Articles | Volume 9, issue 7
https://doi.org/10.5194/amt-9-3263-2016
https://doi.org/10.5194/amt-9-3263-2016
Research article
 | 
25 Jul 2016
Research article |  | 25 Jul 2016

Applications and limitations of constrained high-resolution peak fitting on low resolving power mass spectra from the ToF-ACSM

Hilkka Timonen, Mike Cubison, Minna Aurela, David Brus, Heikki Lihavainen, Risto Hillamo, Manjula Canagaratna, Bettina Nekat, Rolf Weller, Douglas Worsnop, and Sanna Saarikoski

Abstract. The applicability, methods and limitations of constrained peak fitting on mass spectra of low mass resolving power (m∕Δm50 ∼ 500) recorded with a time-of-flight aerosol chemical speciation monitor (ToF-ACSM) are explored. Calibration measurements as well as ambient data are used to exemplify the methods that should be applied to maximise data quality and assess confidence in peak-fitting results. Sensitivity analyses and basic peak fit metrics such as normalised ion separation are employed to demonstrate which peak-fitting analyses commonly performed in high-resolution aerosol mass spectrometry are appropriate to perform on spectra of this resolving power. Information on aerosol sulfate, nitrate, sodium chloride, methanesulfonic acid as well as semi-volatile metal species retrieved from these methods is evaluated. The constants in a commonly used formula for the estimation of the mass concentration of hydrocarbon-like organic aerosol may be refined based on peak-fitting results. Finally, application of a recently published parameterisation for the estimation of carbon oxidation state to ToF-ACSM spectra is validated for a range of organic standards and its use demonstrated for ambient urban data.

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Short summary
The applicability, methods and limitations of constrained peak fitting on mass spectra of low mass resolving power (m∕Δm50 ∼ 500) recorded with a time-of-flight aerosol chemical speciation monitor (ToF-ACSM) are explored. Calibration measurements and ambient data are used to exemplify the methods that should be applied to maximise data quality and assess confidence in peak-fitting results.