the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 24 Feb 2025
Insights into microphysical and optical properties of typical mineral dust within industrial-polluted snowpack via wet/dry deposition in Changchun, Northeastern China
Abstract. This study utilizes the computer-controlled scanning electron microscope software IntelliSEM-EPASTM, combined with K-means cluster analysis and manual experience, reports for the first time that the dust in the snow accumulation from a typical industrial city in China is mainly composed of kaolinite-like (36 %), chlorite-like (19 %), quartz-like (15 %), illite-like (14 %), hematite-like (5 %), and clay-minerals-like (4 %) and other components. It was also found that the size distribution and aspect ratio of the dust did not undergo significant changes during dry and wet deposition, but they exhibited great variability among the different mineral composition groups. Subsequently, these observed microphysical parameters were used to constrain the optical absorption of dust, and the results showed that under low (high) snow grain size scenarios, the albedo reductions caused by dust concentrations of 1, 10, and 100 ppm in snow were 0.007 (0.022), 0.028 (0.084), and 0.099 (0.257), respectively. These results emphasize the importance of dust composition and size distribution characteristics in constraining snowpack light absorption and radiation processes.
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RC1: 'Comment on egusphere-2025-124', Anonymous Referee #1, 05 Mar 2025
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General comments
In this study, mineral dust particles from snowpack were analyzed by SEM. They classified the measured particles based on their compositions and discussed the effects on their optical properties. This study is generally well designed and the topic is interesting. However, I have a concern with the classification of the particles and the subsequent discussions based on the classification. I suggest having more description of the classification and providing SEM chemical and image data of the particles that they classified.Specific comments
Page 1, line 2: "industrial-polluted snowpack" Throughout the manuscript, there is not much discussion and results about the influence of industrial pollution. In an industrially polluted city, there should be many anthropogenic pollutants, but no results are provided. Please provide any results and discussion on anthropogenic pollutants.
Page 2 lines 2-7: I do not think it is appropriate to include the name of commercial products in the first sentence of the abstract, although it is up to the author. The first sentence is long and needs to be checked for grammar.
Page 3, lines 7-10: This sentence is misleading and needs to be revised. Many aerosols are deposited on land and in the ocean.
Page 4, lines 16-18: This sentence is awkward. Perhaps "while" is not needed.
Page 6, line 21: Please describe the sampling location in more detail. Is it near a road or industrial facility?
Page 7, line 1: Aged surface snow should include both wet and dry deposition.
Page 7, lines 4-6: How much snow did you use for the measurements?
Page 7, line 12: Please provide information about the SEM (e.g., company).
Page 7, line 18: The selected elements are 29, not 24. Some elements (e.g. Rh) may be misclassified because they are very rare. Please check them from the original SEM data.
Page 7 line 21: "thousands of particles per hour" How many seconds did you use for chemical analysis? In general, 1 or 2 seconds for EDS is not enough to qualify the elements.
Page 8, line 5: "particle concentration level" Did you measure particle concentrations?
Page 9, line 1: "K-means clustering algorithms and manual experience" Although I understand that it is difficult to classify particles based only on the K-means clustering technique, please describe the "manual experience". Also, I strongly suggest showing, for example, average compositions of the particle group (e.g., quartz-like,...) and representative SEM images of the particles. In the current discussion, there is no such data, and I cannot judge whether the classification worked well or not. Thus, I question whether the particle size distributions are adequate or not, i.e., whether the distributions are similar for all particle types.
Page 11, lines 13-16: Similar to the comment above, please describe how you classify the mineral dust particles into these categories.
Page 23 lines 12-22: "It is worth noting that there is currently no strict set of criteria in the scientific community for classifying dust mineral components" These discussions are misleading. In mineralogy, there are strict definitions of each mineral phase based on composition and crystal structure. The problem with the mineral dust particles used in this study is that they are mixtures and not simple components, resulting in the difficulty of particle classification.
Citation: https://doi.org/10.5194/egusphere-2025-124-RC1 -
RC2: 'Comment on egusphere-2025-124', Anonymous Referee #2, 21 Mar 2025
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The authors measured and analyzed the dust composition and microphysical features based on samples in the snowpack from a typical industrial city in China. They found that size distribution and aspect ratio of the dust did not undergo significant changes during dry and wet deposition but exhibited great variability among the different mineral composition groups. The impact of dust composition on snow albedo effect has been less studied in the past. This study using the observations to constrain dust size distribution and composition provides a useful framework to assess dust-snow albedo effect. Overall, the manuscript is well organized, but there are still some places that need more descriptions and clarifications.
Comments:
- Introduction: One important missing reference here is the recent review paper (https://doi.org/10.1038/s43017-022-00379-5) on dust climatic effects, which quantifies the dust-snow radiative effects and uncertainties. This could be discussed as a broad context here for the dust-snow albedo effect problem.
- Section 2.2: (1) I would suggest presenting an example for the SEM images of the dust samples and the energy spectrum demonstrating the signals for each key dust composition elements. (2) Also, it will be useful if the authors could also discuss the uncertainties associated with the SEM-EPAS measurement-analysis system. (3) It is not very clear how the size distribution and aspect ratio were measured. Is it also derived from SEM images? More descriptions are needed.
- Section 2.3: More descriptions of the SAMDS model are needed. For example, is it assuming very deep snowpack (e.g., semi-infinite)? What is the accuracy of this model? Does the model assume dust-snow external mixing as previous studies (e.g., https://doi.org/10.1029/2019MS001737) highlighted the importance of dust-snow internal mixing? How many spectral bands are used in SAMDS? Could the SAMDS handle non-spherical snow grains (I believe so)? If so, maybe a sensitivity test by using a nonspherical snow grain assumption will be very useful to quantify the uncertainty caused by snow grain shape.
- Section 3.1: Based on the dust composition in this study and literature, would the authors be able to add a small discussion on potential sources for these dust particles (e.g., local or long-range transported? Anthropogenic or natural dust?)?
- Section 3.2: It is interesting to see that different mineral components show large differences in size spectra. Any physical explanation for this?
- Figure 4: How do these MAC_dust values compare with previous literature reported values? It will be useful to know this information. This may reflect some uniqueness of dust in this region.
Citation: https://doi.org/10.5194/egusphere-2025-124-RC2
Data sets
The data used in study of Insights into microphysical and optical properties of typical mineral dust within industrial-polluted snowpack via wet/dry deposition in Changchun, Northeastern China Tenglong Shi https://doi.org/10.5281/zenodo.14633496
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