CIFICEN   24414
CENTRO DE INVESTIGACIONES EN FISICA E INGENIERIA DEL CENTRO DE LA PROVINCIA DE BUENOS AIRES
Unidad Ejecutora - UE
artículos
Título:
Estimation of long-term methane emissions from Mechanical-Biological Treatment waste through Biomethane Potential Test
Autor/es:
CÓRDOBA, VERÓNICA ELIZABETH; SANTALLA, ESTELA MERCEDES
Revista:
ENVIRONMENTAL TECHNOLOGY
Editorial:
TAYLOR & FRANCIS LTD
Referencias:
Año: 2021 p. 1 - 27
ISSN:
0959-3330
Resumen:
Mechanical-Biological Treatment (MBT) is a technology applied to reduce the environmental impacts of urban waste based on stabilising the organic matter content. As the process is not entirely efficient, the residue can generate methane when it is landfilled. Long-term methane emissions estimation based on models is usually over or underestimated because the actual waste composition after stabilisation is generally unknown. This work proposes a single tool to improve the emission estimations of the landfilled MBT waste based on the determination of the biomethane potential test (BMP). Experimental BMP of the crude and stabilised organic fractions of municipal solid waste obtained from an MBT plant were carried out, and the results were used to predict the methane emission from two models, LandGEM (2005) and IPCC (2006). In the LandGEM model the experimental value of BMP represents the methane potential L0 while in the IPCC model it allowed to obtain the ultimate organic carbon anaerobically degraded (DOCf), based on a linear correlation (R2=0.944, p-value< 0.05) that can be used to obtain the DOCf in a waste of any composition. The results of the long-term (40 years) methane emissions of the stabilised waste disposed on land showed overestimations of up 56.0% (IPCC model) and 259.5% (Landgem model) when default data, instead the actual DOCf were applied in stabilized waste; similar behaviour was observed for the crude waste (23.3 and 241.3% overestimations). Moreover, the impact of the stabilisation process revealed methane emission reductions of 5.1% and 20.9% based on LandGEM and IPCC models respectively.