the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 07 Oct 2025
Highly time-resolved chemical characteristics and aging process of submicron aerosols over the central Himalayas
Abstract. Aerosol particles transported from South Asia, especially biomass burning (BB) emission related aerosols during pre-monsoon, have significant climate effect in the Himalayas. The details on complicated physicochemical properties and aging process of aerosols are important for understanding this climate effect. An Aerodyne high-resolution time-of-flight aerosol mass spectrometer co-located with gas analyzers was deployed during 25 April 2022 to 25 May 2022 to study the highly time-resolved chemical characteristics and aging process of submicron aerosols (PM1) on the northern slope of the Himalayas. The 10-min resolution mass concentration of PM1 varied from 0.1 to 12.2 µg m−3 during this study, with an average of 1.7 ± 1.6 µg m−3. Organic aerosols (OA) showed a dominant contribution (46.2 %) to PM1 following by sulfate (20.8 %), BC (19.4 %), ammonium (8.5 %), nitrate (4.8 %) and chloride (0.4 %). Evolution of bulk OA in the f44 vs. f60 space showed clear aging process from less aged BB plumes to highly oxidized state in polluted period. Positive matrix factorization (PMF) on the high-resolution organic mass spectra resolved two oxygenated OA (OOA) factors, i.e., a less-oxidized OOA influenced by biomass burning (OOA-BB) and a more-oxidized OOA (MO-OOA). We performed a case study to explore the OOA formation mechanism during long-range transport. The results indicated aqueous‐phase process and photochemical reaction together elevated OOA concentrations and ageing processing, consistent with secondary inorganic aerosol production. This study underscores the significant occurrence of BB aerosols in Himalayas and provides insights into the oxidative processing in this remote region.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2025-4785', Anonymous Referee #1, 16 Oct 2025
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RC2: 'Comment on egusphere-2025-4785', Anonymous Referee #2, 03 Dec 2025
In this study, an intensive field campaign was conducted at the Qomolangma Station for Atmospheric and Environmental Observation and Research (QOMS) on the northern slope of the Himalayas (4276 m a.s.l.), during the pre-monsoon period, using HR-ToF-AMS and gas analyzers to study the organic aerosol (OA) oxidation pathways and secondary OA (SOA) formation mechanism under the influence of biomass burning emission. The authors found two periods with different source regions based on HYSPLIT back trajectories and concentration-weighted trajectory (CWT) analysis. Using MODIS fire hotspots and aerosol vertical distributions from CALIPSO, the atmosphere at QOMS was influenced by biomass burning plumes from South Asia in the polluted period. Source apportionment of OA identified two relatively oxidized OAs, a less-oxidized OOA influenced by biomass burning (OOA-BB) and a more-oxidized OOA (MO-OOA). Aqueous‐phase process and photochemical reaction together elevated OOA concentrations and ageing processing, consistent with secondary inorganic aerosol production indicators (SOR and NOR). The photochemical reaction was confirmed to be an important contributor to MO-OOA formation via a box model. Overall, the dataset including synchronous AMS and trace gas results provided by this work is valuable. The manuscript is well written and the topic fits well in the scope of ACP. I recommend this manuscript can be published after some minor points.
- Section 3.2: Were the trajectories calculated and clustered during the entire study, or independently in the clean period and polluted period as shown in Fig. 4?
- The average OA concentration after PMF analysis in Figure 8 is 0.5 μg m−3, which is obviously lower than the value in the abstract “46.2% of 1.7 ± 1.6 μg m−3”. Did the authors apply RIE and CE after PMF analysis? Or the contribution of residual factor in PMF result is very high?
- Line 10: replace “during 25 April 2022 to…” with “from 25 April 2022 to…”.
- Line 146: replace “BC (19.4 %) ammonium (8.5 %)” with “BC (19.4 %), ammonium (8.5 %)”.
- Line 179: replace “C2 (from north), C3 (from southeast)” with “C2 (from north) and C3 (from southeast)”.
- Line 188: replace “gradually” with “gradual”.
- Line 269: replace “Although its high degree of oxidation” with “Although its oxidation degree was high”.
- Line 310: replace “in different period” with “in different periods”.
- Line 330: replace “increase with NOR” with “increased with NOR”.
- Line 334: replace “Fig 10e–g” with “Fig. 10e–g”.
- Line 350: replace “in this process” with “in these processes”.
- Lines 374-385: polluted period and clean period? This study only identified one polluted period and one clean period.
Citation: https://doi.org/10.5194/egusphere-2025-4785-RC2
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The manuscript provides valuable insights into the chemical composition and aging processes of submicron aerosols in the Himalayas, particularly focusing on the impact of biomass burning (BB) aerosols during the pre-monsoon period. The use of high-resolution mass spectrometry along with real-time gas analyzers offers a comprehensive view of aerosol characteristics and their formation mechanisms. Additionally, the combination of positive matrix factorization and air mass trajectory analyses provides a clear understanding of the sources and transport pathways of these aerosols. The manuscript is generally well written, and I recommend it for publication after addressing the following comments:
Comments:
The authors used default RIEs for mass quantification. Did the authors perform any calibration of the AMS? It should be possible to obtain the actual RIE for NH4 from the calibration data.
Regarding the diurnal cycles, could the authors check those during the clean periods? The reason for this is that the sources and processes in the clean and polluted periods are different. The diurnal cycle for the entire study could be primarily influenced by the first polluted period with high mass loadings. Similarly, I suggest the authors examine the contributions of different processes in the two distinct periods (Figure 11).
The SOR is notably low compared to previous studies. It is somewhat surprising that the SO2 mixing ratio is relatively high (~3 ppb on average) in this study. Could this be a real measurement, or might it be influenced by instrument uncertainties? It’s like a baseline there?
For Figure 9, could the authors clarify the labels in the first two plots? Specifically, is it f44 or f(CO2+), and f60 or f(C2H4O2+)? These two have some differences.