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

Diez, Sebastian; Bannan, Thomas J.; Chacon-Mateos, Miriam; Edwards, Pete M.; Ferracci, Valerio; Kilic, Dogushan; Lewis, Alastair C.; Malings, Carl; Martin, Nicholas A.; Popoola, Olalekan; Rosales, Colleen; Schmitz, Sean; Schneider, Philipp; von Schneidemesser, Erika

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. © The Author(s) 2025.

Más información

Título según WOS: A framework for advancing independent air quality sensor measurements via transparent data generating process classification
Título según SCOPUS: A framework for advancing independent air quality sensor measurements via transparent data generating process classification
Título de la Revista: npj Climate and Atmospheric Science
Volumen: 8
Número: 1
Editorial: Nature Research
Fecha de publicación: 2025
Idioma: English
DOI:

10.1038/s41612-025-01161-2

Notas: ISI, SCOPUS