Calibration and Inter-Unit Consistency Assessment of an Electrochemical Sensor System Using Machine Learning.

ABSTRACT: This paper addresses the challenges of calibrating low-cost electrochemical sensor systems for air quality monitoring. The proliferation of pollutants in the atmosphere necessitates efficient monitoring systems, and low-cost sensors offer a promising solution. However, issues such as drift, cross-sensitivity, and inter-unit consistency have raised concerns about their accuracy and reliability. The study explores the following three calibration methods for converting sensor signals to concentration measurements: utilizing manufacturer-provided equations, incorporating machine learning (ML) algorithms, and directly applying ML to voltage signals. Experiments were performed in three urban sites in Greece. High-end instrumentation provided the reference concentrations for training and evaluati

SUBMITTER: Apostolopoulos ID 

PROVIDER: S-EPMC11244084 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature