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This study presents a real-time Internet of Things (IoT)-based air quality monitoring system designed for industrial and research environments. The system integrates a Raspberry Pi with low-cost sensors, namely the BME688, PM2.5, MQ-2, and MQ-135, to monitor key environmental parameters such as particulate matter, temperature, humidity, pressure, and concentrations of harmful gases. Real-time data are visualized through a custom dashboard, and automated email alerts are generated when air quality measurements exceed predefined safety thresholds, specifically PM2.5 levels greater than 30 μg/m3 and harmful gas concentrations exceeding 30 ppm (PPM). The system was operated continuously for 24 h in a controlled indoor environment and successfully detected critical air quality fluctuations, following World Health Organization (WHO) and National Institute for Occupational Safety and Health (NIOSH) thresholds. Results demonstrate its responsiveness, accuracy, and cost-effectiveness for localized monitoring. This work contributes a scalable IoT-based framework for continuous air quality assessment and proactive safety interventions.

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IoT-Driven Real-Time Indoor Air Quality Monitoring System for Enhanced Environmental Safety

  • 会议论文

  • 发表日期：

    2026年

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  • Md Abubakar Siddique,
  • Amit Joe Lopes

摘要

This study presents a real-time Internet of Things (IoT)-based air quality monitoring system designed for industrial and research environments. The system integrates a Raspberry Pi with low-cost sensors, namely the BME688, PM2.5, MQ-2, and MQ-135, to monitor key environmental parameters such as particulate matter, temperature, humidity, pressure, and concentrations of harmful gases. Real-time data are visualized through a custom dashboard, and automated email alerts are generated when air quality measurements exceed predefined safety thresholds, specifically PM2.5 levels greater than 30 μg/m3 and harmful gas concentrations exceeding 30 ppm (PPM). The system was operated continuously for 24 h in a controlled indoor environment and successfully detected critical air quality fluctuations, following World Health Organization (WHO) and National Institute for Occupational Safety and Health (NIOSH) thresholds. Results demonstrate its responsiveness, accuracy, and cost-effectiveness for localized monitoring. This work contributes a scalable IoT-based framework for continuous air quality assessment and proactive safety interventions.