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.
Highlights
Today, sensor technology installed on wireless nodes is beginning to mature with applications in various fields including precision agriculture, air pollution, location, water flow monitoring, etc. [1,2,3,4].Air pollution is one of the major concerns in modern society due to its economic impact and on people’s health
Due to the high cost of these reference stations, researchers have focused their studies on low-cost air pollution sensors for pollutants such as O3, CO, nitrogen oxides (NOx), CO2, PM2.5, etc., whose objective is to complement the data obtained by reference stations
The reason for these behaviors, and which have a strong impact on the RMSE obtained, is the reaction that measurements have to different environmental conditions, e.g., temperature and relative humidity, making it difficult to estimate long-term ozone concentrations
Summary
Sensor technology installed on wireless nodes is beginning to mature with applications in various fields including precision agriculture, air pollution, location, water flow monitoring, etc. [1,2,3,4]. There is little knowledge, even using linear models, of how long-term estimation behaves This problem is relevant in a deployment of sensors whose objective is not to investigate how a sensor performs but rather to calibrate the sensor with a small data set (calibration period) near a reference air quality monitoring station and deploy it elsewhere. In this case the calibration parameters are used to estimate long-term pollution concentrations without being able to verify the quality and accuracy of the measurements taken.
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