ESCUELA DE DOCTORADO

 
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VALIDATING FULL SCALE METLAND SOLUTIONS FOR DECENTRALIZED SUSTAINABLE WASTEWATER TREATMENT: TECHNO-ENVIRONMENTAL AND GEOSPATIAL ANALYSIS
Autor/aPeñacoba Antona, Lorena
DepartamentoQuímica Analítica,quím.física e Ing.quím
Director/aEsteve Núñez, Abraham
Codirector/aGarcía Calvo, Eloy
Fecha de defensa12/11/2021
CalificaciónSobresaliente Cum Laude
ProgramaHidrología y Gestión de los Recursos Hídricos (RD 99/2011)
Mención internacionalSi
ResumenIn 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 ecmaterials 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 decisionmakers 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