Advanced IoT Pressure Monitoring System for Real-Time Landfill Gas Management

Landfill bacteriology: Role in waste bioprocessing elevated landfill gaseselimination and heat management

This study delves into the critical role of microbial ecosystems in landfills, which are pivotal for handling municipal solid waste (MSW). Within these landfills, a complex interplay of several microorganisms (aerobic/anaerobic bacteria, archaea or methanotrophs), drives the conversion of complex substrates into simplified compounds and complete mineralization into the water, inorganic salts, and gases, including biofuel methane gas. These landfills have dominant biotic and abiotic environments where various bacterial, archaeal, and fungal groups evolve and interact to decompose substrate by enabling hydrolytic, fermentative, and methanogenic processes. Each landfill consists of diverse bio-geochemical environments with complex microbial populations, ranging from deeply underground anaerobic methanogenic systems to near-surface aerobic systems. These kinds of landfill generate leachates which in turn emerged as a significant risk to the surrounding because generated leachates are rich in toxic organic/inorganic components, heavy metals, minerals, ammonia and xenobiotics. In addition to this, microbial communities in a landfill ecosystem could not be accurately identified using lab microbial-culturing methods alone because most of the landfill's microorganisms cannot grow on a culture medium. Due to these reasons, research on landfills microbiome has flourished which has been characterized by a change from a culture-dependent approach to a more sophisticated use of molecular techniques like Sanger Sequencing and Next-Generation Sequencing (NGS). These sequencing techniques have completely revolutionized the identification and analysis of these diverse microbial communities. This review underscores the significance of microbial functions in waste decomposition, gas management, and heat control in landfills. It further explores how modern sequencing technologies have transformed our approach to studying these complex ecosystems, offering deeper insights into their taxonomic composition and functionality.

Green IoT Event Detection for Carbon-Emission Monitoring in Sensor Networks

This research addresses the intersection of low-power microcontroller technology and binary classification of events in the context of carbon-emission reduction. The study introduces an innovative approach leveraging microcontrollers for real-time event detection in a homogeneous hardware/firmware manner and faced with limited resources. This showcases their efficiency in processing sensor data and reducing power consumption without the need for extensive training sets. Two case studies focusing on landfill CO2 emissions and home energy usage demonstrate the feasibility and effectiveness of this approach. The findings highlight significant power savings achieved by minimizing data transmission during non-event periods (94.8-99.8%), in addition to presenting a sustainable alternative to traditional resource-intensive AI/ML platforms that comparatively draw and produce 20,000 times the amount of power and carbon emissions, respectively.