A framework for advancing independent air quality sensor measurements via transparent data generating process classification - 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; et al. (npj Climate and Atmospheric Science)
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.