Surface water contains a large number of pollutants, particularly human pathogens, organic toxicants and heavy metals. Due to the toxic nature of heavy metals towards marine organisms, its removal from the environment has been a growing issue. The biosorption of heavy metal ions from surface water using fish scales has emerged as an environmentally friendly technique. This study assessed the degree of heavy metals accumulation in the scales of Oreochromis niloticus and determining its efficiency as a bioindicator for Cu, Mn and Fe ions removal in the environment of Wewe and Owabi rivers. This study shows that the levels of Cu, Mn, Fe adsorbed from the Owabi river were 685.70 ± 16.51, 247.06 ± 50.46 and 892.90 ± 96.29 mg/kg, respectively. Moreover, the levels of Cu, Mn and Fe adsorbed from Wewe river were 501.60 ± 77.78, 300.89 ± 54.61     and 413.04 ± 9.92 mg/kg, respectively. Under best optimum adsorption conditions, Cu was the best removed heavy metal ions in both surface water reservoirs. Multivariate analysis showed that Cu and Mn showed association in Owabi river, while Mn and Fe were correlated in Wewe river signifying their similarities to a common anthropogenic activity. The Fourier–transform infrared spectrum revealed the existence of a nitro, amine, and carbonyl groups in the biosorption process. This study highlighted that Oreochromis niloticus scales was an efficient bio–sorbent in removing Cu, Mn and Fe ions from Owabi and Wewe rivers.

The study was conducted to assess surface water quality in Ca Mau peninsula using multivariate statistical analysis. Fifty-one water samples with the parameters of pH, dissolved oxygen (DO), total suspended solids (TSS), biochemical oxygen demand (BOD5), chemical oxygen demand (COD), ammonium (N-NH4+), orthophosphate (P-PO43-) and total coliform were used in the evaluation. Water quality is assessed using national standard and water quality index (WQI). The methods of cluster (CA), discriminant (DA), principal component analysis (PCA) were used to analyze the variation patterns of water quality. The surface water was contaminated with organic matters, suspended solids, nutrients, and microorganisms. DA revealed that DO, TSS, BOD5 and pH contributed 76.91% to the seasonal variation of water quality. Water quality is classified from bad to heavily polluted. CA grouped water quality into 7 clusters and DO, TSS, BOD5, COD and coliform of the clusters 1-3 were significantly higher than those of the clusters 4-7. PCA presented that PC1-PC3 was the main sources affecting water quality, explaining 85.45% of the variation in water quality. The sources of pollution can be human (domestic wastewater, waste from agriculture, fisheries, industry, landfills), natural (hydrological regime, rainwater overflow, river bank erosion). pH, DO, BOD5, COD, TSS, N-NH4+, P-PO43- and coliform have an impact on water quality and need to be continuously monitored. However, for the multivariate statistical method to be more effective, an initial data set with several water quality parameters sampling locations is needed. The current results provide scientific information and support local water quality monitoring activities.