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IntroductionToxic heavy metal pollution has been considered a major ecosystem pollution source. Unceasing or rare performance of Pb2+ to the surrounding environment causes damage to the kidney, nervous, and liver systems. Microbial remediation has acquired prominence in recent decades due to its high efficiency, environment-friendliness, and cost-effectiveness.MethodsThe lead biosorption by Bacillus subtilis was optimized by two successive paradigms, namely, a definitive screening design (DSD) and an artificial neural network (ANN), to maximize the sorption process.ResultsFive physicochemical variables showed a significant influence (p < 0.05) on the Pb2+ biosorption with optimal levels of pH 6.1, temperature 30°C, glucose 1.5%, yeast extract 1.7%, and MgSO4.7H2O 0.2, resulting in a 96.12% removal rate. The Pb2+ biosorption mechanism using B. subtilis biomass was investigated by performing several analyses before and after Pb2+ biosorption. The maximum Pb2+ biosorption capacity of B. subtilis was 61.8 mg/g at a 0.3 g biosorbent dose, pH 6.0, temperature 30°C, and contact time 60 min. Langmuir’s isotherm and pseudo-second-order model with R2 of 0.991 and 0.999 were suitable for the biosorption data, predicting a monolayer adsorption and chemisorption mechanism, respectively.DiscussionThe outcome of the present research seems to be a first attempt to apply intelligence paradigms in the optimization of low-cost Pb2+ biosorption using B. subtilis biomass, justifying their promising application for enhancing the removal efficiency of heavy metal ions using biosorbents from contaminated aqueous systems.
IntroductionToxic heavy metal pollution has been considered a major ecosystem pollution source. Unceasing or rare performance of Pb2+ to the surrounding environment causes damage to the kidney, nervous, and liver systems. Microbial remediation has acquired prominence in recent decades due to its high efficiency, environment-friendliness, and cost-effectiveness.MethodsThe lead biosorption by Bacillus subtilis was optimized by two successive paradigms, namely, a definitive screening design (DSD) and an artificial neural network (ANN), to maximize the sorption process.ResultsFive physicochemical variables showed a significant influence (p < 0.05) on the Pb2+ biosorption with optimal levels of pH 6.1, temperature 30°C, glucose 1.5%, yeast extract 1.7%, and MgSO4.7H2O 0.2, resulting in a 96.12% removal rate. The Pb2+ biosorption mechanism using B. subtilis biomass was investigated by performing several analyses before and after Pb2+ biosorption. The maximum Pb2+ biosorption capacity of B. subtilis was 61.8 mg/g at a 0.3 g biosorbent dose, pH 6.0, temperature 30°C, and contact time 60 min. Langmuir’s isotherm and pseudo-second-order model with R2 of 0.991 and 0.999 were suitable for the biosorption data, predicting a monolayer adsorption and chemisorption mechanism, respectively.DiscussionThe outcome of the present research seems to be a first attempt to apply intelligence paradigms in the optimization of low-cost Pb2+ biosorption using B. subtilis biomass, justifying their promising application for enhancing the removal efficiency of heavy metal ions using biosorbents from contaminated aqueous systems.
In recent years, overexplorations of ore and the growth of industries are the prime factors in the release of heavy metals in environments. As a result, the food crops and water bodies are contaminated with metals which may have several adverse effects on the health of humans and other living species. These metals and metalloids, such as Zn, Cu, Mn, Ni, Cr, Pb, Cd, and As, upset the biochemical pathways of metabolite synthesis in living organisms and contribute to the etiology of different diseases. Microorganisms include bacteria, archaea, viruses, and many unicellular eukaryotes, which can span three domains of life—Archaea, Bacteria, and Eukarya—and some microorganisms, such as cyanobacteria, have shown high efficiency in the biosorption rate of heavy metals. Cyanobacteria are suitable for bioremediation as they can grow in adverse environments, have a less negative impact on the surrounding environment, and are relatively cheaper to manage. The structure of cyanobacteria has shown no extensive internal-bound membranes, so it can directly employ the physiological mechanisms to uptake heavy metals from contamination sites. Such biochemical makeups are suitable for managing and bioremediating heavy metal concentrations in polluted environments. This review aims to explore the potential of cyanobacteria in the bioremediation of heavy metals and metalloids in water bodies. Additionally, we have identified the prospects for enhancing bioremediation effectiveness.
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