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Water pollution is a pressing global concern, with per- and polyfluoroalkyl substances (PFAS) being considered as "forever contaminants." Among them, perfluorooctanesulfonic acid (PFOS) has received significant attention for its adverse effects on human health and aquatic ecosystems. This study aimed to design an innovative adsorbent for effective PFOS removal with exceptional water stability, improving its cost-performance trade-off. The current work simultaneously improved the stability of water of Cu-based metal–organic framework (CMOF) and increased its PFOS removal capacity by modifying it with amine-functionalized SiO2 nanoparticles (AF-CMOF). AF-CMOF presented a lower specific surface area of 999 m2 g−1 compared to CMOF with a surface area of 1098 m2 g−1. AF-CMOF showed remarkable PFOS uptake performance of 670 mg/g compared to the performance of the Cu-based MOF which exhibited a PFOS uptake capacity of only 22 mg/g. The most suitable pH for PFOS removal using both adsorbents was determined to be 3. In addition, AF-CMOF demonstrated excellent water stability, retaining its structural integrity even after seven days of water contact, while CMOF structure collapsed rapidly after four days of water exposure. Moreover, the study identified the significant pH influence on the PFOS uptake process, with electrostatic interactions between protonated amine functionalities and PFOS molecules identified as the dominant mechanism. The study's findings present the potential of synthesized adsorbent as a superior candidate for PFOS uptake and contribute to the development of effective water treatment technologies. [ABSTRACT FROM AUTHOR]

This study breaks new ground by developing a multi-hazard vulnerability map for the Tensift watershed and the Haouz plain in the Moroccan High Atlas area. The unique juxtaposition of flat and mountainous terrain in this area increases sensitivity to natural hazards, making it an ideal location for this research. Previous extreme events in this region have underscored the urgent need for proactive mitigation strategies, especially as these hazards increasingly intersect with human activities, including agriculture and infrastructure development. In this study six advanced machine learning (ML) models were used to comprehensively assess the combined probability of three significant natural hazards: flooding, gully erosion, and landslides. These models rely on causal factors derived from reputable sources, including geology, topography, meteorology, human activities, and hydrology. The research's rigorous validation process, which includes metrics such as specificity, precision, sensitivity, and accuracy, underlines the robust performance of all six models. The validation process involved comparing the model's predictions with actual hazard occurrences over a specific period. According to the outcomes in terms of the area under curve (AUC), the XGBoost model emerged as the most predictive, with remarkable AUC values of 93.41% for landslides, 91.07% for gully erosion and 93.78% for flooding. Based on the overall findings of this study, a multi-hazard risk map was created using the relationship between flood risk, gully erosion, and landslides in a geographic information system (GIS) architecture. The innovative approach presented in this work, which combined ML algorithms with geographical data, demonstrates the power of these tools in sustainable land management and the protection of communities and their assets in the Moroccan High Atlas and regions with similar topographical, geological, and meteorological conditions that are vulnerable to the aforementioned risks. [ABSTRACT FROM AUTHOR]

This study explores and compares the predictive capabilities of various ensemble algorithms, including SVM, KNN, RF, XGBoost, ANN, DT, and LR, for assessing flood susceptibility (FS) in the Houz plain of the Moroccan High Atlas. The inventory map of past flooding was prepared using binary data from 2012 events, where "1" indicates a flood-prone area and "0" a non-flood-prone or extremely low area, with 762 indicating flood-prone areas. 15 different categorical factors were determined and selected based on importance and multicollinearity tests, including slope, elevation, Normalized Difference Vegetation Index, Terrain Ruggedness Index, Stream Power Index, Land Use and Land Cover, curvature plane, curvature profile, aspect, flow accumulation, Topographic Position Index, soil type, Hydrologic Soil Group, distance from river and rainfall. Predicted FS maps for the Tensift watershed show that, only 10.75% of the mean surface area was predicted as very high risk, and 19% and 38% were estimated as low and very low risk, respectively. Similarly, the Haouz plain, exhibited an average surface area of 21.76% for very-high-risk zones, and 18.88% and 18.18% for low- and very-low-risk zones respectively. The applied algorithms met validation standards, with an average area under the curve of 0.93 and 0.91 for the learning and validation stages, respectively. Model performance analysis identified the XGBoost model as the best algorithm for flood zone mapping. This study provides effective decision-support tools for land-use planning and flood risk reduction, across globe at semi-arid regions. [ABSTRACT FROM AUTHOR]

