A calibration methodology of low-cost air pollutant sensor using neural networks

International audience Air quality Low cost sensors (LCSs) are cheap and can map extensive areas. They alert people about pollution spikes in smart city buildings (schools, universities, hospitals. . .) or industrial areas. Before using them for a specified task, they must be calibrated to give accurate readings, i.e. they must be aligned with a measure based on a reference machine. Unfortunately, classic calibration is limited by interferences with other pollutants or can be affected by atmosphere constants in the case of uncontrolled environments. This paper proposes a calibration solution based on artificial neural networks (ANN).