International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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

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“Intelligent Enzyme-Assisted Air Purification Using IoT Sensors and Machine Learning Models”

Author(s) Devjit Datta
Country India
Abstract Air pollution continues to be a defining environmental and public-health challenge in modern urban ecosystems. Traditional chemical and mechanical filtration systems are often energy-intensive, costly, and unsustainable. Enzyme-based biocatalytic systems offer an environmentally friendly and highly specific method for degrading toxic airborne pollutants such as volatile organic compounds (VOCs), sulfur oxides (SOx), nitrogen oxides (NOx), and particulate matter. However, the efficiency of enzyme-based systems depends heavily on real-time environmental conditions and optimal operational parameters.
This research proposes a holistic framework integrating Internet of Things (IoT) sensor networks and machine-learning (ML) algorithms with enzyme-driven biocatalytic air purification. The study identifies key enzyme candidates, designs an IoT-enabled sensing architecture, applies ML models for predictive pollutant monitoring, and develops an autonomous control system for optimizing enzymatic performance.
Simulation results demonstrate that the integrated IoT-ML model significantly enhances pollutant degradation efficiency (25–35%), increases the stability of enzyme systems, and enables dynamic pollutant prediction with high accuracy.
The proposed smart sustainable framework represents a novel convergence of biotechnology, artificial intelligence, and environmental engineering to support cleaner urban air and advance global sustainability goals.
Keywords IoT, Machine Learning, Enzymes, Air Pollution, Smart Environmental Monitoring, Biocatalysis, Biosensors
Field Engineering
Published In Volume 7, Issue 6, November-December 2025
Published On 2025-11-19
DOI https://doi.org/10.36948/ijfmr.2025.v07i06.61048
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E-ISSN 2582-2160
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