Authors : Sruthi S.; Bose V. V.

Volume/Issue : Volume 11 - 2026, Issue 5 - May

Google Scholar : https://tinyurl.com/mv4z5yac

Scribd : https://tinyurl.com/mr3ywjxb

DOI : https://doi.org/10.38124/ijisrt/26May317

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Abstract : Industrial carbon emissions significantly impact environmental sustainability and human health, creating an urgent need for advanced systems capable of efficient monitoring and mitigation. This study proposes an artificial intelligence-based carbon capture and surveillance framework that combines IoT-enabled embedded systems with machine learning techniques for continuous industrial emission supervision. The architecture utilizes an ESP32 microcontroller integrated with essential environmental sensors, including the MQ135 gas sensor for air quality assessment, the DHT11 sensor for temperature and humidity measurement, and a flame sensor for hazard detection. Real-time sensor readings are transmitted to the ThingSpeak cloud platform, allowing remote access and monitoring through mobile devices. A Pythondriven machine learning model analyzes the collected data to identify, categorize, and forecast pollution conditions with improved precision. When emission values surpass established safety limits, the system generates immediate alerts to support rapid intervention measures. Through the integration of edge-level sensing and cloud-based intelligence, the proposed framework offers an economical, expandable, and efficient approach to industrial carbon management while improving workplace safety. This research highlights the importance of AI-powered IoT systems in promoting smarter environmental governance and sustainable industrial operations.

Keywords : Industrial Emissions, IoT, ESP32, Carbon Monitoring, Machine Learning, Thingspeak Cloud, Industrial Safety.

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