Diez, S; Bannan, T J; Chacón-Mateos, M; Edwards, P M; Ferracci, V; Kılıç, D; Lewis, A C; Malings, C; Martin, N A; Popoola, O; Rosales, C; Schmitz, S; Schneider, P; von Schneidemesser, E (2025) A framework for advancing independent air quality sensor measurements via transparent data generating process classification. npj Climate and Atmospheric Science, 8 (1). 285 ISSN 2397-3722

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Abstract

Lower-cost air quality sensors offer potential for expanding global measurements, especially in regions with limited monitoring. However, sensor-derived data quality varies widely, with data processing methods are often opaque. These methods can produce outputs closer to software-driven predictions than independent hardware-based measurements, affecting data integrity and transferability. This work proposes operational definitions and a classification framework for sensor-derived data products, to aid users in interpreting sensor data and selecting suitable products for their applications. We focus on clearly differentiating independent sensor measurements (ISM) from other data products, emphasizing transparency and traceability. Recommendations are outlined for manufacturers, academia, and standardization bodies to adopt these definitions, fostering product differentiation and incentivizing the advancement of sensor hardware. Establishing these criteria aims to enhance user confidence, support policymaking, facilitate market differentiation, and drive the development of robust and reliable sensor systems, particularly benefiting underserved regions.

Item Type:	Article
Subjects:	Environmental Measurement > Air Quality and Airborne Particulates
Divisions:	Atmospheric Environmental Sciences
Identification number/DOI:	10.1038/s41612-025-01161-2
Last Modified:	29 Jul 2026 09:47
URI:	https://eprintspublications.npl.co.uk/id/eprint/10490