the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
29 Mar 2021
29 Mar 2021
Saccharide composition in atmospheric fine particulate matter at the remote sites of Southwest China and estimates of source contributions
- 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
- 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
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 PM2.5. 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: open (until 24 May 2021)
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CC1: 'Comment on acp-2021-83', Samuel Weber, 30 Mar 2021
reply
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 PM2.5 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, PM2.5), 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.
Zhenzhen Wang et al.
Zhenzhen Wang et al.
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