Size-segregated atmospheric aerosol samples collected with cascade impactors can provide valuable information for the study of aerosol emission sources and processes, especially when size distributions of chemical compounds are retrieved. Nevertheless, such data are rare in the literature and, when available, they are often presented as discrete size distributions since data analysis is not straightforward, thus preventing to retrieve detailed modal structure information. For this reason, all the main steps for a robust analysis of size-segregated data are illustrated in this paper. The MICRON code was used to perform data inversion and a sensitivity test on the main input parameters (weights, smoothing parameter, and derivative order) was carried out to investigate how the output is affected by their variations. A method to fit the inverted distribution with log-normal functions and to validate the final output has been implemented and discussed through a case-study

Improving data analysis for size-segregated atmospheric aerosol samples / F. Crova, V. Bernardoni, A.C. Forello, S. Valentini, G. Valli, R. Vecchi. - In: IL NUOVO CIMENTO C. - ISSN 2037-4909. - 44:1(2021 May 11), pp. 13.1-13.10. [10.1393/ncc/i2021-21013-x]

Improving data analysis for size-segregated atmospheric aerosol samples

F. Crova
Primo
;
V. Bernardoni
Secondo
;
A.C. Forello;S. Valentini;G. Valli
Penultimo
;
R. Vecchi
Ultimo
2021-05-11

Abstract

Size-segregated atmospheric aerosol samples collected with cascade impactors can provide valuable information for the study of aerosol emission sources and processes, especially when size distributions of chemical compounds are retrieved. Nevertheless, such data are rare in the literature and, when available, they are often presented as discrete size distributions since data analysis is not straightforward, thus preventing to retrieve detailed modal structure information. For this reason, all the main steps for a robust analysis of size-segregated data are illustrated in this paper. The MICRON code was used to perform data inversion and a sensitivity test on the main input parameters (weights, smoothing parameter, and derivative order) was carried out to investigate how the output is affected by their variations. A method to fit the inverted distribution with log-normal functions and to validate the final output has been implemented and discussed through a case-study
Settore FIS/07 - Fisica Applicata(Beni Culturali, Ambientali, Biol.e Medicin)
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/844575
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