Distributed Multi-Scale Calibration of Low-Cost Ozone Sensors in Wireless Sensor Networks

Abstract

New advances in sensor technologies and communications in wireless sensor networks have favored the introduction of low-cost sensors for monitoring air quality applications. In this article, we present the results of the European project H2020 CAPTOR, where three testbeds with sensors were deployed to capture tropospheric ozone concentrations. One of the biggest challenges was the calibration of the sensors, as the manufacturer provides them without calibrating. Throughout the paper, we show how short-term calibration using multiple linear regression produces good calibrated data, but instead produces biases in the calculated long-term concentrations. To mitigate the bias, we propose a linear correction based on Kriging estimation of the mean and standard deviation of the long-term ozone concentrations, thus correcting the bias presented by the sensors.

Graph Learning Techniques Using Structured Data for IoT Air Pollution Monitoring Platforms

“…Since the lowcost sensors have been calibrated on specific chambers or have not been directly calibrated by the manufacturer, they have to be calibrated in-situ with reference stations in the deployment field [3], [4]. In the process of calibrating low-cost sensors in uncontrolled environments, sensors are calibrated by positioning the sensor in a reference station and comparing both data using machine learning techniques such as multiple linear regression (MLR) [5], [18], [19], [20], k-nearest neighbors (KNN) [6], [19], support vector regression (SVR) [6], [15], [21], random forest (RF) [6], [15] or artificial neural networks (ANN) [5].…”

Multisensor Data Fusion Calibration in IoT Air Pollution Platforms

“…The concept is that in this context of calibration, a number of sensors (called array of sensors) participate in the calibration of the target sensor to reduce calibration errors. The sensor array principle is widely used [4], [11], [13], [17], [18], [19], [26] in the calibration of air pollution sensors, where the sensor calibration consists of measuring all cross sensitivities of the sensor array to compensate for all interfering contaminants and environmental conditions. Different calibration methods covering linear and non-linear models have been used with arrays of sensors depending on the type of contaminant and cross sensitivities.…”

“…Most of these papers use nodes that mount an array of sensors since many air pollutants are directly or inversely related to other pollutants (e.g. ozone is inversely related to nitrogen oxide due to titration) or to environmental parameters (temperature and relative humidity) [11], [18], [19]. The results of these studies indicate that the air pollution sensor technology is still not mature enough for the sensors to give results with high accuracy.…”

Establishing A Sustainable Low-Cost Air Quality Monitoring Setup: A Survey of the State-of-the-Art

“…Sensor boxes/nodes/motes are constructed by integrating LCS with microcontroller and additional components (Global positioning system (GPS), Global System for Mobile communication (GSM) etc.). Real-time affordable multi-pollutant monitor (RAMP) [ 20 ], AirU pollution monitor [ 21 ], Particulate monitor devices (Atmos) [ 22 ], ARISense [ 23 ] and captor nodes [ 24 ] are examples of such sensor boxes/nodes constructed for air quality measurement. Several researchers assessed the feasibility of air pollution measurement with LCS in long-term deployments with larger area coverage.…”