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Low-cost sensing is a relatively new paradigm for air quality monitoring at high spatial and temporal resolutions. However, data obtained by using this technique is less reliable due to various error sources such as atmospheric conditions and parameter drift. Calibration, transforming raw sensor measurements to the one generated by reference-grade instruments, is a promising approach to improving the accuracy of low-cost sensors (LCS) data. The calibration of LCS can be framed as a machine learning model to minimize the error between the reference instrument and LCS. This talk presents a quantitative analysis of regression and classification models in machine learning to calibrate LCS in air quality monitoring. Our approach is application-specific, where we divide the stationary applications of LCS into low, moderate and high concentration applications to determine a better calibration model for respective applications. Results are verified with real-time data obtained from SensurAir, an LCS device that we designed and deployed in two different locations in Chennai.
M V Narayana EE18D302