AI-Integrated Nanosensors for Real-Time Monitoring of Pharmaceutical Residues in Urban Wastewater: Advances and Environmental Implications
نویسندگان
1 Saveetha College of Pharmacy, Saveetha Institute of Medical and Technical Sciences (Deemed to be University), SIMATS Deemed University, India, Chennai-602105.
2 Saveetha College of Pharmacy, Saveetha Institute of Medical and Technical Sciences (Deemed to be University), SIMATS Deemed University, India, Chennai-602105.
3 Saveetha College of Pharmacy, Saveetha Institute of Medical and Technical Sciences (Deemed to be University), SIMATS Deemed University, India, Chennai-602105.
4 Saveetha College of Pharmacy, Saveetha Institute of Medical and Technical Sciences (Deemed to be University), SIMATS Deemed University, India, Chennai-602105.
5 Saveetha College of Pharmacy, Saveetha Institute of Medical and Technical Sciences (Deemed to be University), SIMATS Deemed University, India, Chennai-602105.
6 Saveetha College of Pharmacy, Saveetha Institute of Medical and Technical Sciences (Deemed to be University), SIMATS Deemed University, India, Chennai-602105.
doi
10.22090/jwent.2026.2075989.1991چکیده
The occurrence of pharmaceutical residues in urban wastewater is an emerging environmental concern due to their persistence and potential ecological and human health impacts. Conventional analytical methods, such as chromatographic techniques, provide high accuracy but are limited by high costs, long analysis times, and restricted suitability for continuous monitoring. AI-integrated nanosensors represent a promising alternative for detecting trace pharmaceutical contaminants in complex wastewater matrices. These systems combine functional nanomaterials, including graphene, metal–organic frameworks, and molecularly imprinted polymers, with artificial intelligence algorithms for signal interpretation and pattern recognition. Machine learning-based data processing improves analyte discrimination and supports adaptive calibration, thereby reducing signal drift during prolonged operation. Recent field evaluations indicate good agreement between nanosensor responses and laboratory reference measurements, suggesting their potential for in situ monitoring applications. Overall, AI-integrated nanosensors offer a feasible approach for real-time wastewater surveillance and may support improved monitoring strategies in environmental and regulatory contexts.
