The synthetic utility of tris-((2-hydroxybenzyl)-aminoethyl)amine (H6TrenSal) and tris-((2-hydroxy-5-bromobenzyl)-aminoethyl)amine (H6Tren5BrSal) are investigated. A range of monomeric complexes with general formula [(H6TrenSal) M][NO3] with nickel, copper and zinc are reported and crystallographically analysed. Nickel adopts three motifs which are different to that observed for copper and zinc. The use of these species as platforms for the synthesis of more complex systems in conjunction with the lanthanoids are explored. Copper and zinc do not follow a similar reaction pathway to nickel. While nickel forms the expected trimetallic motif [{(TrenSal)Ni}(2)Ln(HOMe)](+), copper forms a copper trimetallic motif. In contrast to both nickel and copper, reactions with [(H6TrenSal)Zn](+) produce lanthanoid based products namely [(H6Tren5BrSal)Gd(NO3)(3)] and [{(H6TrenSal)Ce}(2)-mu(2)-O-2]

Jakara River Basin has been extensively studied to assess the overall water quality and to identify the major variables responsible for water quality variations in the basin. A total of 27 sampling points were selected in the riverine network of the Upper Jakara River Basin. Water samples were collected in triplicate and analyzed for physicochemical variables. Pearson product-moment correlation analysis was conducted to evaluate the relationship of water quality parameters and revealed a significant relationship between salinity, conductivity with dissolved solids (DS) and 5-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), and nitrogen in form of ammonia (NH4). Partial correlation analysis (r p) results showed that there is a strong relationship between salinity and turbidity (r p=0.930, p=0.001) and BOD5 and COD (r p=0.839, p=0.001) controlling for the linear effects of conductivity and NH4, respectively. Principal component analysis and or factor analysis was used to investigate the origin of each water quality parameter in the Jakara Basin and identified three major factors explaining 68.11 % of the total variance in water quality. The major variations are related to anthropogenic activities (irrigation agricultural, construction activities, clearing of land, and domestic waste disposal) and natural processes (erosion of river bank and runoff). Discriminant analysis (DA) was applied on the dataset to maximize the similarities between group relative to within-group variance of the parameters. DA provided better results with great discriminatory ability using eight variables (DO, BOD5, COD, SS, NH4, conductivity, salinity, and DS) as the most statistically significantly responsible for surface water quality variation in the area. The present study, however, makes several noteworthy contributions to the existing knowledge on the spatial variations of surface water quality and is believed to serve as a baseline data for further studies. Future research should therefore concentrate on the investigation of temporal variations of water quality in the basin.

Groun in a very complex way. In this work, multivariate statistical analysis was used to evaluate the factors controlling the groundwater chemistry of Kapas Island (Malaysia). Principal component analysis (P dwater chemistry of small tropical islands is influenced by many factors, such as recharge, weathering and seawater intrusion, among others, which interact with each other CA) was applied to 17 hydrochemical parameters from 108 groundwater samples obtained from 18 sampling sites. PCA extracted four PCs, namely seawater intrusion, redox reaction, anthropogenic pollution and weather factors, which collectively were responsible for more than 87% of the total variance of the island’s hydrochemistry. The cluster analysis indicated that three factors (weather, redox reaction and seawater intrusion) controlled the hydrochemistry of the area, and the variables were allocated to three groups based on similarity. A Piper diagram classified the island’s water types into Ca-HCO3 water type, Na-HCO3 water type, Na-SO4-Cl water type and Na-Cl water type, indicating recharge, mixed, weathering and leached from sewage and seawater intrusion, respectively. This work will provide policy makers and land managers with knowledge of the precise water quality problems affecting the island and can also serve as a guide for hydrochemistry assessments of other islands that share similar characteristics with the island in question.

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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.

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.