the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 03 Jul 2025
Measurement report: Impact of domestic heating on dust deposition sources in hyper-arid Qaidam Basin, northern Qinghai-Xizang Plateau
Abstract. Given the unique energy profile of the Qaidam Basin (QDB), it is crucial to examine the impacts of domestic heating on, the Qinghai-Xizang Plateau (QXP), and global atmospheric systems. This study collected monthly dust deposition six sites in the southern QDB between 2020 and 2023. We identified the sources of dust-fall during domestic heating (HP) and non-heating periods (NHP) in urban and rural and its environmental effects. The results demonstrated that domestic heating increased the concentration of water-soluble ions in rural, trace elements in urban, and carbon emissions in both. Among various carbon indicators, organic carbon (OC) and element carbon (EC) levels rose during the HP, with Char-EC as the primary component of EC. Char-EC concentrations were higher in urban areas, while secondary organic carbon, the main contributor to OC, was more prevalent in rural. The OC/EC and char-EC/soot-EC ratios, along with PMF results, indicated that coal and biomass burning were the main contributors to dust deposition in rural, strongly influenced by domestic heating, whereas urban dust predominantly originated from vehicle and industrial emissions. Coal consumption in QDB was greater during the HP than that of other dust sources in the QXP. This increased consumption leads to higher emissions of atmospheric pollutions, which may accelerate glacier melting in the region. Consequently, integrating QDB carbon aerosols into future environmental policies and climate models for the QXP is essential. This study provides a reference for investigating carbonaceous aerosols in climatically similar hyper-arid basins with intensive human activity and salt lake regions.
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RC1: 'Comment on egusphere-2025-1561', Anonymous Referee #2, 14 Jul 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1561/egusphere-2025-1561-RC1-supplement.pdfCitation: https://doi.org/
10.5194/egusphere-2025-1561-RC1 -
RC2: 'Comment on egusphere-2025-1561', Anonymous Referee #3, 29 Sep 2025
This measurement report collects dust deposition samples from six urban and rural sites in the southern Qaidam Basin over the period from January 2020 to March 2023. It analyzes dust flux, water-soluble ions, trace elements, and carbonaceous components during domestic heating and non-heating periods, with the aims of identifying dust sources and assessing the impacts of heating activities. The report yields some interesting findings; however, it contains inconsistencies, and its PMF results require refinement—issues that may undermine the validity of its overall conclusions. First, OC and EC are typically the dominant components of aerosols from biomass or coal combustion (https://doi.org/10.3390/atmos13101595), yet the PMF source profiles presented here show higher trace metals and soluble ions instead. Second, aerosols from biomass or coal combustion are generally in the submicron size range (PM1), which should correspond to lower mass compared to soil dust (primarily in the PM₁₀ size mode, i.e., larger particles). This size-mass relationship is not adequately addressed, casting doubt on the accuracy of source contribution estimates. Third, the distinction between road dust and traffic-related emissions is blurred in the study, leading to an implausible conclusion. Vehicle-road interactions primarily drive the resuspension of road dust—a coarse-particle fraction—and brake emissions from vehicles are also a likely contributor. By contrast, traffic emissions from engine exhaust are dominated by submicron (PM₁) or ultrafine aerosols (https://acp.copernicus.org/articles/20/12721/2020/).
Additionally, the figures lack clear organization, hampering readability and the ability to cross-verify results with data. Given these critical issues, I recommend a major revision to address the concerns outlined above before the manuscript is suitable for publication.
Line 145. Please provide more details about the dust collection techniques, including the dust size cut. Are you measuring total atmospheric dust with no size cut? Specify the dust collection efficiency, and the duration of each collection—for example, is it 24 hours?
Line 204. Is OC fully collected on the filter samples? It is unclear whether the filter samples can collect all OC in the dust samples.
Line 231. For biomass burning, OC/EC can exceed 2 even for primary emissions.
