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A framework for advancing independent air quality sensor measurements via transparent data generating process classification
by
Schmitz, Sean
, Chacón-Mateos, Miriam
, Schneider, Philipp
, Diez, Sebastiàn
, Bannan, Thomas J.
, Popoola, Olalekan
, Malings, Carl
, Lewis, Alastair C.
, Ferracci, Valerio
, Rosales, Colleen Marciel F.
, Edwards, Pete M.
, Kilic, Dogushan
, von Schneidemesser, Erika
, Martin, Nicholas A.
in
639/166
/ 704/106/35/824
/ 704/172/169
/ Air pollution
/ Air quality
/ Air quality measurements
/ Algorithms
/ Artificial intelligence
/ Atmospheric Sciences
/ Classification
/ Climate Change/Climate Change Impacts
/ Climatology
/ Data integrity
/ Data processing
/ Earth and Environmental Science
/ Earth Sciences
/ Hypotheses
/ Outdoor air quality
/ Perspective
/ Product differentiation
/ Restitution
/ Sensors
/ Software
2025
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A framework for advancing independent air quality sensor measurements via transparent data generating process classification
by
Schmitz, Sean
, Chacón-Mateos, Miriam
, Schneider, Philipp
, Diez, Sebastiàn
, Bannan, Thomas J.
, Popoola, Olalekan
, Malings, Carl
, Lewis, Alastair C.
, Ferracci, Valerio
, Rosales, Colleen Marciel F.
, Edwards, Pete M.
, Kilic, Dogushan
, von Schneidemesser, Erika
, Martin, Nicholas A.
in
639/166
/ 704/106/35/824
/ 704/172/169
/ Air pollution
/ Air quality
/ Air quality measurements
/ Algorithms
/ Artificial intelligence
/ Atmospheric Sciences
/ Classification
/ Climate Change/Climate Change Impacts
/ Climatology
/ Data integrity
/ Data processing
/ Earth and Environmental Science
/ Earth Sciences
/ Hypotheses
/ Outdoor air quality
/ Perspective
/ Product differentiation
/ Restitution
/ Sensors
/ Software
2025
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Do you wish to request the book?
A framework for advancing independent air quality sensor measurements via transparent data generating process classification
by
Schmitz, Sean
, Chacón-Mateos, Miriam
, Schneider, Philipp
, Diez, Sebastiàn
, Bannan, Thomas J.
, Popoola, Olalekan
, Malings, Carl
, Lewis, Alastair C.
, Ferracci, Valerio
, Rosales, Colleen Marciel F.
, Edwards, Pete M.
, Kilic, Dogushan
, von Schneidemesser, Erika
, Martin, Nicholas A.
in
639/166
/ 704/106/35/824
/ 704/172/169
/ Air pollution
/ Air quality
/ Air quality measurements
/ Algorithms
/ Artificial intelligence
/ Atmospheric Sciences
/ Classification
/ Climate Change/Climate Change Impacts
/ Climatology
/ Data integrity
/ Data processing
/ Earth and Environmental Science
/ Earth Sciences
/ Hypotheses
/ Outdoor air quality
/ Perspective
/ Product differentiation
/ Restitution
/ Sensors
/ Software
2025
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A framework for advancing independent air quality sensor measurements via transparent data generating process classification
Journal Article
A framework for advancing independent air quality sensor measurements via transparent data generating process classification
2025
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Overview
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.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
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