Low Cost Sensor With IoT LoRaWAN Connectivity and Machine Learning-Based Calibration for Air Pollution Monitoring

• Published in: IEEE transactions on instrumentation and measurement Vol. 70; pp. 1 - 11
• Main Authors: Ali, Sharafat, Glass, Tyrel, Parr, Baden, Potgieter, Johan, Alam, Fakhrul
• Format: Journal Article
• Language: English
• Published: New York IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Air monitoring; Air pollution; Air pollution monitoring; Air quality; air quality monitor (AQM); Airborne particulates; Artificial neural networks; Atmospheric measurements; Calibration; Carbon monoxide; Chemical sensors; Communication; Environmental monitoring; Equipment costs; Error correction; Infrared detectors; Internet of Things; Internet of Things (IoT); Learning theory; long-range wide area network (LoRaWAN); Low cost; low-cost sensor; Machine learning; Monitoring; Nitrogen dioxide; Particulate emissions; Pollution monitoring; remote sensing; sensor calibration; Sensors; Temperature measurement; Temperature sensors; Wide area networks; Wireless communication systems; Wireless communications
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2020.3034109

Air pollution poses significant risk to environment and health. Air quality monitoring stations are often confined to a small number of locations due to the high cost of the monitoring equipment. They provide a low fidelity picture of the air quality in the city; local variations are overlooked. However, recent developments in low-cost sensor technology and wireless communication systems like Internet of Things (IoT) provide an opportunity to use arrayed sensor networks to measure air pollution, in real time, at a large number of locations. This article reports the development of a novel low-cost sensor node that utilizes cost-effective electrochemical sensors to measure carbon monoxide (CO) and nitrogen dioxide (NO 2 ) concentrations and an infrared sensor to measure particulate matter (PM) levels. The node can be powered by either solar-recharged battery or mains supply. It is capable of long-range, low power communication over public or private long-range wide area network (LoRaWAN) IoT network and short-range high data rate communication over Wi-Fi. The developed sensor nodes were co-located with an accurate reference CO sensor for field calibration. The low-cost sensors' data, with offset and gain calibration, show good correlation with the data collected from the reference sensor. Multiple linear regression (MLR)-based temperature and humidity correction results in mean absolute percentage error (MAPE) of 48.71% and R 2 of 0.607 relative to the reference sensor's data. Artificial neural network (ANN)-based calibration shows the potential for significant further improvement with MAPE of 38.89% and R 2 of 0.78 for leave-one-out cross-validation.