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.

Standort
Deutsche Nationalbibliothek Frankfurt am Main
Umfang
Online-Ressource
Sprache
Englisch

Erschienen in
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)

Urheber
Bean, Jeffrey K.

DOI
10.5194/amt-14-7369-2021
URN
urn:nbn:de:101:1-2021120204535995549389
Rechteinformation
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
Letzte Aktualisierung
15.08.2025, 07:28 MESZ

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Beteiligte

  • Bean, Jeffrey K.

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