Adamu Mustapha

Evaluation of Factors Influencing the Groundwater Chemistry in a Small Tropical Island of Malaysia

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.

Spatial aspects of surface water quality in the Jakara Basin, Nigeria using chemometric analysis.

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 (BOD5), Faecal coliform, total solids (TS), nitrates (NO3 −), phosphates (PO4 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.

Spatial-temporal variation of surface water quality in the downstream region of the Jakara River, north-western Nigeria: A statistical approach

The pollution status of the downstream section of the Jakara River was investigated. Dissolved oxygen (DO), 5-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), suspended solids (SS), pH, conductivity, salinity, temperature, nitrogen in the form of ammonia (NH3), turbidity, dissolved solids (DS), total solids (TS), nitrates (NO3), chloride (Cl) and phosphates (PO3− 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 Pearsons correlation matrix, principal component analysis (PCA) and paired sample t-tests. Positive correlations were observed for BOD5, NH3, 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.

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.

Temporal Aspects of Surface Water Quality Variation Using Robust Statistical Tools

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 (BOD5) and chemical oxygen demand (COD), total solids (TS) and dissolved solids (DS) controlling for the linear effect of nitrogen in the form of ammonia (NH3) 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.