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

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.

Description

Acknowledgements: This work was funded by the National Agency for Research and Development (ANID), through the FONDEQUIP Mayor Fund, under Grant No. EQY200021. The authors would like to express their sincere gratitude to Priscilla Adong, Alexandre Caseiro, Gayle Hagler, David Harrison, Stuart Lacy, Gustavo Olivares, Pallavi Pant, Jorge Saturno, Saumya Singh, and Brian Stacey for their valuable contributions and constructive feedback during the development of this work.

Keywords

37 Earth Sciences, 3701 Atmospheric Sciences, 3702 Climate Change Science, Generic health relevance

Journal Title

npj Climate and Atmospheric Science

Journal ISSN

2397-3722
2397-3722

Volume Title

8

Publisher

Springer Science and Business Media LLC

Publisher DOI

https://doi.org/10.1038/s41612-025-01161-2

Rights and licensing

Except where otherwised noted, this item's license is described as http://creativecommons.org/licenses/by-nc-nd/4.0/

Sponsorship

Agencia Nacional de Investigación y Desarrollo (EQY200021)
NASA Health and Air Quality Applied Sciences Program (80NSSC22K1473)

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