Nano Sensors and Artificial Intelligence Driven Environmental Intelligence for Real-Time Water Quality and Ecosystem Monitoring: A Critical Review

The deterioration of aquatic ecosystems and the emergence of new pollutants necessitate continuous monitoring, which goes beyond the capabilities of traditional analytical and sampling approaches. As nanotechnology has successfully promoted the creation of highly sensitive sensors, and Artificial Intelligence has transformed the landscape of data analysis, the fusion of the two concepts into efficient Environmental Intelligence (EI) systems is still at an immature level. This critical review brings together the merging of nanomaterials sensing platforms and Artificial Intelligence signal processing techniques for real-time, autonomous water quality analysis. The critical analysis of the whole process will further emphasize how carbon and metal oxide nano sensors can be effective in real-world scenarios, including Machine Learning strategies in addressing the inherent hardware-related difficulties of sensing platforms, including signal drift, cross-sensitivity, and fouling. This study also describes the translation of passive data analysis techniques towards predictive EI, including the successful application of EI in the prediction of heavy metals and algal bloom warning systems. Lastly, the translation of current data standardization approaches, sensing platform reliability, and algorithmic transparency is emphasized and necessary to facilitate the development of intelligent environmental networks from the current analytical environment.

کلیدواژه ها:

Environmental Intelligence (EI) ، Nanomaterial based Sensing ، Machine Learning ، real time monitoring ، Water quality