This study delves into the realm of water treatment by conducting a comprehensive techno-economic evaluation of direct contact membrane distillation (DCMD) and nanofiltration (NF) processes. While previous research has explored the technical aspects of membrane distillation (MD) and nanofiltration, there remains a notable gap in economic analyses. Our research aims to bridge this gap by assessing the financial feasibility of employing MD and NF technologies for water desalination. Specifically, we scrutinize the performance of hydrophobic microporous flat sheet membranes crafted from polytetrafluoroethylene (PTFE) supported by non-woven polypropylene (PP) in desalinating brackish water through DCMD and NF processes. By varying operating conditions such as flow rate and feed temperature, we evaluate the membrane's efficacy. Employing an analytical model based on heat and mass transfer equations, we predict process performance across diverse scenarios. Our model demonstrates a high level of accuracy, with flux predictions deviating by less than 10% when utilizing the Knudsen-molecular mechanism model. Furthermore, through a detailed design and economic analysis of industrial-scale units for both processes, we reveal that the cost of permeated water is lower with NF compared to DCMD. Specifically, our calculations indicate a water cost of 1.34 USD/m3 for DCMD at a feed temperature of 65 °C with an 80% recovery rate, positioning it as a competitive option among conventional desalination methods. Notably, our financial assessment highlights that steam cost constitutes the primary expense in DCMD operations, contingent upon heating value and fuel prices. Noteworthy findings suggest that natural gas emerges as the most cost-effective fuel for steam production in a DCMD plant. This study underscores the economic viability and potential cost efficiencies associated with NF over DCMD in water treatment applications. [ABSTRACT FROM AUTHOR]

Purpose: Particle size distribution (PSD) assessment, which affects all physical, chemical, biological, mineralogical, and geological properties of soil, is crucial for maintaining soil sustainability. It plays a vital role in ensuring appropriate land use, fertilizer management, crop selection, and conservation practices, especially in fragile soils such as those of the North-Western Himalayas. Materials and methods: In this study, the performance of eleven mathematical and three Machine Learning (ML) models used in the past was compared to investigate PSD modeling of different soils from the North-Western Himalayan region, considering that an appropriate model must fit all PSD data. Results and discussion: Our study focuses on the significance of evaluating the goodness of fit in particle size distribution modeling using the coefficient of determination (R2adj = 0.79 to 0.45), the Akaike information criterion (AIC = 67 to 184), and the root mean square error (RMSE = 0.01 to 0.09). The Fredlund, Weibull, and Rosin Rammler models exhibited the best fit for all samples, while the performance of the Gompertz, S-Curve, and Van Genutchen models was poor. Of the three ML models tested, the Random Forest model performed the best (R2 = 0.99), and the SVM model was the lowest (R2 = 0.95). Thus, the PSD of the soil can be best predicted by ML approaches, especially by the Random Forest model. Conclusion: The Fredlund model exhibited the best fit among mathematical models while random forest performed best among the machine learning models. As the number of parameters in the model increased better was the accuracy. [ABSTRACT FROM AUTHOR]

In twenty-first century buildings, green roof systems are envisioned as great solution for improving Environmental sustainability in urban ecosystems and it helps to mitigate various health hazards for humans due to climatic pollution. This study determines the feasibility of using five domestic organic wastes, including sawdust, wood bark, biochar, coir, and compost, as sustainable substrates for green roofs as compared to classical Sri Lankan base medium (fertiliser + potting mix) in terms of physicochemical and biological parameters associated with growing mediums. Comprehensive methodologies were devised to determine the thermal conductivity and electric conductivity of growing mediums. According to preliminary experimental results, the most suitable composition for green roof substrates comprised 60% organic waste and 40% base medium. Sawdust growing medium exhibited the highest moisture content and minimum density magnitudes. Biochar substrate was the best performing medium with the highest drought resistance and vegetation growth. The wood bark substrate had the highest thermal resistance. Growing mediums based on compost, sawdust, and coir produced the best results in terms of nitrate, phosphate, pH, and electric conductivity (EC) existence. This study provided a standard set of comprehensive comparison methodologies utilising physicochemical and biological properties required for substrate characterization. The findings of this research work have strong potential in the future to be used in selecting the most suitable lightweight growing medium for a green roof based on stakeholder requirements. [ABSTRACT FROM AUTHOR]

Land use changes have been investigated in the surroundings of 14 rural Montenegrin settlements in order to get specific information about trends in land abandonment since around 1950. Permanently, seasonally and less inhabited settlements with different geographic conditions were studied. This was done by interviewing local inhabitants, which enabled a holistic approach to reveal the underlying processes of land abandonment. According to the observed patterns of land use change, the study sites can be categorized into intensified, urbanized, extensified, overgrown and forested cases. The category of extensified settlements is characterized by a highly reduced agricultural management intensity, resulting in an increase in grasslands and fruit trees at the expense of cropland. This land use change is mainly related to emigrating and aging inhabitants, having less livestock. Such extensive land use is found in both permanently inhabited and abandoned villages. Only some studied settlements became largely overgrown by bushes and forest. The steep average slope gradients and a large distance to the nearest city are explanatory factors of such land abandonment. Land use intensification takes place in low-lying areas located nearby towns. [ABSTRACT FROM AUTHOR]