Heavy Metals Pollution Index in African River Prawn (Macrobrachium vollenhovenii) collected from Calabar River, Nigeria
Journal: International Journal of Environment, Agriculture and Biotechnology (Vol.5, No. 3)Publication Date: 2020-05-15
Authors : Onwubiko C. C. Onuoha E. M. Anukwa F. A.;
Page : 647-653
Keywords : Heavy metals; Prawn; Bioaccumulation; Pollution; Bioindicators.;
Abstract
Studies on the accumulation of some heavy metals in African river prawn (Macro brachium Vollenhoven Ii), in Calabar River, Calabar, Cross River State, Nigeria, A total of 54 prawn samples, were collected during the study. The heavy metals in the samples were analyzed using atomic absorption spectrophotometer for cadmium, cobalt, chromium, copper, mercury, manganese, nickel and lead while total hydrocarbon (THC) was analysed using UV-spectrophotometer. The heavy metal concentrations in prawn varied across sampling stations and between seasons. The mean metal concentrations in prawns were: 0.02 ± 0.01 mg/kg (Cd), 0.45 ± 0.04 mg/kg (Co), 0.06 ± 0.04 mg/kg (Cr), 0.56 ± 0.04 mg/kg (Cu), 0.63 ± 0.03 mg/kg (Mn), 0.67 ± 0.03 mg/kg (Ni), 0.08 ± 0.01 mg/kg (Pb) and 0.69 ± 0.19 mg/kg (THC). Mercury was not detected in the prawns. The prawns from Calabar River have high chromium, nickel and THC concentrations according to WHO standard and as such consumption of the prawns is not safe. There should be increase awareness on the impact of unlawful dumping of wastes in the study areas. More studies in the Calabar River aimed at monitoring of pollution should be carried out and properly funded to give an insight into whether the fishery resources in the study area are safe for consumption or not.
Other Latest Articles
- Robust Prior Stage Epileptic Seizure Diagnosis System using Resnet and Backpropagation Techniques
- Nagpur Metro Tracks Construction Monitoring System
- Mobile Device Integration in IIUM Service
- Analysis, Prediction and Evaluation of COVID-19 Datasets using Machine Learning Algorithms
- Improving the Accuracy of Spam Message Filtering using Hybrid CNN Classification
Last modified: 2020-06-17 16:34:00