Nanoparticles (NPs) are gaining popularity due to their exceptional size-to-volume ratio, which enables them to efficiently perform a wide range of chemical reactions. The application of these particles has expanded rapidly in various sectors. However, the traditional methods for synthesizing NPs often involve the use of toxic chemicals. Although these toxic chemicals can produce useful target NPs, the production of hazardous byproducts is inevitable. Green synthesis processes always exclude toxic materials from the synthesis procedures and use supplementary materials that are natural or less harmful. This study focuses on the synthesis of these materials without the use of toxic chemicals and the production of NPs from natural resources, such as peels, leaves, petals of flowers, fruits, and roots. These starting materials are cheap and safe and may reduce the impact of waste on the environment. This study also focuses on the application of such NPs in a variety of industries. Some examples of these industries include agriculture, food, and pharmaceuticals.

Nanoparticles have become more widely applied in industrial, consumer, and therapeutic products over the past decade, and this trend is presumed to persist due to the rapid population growth, industry, urbanization, and intensive agriculture. The manufacturing of nanomaterials is not necessarily accomplished through eco-friendly processes. Certain nanomaterials involve heavy metals. The release of nanomaterials into the environment could result in soil and aquatic system contamination. Once released into water and soil matrices, nanoparticles undergo dynamic transformations, including aggregation, dissolution, and surface modification, which determine their transport and bioavailability and their toxicological profiles. Different studies have consistently reported adverse impacts of metal, carbon, and plastic-based nanomaterials on aquatic organisms, soil microbial community, enzymatic activities, and nutrient cycling processes, mainly through oxidative stress, disruption of the membrane, and release of metal ions. These problems have stimulated intensive research aimed at the prediction of environmental concentrations of nanoparticles in water and soil and for their ecotoxicological effect on aquatic and terrestrial ecosystems. On the other hand, nanomaterials are also showing great potential for sustainable use, such as water purification, soil remediation, immobilization of contaminants, and geotechnical soil improvement, referring to soil stabilization, strength enhancement, permeability reduction, and ground improvement, where low dosages can improve the mechanical properties and respected environmental performance. This paper deals with current research on these competing roles, examining the causes of nanotoxicity as well as their positive geotechnical and remedial applications in water and soil systems.

The field of climate modeling is undergoing a significant transformation, moving away from the traditional General Circulation Models (GCMs) and toward the use of sophisticated artificial intelligence (AI)-based prediction systems. Research has shown that AI has the potential to improve climate modeling’s regional accuracy and computing efficiency. Machine learning downscaling better captures local precipitation extremes than GCMs, while hybrid AI–physics models cut ensemble costs by reducing computational demand without sacrificing accuracy. Nevertheless, these investigations have frequently functioned in discrete settings and oversimplified situations without a thorough connection with basic physical concepts. This drawback emphasizes the necessity of a more comprehensive strategy that can handle the intricacies of climatic variability and guarantee reliable model validation. In order to assess the possibilities and challenges of hybrid models in comparison to conventional GCMs, highlighting that AI complements GCMs in regional downscaling and extremes, while GCMs provide stronger global consistency, this study synthesizes proven climate models, AI methodologies, and their accuracy in climate predictions and analyzes existing climate models to evaluate the potential and limitations of hybrid models compared to traditional GCMs. Integrated AI-driven models show notable improvements in predicting regional variations in climate and accelerating simulation processes, especially when dealing with the growing presence of extreme weather occurrences. However, it is important to have consistent datasets and open evaluation procedures in order to guarantee accuracy and deal with the difficulties that come with model benchmarking. This research highlights how crucial it is to maintain interdisciplinary cooperation in order to properly utilize what AI has to offer in climate modeling. This partnership is essential to creating more accurate and useful climate projections, which will eventually guide successful mitigation and adaptation plans for a changing global environment. In order to have a greater understanding of our climate’s future, we must keep pushing the limits of the existing modeling tools.

