Saccharide composition in atmospheric fine particulate matter at the remote sites of Southwest China and estimates of source contributions

Wang, Zhenzhen; Wu, Di; Li, Zhuoyu; Shang, Xiaona; Li, Qing; Li, Xiang; Chen, Renjie; Kan, Haidong; Ouyang, Huiling; Tang, Xu; Chen, Jianmin

doi:https://doi.org/10.5194/acp-2021-83

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https://doi.org/10.5194/acp-2021-83

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29 Mar 2021

Review status: a revised version of this preprint is currently under review for the journal ACP.

Saccharide composition in atmospheric fine particulate matter at the remote sites of Southwest China and estimates of source contributions

Zhenzhen Wang¹, Di Wu¹, Zhuoyu Li¹, Xiaona Shang¹, Qing Li¹, Xiang Li¹, Renjie Chen², Haidong Kan², Huiling Ouyang³, Xu Tang³, and Jianmin Chen^1,3 Zhenzhen Wang et al.

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¹Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Department of Environmental Science & Engineering, Fudan Tyndall Centre, Fudan University, Shanghai 200438, China
²School of Public Health, Key Lab of Public Health Safety of the Ministry of Education, NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai 200032, China
³IRDR International Center of Excellence on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, Institute of Atmospheric Sciences, Fudan University, Shanghai 200438, China

Received: 29 Jan 2021 – Accepted for review: 03 Mar 2021 – Discussion started: 29 Mar 2021

Abstract. Based on source-specific saccharide tracers, the characteristic of biomass burning (BB) and biogenic emissions to saccharides was investigated in three rural sites at Lincang, where covered with 65 % of forest in the southwest border of China. The total saccharides accounted for 8.4 ± 2.7 % of OC, and 1.6 ± 0.6 % of PM_2.5. The measured anhydrosugars accounted for 48.5 % of total saccharides, among which levoglucosan was the most dominant species. The high level of levoglucosan was both attributed to the local BB activities and biomass combustion smoke transported from the neighboring regions of Southeast Asia (Myanmar) and the northern Indian Peninsula. The measured mono (di) saccharides and sugar alcohols accounted for 24.9 ± 8.3 % and 26.6 ± 9.9 % of the total saccharides, respectively, were both proved to be mostly emitted by direct biogenic volatilization from plant materials/surface soils, rather than as byproducts of polysaccharides breakdown during BB processes. Five sources of saccharides were resolved by non-negative matrix factorization (NMF) analysis, including BB, soil microbiota, plant senescence, airborne pollen and plant detritus with the contribution of 34.0 %, 16.0 %, 21.0 %, 23.7 % and 5.3 %, respectively. The results provide the information on the magnitude of levoglucosan and contributions of BB, as well as the characteristic of biogenic saccharides, at the remote sites of Southwest China, which can be further applied to regional source apportionment models and global climate models.

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Zhenzhen Wang et al.

Status: final response (author comments only)

CC1:
'Comment on acp-2021-83', Samuel Weber, 30 Mar 2021
In this study the authors reported measurment of PM2.5 component over 3 different sites in China during a sampling period of 1 month, during spring 2019. Different saccharides were measured, including biomass burning proxy such as levoglucosan, manossan and galactosan, as well as more uncommon mono(di)saccharide, aiming at tracing the promary biogenic and possibly secondary biogenic sources. After a discussion on the potential link between emissions sources based on correlation and ratio of species, the authors attempt a source-apportioment of the different saccharide using a Non-Negative matrix Factorization (NMF) method and succesfully identify 5 differents factors of saccharides.
This interesting study reports a comprehensive observational dataset (although not covering the full year) and gives usefull insight concerning the sources of organic components thanks to the use of proxy species not-usually used in the litterature.
Specific comments:
Samake et al. (2019) highlight that the different polyols are mostly in the coarse fraction of the PM. Also, it has been hypothesis that the different size distribution of polyols may be a proxy of the different microbiota. Did the authors have also sampled the PM₁₀ fraction and could provide the size distribution of the different saccharides?

The source apportioment (SA) is a very interesting part, although it lacks of important information that should be reported:

Why didn’t you included the whole species available in the SA? It could help identify more robustly BB, but also saccharides from soil resuspention (with Ca²⁺), and moreover quantify the apportionment of the different factors to the total PM_2.5 mass.

It is stated that the SA is still uncertain, but no estimation of the uncertainties is given. It would be of great interest to report the species uncertainties, for instance with bootstraping your input data.

The timeserie contribution would also be of great interrest. Even if the authors did not include a total variable (namely, PM_2.5), the timeserie of the total saccharide for the 5 factors would be informative.