Line 247. Provide more details about the minimum R². Were all data analyzed together, or separately for the 6 sites? What is the specific minimum R² value for the data? Given the low time resolution of the method, I suspect OC and EC are well correlated, so the minimum R² method may not be suitable for this type of data.
In the Supporting Text, should Figure S14 be Figure S13?
Line 295. How many runs were conducted for BS? What are the uncertainties of the PMF analysis? This should be evaluated, considering the small number of data points for each PMF run.
Why perform PMF separately for heating/non-heating periods and urban/non-urban areas? The factors appear similar except for coal combustion. Can all data be combined for a single PMF analysis? What type of industry does the expected industry factor correspond to?
Line 309. Can the hypothesis be supported by the PMF results?
Line 335. Are you suggesting that urban residents use more coal than rural residents? I would expect rural residents to use more coal, while urban areas have municipal central heating. This seems consistent with Line 377, which states rural carbon emissions are higher than urban levels.
Line 398. The lower OC/EC ratios may be due to the low OC collection efficiency of the method used.
Line 419. I don’t think comparing dust deposition (presumably TSP) with PM2.5, PM10, or filter samples is meaningful. Particles of different sizes have different sources, so you should compare with dust deposition data from literature, not filter samples.
Line 438. It is confusing that this study focuses on PM larger than 10 μm, as the size cut for dust sampling is not clearly stated. Biomass burning OA and EC are mainly in the PM2.5 size range.
Line 449. In the Method section (Line 223), soot EC is defined as EC1-OPC, which is very confusing. What about Ash EC (mentioned in Line 224)?
Line 475. When stating “supporting previous findings”, please add references. Do these previous findings refer to the same site?
Line 483. This content is repetitive.
Line 485. Check the references—do they use MSR?
Line 516. Are you referring to Table S3 instead of Table S1?
Line 516. Why are the heavy metal levels lower in this study?
Line 523. You mentioned in Line 500 that coal combustion is more intense in rural areas, so these metal levels should be higher in rural areas rather than urban areas. Please clarify this inconsistency.
Line 553. Si and Al are trace elements, not ions—this classification is incorrect.
PMF results:
- Why was secondary formation only identified in Urban-NHP? Additionally, this attribution to secondary sources is highly problematic, as Figure S13 shows SOC was nearly 0 while POC was higher than SOC.
- Why does the salt lake factor have EC and SOC in its profile? As shown in Figure S13 for Rural-NHP, these should be 0.
- As shown in Figure S13, the soil dust factor has nearly 40% SOC contribution for Rural-HP—why is this the case?
Line 561. Are these elements from internal combustion engines or traffic-induced road dust? I suspect it is the latter.
Line 669. Vehicle-road interactions should cause dust suspension (often defined as road dust), and this should contribute more than vehicle emissions.
Figure 7a. How was Figure 7a generated? The PMF factor profiles in Figure S13 differ across different sites—please explain the integration process.
Figure 7b. Rename “Vehicle combustions” to “Vehicle”. The term “Vehicle combustions” is inaccurate, as vehicles themselves are not burned.
Figure S4. Reorganize this figure by combining its subpanels into one layout that fits on a single page. Apply the same reorganization to Figure S5.
Figure S11. By definition, SOC+POC=OC and Soot-EC+Char-EC=total EC. The fractional contributions in this figure involve double counting of OC and EC, which is incorrect.
Table S2. It is impossible for PMF to have 0% uncertainty—this value is erroneous and needs correction.
Table S3. Provide mean values instead of ranges to facilitate better comparison.
Figure S13. Reorganize this figure to improve readability, as it currently spans 4 pages. In addition to factor profiles, can you provide time series of factor contributions? Note that the left y-axis is labeled “concentration of species”, but factor profiles are normalized values, not actual concentrations—this mislabeling should be fixed.
Citation: https://doi.org/10.5194/egusphere-2025-1561-RC2
Data sets
The dust deposition data of the Qaidam Basin [Data set] H.-X. Zhu https://doi.org/10.5281/zenodo.14382853
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