Design, Implementation, and Performance Evaluation of a Real-time Industrial Air Quality Monitoring System using ESP32 and Multi-gas Sensors

Abstract

Industrial air pollution poses significant risks to human health and environmental sustainability, particularly in regions affected by mining and other industrial activities. Pollutants such as ozone (O3), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and particulate matter, as observed in Ikpeshe and Iloke due to mining operations in Edo State, Nigeria, contribute to both acute and chronic health conditions, including cardiovascular diseases, lung cancer, and respiratory infections. Conventional air-quality monitoring systems are often expensive, difficult to deploy, and limited in their ability to provide continuous, remotely accessible data. This study presents the design, implementation, and performance evaluation of a cost-effective, real-time industrial air-quality monitoring system based on the Internet of Things (IoT). The system utilizes an ESP32 Wi-Fi microcontroller integrated with MQ-135 and MQ-2 gas sensors to detect variations in air pollutants and combustible gases. An OLED display and the Blynk IoT platform facilitate both local and remote access to monitoring data. The system was designed and simulated in Proteus and implemented in a compact 3D-printed enclosure. Experimental evaluation examined sensor responses to different gas-source proximities, carbon monoxide generated during wood combustion, and changes in the air quality index (AQI). Results demonstrated a direct correlation between pollutant-source proximity and sensor response: sensor resistance decreased, and output voltage increased as the gas source approached the sensing elements. In the carbon monoxide experiment, measured concentrations increased from 10 ppm as smoke accumulated in an enclosed environment, confirming the system’s ability to detect declining air quality. Similarly, the AQI rose from 24 under baseline conditions to hazardous levels after petroleum exposure, then decreased after the pollutant source was removed. The complete system consumed approximately 3.33 W, with an estimated battery operating time of 5.3 hours. These findings suggest that the proposed IoT-enabled system provides a compact, low-power, and remotely accessible solution for real-time air-quality monitoring and early pollution alerts in industrial environments. The system offers potential for affordable environmental monitoring and could be further enhanced through sensor calibration, expanded pollutant detection, and integration with advanced data analytics.