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 River's WQ and accentuated the roles of weathering and surface runoff in determining the river's 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.
Multivariate statistical techniques such as hierarchical Agglomerated cluster analysis (HACA), discriminant analysis (DA), principal component analysis (PCA), and factor analysis (FA) were applied to identify the spatial variation and pollution sources of Jakara River, Kano, Nigeria. Thirty surface water samples were collected: 23 along Getsi River and 7 along the main channel of River Jakara. Twenty-three water quality parameters, namely pH, temperature, turbidity, electrical conductivity (EC), dissolved oxygen (DO), 5-day biochemical oxygen demand (BOD(5)), Faecal coliform, total solids (TS), nitrates (NO(3)(-)), phosphates (PO(4)(3-)), cobalt (Co), iron (Fe), nickel (Ni), manganese (Mn), copper (Cu), sodium (Na), potassium (K), mercury (Hg), chromium (Cr), cadmium (Cd), lead (Pb), magnesium (Mg), and calcium(Ca) were analysed. HACA grouped the sampling points into three clusters based on the similarities of river water quality characteristics: industrial, domestic, and agricultural water pollution sources. Forward and backward DA effectively discriminated 5 and 15 water quality variables, respectively, each assigned with 100% correctness from the original 23 variables. PCA and FA were used to investigate the origin of each water quality parameter due to various land use activities, 7 principal components were obtained with 77.5% total variance, and in addition PCA identified 3 latent pollution sources to support HACA. From this study, one can conclude that the application of multivariate techniques derives meaningful information from water quality data.
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.
This paper determines the controlling factors that influence the metals' behavior water-sediment interaction facies and distribution of elemental content ((75)As, (111)Cd, (59)Co, (52)Cr, (60)Ni, and (208)Pb) in water and sediment samples in order to assess the metal pollution status in the Langat River. A total of 90 water and sediment samples were collected simultaneously in triplicate at 30 sampling stations. Selected metals were analyzed using ICP-MS, and the metals' concentration varied among stations. Metal concentrations of water ranged between 0.08-24.71 μg/L for As, <0.01-0.53 μg/L for Cd, 0.06-6.22 μg/L for Co, 0.32-4.67 μg/L for Cr, 0.80-24.72 μg/L for Ni, and <0.005-6.99 μg/L for Pb. Meanwhile, for sediment, it ranged between 4.47-30.04 mg/kg for As, 0.02-0.18 mg/kg for Cd, 0.87-4.66 mg/kg for Co, 4.31-29.04 mg/kg for Cr, 2.33-8.25 mg/kg for Ni and 5.57-55.71 mg/kg for Pb. The average concentration of studied metals in the water was lower than the Malaysian National Standard for Drinking Water Quality proposed by the Ministry of Health. The average concentration for As in sediment was exceeding ISQG standards as proposed by the Canadian Sediment Quality Guidelines. Statistical analyses revealed that certain metals (As, Co, Ni, and Pb) were generally influenced by pH and conductivity. These results are important when making crucial decisions in determining potential hazardous levels of these metals toward humans.
Robust statistical tools were applied on the water quality datasets with the aim of determining the most significance parameters and their contribution towards temporal water quality variation. Surface water samples were collected from four different sampling points during dry and wet seasons and analyzed for their physicochemical constituents. Discriminant analysis (DA) provided better results with great discriminatory ability by using five parameters with (P < 0.05) for dry season affording more than 96% correct assignation and used five and six parameters for forward and backward stepwise in wet season data with P-value (P < 0.05) affording 68.20% and 82%, respectively. Partial correlation results revealed that there are strong (r(p) = 0.829) and moderate (r(p) = 0.614) relationships between five-day biochemical oxygen demand (BOD(5)) and chemical oxygen demand (COD), total solids (TS) and dissolved solids (DS) controlling for the linear effect of nitrogen in the form of ammonia (NH(3)) and conductivity for dry and wet seasons, respectively. Multiple linear regression identified the contribution of each variable with significant values r = 0.988, R(2) = 0.976 and r = 0.970, R(2) = 0.942 (P < 0.05) for dry and wet seasons, respectively. Repeated measure t-test confirmed that the surface water quality varies significantly between the seasons with significant value P < 0.05.
The pollution status of the downstream section of the Jakara River was investigated. Dissolved oxygen (DO), 5-day biochemical oxygen demand (BOD(5)), chemical oxygen demand (COD), suspended solids (SS), pH, conductivity, salinity, temperature, nitrogen in the form of ammonia (NH(3)), turbidity, dissolved solids (DS), total solids (TS), nitrates (NO(3)), chloride (Cl) and phosphates (PO(3-)(4)) were evaluated, using both dry and wet season samples, as a measure of variation in surface water quality in the area. The results obtained from the analyses were correlated using Pearson's correlation matrix, principal component analysis (PCA) and paired sample t-tests. Positive correlations were observed for BOD(5), NH(3), COD, and SS, turbidity, conductivity, salinity, DS, TS for dry and wet seasons, respectively. PCA was used to investigate the origin of each water quality parameter, and yielded 5 varimax factors for each of dry and wet seasons, with 70.7 % and 83.1 % total variance, respectively. A paired sample t-test confirmed that the surface water quality varies significantly between dry and wet season samples (P < 0.01). The source of pollution in the area was concluded to be of anthropogenic origin in the dry season and natural origins in the wet season.
The present study deals with the assessment of Langat River water quality with some chemometrics approaches such as cluster and discriminant analysis coupled with an artificial neural network (ANN). The data used in this study were collected from seven monitoring stations under the river water quality monitoring program by the Department of Environment (DOE) from 1995 to 2002. Twenty three physico-chemical parameters were involved in this analysis. Cluster analysis successfully clustered the Langat River into three major clusters, namely high, moderate and less pollution regions. Discriminant analysis identified seven of the most significant parameters which contribute to the high variation of Langat River water quality, namely dissolved oxygen, biological oxygen demand, pH, ammoniacal nitrogen, chlorine, E. coli, and coliform. Discriminant analysis also plays an important role as an input selection parameter for an ANN of spatial prediction (pollution regions). The ANN showed better prediction performance in discriminating the regional area with an excellent percentage of correct classification compared to discriminant analysis. Multivariate analysis, coupled with ANN, is proposed, which could help in decision making and problem solving in the local environment.
Spatial variations in estuarine intertidal sediment have been often related to such environmental variables as salinity, sediment types, heavy metals and base cations. However, there have been few attempts to investigate the difference condition between high and low tides relationships and to predict their likely responses in an estuarine environment. This paper investigates the linkages between environmental variables and tides of estuarine intertidal sediment in order to provide a basis for describing the effect of tides in the Mengkabong lagoon, Sabah. Multivariate statistical technique, principal components analysis (PCA) was employed to better interpret information about the sediment and its controlling factors in the intertidal zone. The calculation of Geoaccumulation Index (I(geo)) suggests the Mengkabong mangrove sediments are having background concentrations for Al, Cu, Fe, and Zn and unpolluted for Pb. Extra efforts should therefore pay attention to understand the mechanisms and quantification of different pathways of exchange within and between intertidal zones.