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.

The shift from fossil fuels to renewable energy is a key component in achieving global sustainability and dealing with climate change. Natural resources, such as sunlight, air, water, and biomass, have tremendous potential to create clean energy; however, exploiting these resources in an efficient, stable, and large-scale integration manner is difficult due to their variable and distributed nature. Artificial intelligence (AI) approaches that mimic human learning and decision-making have recently become viable approaches to solving renewable energy problems. This study mainly examines the current landscape of AI applications across solar, wind, hydro, geothermal, ocean, hydrogen, bioenergy, and hybrid energy systems. AI enhances renewable energy forecasting, improves power system frequency analysis and stability assessments, and optimizes dispatch and distribution networks. Beyond technical optimization, AI also contributes to broader sustainability goals, including energy efficiency improvements, intelligent smart grid management, and enabling mechanisms such as carbon trading and circular economy practices to reduce exposure to climate extremes. Drawing on evidence from a range of renewable energy areas, this review highlights the importance of AI in bridging the link between technological innovation and sustainable energy management. This paper discusses potential future research avenues, such as building sophisticated AI designs, energy-efficient chips, and data communication networks. Ultimately, the synergy between AI and renewable energy systems represents not only a technological advancement but also a transformative pathway toward a resilient, low-carbon future.

Artificial intelligence (AI) is rapidly redefining the management of urban energy systems by coupling predictive analytics with closed-loop control across buildings, power grids, and mobility networks, positioning cities as critical leverage points in global decarbonization efforts. Contemporary urban environments generate vast, heterogeneous datasets that enable advanced machine learning applications; however, limitations remain, including interpretability–fairness trade-offs, fragmented data governance, interoperability gaps, cybersecurity risks, and insufficient long-term validation across diverse climatic and socio-economic contexts. This review evaluates AI-driven strategies for energy-efficient urban systems and identifies the technical and governance conditions required for scalable impact. The evidence synthesized indicates that supervised and ensemble learning models achieve high predictive accuracy for electricity demand and chiller performance, with models such as Random Forest Regression achieving R² values up to 0.9835 in electricity consumption forecasting, while unsupervised approaches detect latent inefficiencies in HVAC systems, delivering measurable savings typically around 6% under controlled benchmarking conditions. Deep learning architectures improve multi-building forecasting and real-time control, with hybrid CNN–LSTM models achieving prediction accuracies up to 97% and outperforming traditional statistical approaches in weekly energy demand forecasting achieving higher prediction accuracy and significant energy savings in complex urban subsystems with reported reductions of approximately 21–23% in residential and educational buildings and up to 37% in office HVAC systems. Hybrid and physics-informed AI models embed thermodynamic principles into data-driven frameworks, improving robustness, interpretability, and generalization. IoT sensor networks and edge-computing architectures support adaptive HVAC, demand–response, and smart-grid management, while integrated building–grid–mobility systems enhance load balancing, storage use, and carbon reduction. AI-enhanced strategies offer a credible pathway toward measurable reductions in urban energy use and emissions with deep reinforcement learning in digital twin environments reducing HVAC energy demand by 10–35% while maintaining thermal comfort within ±0.5 °C in line with ASHRAE standards, and overall energy savings reaching up to 44% in optimized systems when supported by interoperable infrastructures, secure digital-twin architectures, and standardized measurement and verification protocols.