REMIPLASWAS · Removal of microplastics from the environment using autochthonous wastewater-derived microbial consortia

The accumulation of microplastics (MPs) in the environment poses a great threat to the biosphere. Known and demonstrated conventional methodologies used in the treatment of MPs in wastewater have several limitations. Therefore, there is the need to develop new eco-friendly, cost-effective biobased MPs (Polyethylene terephthalate-PET) removal strategies. Microorganisms can transform organic matter as well as remove pollutants through a chain of different catabolic reactions energized by the induction of non-stereoselective enzymes. Hence, this project will be the first attempt to deploy microbial autochthonous consortia as an eco-friendly remediation technique and strategy in the removal of MPs in the wastewaters. However, to overcome the limitations associated with cultivation and identification of MP degraders, I propose the use of a natural microbial consortium that harbours a plastic-degrading microbial community in wastewater after an enrichment process. Thus, in this project, an enhancement of microorganisms with catabolic capacity to transform MPs will be used for the development of a bio-based tailored technology, which represents the promising potential of this project. The overall objectives of this project are to: (1) characterize MPs in wastewater (2) isolate autochthonous consortium of MPs (PET) degraders in a short term enrichment experiment, study the microbial communities participating in MPs removal and identification of key genes in the catabolic process (3) select a microbial consortium with the ability to remove MPs and study of the fate of MPs in batch experiments (4) scale-up the process into a bench-scale bioreactor to develop a bioaugmentation technology with a microbial native consortium based on their ability to degrade MPs. (5) assess the toxicity of the resulting effluent after MPs process (6) verify the implementation and validate the design of the bioreactor, calculating the effectiveness of the equipment through a predictive model