The rising demand for clean water and the environmental challenges associated with fossil fuels have encouraged the application of renewable and greener energy systems in desalination. Moreover, the small footprint and high productivity favored the membrane-based process in the water industry. In the past few decades, noticeable work has been performed on the development and applicability of membrane-based desalination processes powered by renewable energy sources such as solar, wind, tidal, and geothermal. Several integrated membrane desalination processes for producing clean water with sustainable and clean energy are introduced. This review details the source and performance efficiencies of existing renewable energy technologies and their application in membrane-based desalination processes, with a special focus on current advancements and challenges. This study reviews the interconnections between water, energy, and the environment and explores future energy-efficient desalination options for energy savings and environmental protection.

This comprehensive review focuses on treatment technologies for produced water, with a particular emphasis on membrane-based systems. These systems offer significant advantages, including high contaminant removal efficiencies, compact design, and the potential for resource recovery. The review emphasizes the application of these technologies, their performance in meeting regulatory standards, and the challenges they face, such as operational efficiency and fouling. It highlights the need for further research and for the optimization of processes to enhance their efficiency. The integration of conventional methods with advanced treatment processes is also explored, with a vision toward developing hybrid systems for improved treatment efficiency. Overall, membrane-based systems show great promise for the treatment of produced water, but further advancements, sustainability considerations, and integration with other technologies are essential for their successful implementation in large-scale applications.

This study was conducted to set up a new reference line for municipal solid waste quantification and characterization in the least urbanized cities of a developing nation. A survey was run to collect baseline data from 60 new municipalities of Nepal. The study covered a sample size of 3300 households, 600 institutions and 600 commercial establishments in those municipalities. The municipalities were further stratified according to geographical location, the degree of urbanization, household expenditure and population size to assess the influence on waste generation. The results indicated that the average per capita household waste generation is 115 g day^-1 while the average total municipal waste generation was estimated to be 180 g day^-1 per capita.The study also revealed that size of municipal population, geographic location, household expenditure and degree of urbanization were found to have a significant influence on the unit waste generation. The larger the size of municipal population the higher the per capita household waste generation, degree of urbanization and per capita waste production. Increasing household expenditure revealed an increase in the unit waste generation. The waste categories included organic waste, plastics, paper and paper products, textiles, rubber and leather, metals, glass, and others. The results indicated that organic waste dominated the characterization (62%), followed by plastics (12%) and paper/paper products (11%).

Accurate estimation of climate change impacts on catchment hydrology is essential for effective future water management. The efficacy of such estimations is dependent on proper climate model selection. In this study, an attempt was made to formulate a methodology for climate model selection, evaluating eight climate models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The models were assessed for their ability to simulate variables used in hydrological studies and large-scale atmospheric circulation influencing rainfall in Australia. Five statistical indicators Root Mean Square Error (RMSE), Spatial Correlation (SC), Percentage Bias (Pbias), Normalized Root Mean Square Error (NRMSE), and Nash-Sutcliffe Efficiency (NSE) were used to evaluate the performance, and the models were ranked through Compromise Programming (CP), a multiple criteria decision making technique. Results show that HadGEM3-GC31-LL performed well in most of the categories considered and was top top-ranked model overall followed by GFDL-ESM4, CESM2-CAM6-RT, and CanESM5 for Australia. Conversely, MIROC6 consistently ranked lower in most of the categories. In the context of simulating hydrological variables, CESM2-CAM6-RT, HadGEM3-GC31-LL, and GFDL-ESM4 emerged as the top three models. The robustness of the proposed methodology suggests its applicability for model selection, making it a replicable approach for climate change impact assessment studies in diverse regions.