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Shaikh A. Hakim A,
Kazi Kutubuddin Sayyad Liyakat,
- Assistant Professor, Department of Electrical Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India
- Professor, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India
Abstract
Traditional environmental toxicology relies heavily on labor-intensive, often retrospective, sampling and analysis, limiting our understanding of dynamic pollutant behaviors and their real-time impact on ecosystems and human health. This study presents a novel, integrated framework leveraging advanced sensor networks and artificial intelligence (AI) to revolutionize the monitoring, assessment, and predictive modeling of environmental contaminants. We deployed a sophisticated array of multi-parameter sensors (e.g., electrochemical, optical, biosensors for heavy metals, organic pollutants, and physicochemical parameters) across a representative aquatic ecosystem. The high-volume, high-velocity data stream generated by these sensors was continuously fed into an AI-driven analytical platform. This platform utilizes deep learning algorithms for anomaly detection and complex pattern recognition, coupled with machine learning models for predictive ecotoxicological risk assessment, source identification, and forecasting contaminant dispersion. Our study reveals the AI-enhanced system’s superior capability in detecting subtle pollutant fluctuations, identifying emergent contaminant mixtures, and predicting acute and chronic ecotoxicological effects with unprecedented accuracy and temporal resolution. This research underscores the study of the transformative potential of combining ubiquitous sensing with intelligent data interpretation, moving environmental toxicology from reactive assessment to proactive, preventative risk management. This is the promise of an environmental toxicology study powered by the synergistic might of artificial intelligence and advanced sensor networks.
Keywords: Toxicology, environment, artificial intelligence, sensor, risk assessment
[This article belongs to Research and Reviews: A Journal of Toxicology ]
Shaikh A. Hakim A, Kazi Kutubuddin Sayyad Liyakat. A Study on AI-Enhanced Environmental Toxicology: Sensor-Driven Predictive Framework. Research and Reviews: A Journal of Toxicology. 2025; 15(03):-.
Shaikh A. Hakim A, Kazi Kutubuddin Sayyad Liyakat. A Study on AI-Enhanced Environmental Toxicology: Sensor-Driven Predictive Framework. Research and Reviews: A Journal of Toxicology. 2025; 15(03):-. Available from: https://journals.stmjournals.com/rrjot/article=2025/view=231797
References
- Shaikh HT, Liyakat KKS. Sensor-based Intelligent Wearable Glasses. J Dig Circ Innov Elect Devices. 2025;1(2):16–24.
- Liyakat KK Sayyad. Nanorobots: The Fight Against Cholesterol. Nano Trends. 2025;27(2).
- Shaikh HT, Liyakat KKS. Pre-Detection Systems Transfiguring Intoxication and Smoking Using Sensor and AI. J Instrum Innov Sci. 2025;10(2):19–31.
- Shaikh HT, Liyakat KKS. Millimetre Wave: A Study on the Backbone of Future IoT Connectivity. Adv Res Analog Digit Commun. 2025;2(2):20–31.
- Mulani AK, Liyakat KKS. Microwave Signals: A New Frontier in Non-Invasive Medical Diagnostics: A Study. J Microwave Eng Technol. 2025;12(3):27–41.
- Mulani AK, Liyakat KKS. Revolutionizing Optical Fibre Field Distribution with Linear Finite Element Method. Trends Opto-Electro & Opt Commun. 2025;15(3):31–41.
- Shaikh HT, Liyakat KKS. Robust Access Control Mechanisms in IoT Security using VHDL Programming. J VLSI Des Signal Process. 2025;11(2):31–40.
- Liyakat KKS. Advancing Towards Sustainable Energy With Hydrogen Solutions: Adaptation and Challenges. In: Özsungur F, Chaychi Semsari M, Küçük Bayraktar H, editors. Geopolitical Landscapes of Renewable Energy and Urban Growth. IGI Global Sci Publ; 2025. 357–394.
- Liyakat KKS. KK Approach for IoT Security: T-Cell Concept. In: Kumar R, Peng S, Jain P, Elngar A, editors. Deep Learning Innovations for Securing Critical Infrastructures. IGI Global Sci Publ; 2025. 369–390.
- Liyakat KKS. Hydrogen Energy: Adaptation and Challenges. In: Mabrouki J, editor. Obstacles Facing Hydrogen Green Systems and Green Energy. IGI Global Sci Publ; 2025. 205–236.
- Liyakat KKS. Role of Carbon-Based Supercapacitors in Regenerative Braking for Electrical Vehicles. In: Mhadhbi M, editor. Innovations in Next-Generation Energy Storage Solutions. IGI Global Sci Publ; 2025. 523–572.
- Liyakat KS. AI-Driven-IoT (AIIoT)-Based Decision Making in Drones for Climate Change: KSK Approach. In: Aouadni S, Aouadni I, editors. Recent Theories and Applications for Multi-Criteria Decision-Making. IGI Global Sci Publ; 2025. 311–340.
- Liyakat KS. Machine Learning-Driven Internet of Medical Things (ML-IoMT)-Based Healthcare Monitoring System. In: Soufiene B, Chakraborty C, editors. Responsible AI for Digital Health and Medical Analytics. IGI Global Sci Publ; 2025. 49–86.
- Liyakat KS. Transformation of Agriculture Effectuated by Artificial Intelligence-Driven Internet of Things (AIIoT). In: Garwi J, Dzingirai M, Masengu R, editors. Integrating Agriculture, Green Marketing Strategies, and Artificial Intelligence. IGI Global Sci Publ; 2025. 449–484.
- Upadhyaya AN, Surekha C, Malathi P, Suresh G, Suriyan K, Liyakat KKS. Pioneering Cognitive Computing for Transformative Healthcare Innovations. SSRN Electron J. 2025.
- Vaishnavi Ashok Desai. AI and Sensor Systems Revolutionizing Intoxication and Smoking Pre-Detection. J Control Instrum. 2025;16(3):15–26.
Research and Reviews: A Journal of Toxicology
| Volume | 15 |
| Issue | 03 |
| Received | 18/09/2025 |
| Accepted | 19/09/2025 |
| Published | 14/11/2025 |
| Publication Time | 57 Days |
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