IoT-Based Air Quality Monitoring and Surveillance System
Keywords:
Internet of Things, air quality, PM2.5, carbon dioxide, Raspberry Pi, GrafanaAbstract
This study aimed to develop and evaluate an Internet of Things (IoT)-based system for monitoring indoor particulate matter with an aerodynamic diameter of less than 2.5 µm (PM2.5) and carbon dioxide (CO2). The system integrated PMS3003 and MH-Z14A sensors with a Raspberry Pi for data acquisition and processing. Measurements were displayed locally on an LCD, transmitted via Wi-Fi to a database, and visualized through a Grafana Dashboard. Telegram was used to report events when the measurements exceeded the predefined operational thresholds. The evaluation included a comparison of the sensor readings with a commercial monitoring device using 10 paired observations and a functional verification of data transmission, visualization, and event reporting. Within the tested ranges, the PMS3003 achieved an of 0.974, a mean absolute error (MAE) of 0.58 µg/m³, and a root mean square error (RMSE) of 0.78 µg/m³. The MH-Z14A achieved an of 1.000, an MAE of 1.90 ppm, and an RMSE of 2.07 ppm. The values displayed on the LCD and Grafana Dashboard were consistent, and the system changed its status and sent Telegram messages when PM2.5 and CO2 exceeded the operational thresholds of 50 µg/m³ and 1,200 ppm, respectively. These findings demonstrate the feasibility of the prototype for monitoring, visualizing, and reporting indoor air-quality events within the experimental setting. However, the comparison represents a preliminary assessment of agreement with a commercial device and should not be interpreted as calibration against a traceable reference instrument.
This study aimed to develop and evaluate an Internet of Things (IoT)-based system for monitoring indoor particulate matter with an aerodynamic diameter of less than 2.5 µm (PM2.5) and carbon dioxide (CO2). The system integrated PMS3003 and MH-Z14A sensors with a Raspberry Pi for data acquisition and processing. Measurements were displayed locally on an LCD, transmitted via Wi-Fi to a database, and visualized through a Grafana Dashboard. Telegram was used to report events when the measurements exceeded the predefined operational thresholds. The evaluation included a comparison of the sensor readings with a commercial monitoring device using 10 paired observations and a functional verification of data transmission, visualization, and event reporting. Within the tested ranges, the PMS3003 achieved an of 0.974, a mean absolute error (MAE) of 0.58 µg/m³, and a root mean square error (RMSE) of 0.78 µg/m³. The MH-Z14A achieved an of 1.000, an MAE of 1.90 ppm, and an RMSE of 2.07 ppm. The values displayed on the LCD and Grafana Dashboard were consistent, and the system changed its status and sent Telegram messages when PM2.5 and CO2 exceeded the operational thresholds of 50 µg/m³ and 1,200 ppm, respectively. These findings demonstrate the feasibility of the prototype for monitoring, visualizing, and reporting indoor air-quality events within the experimental setting. However, the comparison represents a preliminary assessment of agreement with a commercial device and should not be interpreted as calibration against a traceable reference instrument.
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