Abstract
Sewage systems generate hazardous gases such as methane (CH₄) and ammonia (NH₃), which pose significant risks including toxic exposure, fire hazards, and explosion threats. This paper proposes an AI-integrated Internet of Things (IoT)-based sewage gas detection and emergency response system aimed at enhancing worker safety and enabling intelligent hazard mitigation. The system utilizes MQ-4 and MQ-135 gas sensors interfaced with an ESP32 microcontroller to continuously monitor gas concentration levels in real time. Upon exceeding predefined safety thresholds, the system triggers local alerts and transmits sensor data to a cloud-based artificial intelligence (AI) agent for risk evaluation and decision support. The AI module classifies hazard severity and generates dynamic safety instructions through a chat-based interface. Simultaneously, automated SMS notifications are dispatched to the sewage coordinator and fire service authorities to ensure rapid emergency response. The integration of IoT communication, AI-driven analytics, and automated alert mechanisms provides a scalable, cost-effective, and intelligent safety solution for sewage monitoring applications. Experimental results demonstrate reliable detection capability and effective real-time emergency alert generation, contributing to improved occupational safety and smart infrastructure management.
Keywords
Artificial Intelligence (AI), Internet Of Things (Iot), Sewage Gas Detection, Methane Detection, Ammonia Monitoring, MQ-4 Sensor, MQ-135 Sensor, Emergency Alert System, Smart Safety Systems, Real-Time Monitoring.
Citations
IRE Journals:
Pradeep J, Harshini R, Priyanga, Divya Pranav A, Prabakaran T "Automated Sewage Gas Monitoring and Alerting System" Iconic Research And Engineering Journals Volume 9 Issue 8 2026 Page 1899-1908 https://doi.org/10.64388/IREV9I8-1714380
IEEE:
Pradeep J, Harshini R, Priyanga, Divya Pranav A, Prabakaran T
"Automated Sewage Gas Monitoring and Alerting System" Iconic Research And Engineering Journals, 9(8) https://doi.org/10.64388/IREV9I8-1714380
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