Application of Machine Learning methods to correct the readings of low-cost air pollution sensors

Abstract

The study is devoted to the analysis of the application of machine learning methods for correcting the readings of inexpensive sensors that record the concentration of suspended particles PM2.5 in the surface layer of the atmosphere, relative to readings of reference stations. The analysis was carried out on the example of coupled sensors (an inexpensive CityAir sensor and a reference E-BAM) located in Krasnoyarsk (Russia) based on observational data from January 1, 2019 to December 31, 2022. Statistical analysis of the data and comparison of parametric (Linear, Ridge, Lasso, Support vectors machine, Elastic net regressions) and nonparametric (regressions of Nearest Neighbor, Decision Tree and Random Forest) methods for establishing the relationship between two samples was carried out.