The « Soil microbiota » factor, identified mainly by the presence of Threalose and Mannitol (and Arabitol) denotes with the finding of Samake et al. (2020) that found that Arabitol and Mannitol are associated with fungi and bacteria from the leaves and not with the soil (even if some mixing are probable). I would suggest naming it « Soil and leave microbiota ».

Overall, the naming of the different factors identified is too rapidly explained, and more detailed could be written to ease the interpretation of the different factors.

Minor comment :
Please provide the pie chart of Figure 6b in a non-3D way, as the relative proportion is much harder to see in 3D compare to regular 2D graph.

Sincerly,
References:
Samaké, A., Jaffrezo, J.-L., Favez, O., Weber, S., Jacob, V., Albinet, A., Riffault, V., Perdrix, E., Waked, A., Golly, B., Salameh, D., Chevrier, F., Oliveira, D. M., Bonnaire, N., Besombes, J.-L., Martins, J. M. F., Conil, S., Guillaud, G., Mesbah, B., Rocq, B., Robic, P.-Y., Hulin, A., Meur, S. L., Descheemaecker, M., Chretien, E., Marchand, N., and Uzu, G.: Polyols and glucose particulate species as tracers of primary biogenic organic aerosols at 28 French sites, 19, 3357–3374, https://doi.org/10.5194/acp-19-3357-2019, 2019.
Samaké, A., Bonin, A., Jaffrezo, J.-L., Taberlet, P., Weber, S., Uzu, G., Jacob, V., Conil, S., and Martins, J. M. F.: High levels of primary biogenic organic aerosols are driven by only a few plant-associated microbial taxa, 20, 5609–5628, https://doi.org/10.5194/acp-20-5609-2020, 2020.
Citation: https://doi.org/10.5194/acp-2021-83-CC1
- CC2: 'Reply on CC1', Jianmin Chen, 26 May 2021
  
  The comment was uploaded in the form of a supplement: https://acp.copernicus.org/preprints/acp-2021-83/acp-2021-83-CC2-supplement.pdf
  
  Citation: https://doi.org/10.5194/acp-2021-83-CC2
RC1:
'Comment on acp-2021-83', Anonymous Referee #3, 20 May 2021
The paper entitled “Saccharide composition in atmospheric fine particulate matter at the remote sites of Southwest China and estimates of source contributions” by Zhenzhen Wang and colleagues provide the characteristic of saccharides during spring 2019 at Lincang, a rural site in Southwest China. The authors reported molecule tracers including anhydrosugars, mono (di) saccharides and sugar alcohols, combined with statistical analysis and HYSPLIT model, they concluded that biofuel and open biomass burning (BB) activities could have a significant impact on ambient aerosol levels at Lincang. Overall, this paper is logically organized, and knowledge of this work is needed and helpful for better understanding air conditions in Southwest China. The topic of this paper is within the scope of the journal Atmospheric Physics and Chemistry. I would like to recommend this paper published after the following of my concerns be resolved.

Major comments:

The surrounding environmental condition is crucial for understanding the results, I strongly suggest the authors added a figure to show the sampling sites as Figure 1. This figure should include some necessary information about the topography, vegetation, residential area nearby Lincang, and photos of three sampling sites are also crucially needed.

The source appointment is mainly based on the 72h backward trajectories of HYSPLIT model. However, high uncertainty existent for the application of HYSPLIT model at high elevation site because topographic relief. The frequencies of HYSPILT or meteorological analysis should provide more creditable results.

Minor comments:

The samples of this work are mainly in spring, the title should be changed to “Saccharide composition in atmospheric fine particulate matter during spring at the remote sites of Southwest China and estimates of source contributions”.

Line 62, Wu et al., 2020 is not cited in references.

Line 71-72, “10.1-383.4 ng m^-3 over the Tibetan Plateau (Li et al., 2019)”, the reference Li et al., 2019, EP is glacier cryoconites not aerosol samples.

Line 75, Sichuan Basin, not “Chengdu Basin”.

Line 79-81, Levoglucosan emission of China is estimated by BB activities by Wu et al., 2021, this sentence is not rigorous.

Line 109-112, you should better add some references.

Line 116, do you have samples over other period?

Line 126-130, please add a figure for sample sites.

Line 183, why do not use meteorological data at Lincang?

Line 231-233, “no distinct variation”, has statistical significance?

Line 239-248, samples in those references are not collected at the same period.

Line 276-277, how about the L/M for burned ghost money?

Line 290-291, references for L/K⁺?

Line 431-441, Figure 4, only one air mass from Hengduan Mountain region. Maybe frequency is better for understanding air sources.

