Increased food demand, reflecting a rising global human population, attaining 8 billion in 2022, has furthered the intensification of farmed aquatic animal production. Intensification practices can be resource demanding for micro and macronutrients, dedicated feeds, fossil fuels, chemotherapeutics, and water. Water replaces evaporative losses and may be used to dilute nutrient‐rich wastewater. As an alternative to wastewater discharge, biofloc technology (BFT) uses microbes to manage nutrient levels in production systems. A microbial community grown at high densities can assimilate waste metabolites for growth. This is promoted by increasing the carbon–nitrogen (C:N) ratio using a combination of aquafeed fed to the fish and an additional carbohydrate source, for example, starch, molasses, or bran. The microbial biomass offers a continuous and additional food source to the farmed animal, thus reducing the need for finished aquafeeds and lowering the feed conversion ratio. Although the approach relies on multiple interacting species, BFT can be deployed in relatively low technology settings. This review considers the basis of BFT and identifies areas for innovation and expansion. For example, control of light quantity and quality can influence the biofloc and hence the growth of cultured species. Research on biofloc aquaculture is dominated by studies using tilapia and shrimp, but the technology could be applied to other species, particularly species that are tolerant of biofloc conditions and not highly carnivorous. Biofloc may be a useful biosecurity tool for all or part of the life cycle, and meals made from dried biofloc may enhance the production of cultured species. The key benefits of BFT can potentially be seen in reduced water consumption, lower feed requirements, and improved fish health. This review differs from current review papers in proposing the use of a life cycle assessment in conjunction with BFT, which may be a useful tool for describing and communicating the relative benefits of biofloc systems and their wider environmental impact. This study will serve as a useful knowledge base of BFT information for students, researchers, and stakeholders alike, offering a central source of the main aspects around the culturing of biofloc, key parameters, common species used, areas of potential improvement, and a discussion on where the future of BFT may lie.

Packaging is fundamental for food preservation and transportation but generates an environmental burden from its production and end‐of‐life management. This review evaluates packaging contribution to the environmental performance of seafood products. Life cycle assessment (LCA) studies were evaluated by both qualitative and quantitative analysis. The qualitative analysis assessed how direct (e.g., packaging material) and indirect impacts (e.g., influence on seafood loss and waste) have been considered, while the quantitative analysis evaluated packaging contribution to products’ weight and climate change impact. Qualitative analysis revealed that seafood LCAs focus mainly on direct environmental impacts arising from packaging materials, for which some articles conducted sensitivity analysis to assess materials substitution. Recycling was found to be the most common recommendation to diminish direct potential environmental impacts arising from packaging end‐of‐life. However, standardized recovery rates and other end‐of‐life options (e.g., reuse), should be considered. Quantitative analysis revealed that cans' production contributes significantly to the overall climate change impact for canned products. On average, it contributes to 42% of a product's climate change impact and 27% of a product's weight. Packaging has a lower contribution when considering freezing, chilling, and other post‐harvesting processing. It represents on average less than 5% of a product's climate change impact (less than 1 kg CO ₂ eq/kg) and 6% of a product's weight. Packaging material production is more relevant to aluminum, tinplate, and glass than for plastic and paper. Therefore, it is essential to accurately include these materials and their associated processes in inventories to improve the environmental assessment of seafood products.

Fisheries and aquaculture are becoming a focus of societal concern driven by globalization and increasing environmental degradation, mainly caused by climate change and marine litter. In response to this problem, the European Atlantic Area NEPTUNUS project aims to support and inform about the sustainability of the seafood sector, boosting the transition towards a circular economy through defining eco-innovation approaches and a steady methodology for eco-labelling products. This timely trans-regional European project proposes key corrective actions for positively influencing resource efficiency by addressing a life cycle thinking and involving all stakeholders in decision-making processes, harnessing the water-energy-seafood nexus. This paper presents inter-related objectives, methodologies and cues to action that will potentially meet these challenges that are aligned with many of the United Nations Sustainable Development Goals and European policy frameworks (e.g., Farm to Fork, European Green Deal).

Real-time control of wastewater treatment plants (WWTPs) can have significant environmental and cost advantages. However, its application to small and decentralised WWTPs, which typically have highly varying influent characteristics, remains limited to date due to cost, reliability and technical restrictions. In this study, a methodology was developed using numerical models that can improve sustainability, in real time, by enhancing wastewater treatment whilst also optimising operational and energy efficiency. The methodology leverages neural network and regression modelling to determine a suitable soft sensor for the prediction of ammonium-nitrogen trends. This study is based on a case-study decentralised WWTP employing sequencing batch reactor (SBR) treatment and uses pH and oxidation-reduction potential sensors as proxies for ammonium-nitrogen sensors. In the proposed method, data were pre-processed into 15 input variables and analysed using multi-layer neural network (MLNN) and regression models, creating 176 soft sensors. Each soft sensor was then analysed and ranked to determine the most suitable soft sensor for the WWTP. It was determined that the most suitable soft sensor for this WWTP would achieve a 67% cycle-time saving and 51% electricity saving for each treatment cycle while meeting the criteria set for ammonium discharges. This proposed soft sensor selection methodology can be applied, in full or in part, to existing or new WWTPs, potentially increasing the adoption of real-time control technologies, thus enhancing their overall effluent quality and energy performance.