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The present chapter examines the significant influence of Artificial IntelligenceArtificial Intelligence (AI) (AI) on the application of biosensorsBiosensor for monitoringMonitoring environmentalEnvironmental pollutantsPollutants. As risks from industrial and agricultural pollutionPollution escalate, traditional analytical techniques are often too slow and expensive for effective, real-time analysis. While biosensorsBiosensor present a swift, portable, and economical solutionSolutions, their effectiveness in challenging settings can be compromised by issues such as signal interference and background noise. The incorporation of AIArtificial Intelligence (AI), especially machine learningMachine Learning (ML) (ML) and deep learning (DL) frameworks, marks a pivotal shift, enabling the creation of smart sensor systems with advanced functionalities. This text traces the progression from conventional to AI-enhanced biosensorsBiosensor, highlighting key AIArtificial Intelligence (AI) methods that improve data handling, feature identification, and signal analysis. It investigates how AI-based strategies refine the essential components of biosensorsBiosensor, resulting in superior specificitySpecificity, sensitivity, and stability. Through case studies focused on waterWater, air, and soilSoil quality, the chapter illustrates the real-world utility of this synergy, covering applications from forecasting contaminationContamination events to the immediate classification of pollutantsPollutants. In conclusion, the fusion of AIArtificial Intelligence (AI) and biosensorBiosensor technologyTechnology is pioneering new possibilities in environmentalEnvironmental science, paving the way for more proactive, precise, and intelligent management of our planet.

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AI-Assisted Biosensors for Environmental Pollutant Monitoring

  • 章节

  • 发表日期：

    2026年

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  • Chansi,
  • Bazil Gulzar Wani,
  • Maroosha Mir,
  • Tinku Basu

摘要

The present chapter examines the significant influence of Artificial IntelligenceArtificial Intelligence (AI) (AI) on the application of biosensorsBiosensor for monitoringMonitoring environmentalEnvironmental pollutantsPollutants. As risks from industrial and agricultural pollutionPollution escalate, traditional analytical techniques are often too slow and expensive for effective, real-time analysis. While biosensorsBiosensor present a swift, portable, and economical solutionSolutions, their effectiveness in challenging settings can be compromised by issues such as signal interference and background noise. The incorporation of AIArtificial Intelligence (AI), especially machine learningMachine Learning (ML) (ML) and deep learning (DL) frameworks, marks a pivotal shift, enabling the creation of smart sensor systems with advanced functionalities. This text traces the progression from conventional to AI-enhanced biosensorsBiosensor, highlighting key AIArtificial Intelligence (AI) methods that improve data handling, feature identification, and signal analysis. It investigates how AI-based strategies refine the essential components of biosensorsBiosensor, resulting in superior specificitySpecificity, sensitivity, and stability. Through case studies focused on waterWater, air, and soilSoil quality, the chapter illustrates the real-world utility of this synergy, covering applications from forecasting contaminationContamination events to the immediate classification of pollutantsPollutants. In conclusion, the fusion of AIArtificial Intelligence (AI) and biosensorBiosensor technologyTechnology is pioneering new possibilities in environmentalEnvironmental science, paving the way for more proactive, precise, and intelligent management of our planet.