Abdusssamad Abubakar, H. Yakasai, G. Uba, Ibrahim Sabo
{"title":"硝化假单胞菌在辛基酚聚氧乙烯醚上生长的底物抑制模型","authors":"Abdusssamad Abubakar, H. Yakasai, G. Uba, Ibrahim Sabo","doi":"10.54987/jemat.v11i1.841","DOIUrl":null,"url":null,"abstract":"Octylphenol polyethoxylates (OPEs) constitute a class of non-ionic surfactants extensively employed in various industrial applications. However, concerns have arisen regarding the potential environmental and human health impacts of OPEs because of their widespread use and persistence in aquatic environments. Bioremediation of OPE in the environment using OPE-degrading bacterium is appealing as bacterial metabolism converts OPE to harmless carbon dioxide and water as byproducts. In this study, various secondary growth models such as Luong, Yano, Teissier-Edward, Aiba, Haldane, Monod, Han, and Levenspiel were employed to model the inhibitory effect of high OPE concentrations to the growth rate of Pseudomonas nitroreducens TX1 the bacterium on OPE. Following thorough statistical analyses such as root-mean-square error (RMSE), adjusted coefficient of determination (adjR2), bias factor (BF), and accuracy factor (AF), the Teissier model emerged as the most optimal choice. All of the studied models showed good fittings except Moser, Monod and Hinshelwood which showed the poorest curve fitting. The Teissier model emerged as the most suitable model, as indicated by its remarkably low values for RMSE, AICc, and modified adjR2. Furthermore, the model's AF and BF values were close to unity (Table 2). The experimental data obtained indicates that OPE is toxic and slows down the rate of growth at higher concentrations. The maximum OPE specific growth rate (max), half-saturation concentration (KS), half inhibition concentration (Ki) was 0.613 h-1 (95% Confidence Interval or C.I. from 0.519 to 0.707), 2352.8 mg/L (95% C.I. from 1668.8 to 3036.8) and 52,456.7 mg/L (95% C.I. from 38395.0 to 66518.5), respectively. It is possible that these new constants found when modeling could be useful inputs for future modeling efforts.","PeriodicalId":393012,"journal":{"name":"Journal of Environmental Microbiology and Toxicology","volume":"16 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Substrate Inhibition Modelling of Pseudomonas nitroreducens Growth on Octylphenol Polyethoxylates\",\"authors\":\"Abdusssamad Abubakar, H. Yakasai, G. Uba, Ibrahim Sabo\",\"doi\":\"10.54987/jemat.v11i1.841\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Octylphenol polyethoxylates (OPEs) constitute a class of non-ionic surfactants extensively employed in various industrial applications. However, concerns have arisen regarding the potential environmental and human health impacts of OPEs because of their widespread use and persistence in aquatic environments. Bioremediation of OPE in the environment using OPE-degrading bacterium is appealing as bacterial metabolism converts OPE to harmless carbon dioxide and water as byproducts. In this study, various secondary growth models such as Luong, Yano, Teissier-Edward, Aiba, Haldane, Monod, Han, and Levenspiel were employed to model the inhibitory effect of high OPE concentrations to the growth rate of Pseudomonas nitroreducens TX1 the bacterium on OPE. Following thorough statistical analyses such as root-mean-square error (RMSE), adjusted coefficient of determination (adjR2), bias factor (BF), and accuracy factor (AF), the Teissier model emerged as the most optimal choice. All of the studied models showed good fittings except Moser, Monod and Hinshelwood which showed the poorest curve fitting. The Teissier model emerged as the most suitable model, as indicated by its remarkably low values for RMSE, AICc, and modified adjR2. Furthermore, the model's AF and BF values were close to unity (Table 2). The experimental data obtained indicates that OPE is toxic and slows down the rate of growth at higher concentrations. The maximum OPE specific growth rate (max), half-saturation concentration (KS), half inhibition concentration (Ki) was 0.613 h-1 (95% Confidence Interval or C.I. from 0.519 to 0.707), 2352.8 mg/L (95% C.I. from 1668.8 to 3036.8) and 52,456.7 mg/L (95% C.I. from 38395.0 to 66518.5), respectively. 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Substrate Inhibition Modelling of Pseudomonas nitroreducens Growth on Octylphenol Polyethoxylates
Octylphenol polyethoxylates (OPEs) constitute a class of non-ionic surfactants extensively employed in various industrial applications. However, concerns have arisen regarding the potential environmental and human health impacts of OPEs because of their widespread use and persistence in aquatic environments. Bioremediation of OPE in the environment using OPE-degrading bacterium is appealing as bacterial metabolism converts OPE to harmless carbon dioxide and water as byproducts. In this study, various secondary growth models such as Luong, Yano, Teissier-Edward, Aiba, Haldane, Monod, Han, and Levenspiel were employed to model the inhibitory effect of high OPE concentrations to the growth rate of Pseudomonas nitroreducens TX1 the bacterium on OPE. Following thorough statistical analyses such as root-mean-square error (RMSE), adjusted coefficient of determination (adjR2), bias factor (BF), and accuracy factor (AF), the Teissier model emerged as the most optimal choice. All of the studied models showed good fittings except Moser, Monod and Hinshelwood which showed the poorest curve fitting. The Teissier model emerged as the most suitable model, as indicated by its remarkably low values for RMSE, AICc, and modified adjR2. Furthermore, the model's AF and BF values were close to unity (Table 2). The experimental data obtained indicates that OPE is toxic and slows down the rate of growth at higher concentrations. The maximum OPE specific growth rate (max), half-saturation concentration (KS), half inhibition concentration (Ki) was 0.613 h-1 (95% Confidence Interval or C.I. from 0.519 to 0.707), 2352.8 mg/L (95% C.I. from 1668.8 to 3036.8) and 52,456.7 mg/L (95% C.I. from 38395.0 to 66518.5), respectively. It is possible that these new constants found when modeling could be useful inputs for future modeling efforts.