Rauber, M.; Strähl, J.; Salazar, G.; Szidat, S. (6 May 2022). Sunset-calc: An R Shiny Application for Processing Thermo-Optical Analysis Data from Atmospheric Aerosol Measurements (Unpublished). In: Bern Data Science Day 2022. University of Bern. 06 May 2022.
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BDSD2022-Poster-Martin-Rauber.pdf - Other Available under License Creative Commons: Attribution (CC-BY). Download (1MB) | Preview |
Atmospheric aerosols are harmful to human health and affect the climate. We use radiocarbon for the source apportionment of carbonaceous aerosols to unequivocally separate fossil form non-fossil sources. We use thermal-optical analysis (TOA) for radiocarbon measurement of the organic carbon (OC) and elemental carbon (EC) fractions, which requires physical OC/EC separation. TOA relies on the changes in the optical behaviour of carbon when OC is thermally separated from EC. Thermal-optical OC/EC separation leads to partial EC-loss and the conversion of some OC to EC (charring). EC-loss and charring are artifacts which falsify the results of the quantification and must be corrected for. Furthermore, quantifications with custom TOA protocols are not supported with the provided device software. These calculations were previously performed with various spreadsheet style templates and other tools. With Sunset-calc, we aimed to bundle the data processing and develop an extendable and simple web application. With R Shiny, we found a powerful and simple language also for people with little prior programming skills to build rich web applications. We have deployed Sunset-calc on an on-premises R server (14c.unibe.ch/sunsetcalc), which is publicly accessible and particularly useful for our collaborators outside of the University. Sunset-calc is available on GitHub (github.com/martin-rauber/sunset-calc).
Item Type: |
Conference or Workshop Item (Poster) |
|---|---|
Division/Institute: |
08 Faculty of Science > Department of Chemistry, Biochemistry and Pharmaceutical Sciences (DCBP) |
UniBE Contributor: |
Rauber, Martin, Strähl, Jan, Salazar Quintero, Gary Abdiel, Szidat, Sönke |
Subjects: |
500 Science > 570 Life sciences; biology 500 Science > 540 Chemistry |
Projects: |
[1587] Bern Data Science Day 2022-05-06 Official URL |
Language: |
English |
Submitter: |
Martin Rauber |
Date Deposited: |
25 May 2022 15:11 |
Last Modified: |
05 Dec 2022 16:20 |
Additional Information: |
Bern Data Science Day 2022-05-06 collection |
BORIS DOI: |
10.48350/170244 |
URI: |
https://boris.unibe.ch/id/eprint/170244 |
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