A framework for advancing independent air quality sensor measurements via transparent data generating process classification

A framework for advancing independent air quality sensor measurements via transparent data generating process classification

Abstract We propose operational definitions and a classification framework for air quality sensor-derived data, thereby aiding users in interpreting and selecting suitable data products for their applications. We focus on differentiating independent sensor measurements (ISM) from other data products...

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Main Authors: Sebastian Diez, Thomas J. Bannan, Miriam Chacón-Mateos, Pete M. Edwards, Valerio Ferracci, Doğuşhan Kılıç, Alastair C. Lewis, Carl Malings, Nicholas A. Martin, Olalekan Popoola, Colleen Rosales, Sean Schmitz, Philipp Schneider, Erika von Schneidemesser
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:npj Climate and Atmospheric Science
Online Access:https://doi.org/10.1038/s41612-025-01161-2
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Summary:Abstract We propose operational definitions and a classification framework for air quality sensor-derived data, thereby aiding users in interpreting and selecting suitable data products for their applications. We focus on differentiating independent sensor measurements (ISM) from other data products, emphasizing transparency and traceability. Recommendations are provided for manufacturers, academia, and standardization bodies to adopt these definitions, fostering data product differentiation and incentivizing the development of more robust, reliable sensor hardware.
ISSN:2397-3722

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