the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 25 Nov 2024
Measurement report: size-resolved particle effective density measured by the AAC-SMPS and implications for chemical composition
Abstract. The effective density (ρeff) is closely associated with the aging process and can serve as a tracer for chemical composition. Recently, studies investigating the effect of particle size on density have been limited. In this study, size-resolved ρeff was characterized with a tandem of an aerodynamic aerosol classifier (AAC) and a scanning mobility particle sizer (SMPS) system during one month of observation in Hangzhou. The results indicate that the ρeff values of the particles exhibit a unimodal distribution, with average values ranging from 1.47 g/cm3 to 1.63 g/cm3, increasing as the particle diameter increases. The diurnal variation is more pronounced for small particles (dae <350 nm), which generally exhibit lower density during the day and higher density at night. The relationship between ρeff and particle diameter varies under different pollution conditions due to differences in the chemical composition of the particles. additive explanations (SHAPs) revealed good relationships between ρeff and the bulk composition of particles with diameters smaller than 350 nm. Since the size-resolved chemical composition of particles is still challenging, a new method to investigate the size-resolved chemical composition was proposed, in which the size-resolved composition can be derived from the ρeff and fixed material density of secondary inorganic aerosols (SIAs), organic aerosols (OAs) and black carbon (BC).
Competing interests: At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.-
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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.
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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.
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Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-3298', Anonymous Referee #1, 05 Dec 2024
The study investigated size-resolved particle effective density and its relationship with chemical composition, which has implications for air quality, climate, and health. The use of AAC-SMPS in tandem with machine learning (ML) techniques, such as SHAP analysis, demonstrates a commendable level of innovation. However, I have two concerns: first, I am somewhat worried about the accuracy of the measurements from the entire system. Second, one of the highlights of the paper is using size-resolved effective density to infer particle composition, but the paper does not provide composition information for different sizes for comparison. I recommend a major revision of the paper.
Comments:
Why does the effective density of ammonium sulfate increase with particle size, while ammonium nitrate remains relatively stable (Fig. S2 )? I’m curious about the measurement results of this system for the effective density of PSL (polystyrene latex) spheres with different particle sizes. Since PSL spheres are essentially regular spherical shapes, their effective density should theoretically equal the material density (1.05 g/cm³). If the effective density of PSL spheres with different sizes cannot remain stable, I suspect that the observed relationship between effective density and particle size may not reflect the true situation but rather a bias in the observation system.
Line 85 Please specify the resolution (Rs) of AAC, the sheath of DMA, and the scanning settings of SMPS(such as diameter range, time for one scan, how many SMPS scans in 5 min). The Rs and DMA sheath could both influence the uncertainty of the results. Besides, After the authors provide these parameters, it is recommended that they perform an uncertainty analysis.
Line 98 The effective density of BC (could be smaller than 0.5 g/cm3 for fresh BC) is totally different from BC’s material density (~1.8 g/cm3), Is it reasonable to use the material density of black carbon to calculate the overall effective density?
Line 128-143 Another possibility for the unimodal distribution is that the minimum particle size setting for this observation was Dae = 200 nm, and the aerodynamic diameter of most fresh black carbon particles is smaller than this, so the bimodal distribution could not be detected.
Line 165 Please improve the clarity of Figure 3, especially as the figure legend is a bit blurry.
Line 190 I have two questions regarding the measurement and discussion of large particles (Dae = 531 nm):
(1) It was mentioned earlier (line 92) that particles with a mobility diameter exceeding 600 nm were not used in Gaussian fitting. How significant is the impact of this on the effective density determination at 531 nm?
(2) The number concentration of large particles is relatively small. After AAC selection, can the size distribution measured by SMPS still be fitted with a Gaussian function? I would like to see the size distribution scans by SMPS at different Dae values."Citation: https://doi.org/10.5194/egusphere-2024-3298-RC1 -
AC1: 'Reply on RC1', Zhibin Wang, 08 Feb 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3298/egusphere-2024-3298-AC1-supplement.pdf
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AC1: 'Reply on RC1', Zhibin Wang, 08 Feb 2025
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RC2: 'Comment on egusphere-2024-3298', Anonymous Referee #2, 17 Dec 2024
The current manuscript presents the characteristics of the ambient aerosol density of one-month online measurements in Hangzhou. The ms is generally written well. I have mainly questions for clarification.
Do the SHAPs work for particles larger than 350 nm? If not, it deserves to be mentioned in the abstract.
Lines 33–34: Please specify the size range.
The current system uses a dryer before the instruments. When RH is high enough, ambient particles may exist as aqueous droplets. Is there any effect of the particle phase state on density measurements?
Please define the sub-density mode.
The relationship between density distribution and particle mixing state is unclear. Please elaborate on what the bimodal distribution means.
The authors conclude no significant diurnal variation in the density values. However, Figure 4a noted a gradual increase in the density for 531 nm particles, which deserves more discussion and comparisons with other studies.
In this study, only small particles showed a strong correlation between feature values and the prediction. Please specify the feature values. Does the weak correlation for big particles mean that the chemical composition is invariable compared to that of small particles?
Minor comments:
Line 58: Please rewrite “machine learning (ML) methods, such as ozone pollution and potential aerosol sources.” Ozone pollution and potential aerosol sources are not ML methods.
