the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 11 Oct 2023
A new software toolkit for optical apportionment of carbonaceous aerosol
Abstract. Instruments measuring aerosol light absorption, such as the Aethalometer and the Multi-Wavelength Absorbance Analyzer (MWAA), have been extensively used to characterize optical absorption of atmospheric particulate matter. Data retrieved with such instruments can be analysed with mathematical models to apportion different aerosol sources (Aethalometer model) and components (MWAA model). In this work we present an upgrade to the MWAA optical apportionment model. In addition to the apportionment of the absorption coefficient babs in its components (Black Carbon and Brown Carbon) and sources (Fossil Fuel and Wood Burning), the extended model allows the retrieval of the Absorption Ångström Exponent of each component and source, thereby avoiding initial assumptions regarding these parameters. We also present a new open-source software toolkit, the MWAA Model Toolkit, written in both Python and R, that performs the entire apportionment procedure.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(1438 KB) - Metadata XML
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Supplement
(364 KB) - BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2023-1936', Anonymous Referee #1, 04 Nov 2023
General Comments:
Optical apportionment of carbonaceous aeroso" is an important process in the measurement of aerosol absorption properties. This manuscript presents an improved model without initial assumptions of parameters for distinguishing the composition and sources of light-absorbing carbonaceous aerosols based on the traditional optical apportionment model used for multi-wavelength absorption coefficient detection. From a scientific perspective, this study lacks significant innovation. Additionally, the deployment and application of this improved model toolkit holds some technical value. Therefore, it is recommended to reconsider the acceptance of this study after the following issues have been well solved.
Specific Comments:
1) The algorithm presented in this paper requires at least one additional independent measurement result (e.g., Levoglucosan), which significantly limits the application of this method. In Equations 1 and 2 within the text, each of them has four unknowns. In theory, the detection results from five wavelengths are sufficient to solve these unknowns. Why didn't the authors use the results from a multi-wavelength absorption analyzer for independent calculations?
2) In the algorithm described in this paper, αBC, αFF, and αWB remain constant over a certain period of time (such as during a field experiment), while αBrC varies with time. This is not reasonable. For example, in the observation example in Milan, αBrC clearly varies with time, while αWB is assumed to be a constant value in this period. This can introduce significant errors into the calculations. For example, in Figure 6, the trends of BrC and BCWB are nearly identical, while in Figure 7, there is a significant difference between them. This distinction may be a result of the algorithm rather than the environmental conditions themselves.
3) In Figure 3, in the left panel, αBC has a higher R2 value around 0.93, while in the right panel, αWB has a higher R2 value around 1.67. Why not use these two values as fitting results?
4) In P6L178, the author mentioned that the Milan campaign had 20 samples, but in Figure 5, there are 25 samples. Why is that?
5) The abbreviations BC, BrC, FF, and WB should only be introduced with their full names the first time they appear, and there's no need to reintroduce them later (e.g., as in P3L90). Similarly, EC should be introduced with its full name the first time it appears.
Citation: https://doi.org/10.5194/egusphere-2023-1936-RC1 - AC2: 'Reply on RC1', Federico Mazzei, 30 Nov 2023
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RC2: 'Comment on egusphere-2023-1936', Anonymous Referee #2, 12 Nov 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1936/egusphere-2023-1936-RC2-supplement.pdf
- AC1: 'Reply on RC2', Federico Mazzei, 30 Nov 2023
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-1936', Anonymous Referee #1, 04 Nov 2023
General Comments:
Optical apportionment of carbonaceous aeroso" is an important process in the measurement of aerosol absorption properties. This manuscript presents an improved model without initial assumptions of parameters for distinguishing the composition and sources of light-absorbing carbonaceous aerosols based on the traditional optical apportionment model used for multi-wavelength absorption coefficient detection. From a scientific perspective, this study lacks significant innovation. Additionally, the deployment and application of this improved model toolkit holds some technical value. Therefore, it is recommended to reconsider the acceptance of this study after the following issues have been well solved.
Specific Comments:
1) The algorithm presented in this paper requires at least one additional independent measurement result (e.g., Levoglucosan), which significantly limits the application of this method. In Equations 1 and 2 within the text, each of them has four unknowns. In theory, the detection results from five wavelengths are sufficient to solve these unknowns. Why didn't the authors use the results from a multi-wavelength absorption analyzer for independent calculations?
2) In the algorithm described in this paper, αBC, αFF, and αWB remain constant over a certain period of time (such as during a field experiment), while αBrC varies with time. This is not reasonable. For example, in the observation example in Milan, αBrC clearly varies with time, while αWB is assumed to be a constant value in this period. This can introduce significant errors into the calculations. For example, in Figure 6, the trends of BrC and BCWB are nearly identical, while in Figure 7, there is a significant difference between them. This distinction may be a result of the algorithm rather than the environmental conditions themselves.
3) In Figure 3, in the left panel, αBC has a higher R2 value around 0.93, while in the right panel, αWB has a higher R2 value around 1.67. Why not use these two values as fitting results?
4) In P6L178, the author mentioned that the Milan campaign had 20 samples, but in Figure 5, there are 25 samples. Why is that?
5) The abbreviations BC, BrC, FF, and WB should only be introduced with their full names the first time they appear, and there's no need to reintroduce them later (e.g., as in P3L90). Similarly, EC should be introduced with its full name the first time it appears.
Citation: https://doi.org/10.5194/egusphere-2023-1936-RC1 - AC2: 'Reply on RC1', Federico Mazzei, 30 Nov 2023
-
RC2: 'Comment on egusphere-2023-1936', Anonymous Referee #2, 12 Nov 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1936/egusphere-2023-1936-RC2-supplement.pdf
- AC1: 'Reply on RC2', Federico Mazzei, 30 Nov 2023
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Tommaso Isolabella
Vera Bernardoni
Alessandro Bigi
Marco Brunoldi
Federico Mazzei
Franco Parodi
Paolo Prati
VIrginia Vernocchi
Dario Massabò
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(1438 KB) - Metadata XML
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Supplement
(364 KB) - BibTeX
- EndNote
- Final revised paper