Intelligent Environmental Chemistry: AI-Assisted Detection and Destruction of Persistent Pollutants

Adeyemi Adeleye

Intelligent Environmental Chemistry: AI-Assisted Detection and Destruction of Persistent Pollutants

Author(s)	Dr. Adeyemi Adeleye
Country	United States
Abstract	Persistent pollutants present a long-term environmental challenge because they resist natural degradation, migrate across environmental compartments and accumulate in organisms and food webs. Per- and polyfluoroalkyl substances, polychlorinated biphenyls, chlorinated pesticides, pharmaceutical residues and brominated flame retardants may occur at trace concentrations within chemically complex water, soil, sediment and biological samples. Conventional environmental analysis provides highly sensitive and selective measurements, but laboratory workflows can be time-intensive, dependent on predetermined target lists and difficult to adapt to rapidly changing contaminant profiles. Treatment systems face a related limitation: fixed operating conditions may remove contaminants from water without completely destroying them, or may generate transformation products whose toxicity and persistence remain uncertain. Artificial intelligence offers a complementary capability for environmental chemistry. Machine-learning models can support spectral interpretation, chromatographic peak recognition, non-target screening, source attribution, toxicity prediction and treatment-process optimization. This study develops a conceptual architecture connecting AI-assisted pollutant detection with adaptive destruction technologies. The framework integrates sensor data, high-resolution mass spectrometry, chemical descriptors, environmental metadata, mechanistic reaction knowledge and feedback from advanced oxidation, photocatalytic and electrochemical treatment systems. An author-generated simulation compares conventional fixed-parameter treatment with an AI-optimized hybrid process across five persistent-pollutant classes. Illustrative destruction or mineralization efficiency increases from 58% to 91% for a PFAS mixture, from 63% to 89% for PCB congeners, from 69% to 94% for chlorinated pesticides, from 72% to 96% for pharmaceutical residues and from 61% to 90% for flame retardants. The simulated average increases from 64.6% to 92.0%. These values are illustrative and do not represent measured treatment-plant performance.The study concludes that intelligent environmental chemistry should connect detection, identification, risk prioritization, treatment selection and post-treatment verification within a closed analytical loop. AI should augment rather than replace validated chemical analysis and mechanistic reasoning. Reliable implementation requires representative training data, uncertainty estimation, explainable model outputs, transformation-product monitoring, mass-balance verification, cybersecurity and independent laboratory confirmation.
Keywords	environmental chemistry, artificial intelligence, persistent pollutants, PFAS, non-target screening, mass spectrometry, advanced oxidation, photocatalysis, electrochemical treatment
Field	Engineering
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-04-30

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American Journal of Advanced Multidisciplinary Research and Innovation

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Intelligent Environmental Chemistry: AI-Assisted Detection and Destruction of Persistent Pollutants

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