Asian Journal of Microbiology and Biotechnology

Corrosion is a pervasive phenomenon on offshore metallic structures, driven by chemical, physical, and biological processes. The pivotal role of sulfate-reducing bacteria in accelerating microbiologically influenced corrosion of galvanized steel in marine environments, thereby enhancing the scientific community’s understanding of biocorrosion mechanisms. The findings also contribute to the development of predictive models and practical preventive strategies, opening interdisciplinary research avenues in materials science, engineering, and microbiology. This study investigated the isolation and identification of sulphate-reducing bacteria (SRB) associated with MIC of galvanized steel in seawater environments, with implications for seafood-processing infrastructure. Seawater samples were obtained from the Nigerian Institute for Oceanography and Marine Research (NIOMR), and galvanized steel bars from the Industrial Microbiology Department, University of Lagos. Steel bars were submerged in seawater for 180 days, with sampling every 90 days to monitor bacterial load and physicochemical parameters (pH, TDS, TSS, DO). Corrosion rates were quantified by weight-loss and thickness-reduction analyses, enabling correlation with microbiological activity and aqueous chemistry. SRB strains were isolated using Baar’s medium and characterised via biochemical methods. Results showed that increased bacterial activity corresponded with reduced pH, elevated TDS/TSS, and higher corrosion rates. Identified SRB included Aeromonas and Mycobacterium, capable of biofilm formation on food-contact surfaces and accelerating galvanic and pitting corrosion through sulphur reduction. Corrosion rates in seawater were 2% higher than in sterile distilled water controls, with microstructural zinc layer degradation most pronounced after 180 days. From a materials science and engineering perspective, these findings emphasize the importance of integrating microbial risk analysis into alloy design, protective coating optimization, and electrochemical corrosion modeling.