فهرست نشریات علمی دسترسی آزاد ایران

The increasing pressures of climate change, freshwater scarcity, emerging pollutants, ecosystem degradation, urbanization, and industrialization have fundamentally reshaped research in water and environmental sciences. Conventional approaches to monitoring, treating, and managing water and environmental resources are increasingly faced with the complexity, scalability, dynamics, and inter-relationships of environmental systems. In many cases, these methods are no longer able to respond to current challenges alone. In this context, the convergence of artificial intelligence (AI) and nanotechnology in achieving the Sustainable Development Goals (SDGs) is emerging not just as a technological trend, but as an emerging scientific paradigm; a paradigm that has the potential to fundamentally redefine the way we understand, predict, and challenge environmental concerns [1, 2]. The field of environmental sciences has drastically transformed from primarily a reactive, laboratory-based approach to a more predictive, data-rich, material-enabled, and systems-based discipline [3] over the last decade.AI appears as one of the major driving forces in this paradigm, in which machine learning (ML), deep learning (DL), optimization, and hybrid modeling techniques are successfully utilized for predicting pollutant behavior, designing treatment strategies, understanding complicated sensor signals, and facilitating material discovery and real-time decision-making in water and environmental systems [4-7]. Nanotechnology, on the other hand, has established itself as a mature and multidisciplinary system design paradigm for producing sensitive, selective, and tunable advanced adsorbents, catalysts, photocatalysts, membranes, antibacterial materials, delivery systems, nanosensors [8-10], and nano-agrochemicals such as nanofertilizers, nanopesticides, nanonutrients, etc. [16-20].