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  • Macau’s rapid urban development has significantly altered its natural landscapes, leading to the degradation and fragmentation of wetland ecosystems. Although diminished in size, these urban wetlands continue to deliver vital ecological services and sustain a range of biodiversity. Conventional methods for biodiversity assessment are often labor-intensive and unsuitable for regular monitoring in urban environments. This study utilized environmental DNA (eDNA) metabarcoding as a non-invasive and effective approach to survey biodiversity in 2 coastal and 7 freshwater urban wetlands in Macau. Environmental DNA was extracted from water samples collected from these nine representative wetland sites and analyzed using metabarcoding techniques to detect fish species. Three genetic markers were targeted to enhance taxonomic resolution, which included COI (mlCOIintF/LoboR1, 313 bp), 12S (miFish-U, 170 bp) and 18S (V4, 400 bp). Bioinformatic pipelines were used to process sequencing data, identify taxa, and compute biodiversity indices, providing a comprehensive snapshot of aquatic biodiversity in Macau’s urban wetlands. A total of 90 fish species were detected, including native species, migrants, and invasive taxa. Beta diversity analysis shows site and season together explained 50% of the variation in community composition (PERMANOVA R² = 0.50, p = 0.001), shaped by environmental gradients and seasonal turnover. These results highlight eDNA metabarcoding as an effective tool for biodiversity monitoring in urban wetlands and emphasize the need to preserve habitat diversity and management strategies in Macau.

  • The global food industry generates substantial waste, posing significant environmental, economic, and social challenges. This dissertation explores circular business strategies for food waste management, aiming to develop an efficient model that integrates circular economy principles and innovative technologies. Key research questions include: What are current food waste management practices? How can circular economy principles reduce food waste effectively? What role can technology play in improving these systems? The study also examines barriers to implementation and identifies gaps in existing literature. The methodology involves a comprehensive literature review, case studies, and the development of a detailed mathematical model. The literature review covers circular economy concepts, current food waste treatment technologies, machine learning and Al applications in waste management. Case studies from various countries provide insights into regulatory frameworks and innovative solutions. Central to this research is the mathematical modelling of food waste management systems. The model employs Hamiltonian and/or Lagrangian formulations to optimise waste transportation and processing. This approach allows for the simulation of various scenarios, helping to identify the most efficient pathways for food waste reduction and resource recovery. The model also incorporates phase transitions better to understand the dynamics of waste generation and treatment processes. Phase transitions mark changes on tendencies and in this case they help us to evaluate the viability of the construction of a fast track for the transportation of food waste in any city. Results indicate that adopting circular economy principles in food waste management is feasible and beneficial. Effective strategies include bioplastics, insectutilisation, and machine learning models for waste prediction and management. The developed mathematical model suggests efficient waste transportation through a coupled network approach, ensuring rapid and effective waste evacuation. The research highlights the importance of technological integration and cross-sector collaboration for sustainable food waste management. It also stresses the need for robust regulatory frameworks and consumer education to drive behavioural changes and support circular practices

Last update from database: 12/12/25, 7:01 PM (UTC)

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