Environmental Sustainability

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Browsing Environmental Sustainability by Subject "12 Responsible Consumption and Production"

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    Anaerobic microbial core for municipal wastewater treatment — the sustainable platform for resource recovery
    (Elsevier, 2025-08-01) Conall Holohan, B.; Trego, Anna; Keating, Ciara; Bressani-Ribeiro, Thiago; Chernicharo, Carlos L.; Daigger, Glen; Galdi, Stephen M.; Knörle, Ulrich; Paissoni, Eleonora; Robles, Angel; Rogalla, Frank; Shin, Chungheon; Soares, Ana; Smith, Adam L.; Szczuka, Aleksandra; Hughes, Dermot; O’Flaherty, Vincent
    The requirement for carbon neutrality and bioresource recovery has shifted our views on water treatment from health and pollution avoidance to one of sustainability with water and nutrient circularity. Despite progress, the current process of wastewater treatment is linear, based on core aerobic microbiology, which is unlikely to be carbon neutral due to its large use of energy and production of waste sludge. Here, we outline a shift from aerobic to anaerobic microbiology at the core of wastewater treatment and resource recovery, illustrating the state-of-the-art technologies available for this paradigm shift. Anaerobic metabolism primarily offers the benefit of minimal energy input (up to 50% reduction) and minimal biomass production, resulting in up to 95% less waste sludge compared with aerobic treatment, which is increasingly attractive, given dialogue surrounding emerging contaminants in biosolids. Recent innovative research solutions have made ambient (mainstream) anaerobic treatment a ready substitute for the aerobic processes for municipal wastewater in temperate regions. Moreover, utilising anaerobic treatment as the core carbon removal step allows for more biological downstream resource recovery with several opportunities to couple the process with (anaerobic) nitrogen and phosphorus recovery, namely, potential mainstream anaerobic ammonium oxidation (anammox) and methane oxidation (N-DAMO). Furthermore, these technologies can be mixed and matched with membranes and ion-exchange systems, high-value biochemical production, and/or water reuse installations. As such, we propose the reconfiguration of the wastewater treatment plant of the futurewith anaerobic microbiology. Mainstream anaerobic treatment at the core of a truly sustainable platform for modern municipal wastewater treatment, facilitating circular economy and net-zero carbon goals.
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    Artificial intelligence for prediction of shelf-life of various food products: recent advances and ongoing challenges
    (Elsevier, 2025-05-01) Rashvand, Mahdi; Ren, Yuqiao; Sun, Da-Wen; Senge, Julia; Krupitzer, Christian; Fadiji, Tobi; Miró, Marta Sanzo; Shenfield, Alex; Watson, Nicholas J.; Zhang, Hongwei
    Background: Accurate estimation of shelf-life is essential to maintain food safety, reduce wastage, and improve supply chain efficiency. Traditional methods such as microbial and chemical analysis, and sensory evaluation provide reproducible results but require time and labor and may not be suitable for real-time or high-throughput applications. The integration of artificial intelligence (AI) with advanced analysis techniques offers a suitable alternative for rapid, data-driven estimation of shelf-life in dynamic storage environments. Approach and scope: The current review assesses the application of AI-based techniques such as machine learning (ML), deep learning (DL), and hybrid approaches in food product shelf life prediction. This study highlights how AI can be utilized to examine data from non-destructive testing methods like hyperspectral imaging, spectroscopy, machine vision, and electronic sensors to enhance predictive performance. The review also describes how AI-based techniques contribute to managing food quality, reduce economic losses, and enhance sustainability by ensuring optimized food distribution and reducing waste. Key findings and conclusions: AI techniques overcome conventional techniques by considering intricate, multi-sourced information capturing microbiological, biochemical, and environmental factors influencing food spoilage. Meat, dairy, fruits and vegetables, and beverage case studies illustrate AI techniques' superiority in real-time monitoring and quality assessment. It also identifies limitations such as data availability, model generalizability, and computational cost, constraining extensive applications. Cloud and Internet of Things (IoT) platform integration into future applications has to be considered to enable real-time decision-making and adaptive modeling. AI can be a paradigm-changing tool in food industries with intelligent, scalable, and low-cost interventions in food safety, waste reduction, and sustainability.