Machine learning based calibration techniques for low-cost air quality sensors : thesis for Doctor of Philosophy, Electronic and Computer Engineering, Massey University
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Date
2024-05-28
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Massey University
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Abstract
Breathable air is the single most essential element for life on earth. Polluted air poses numerous risks to health and the environment, especially in urban areas with large populations and many active sources of air pollution. Therefore, researchers from a wide range of disciplines have been working on mitigating the impact of air pollution. Monitoring ambient air pollution is one of the means to ensure public health safety, raise public awareness and build a sustainable urban environment. However, conventional air quality monitoring stations are mostly confined to a few locations due to their costly equipment and large sizes. As a result, although these monitoring stations provide accurate air pollution data, they can only offer a low-fidelity picture of air quality in a large city, leading to a poor spatial resolution of urban pollution data. Low-cost sensor (LCS) technologies aim to address this challenge and intend to make it possible to monitor air quality at a high spatio-temporal resolution. The pollutant data captured by these LCSs are less accurate than their conventional counterparts and thus require calibration techniques to improve their accuracy and reliability. Researchers have proposed different calibration methods and techniques to improve the accuracy of the LCSs, including machine learning based calibration models. This thesis investigates and proposes several machine learning-based calibration techniques and rigorously benchmarks their performance using a robust training, validation and testing method. Based on the findings, One Dimensional Convolutional Neural Network (1DCNN) and Gradient Boosting Regression (GBR) based calibration techniques provide consistently accurate performance. Both of these machine learning techniques, which have not been widely used or evaluated for low-cost ambient gas sensor calibration, can improve the state of the art. This research also demonstrates that readily available and previously unemployed co-variate data, namely the number of days the sensor has been deployed and the time of day at which the reading is taken, can significantly improve the accuracy of Machine Learning based calibration algorithms.
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Keywords
air quality monitoring, machine learning, calibration algorithm, Air, Pollution, Air quality, Measurement, Detectors, Calibration, Air sampling apparatus