the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 07 Jul 2026
A Decadal-Scale Perspective on PM10 Composition and its Variability Drivers at the Alpine High-Altitude Research Station Jungfraujoch
Abstract. Atmospheric aerosols in the free troposphere (FT) exert a disproportionate influence on climate forcing yet remain poorly constrained. Here, we present an 11-year (2011–2021) characterization of PM10 chemical composition at the High Altitude Research Station Jungfraujoch (3580 m above sea level), capturing both FT conditions and episodic planetary boundary layer intrusions (PBLi). We integrate long-term measurements of organic aerosol (OA), elemental carbon, sulfate, crustal elements, trace metals, and bulk and molecular-level organic composition with gas-phase observations and proxies for atmospheric transport and oxidative capacity to quantify the drivers of aerosol loading and composition. The concentrations of primary aerosol species, including metals and elemental carbon, are strongly controlled by episodic PBL-to-FT transport (2-3-fold seasonal amplitude, e.g. 0.15 to 0.3 ng m-3 for Pb). Secondary species, including sulfate and OA, also reflect PBLi impact, but their formation requires sustained oxidative processing, for which the atmospheric humidity ratio (ω) acts as a key control. OA exhibits the strongest seasonal amplitude (10-fold, 0.1 to 1 μg m-3), additionally reflecting enhanced biogenic emission intensities in the PBL. This is accompanied by a systematic shift in C9 and C10 compounds, likely related to seasonal maxima in monoterpene emissions. Together, these results demonstrate that FT aerosol is governed by a dynamic interplay between episodic PBL-FT transport, source emission intensities and oxidative processing. This dataset constrains their relative contributions, and provides decade-scale observational benchmarks for improving the representation of transport and aging in atmospheric models, with implications for reducing uncertainties in climate forcing.
Competing interests: The authors declare that they have no conflict of interest. Part of the funding was provided via a WeMakeIt Science-485 Booster crowdfunding campaign, including contributions from Digitel AG and Camfil GmbH. The funders had no involvement in the study design, data collection, analysis, interpretation, or manuscript preparation.
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 paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
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Status: open (until 05 Sep 2026)
- RC1: 'Comment on egusphere-2026-3660', Anonymous Referee #1, 11 Aug 2026 reply
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RC2: 'Comment on egusphere-2026-3660', Anonymous Referee #2, 27 Aug 2026
reply
A Decadal-Scale Perspective on PM10 Composition and its Variability Drivers at the Alpine High-Altitude Research Station Jungfraujoch
This paper presents an impressive data set over an 11 year period at a site in the FT. various metrics are used to assess influence of PBLi and oxidation potential. The manuscript is well written, the data is thoroughly evaluated to assess, ratios and proxies are identified with strengths and limitations clearly discussed and implications of the data (and the limitations) are clearly stated. This paper should be published after minor revisions.
My primary concern is that there is so much/many important data/figures in the supplemental material. Your figures are clear and needed but if additional figures that address additional key points could be included in the main paper, the reader would more easily be able to assess your methods and assertions.
Minor comments:
Line 37 through not trough
Equations R1 and R2 are unnecessary as this isn’t the focus of the paper. Stating common oxidation pathway is useful but not so for the formation of the oxidant.
Line 87, give dimensions of filters
Line 98, a separate paper on the Saharan dust samples would be interesting. Perhaps limit information published here to provide data for another paper.
Line 108 and rest of paragraph. Please be clear about the date range of samples analyzed for WSOC, by the offline instruments, IPCMS etc. It is not clear if the samples analyzed are limited to the two time windows stated for OC/EC or for all 11 years for organics, ions and elemental measurements.
Figure 1 - suggest adding the composite of ICPMS elements as a trace in Figure 1 to give the abundance of elements. If your ICPMS method measured Si and Al reasonably well, you could estimate soil (dust) using the IMPROVE dust equation (https://vista.cira.colostate.edu/Improve/reconstructed-fine-mass/) or the SPARTAN dust equation (https://doi.org/10.1021/acsestair.3c00069) and then show the remaining elements as another trace.
Title for section 4.2 You introduce the term receptor site in the title, not something you have stated previously. I suggest removing word receptor or define the site as a receptor site in the text.
Line 264. The binning of data is understandable but the drawing binned local median trend lines is less familiar to me. Can you cite other times this has been used in a similar setting or other reference to show its validity as a tool for analyzing this type of data?
Citation: https://doi.org/10.5194/egusphere-2026-3660-RC2 -
RC3: 'Comment on egusphere-2026-3660', Anonymous Referee #3, 02 Sep 2026
reply
This is a very well written article which also has an impressive dataset (more than 10years worth overall) to discuss. The authors discuss the influence of PBLi and atmospheric oxidative capacity (with humidity ratio ω as its proxy) to the measurements at a FT site in Swiss Alps. The strengths and limitations have been discussed adequately. Detailed analysis and reasoning have been provided. The results presented here may not match in an environment with higher pollution levels and frequent episodic events of fresh emissions, and authors do state this limitation. The article may be considered for publication after minor revisions.
A few plots could have been included from the supplementary in the main text. In the current format, readers have to refer to supplementary frequently. I understand that given the size of the dataset and scope of the analysis, it's not feasible to include all the important figures into the main text but adding a couple more figures in the main text would be good (I don't think this should be an issue as the paper is already sufficiently long).