Line 450-452, how about the atmospheric dynamics for aerosol transport from Southeast Asia to Lincang, especially for residential cooking and heating.

Line 512, ng m^-3?

Line 521, only Myanmar.
Citation: https://doi.org/10.5194/acp-2021-83-RC1
- CC3: 'Reply on RC1', Jianmin Chen, 26 May 2021
  
  The comment was uploaded in the form of a supplement: https://acp.copernicus.org/preprints/acp-2021-83/acp-2021-83-CC3-supplement.pdf
  
  Citation: https://doi.org/10.5194/acp-2021-83-CC3
RC2:
'Comment on acp-2021-83', Anonymous Referee #1, 27 May 2021

This manuscript presents measurement results of particulate sugar compounds from a rural region in Southwest China. Individual sugar species concentrations, correlations among each other, as well as diagnostic ratios were utilized together with meteorological parameters, back trajectories, and fire counts to constrain the main emission sources, including biomass burning, microorganisms and plant emissions. Biomass burning emissions were the dominant contributor to the ambient PM2.5, derived from both local burning activities and long-range transport from surrounding countries.

The results presented in this paper are interesting as they give insight into the sources of ambient aerosols in this part of China for which limited data have been reported. The results are based on a sound measurement approach, and include a large number of chemical PM components, while the measurement period is relatively short and doesn't show seasonal patterns. Overall, the manuscript is fairly well written and structured, and should therefore be published in ACP following minor revision based on the comments given below.

Specific comments:

1. It is good to see the utilization of the Metrohm sugar columns (requiring substantially lower eluent concentrations), instead of the usual CarboPak columns from Dionex used in most other studies. Did the authors encounter any co-elution problems of certain sugar species with this system?

2. Lines 276-278: Do the authors know what are the traditional burning practices during the Qingming Festival, i.e., what types of biomass the local residents may be burning that are special for that holiday or is it just enhanced cooking activity, perhaps with more outdoor BBQ cooking?

3. Lines 416-418: While erythritol may have been used as surrogate for the 2-methyltetrols, I believe it was mainly for quantification of the 2-methyltetrol peaks when no authentic standards were available, rather than representing the ambient 2-methyltetrol levels. Since the 2-methyltetrols can be separated by HPAEC-PAD, did the authors see any unidentified peaks in the sugar alcohol region of the chromatogram that could potentially be attributed to the 2-methyltetrols?

4. Lines 495-500: What are the typical crops that are planted in this region? And what kind of burning practices do the local farmers have, e.g., post-harvest burning of straw or other agricultural residues? Knowledge of these practices would be helpful for explaining the BB patterns and specifically the anhydrosugar diagnostic ratios.

Technical corrections:

1. Throughout the manuscript, grammar and wording needs to be polished.

2. Lines 144-145: Please, check the correct supplier of the DRI Model 2015 analyzer -- I don't think that it is "Atmoslytic" anymore but "Magee" or "Aerosol"

Citation: https://doi.org/10.5194/acp-2021-83-RC2
- AC1:
  'Reply on RC2', Jianmin Chen, 02 Jun 2021
  General comments:
  This manuscript presents measurement results of particulate sugar compounds from a rural region in Southwest China. Individual sugar species concentrations, correlations among each other, as well as diagnostic ratios were utilized together with meteorological parameters, back trajectories, and fire counts to constrain the main emission sources, including biomass burning, microorganisms and plant emissions. Biomass burning emissions were the dominant contributor to the ambient PM_2.5, derived from both local burning activities and long-range transport from surrounding countries.
  The results presented in this paper are interesting as they give insight into the sources of ambient aerosols in this part of China for which limited data have been reported. The results are based on a sound measurement approach, and include a large number of chemical PM components, while the measurement period is relatively short and doesn't show seasonal patterns. Overall, the manuscript is fairly well written and structured, and should therefore be published in ACP following minor revision based on the comments given below.
  
  Reply: We appreciate the positive comments and suggestions about the manuscript. We agree with the reviewer’s comments, and have updated the manuscript on the basis of these suggestions.
  
  Specific comments:
  It is good to see the utilization of the Metrohm sugar columns (requiring substantially lower eluent concentrations), instead of the usual CarboPak columns from Dionex used in most other studies. Did the authors encounter any co-elution problems of certain sugar species with this system?
  
