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April 21, 2026Discover Applied SciencesOpen Access

Artificial intelligence–driven sensor systems for heavy metal pollution monitoring: emerging applications and future directions

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Authors

PWPriyanka Wagh

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Overview

Perspective discusses AI-driven strategies to enhance heavy metal detection in environmental monitoring, suggesting implications for governance.

Key Points

  • Examine the role of AI in improving sensor systems for monitoring heavy metal pollution.
  • Discussed AI-driven sensor technologies including electrochemical, optical, spectroscopic, and biosensors.
  • Analyzed machine learning techniques like random forest, support vector machines, and convolutional neural networks.
  • Explored integration with IoT frameworks for real-time monitoring.
  • AI-driven sensors enable better detection and prediction of heavy metal concentrations.
  • Addressed challenges such as data standardization and model interpretability.
  • Highlighted potential for real-time surveillance and enhanced policy decisions.

Cite This Study

Priyanka Wagh (2026) studied this question.

synapsesocial.com/papers/69e71423cb99343efc98d8efhttps://doi.org/10.1007/s42452-026-08670-6
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