Minor comments:
- line 97: R2 value written here is 0.64 but in figure S2, it is 0.87. Please check which one is correct.
- line 247: The authors state "In Fig. 2, the seasonal patterns particularly of OA and sulfate deviate strongly from those of the PBLi tracers (Fig. S16)." I think this sentence need adjustment. There is difference from trends observed for NOy/CO and Rn222 but I wouldn't call it strong deviation. Also, the plot in Fig. 2a is about WSOA and not OA so the same should be used in the text.
- line 269: Again, authors state that CO exhibits a moderate decrease at high ω and O3 increases moderately. I think for CO, it may be acceptable to some extent but for ozone, the difference isn't so pronounced. So it will be good to reword this section highlighting this limitation.
- line 275-277: the sentence needs revision, some VOCs definitely show negative correlation with ω but it's not clear for all. The authors use the term "for all compounds". Correlation with ω for tetrachloroethylene, trichloroethylene and toluene isn't clear.
- line 286: I suggest using the term "possible proxy" here where the sentence states "the strong and systematic dependence of gas-phase composition on ω validates its use as a proxy for atmospheric oxidative capacity."
- Fig. 7b: trendlines need to thicker, right now it's not clearly visible. Also, color mapping for the plots need to be improved, at least for the ones in the main text (Figs 3, 4, 6, 7). Increased size of the colored dots could be tested.
- Measurements of a few VOCs (particularly halogenated VOCs) are showing the levels of only a few ppt which is almost reaching the detection limits. I understand that Medusa GC-MS has been optimized for high sensitivity for atmospheric compounds but I think it's better to state the detection limits of these species in the supplementary information. Similarly, for Pb concentration, ICP-MS MDL should be provided as it's in the MDL range of ICP-MS depending on the specific instrument performance.
- Fig. 9b: Mass defect plots need to be enlarged slightly for better visualization. Right now, it's very difficult to see clearly.
Citation: https://doi.org/10.5194/egusphere-2026-3660-RC3
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- 1
Review of A Decadal-Scale Perspective on PM10 Composition and its Variability Drivers at the Alpine High-Altitude Research Station Jungfraujoch
Julian Weng et al.
This is an interesting and well-written manuscript. The introduction is particularly strong, providing a clear rationale and effectively framing the research question. The paper is carefully written throughout, with a logical organization. Overall, I found the hypothesis of using specific tracers at a mountain site [i.e. NOy/CO for PBLi and w for atmospheric oxidative capacity] to demonstrate and quantify that the influence of the PBL allows for elevated aerosol concentration, while atmospheric oxidative capacity governs the composition of these episodes (in gas and aerosol phase), an interesting and innovative approach. At this point, I believe this paper need further work to clearly demonstrate this approach (additional statistics, etc). The major and minor comments provided below are intended to further strengthen the manuscript and improve its clarity, rigor, and overall impact.
Major comments and questions:
Von der Weiden, S-L., F. Drewnick, and S. J. A. M. T. Borrmann. "Particle Loss Calculator–a new software tool for the assessment of the performance of aerosol inlet systems." Atmospheric Measurement Techniques 2, no. 2 (2009): 479-494.
Has an experimental validation of the JFJ inlet been conducted (like these examples:https://www.tandfonline.com/doi/full/10.1080/02786826.2019.1602718#d1e1040 or https://www.osti.gov/servlets/purl/2375529)?
This is especially concerning as later (line 154) it is suggested that negligible number of accumulation (N90) particles is found in the Free Troposphere at JFJ. This is inconsistent with many other mountain sites, which commonly find coarse mode (especially dust) aerosols in the free troposphere. A few examples are provided below.
Shen, M., Qi, W., Liu, Y., Zhang, Y., Dai, W., Li, L., Guo, X., Cao, Y., Jiang, Y., Wang, Q., Li, S., Wang, Q., and Li, J.: Measurement report: Observational insights into the impact of dust transport on atmospheric dicarboxylic acids in ground region and free troposphere, Atmos. Chem. Phys., 25, 16147–16165, https://doi.org/10.5194/acp-25-16147-2025, 2025.
Betsy, K. B., and Sanjay Kumar Mehta. "Characteristics of dust aerosols within the atmospheric boundary layer and free troposphere over a tropical coastal station." Meteorology and Atmospheric Physics 137, no. 3 (2025): 27.
Tsai, F., Chien, Y.C., Chen, W.N., Notaro, M., Chen, H.Y., Lin, N.H., Hsu, P.C. and Lin, Y.C., 2025. Source and transport of dust to the North Pacific: Observations and analysis from a high mountain. Journal of Geophysical Research: Atmospheres, 130(6), p.e2024JD042415.
Additionally, particle loss may impact the conclusion in line 216 that “on an annual basis, cumulative fine and coarse mode aerosol masses are of comparable magnitude”
This mass comparison depends upon the assumption that aerosol transmission losses are not size dependent, which is most likely incorrect.
Minor comment:
Figure 3b and 3c: Consider starting the y-axis at 60 ppb for CO and 35 ppb for O₃. This adjustment would improve the readability of the plots by reducing the amount of unused white space and emphasizing the variability in the data.
Figure 3, 4, 6, and 7 – Consider a different more distinct color bar. It is difficult to differentiate blue and purple, which is the most commonly used colors on all plots.