CTAB-crafted ZnO nanostructures for environmental remediation and pathogen control.

This study addresses the critical need for efficient and sustainable methods to tackle organic pollutants and microbial contamination in water. The present work aim was to investigate the potential of multi-structured zinc oxide nanoparticles (ZnO NPs) for the combined photocatalytic degradation of organic pollutants and antimicrobial activity. A unique fusion of precipitation-cum-hydrothermal approaches was precisely employed to synthesize the ZnO NPs, resulting in remarkable outcomes. The synthesized CTAB/ZnO NPs demonstrated exceptional properties: they were multi-structured and crystalline with a size of 40 nm and possessed a narrow band gap energy of 2.82 eV, enhancing light absorption for photocatalysis. These nanoparticles achieved an impressive degradation efficiency of 91.75% for Reactive Blue-81 dye within 105 min under UV irradiation. Furthermore, their photocatalytic performance metrics were outstanding, including a quantum yield of 1.73 × 10-4 Φ, a kinetic reaction rate of 3.89 × 102 µmol g-1 h-1, a space-time yield of 8.64 × 10-6 molecules photon-1 mg-1, and a figure-of-merit of 1.03 × 10-9 mol L J-1 g-1 h-1. Notably, the energy consumption was low at 1.73 × 10-4 J mol-1, compared to other systems. Additionally, the ZnO NPs exhibited effective antimicrobial activity against S. aureus and P. aeruginosa. This research underscores the potential of tailored ZnO NPs as a versatile solution for addressing both organic pollution and microbial contamination in water treatment processes. The low energy consumption further enhances its attractiveness as a sustainable solution.

An evaluative technique for drought impact on variation in agricultural LULC using remote sensing and machine learning.

Drought events threaten freshwater reservoirs and agricultural productivity, particularly in semi-arid regions characterized by erratic rainfall. This study evaluates a novel technique for assessing the impact of drought on LULC variations in the context of climate change from 2018 to 2022. Various data sources were harnessed, encompassing Sentinel-2 satellite imagery for LULC classification, climate data from the CHIRPS and AgERA5 databases, geomorphological data from JAXA's ALOS satellite, and a drought indicator (Vegetation Health Index (VHI)) derived from MODIS data. Two classifier models, namely gradient tree boost (GTB) and random forest (RF), were trained and assessed for LULC classification, with performance evaluated by overall accuracy (OA) and kappa coefficient (K). Notably, the GTB model exhibited superior performance, with OA > 90% and a K > 0.9. Over the period from 2018 to 2022, Fez experienced LULC changes of 19.92% expansion in built-up areas, a 34.86% increase in bare land, a 17.86% reduction in water bodies, and a 37.30% decrease in agricultural land. Positive correlations of 0.81 and 0.89 were observed between changes in agricultural LULC, rainfall, and VHI. Furthermore, mild drought conditions were identified in the years 2020 and 2022. This study emphasizes the importance of AI and remote sensing techniques in assessing drought and environmental changes, with potential applications for improving existing drought monitoring systems.