  Reply: We have encountered some co-elution problems when using the Metrohm sugar column. At first, we prepared twenty standard saccharide compounds for the method test, and found that several saccharides co-eluted. By changing the concentration of the eluent and the flow rate, there were still some saccharides compounds that cannot be separated well.
  For example, it was difficult to separate glycerol and sorbitol, the retention times of which were respectively 5.82 and 5.97 under the condition of the method in this paper. Because there could be a ~5% deviation of the peak location, data of sorbitol was not accurate and was not included in this paper. When testing the outfield samples, the sorbitol peak might be attributed to glycerol.
  Under the same condition, we repeated the experiment many times to carefully identify the peak location for every saccharide. The relative deviation of retention time and peak area were less than 1%. When it showed a good linear relationship between peak area and concentration value (R²>99.9%), the saccharides were selected to measure. We finally decided to test thirteen kinds of saccharide compounds in this article. The selected saccharides were inositol, glycerol, erythritol, arabitol, trehalose, manitol, mannose, glucose, fructose, galactosan, levoglucosan, mannosan and sucrose, the retention times of which were 4.88, 5.82, 6.22, 7.84, 8.96, 9.58, 10.93, 11.97, 14.59, 16.94, 17.96, 19.32 and 22.54, respectively.
  
  Lines 276-278: Do the authors know what are the traditional burning practices during the Qingming Festival, i.e., what types of biomass the local residents may be burning that are special for that holiday or is it just enhanced cooking activity, perhaps with more outdoor BBQ cooking?
  
  Reply: The weather around Qingming Day is not very suitable for barbecue. We think the sudden increase in biomass burining may not be a significant cooking activity. The most likely activity is the sacrifice around the Tomb-Sweeping Day, during which large quantities of ghost money, candles and firecrackers were burned. The main raw materials of ghost money are bamboo and wood.
  
  Lines 416-418: While erythritol may have been used as surrogate for the 2-methyltetrols, I believe it was mainly for quantification of the 2-methyltetrol peaks when no authentic standards were available, rather than representing the ambient 2-methyltetrol levels. Since the 2-methyltetrols can be separated by HPAEC-PAD, did the authors see any unidentified peaks in the sugar alcohol region of the chromatogram that could potentially be attributed to the 2-methyltetrols?
  
  Reply: The usage of erythritol was due to the lack of the standard 2-methyltetrols. The retention time of erythritol was very short when using the Metrohm sugar columns. The peak positions of erythritol and sorbitol were often overlapped, so it was difficult for us to find other substances in the peak location of the erythritol.
  
  Lines 495-500: What are the typical crops that are planted in this region? And what kind of burning practices do the local farmers have, e.g., post-harvest burning of straw or other agricultural residues? Knowledge of these practices would be helpful for explaining the BB patterns and specifically the anhydrosugar diagnostic ratios.
  
  Reply：Thank for the reviewer’s suggestion. This region abounds with black tea, nuts, coffee and sugar cane. The main crops in this region are rice, wheat and corn. Crop straw burning is a common phenomenon after the harvest, including the indoor combustion and open burning. We've put these information into the analysis.
  “Previous results showed the emissions from the combustion of crop residuals such as rice straw, wheat straw and corn straw exhibited comparable L/K⁺ ratios, typically below 1.0. The averages of L/K⁺ ratios in this study was 0.48 ± 0.20, which was higher than the ratio for wheat straw (0.10 ± 0.00) and corn straw (0.21 ± 0.08), but was lower than the ratio for Asian rice straw (0.62 ± 0.32) (Cheng et al., 2013). In this study, higher L/K⁺ ratios were observed during 8-10 March (1.20 ± 0.19) than those during 31 March-1 April (0.40 ± 0.13), which suggested that the open fire event during 8-10 March was more possibly due to smoldering combustion of residues at low temperatures.”
  
  Technical corrections:
  Throughout the manuscript, grammar and wording needs to be polished.
  
  Reply：Thank for the reviewer’s correction. We’ll try the best to polish the grammar and wording of this manuscript. The writing has been updated with the help of a colleague scientist whose native language is English.
  
  Lines 144-145: Please, check the correct supplier of the DRI Model 2015 analyzer -- I don't think that it is "Atmoslytic" anymore but "Magee" or "Aerosol"
  
  Reply： We rechecked the relevant information and found that DRI Model 2015 analyzer was produced by the Aerosol Inc.
  Thank for the reviewer’s correction. “Atmoslytic Inc.” have been changed to “Aerosol Inc.”
  
  Citation: https://doi.org/10.5194/acp-2021-83-AC1

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Short summary

This study firstly investigates the composition of sugars in the fine fraction of aerosol over three sites in the southwest China. The result suggested no significant reduction in biomass burning emissions in Southwest Yunnan Province to some extent. The result shown shed a light on the contributions of biomass burning and the characteristic of biogenic saccharides in these regions, which can be further applied to regional source apportionment models and global climate models.


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