Line 111: Please define “RF.”
Line 148: Fig.2 -> Fig.3.
Citation: https://doi.org/10.5194/egusphere-2024-3298-RC2 -
AC2: 'Reply on RC2', Zhibin Wang, 08 Feb 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3298/egusphere-2024-3298-AC2-supplement.pdf
-
AC2: 'Reply on RC2', Zhibin Wang, 08 Feb 2025
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-3298', Anonymous Referee #1, 05 Dec 2024
The study investigated size-resolved particle effective density and its relationship with chemical composition, which has implications for air quality, climate, and health. The use of AAC-SMPS in tandem with machine learning (ML) techniques, such as SHAP analysis, demonstrates a commendable level of innovation. However, I have two concerns: first, I am somewhat worried about the accuracy of the measurements from the entire system. Second, one of the highlights of the paper is using size-resolved effective density to infer particle composition, but the paper does not provide composition information for different sizes for comparison. I recommend a major revision of the paper.
Comments:
Why does the effective density of ammonium sulfate increase with particle size, while ammonium nitrate remains relatively stable (Fig. S2 )? I’m curious about the measurement results of this system for the effective density of PSL (polystyrene latex) spheres with different particle sizes. Since PSL spheres are essentially regular spherical shapes, their effective density should theoretically equal the material density (1.05 g/cm³). If the effective density of PSL spheres with different sizes cannot remain stable, I suspect that the observed relationship between effective density and particle size may not reflect the true situation but rather a bias in the observation system.
Line 85 Please specify the resolution (Rs) of AAC, the sheath of DMA, and the scanning settings of SMPS(such as diameter range, time for one scan, how many SMPS scans in 5 min). The Rs and DMA sheath could both influence the uncertainty of the results. Besides, After the authors provide these parameters, it is recommended that they perform an uncertainty analysis.
Line 98 The effective density of BC (could be smaller than 0.5 g/cm3 for fresh BC) is totally different from BC’s material density (~1.8 g/cm3), Is it reasonable to use the material density of black carbon to calculate the overall effective density?
Line 128-143 Another possibility for the unimodal distribution is that the minimum particle size setting for this observation was Dae = 200 nm, and the aerodynamic diameter of most fresh black carbon particles is smaller than this, so the bimodal distribution could not be detected.
Line 165 Please improve the clarity of Figure 3, especially as the figure legend is a bit blurry.
Line 190 I have two questions regarding the measurement and discussion of large particles (Dae = 531 nm):
(1) It was mentioned earlier (line 92) that particles with a mobility diameter exceeding 600 nm were not used in Gaussian fitting. How significant is the impact of this on the effective density determination at 531 nm?
(2) The number concentration of large particles is relatively small. After AAC selection, can the size distribution measured by SMPS still be fitted with a Gaussian function? I would like to see the size distribution scans by SMPS at different Dae values."Citation: https://doi.org/10.5194/egusphere-2024-3298-RC1 -
AC1: 'Reply on RC1', Zhibin Wang, 08 Feb 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3298/egusphere-2024-3298-AC1-supplement.pdf
-
AC1: 'Reply on RC1', Zhibin Wang, 08 Feb 2025
-
RC2: 'Comment on egusphere-2024-3298', Anonymous Referee #2, 17 Dec 2024
The current manuscript presents the characteristics of the ambient aerosol density of one-month online measurements in Hangzhou. The ms is generally written well. I have mainly questions for clarification.
Do the SHAPs work for particles larger than 350 nm? If not, it deserves to be mentioned in the abstract.
Lines 33–34: Please specify the size range.
The current system uses a dryer before the instruments. When RH is high enough, ambient particles may exist as aqueous droplets. Is there any effect of the particle phase state on density measurements?
Please define the sub-density mode.
The relationship between density distribution and particle mixing state is unclear. Please elaborate on what the bimodal distribution means.
The authors conclude no significant diurnal variation in the density values. However, Figure 4a noted a gradual increase in the density for 531 nm particles, which deserves more discussion and comparisons with other studies.
In this study, only small particles showed a strong correlation between feature values and the prediction. Please specify the feature values. Does the weak correlation for big particles mean that the chemical composition is invariable compared to that of small particles?
Minor comments:
Line 58: Please rewrite “machine learning (ML) methods, such as ozone pollution and potential aerosol sources.” Ozone pollution and potential aerosol sources are not ML methods.
Line 111: Please define “RF.”
Line 148: Fig.2 -> Fig.3.
Citation: https://doi.org/10.5194/egusphere-2024-3298-RC2 -
AC2: 'Reply on RC2', Zhibin Wang, 08 Feb 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3298/egusphere-2024-3298-AC2-supplement.pdf
-
AC2: 'Reply on RC2', Zhibin Wang, 08 Feb 2025
Peer review completion
Journal article(s) based on this preprint
Data sets
Data of measurement report: size-resolved particle effective density measured by the AAC-SMPS and implications for chemical composition Y. Song et al. https://doi.org/10.5281/zenodo.13981448
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Fei Zhang
Zhengning Xu
Ruifang Shi
Ya Wei
Lu Zhang
Lingling Jin
Zhibin Wang
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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