An open-source tool for evaluating calibration techniques used in low-cost air pollutant monitors

Abstract

Low-cost air pollutant sensors suffer several interferences due to the variation of climatic elements. Recent studies look for calibration solutions based on different regression and classification machine learning algorithms. The present work brings together the implementation and extraction of performance metrics from these algorithms in a single open-source tool. Both the input data and parameters for each algorithm are automatically configured. This feature makes the tool compatible with any input dataset and removes the need to interact with complex codes.