Validating full scale METland solutions for decentralized sustainable wastewater treatmenttechno-environmental and geospatial analysis
- Peñacoba Antona, Lorena
- Abraham Esteve Núñez Director
- Eloy García Calvo Director
Defence university: Universidad de Alcalá
Year of defence: 2021
- Miguel Martín Monerris Chair
- Francisco Aguilera Benavente Secretary
- Ismael Leonardo Vera Puerto Committee member
Type: Thesis
Abstract
In recent decades increasing pressures on natural resources has drastically altered demographic dynamics and climate change. Currently, different lines of action are being pursued for the sustainable management and conservation of global water resources. In the field of wastewater treatment, the problem lies in small population centers where the scarcity of technical and economic resources compromises the effectiveness of conventional treatment methods. METland® technology emerges from the integration of Microbial Electrochemical Technologies (METs) into constructed wetlands. Integration improves treatment efficiency by replacing an inert material (gravel) with a biocompatible and electro-conductive material (ec-biochar or coke). Such designs maximize the transfer of electrons between ec-materials and electroactive bacteria. This makes full-scale METlands® a valid, sustainable, efficient, and robust wastewater treatment solution, with low operation and maintenance costs, for small and remote population centers. In this thesis, new strategies have been explored to improve the design and operation of full-scale METland® systems. A Life Cycle Analysis (LCA) was performed, evaluating the impacts of different operational modes on each environmental category. To explore the geospatial application of METlands, a process to evaluate optimal locations for their implementation was developed. The proposed methodology can be used to help decision-makers employ METland® worldwide using multi-criteria evaluation (MCE) techniques applied to Geographic Information Systems (GIS) with a final sensitivity analysis (SA) to optimize and validate the model.