Abstract

In the aquatic environments, heavy metal pollution remains a major challenge across the world since the metals of cadmium, lead, chromium, and mercury are toxic, persistent, and bioaccumulative. Traditional remediation mechanisms such as chemical precipitation, membrane filtration and adsorption can be costly to operate, produce secondary pollutants and are not adaptable to changing environmental conditions. The paper describes a new, integrated approach that will involve bio-engineering of microbial systems alongside a network of IoT to monitor the environment in order to implement intelligent, effective and scalable heavy metal cleanup in water bodies. Modifications to biosorption, bioaccumulation, redox conversion, and metal precipitation of engineered microbial strains to increase metal-responsiveness: engineered strains of Pseudomonas putida, Shewanella oneidensis, Ralstonia metallidurans and metal-binding cyanobacteria were developed by incorporating metal-responsive genetic circuits, observation of metallothionein overexpression and optimization of electron transfer pathways. To make these engineered microbes immobile to guarantee stability and reusability and biocontainment, there was use of sophisticated encapsulation matrices. Refining on the biological system, a distributed IoT network had been implemented with electrochemical heavy metal sensors and environmental probes, to allow real-time and continuous monitoring of the combinations of metal concentrations, pH, temperature, and dissolved oxygen. Machine learning models have been used to identify data sent over low-power communication protocols to predict contamination variation and autonomously control microbial deployment in response to controlled biocapsule release mechanisms. The outcome of prototype simulations and controlled experiments in microcosm showed that the integrated system was able to yield much better efficiencies of metal removal improvements of 25 to 60 percent over the wild-type strains, which held up to reasonable functional stability when faced with variation of environmental conditions. The integrated biological and digital system provides a strong platform to the future, autonomous remediation systems that can intelligently react to contamination dynamics. The study provides a basis of scalable self-regulating environmental biotechnical systems that could be implemented in rivers, lakes, industrial effluents and other susceptible water environment to solve the age-old issues of heavy metal pollution.