Microbial Analysis of Flood Water in Amassoma Community, Bayelsa State
Year: 2026
Type: article
Abstract: ABSTRACT: This study investigates the microbial quality of flood water in Amassoma community, Bayelsa State, Nigeria, in September, 2024. This study presents a microbial analysis of floodwater samples collected from four stations (1–4). A randomized block experimental design was used. Triplicate samples were taken from each location. The results revealed high levels of total coliforms, Escherichia coli, Salmonella spp., and Vibrio spp., indicating significant fecal contamination and potential health risks. Fungal species such as Aspergillus sp. and Candida sp. were also isolated. The microbial counts varied notably across sampling sites, with values ranging ranged from 38.23±1.12 CFU/ml (Station 4- control) to 44.63±1.28 CFU/ml (Station 2) in mean values. The highest mean count in sampling station 2 (52.67± 1.56 CFU/100ml) indicates potential contamination. Again, sampling station 4 (control) had the lowest mean coliform levels (37.67± 2.76), suggesting more stable and cleaner conditions. The distribution of bacterial species across different sampling locations reveals spatial variability in microbial presence. Notably, Bacillus sp. was consistently present across all stations. Escherichia coli was also frequently encountered in all the sampling stations. The presence of Shigella sp., Providencia sp., and Pragia sp. further supports the possibility of contamination with enteric pathogens. The biochemical test results provided valuable confirmation of bacterial identities. Shigella sp. was characterized as a Gram-negative rod, catalase and indole positive, but negative for citrate and gas production. These results highlight the importance of targeted microbial monitoring in flood-prone areas to assess public health risks and guide sanitation responses.
Institution: Niger Delta University
Language: English
Subfield: Water Science and Technology
Field: Environmental Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7155566607
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