Mitigating long-term emissions of landfill aftercare: Preliminary results from experiments combining microbial electrochemical technologies and in situ aeration

Abstract

Landfills still represent the main option for waste disposal in many parts of the world. Anyway, they often pose a significant pollution risk and contribute to potential environmental and human health impacts via gaseous and liquid (leachate) emission pathways if not properly managed. Some innovative technologies can help to reduce these emissions, such as in situ aeration and the application of microbial electrochemical technologies (METs). METs are an emerging field that open the possibility to control microbial reactions, enhancing electron flows from electron donors towards electron acceptors. To this end, several materials with different electrochemically-active properties are used, such as electrical conductivity, capacitance, surface electroactivity and charge. The present project named LA-LA-LAND (Landfill electron-Lapping for a LANDscape requalification) was aimed to apply METs to treat leachate-saturated zones in old landfills. A MET prototype was constructed using a granular anode (graphite) and a cylindrical air-cathode (electroactive biochar). The METs were integrated to three identical laboratory-scale landfill bioreactors coupled with the in situ aeration technique, while three control reactors run without MET. The maximum values of current and power density obtained were 0.015 A·m−2 and 0.00035 W·m−2. The influence of the MET system on the organic matter removal was evident in two reactors, where this technology was applied, with respect to the control ones: total organic carbon decreased on average 13%, while it reduced less than 5% in the control reactors. This preliminary experiment pointed out some critical aspects of MET configuration, such as the weakness of the cathode architecture, which was prone to be flooded by leachate, blocking the aeration flux.

Air Microbial Pollution in Public Places Based on Convolutional Neural Network

“…The output layer calculates the predicted value of the result. If it is a classification problem, the predicted probability is obtained through the Softmax function [17][18]. Convolutional neural networks are characterized by local field-of-view perception, weight sharing in the same convolutional layer, and multiple convolutional layers extracting different features.…”

Revisión de mecanismos de valorización energética para residuos sólidos urbanos caso: relleno sanitario Doña Juana

“…Revisión de mecanismos de valorización energética para residuos sólidos urbanos caso: relleno sanitario Doña Juana Al tener elevadas cantidades de residuos se requiere de un manejo estricto que controle el proceso de descomposición, ya que se producen compuestos lixiviados y gases de efecto invernadero como metano y dióxido de carbono (Emalya et al, 2022). Sin embargo, los desechos son acumulados en lugares para la disposición final donde se produce la descomposición que ocasiona afectación al ecosistema y a las poblaciones cercanas (Pivato et al, 2021). De igual manera, con la producción excesiva y el almacenamiento continuo de los residuos, los lugares de disposición final no lograrán sostener la cantidad de desechos, por esta razón se deben implementar medidas que logren disminuir el volumen de los residuos, las problemáticas socioambientales y a su vez generar un valor agregado en el proceso.…”