Evaluation methods for low-cost particulate matter sensors

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Abstract µ g  m- 3 µ g  m- 3 R 2 R 2 and root mean square error remain the dominant metrics in sensor evaluations, an alternative approach using a prediction interval may offer more consistency between evaluations and a more direct interpretation of sensor data following an evaluation. Ongoing quality assurance for sensor data is needed to ensure that data continue to meet expectations. Observations of trends in linear regression parameters and sensor bias were used to analyze calibration and other quality assurance techniques.

Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Bibliographic citation: Evaluation methods for low-cost particulate matter sensors ; volume:14 ; number:11 ; year:2021 ; pages:7369-7379 ; extent:11
Atmospheric measurement techniques ; 14, Heft 11 (2021), 7369-7379 (gesamt 11)

Creator: Bean, Jeffrey K.

DOI: 10.5194/amt-14-7369-2021

URN: urn:nbn:de:101:1-2021120204535995549389

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:28 AM CEST

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Bean, Jeffrey K.

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