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As water desalination continues to expand globally, desalination plants are continually under pressure to meet the requirements of sustainable development. However, the majority of desalination sustainability research has focused on new desalination projects, with limited research on sustainability performance of existing desalination plants. This is particularly important while considering countries with limited resources for freshwater such as the United Arab Emirates (UAE) as it is heavily reliant on existing desalination infrastructure. In this regard, the current research deals with the sustainability analysis of desalination processes using a generic sustainability ranking framework based on Mamdani Fuzzy Logic Inference Systems. The fuzzy-based models were validated using data from two typical desalination plants in the UAE. The promising results obtained from the fuzzy ranking framework suggest this more in-depth sustainability analysis should be beneficial due to its flexibility and adaptability in meeting the requirements of desalination sustainability.

Changes in microplastic concentrations were examined during various temporal events including heavy rain and snowmelt in a river and an urban stream receiving stormwater. Additionally, microplastic concentrations were measured in an urban river during an active combined sewage overflow event. Microplastic concentrations downstream of a combined sewage outfall were observed to increase seven times compared to ambient conditions. During heavy rainfall an increase of 50 times the microplastic concentration was observed in the urban creek with microplastic concentrations doubling in the urban river. However, the largest increase in microplastic concentration at both locations was observed during the primary snowmelt of spring, with microplastic concentrations increasing 114 times in the urban creek and 11 times in the urban river. These results suggest that more research is required to further establish the influence of both combined sewage overflows and snowmelt as a major temporal conduit of microplastics to freshwater environments.

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This paper addresses the detection of faulty situations that develop in activated sludge wastewater treatment plants (ASWWTP). The Kohonen Self-Organising map (KSOM) has been utilised to detect and track changes in different parameters for real data collected from the Seafield wastewater treatment plant, Edinburgh, UK. The results demonstrate that this method is simple, computationally efficient and provides useful information for the process engineer who is faced with improving the performance of the WWTP.

A community science project in the Ottawa River Watershed in Canada interacted with an existing volunteer base to collect sediment from 68 locations in the watershed over approximately 750 km. Ninety-one percent of the distributed kits were returned with 42 volunteers taking part in the project. After analysis, particle concentrations were relatively low compared to previous freshwater microplastic sediment research, with contributing factors including (but not limited to) the large size of the watershed, a lower population base compared to other researched freshwater watersheds, the relative size and discharge of the Ottawa River and the large seasonal fluxes experienced in the river basin. Utilising community science for sampling large freshwater watersheds demonstrated its advantages in the research, especially spatially. However, careful consideration to research design and implementation is essential for community science projects examining microplastics in freshwater sediments. Research teams should ensure they are responsible for strict quality assurance and quality control protocols, especially in the laboratory with sample preparation and processing. Nonetheless, community science is potentially an extremely useful approach for researchers to use for microplastic sampling projects over large spatial areas.

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