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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月29日

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  • 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

摘要

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.