ABSTRACT The quest for better and advanced materials that comply with economic and environmental sustainability principles has led to the development of several materials, including geopolymers. Derived from aluminosilicates and acid/alkali activators, geopolymers have proven to be a good alternative to several materials, to be environmentally benign and cheap to produce as they can be produced from valorizing waste. It is therefore necessary to know more about geopolymers to facilitate their industrial application and commercialization. In this regard, the paper reviews the physicochemical characteristics of geopolymers to fine‐tune them to specific applications, especially in wastewater treatment. The paper critically and comparatively analyses the difference between geopolymers and alkali‐activated materials, the alternative substrates for geopolymerization and greener ways for developing geopolymers. The paper further discusses the application of geopolymers to wastewater treatment and the mechanisms through which geopolymers remove impurities from wastewater.
This research evaluated machine learning (ML) algorithms to predict the efficiency of a cascade aerating trickling filter (CATF) in removing physicochemical characteristics such as suspended solids (SS), biological oxygen demand (BOD) and chemical oxygen demand (COD). The pre-evaluation of characteristics' data demonstrated that CATF is effective in removing TSS, BOD and COD. Four ML algorithms, that is, random forest (RF), Gaussian process regression (GPR), extreme gradient boosting (XGB) and light gradient boosting machine (LightGBM), were assessed for physicochemical prediction of the CATF. The appraisal of the ML algorithms revealed that RF outperformed the others in predicting 81% SS and 84% COD removal. Nevertheless, GPR was found to be superior, with 66% accuracy in detecting CATF's BOD performance. Ensemble ML algorithms, such as RF and GPR, are recommended as effective solutions for optimizing the performance of wastewater treatment plants.
ABSTRACT Desalination techniques, especially membrane‐based methods, provide effective solution to water scarcity. This research provides a thorough overview of the incorporation of technological advancements like Machine Learning, Internet of Things, Big Data, and Artificial Neural Networks into desalination systems. These methods allow accurate estimation of important parameters like membrane fouling, permeate flux and energy consumption in addition to facilitating real‐time monitoring as well as the early detection of faults. The results reported show an excellent predictive capability from AI models, including ANN, SVM, and SVM, with their coefficients of derivation (R2) that exceed 0.9. Additionally, smart systems help improve water quality and efficiency of processes as well as reduce operating costs and prolong the life of plants and have minimal environmental impact. But issues related to access to data and the ability to interpret models are major obstacles to massive installation. The use of modern technologies will transform traditional desalination processes into more efficient, robust, and smart systems; the existing operational and technical limitations are remediated.
Under global climate change, coastal wetlands face severe dual stress of salinization and drought, whose coupling drives wetland degradation. As a key species, Phragmites australis maintains wetland function, but its community resilience mechanism to salinity-moisture coupling remains unclear. We developed a dynamic growth model for P. australis under these stresses, calibrated and validated via indoor controlled experiments and field surveys. Three scenarios (drought + high salinity, moist + low salinity, flooded + moderate salinity) quantified non-linear impacts on biomass. The model showed high accuracy (R = 0.97, RSR = 0.2452, IOA >= 0.97). Scenario simulations revealed: moist + low salinity yielded the highest biomass peak (similar to 2000 g/m(2)) with gentle decline; flooded + moderate salinity had a slightly lower peak (similar to 1900 g/m(2)) and faster decline; drought + high salinity showed the lowest peak (similar to 1800 g/m(2)) and most rapid post-September decline. This study overcomes static model limitations, providing a scientific tool for adaptive restoration of wetland P. australis communities.
Water-sensitive urban design offers an alternative to traditional stormwater management by integrating blue-green spaces and nature-based solutions into cities. Wider uptake requires shifts in practice supported by public understanding and engagement. Drawing on a national survey of 501 respondents, this study investigates water-sensitive urban design literacy in Aotearoa New Zealand. Findings demonstrate strong public support even in the absence of technical understanding. Overall, despite understanding gaps, participants supported legislation requiring nature-based solutions and welcomed constructed wetlands in their neighbourhood. Furthermore, over half of them indicated willingness to contribute financially to protect urban waterways. Our findings contrast with the assumption that limited technical understanding constitutes a barrier to the implementation of water-sensitive urban design and associated technologies. The paper discusses implications for land use planning, emphasising that engagement strategies leveraging environmental values and multiple co-benefits may prove more effective than technical education alone in accelerating water-sensitive urban design implementation.