As global temperatures continue to rise, understanding the relationship between climate and vegetation is crucial for agriculture and for mitigating and adapting to environmental changes. The complex interaction between vegetation and climate becomes even more significant as temperatures increase, making it essential to comprehend these changes in the environment. This study investigates vegetation dynamics in the Sylhet region of northeastern Bangladesh between 1988 and 2025, focusing on how long-term temperature changes have influenced vegetation cover. The research utilizes Landsat-derived Normalized Difference Vegetation Index (NDVI) datasets from 1988, 1995, 2005, 2015, and 2025, alongside monthly temperature records from NOAA’s NCEI. The temperature data was analyzed using Pearson’s correlation and descriptive statistical method to examine the relationship between vegetation and climate. The results show that dense vegetation declined sharply, with an estimated net loss of ~12.9 km² converting to sparse vegetation and ~1.5 km² converting to urban/barren land between 1988 and 2025. At the same time, sparse vegetation expanded, while urban area/barren land areas increased substantially. Water bodies also showed reductions. Over the same period, the mean annual temperature rose by +0.32 °C. These findings highlight the region’s ecological vulnerability to combined climatic and anthropogenic pressures, underscoring the urgent need for sustainable land management and adaptive strategies.

Quantum dots (QDs) have drawn a lot of attention as photocatalytic materials due to the growing need for environmentally friendly wastewater treatment technologies. Among these, carbon-based QDs, including graphene oxide quantum dots (GOQDs), graphitic carbon nitride (g-C₃N₄), and carbon quantum dots (CQDs), have exceptional optical, electronic, and surface characteristics that increase their suitability for degrading pollutants when exposed to sunlight or visible light. These composites are better at transferring charges, staying stable in light, and breaking down pollutants. Metal-based QDs like ZnO and CdS also have strong photocatalytic activity, but their sustainability remains a concern due to the potential release of toxic ions when they corrode in light. The green synthesis approach addresses these challenges. Using natural extracts, like polyphenols from tea leaves, to biofunctionalize surfaces has been shown to reduce toxicity and improve photocatalytic performance. Green synthesis using renewable precursors solves problems with toxicity, resource depletion, and environmental pollution, which supports a low-impact and circular technological approach. This study examines recent developments in the making, modifying, and use of QD-based photocatalysts in the environment, with a focus on CQD/g-C₃N₄ hybrid systems. Future research should focus on making green, non-toxic, regenerable, and highly active carbon-based QDs for safe large-scale water treatment.

Industrial packaging systems exert substantial environmental pressures, including material resource depletion, greenhouse gas emissions, and the accumulation of post-consumer waste. As global supply chains expand and sustainability regulations intensify, demand for environmentally responsible packaging solutions continues to rise. This study evaluates the environmental footprint of industrial packaging by integrating recent developments in life cycle assessment (LCA), ecological footprint (EF) methodologies, material innovations, and circular economy models. The assessment examines the sustainability performance of conventional and alternative packaging materials, plastics, aluminum, corrugated cardboard, and polylactic acid (PLA). Findings indicate that although corrugated cardboard is renewable, it still presents a measurable environmental burden, with evidence suggesting that incorporating solar energy into production can reduce its footprint by more than 12%. PLA-based trays demonstrate promising environmental performance when sourced from renewable feedstocks and directed to appropriate composting systems. Despite these advancements, several systemic challenges persist, including ecological overshoot in industrial regions where EF may exceed local biocapacity limitations in waste management infrastructure, and significant economic trade-offs. Transportation-related emissions and scalability constraints for bio-based materials further hinder large-scale adoption. Existing research suggests that integrating sustainable packaging across supply chains could meaningfully reduce environmental impacts. Achieving this transition requires coordinated cross-sector collaboration, standardized policy frameworks, and embedding advanced environmental criteria into packaging design and decision-making processes.