the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 15 Apr 2026
Chemical Characterization and Source Apportionment of Carbonaceous Aerosols during Post-Monsoon Biomass Burning and Diwali at an Upwind Site of Delhi
Abstract. Sonipat, located ~40 km northwest of Delhi, lies along the principal transport corridor linking post-monsoon agricultural burning regions of Punjab–Haryana with Delhi and serves as an intermediate receptor for regional pollution. We conducted intensive high time resolution measurements of composition-based PM₂.₅ (non-refractory PM₂.₅ plus black carbon) from 25 October to 15 November 2023 using a ToF-ACSM and an Aethalometer to characterize carbonaceous aerosol sources during the biomass-burning period. Two severe haze episodes occurred, with PM₂.₅ exceeding 300 µg/m³. Organic aerosol dominated the submicron mass (~65 % of non-refractory PM₂.₅), with daily mean concentrations peaking near 140 µg/m³ during the first haze episode. Positive Matrix Factorization resolved five components including hydrocarbon-like, biomass-burning, and solid-fuel combustion organic aerosol, and two oxygenated fractions representing semi-volatile and low-volatility aged aerosol. Secondary organic aerosol accounted for ~57–60 % of organic aerosol mass, with low-volatility oxygenated organic aerosol reaching 42.8 µg/m³ during peak haze, indicating substantial regional aging and accumulation. Biomass-derived black carbon contributed ~78 % of total black carbon (mean 10.9 µg/m³), far exceeding fossil-fuel contributions (~3.1 µg/m³). Trajectory and wind analyses consistently identified northwestern agricultural regions as dominant sources with minor traffic influence, indicating that extreme carbonaceous aerosol over Delhi-NCR largely forms outside the urban core through regional transport biomass and solid-fuel combustion emissions combined with sustained secondary processing, highlighting the need for coordinated airshed-scale emission reductions across the Indo-Gangetic Plain.
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Status: open (until 20 Jun 2026)
- RC1: 'Comment on egusphere-2026-1428', Anonymous Referee #1, 14 May 2026 reply
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RC2: 'Comment on egusphere-2026-1428', Anonymous Referee #2, 05 Jun 2026
reply
The authors report measurements of PM2.5 composition using ToF-ACSM and aethalometer measurements combined with source apportionment and wind analysis at a site upwind of Delhi. They find that combustion emissions (BBOA + SFCOA) are the dominant primary source of PM2.5 as well as a persistent elevated background of aged OA. These measurements at an upwind site are an important effort to understanding the dynamics of haze pollution in Delhi and throughout the IGP, and this dataset appears valuable. I have a few minor comments
One of the author’s main conclusions is that direct reductions of primary emission will have limitations, as a substantial portion of the carbonaceous aerosol is secondary (e.g. line 381). This is an important finding from a policy standpoint; however, discussion is needed about what the dominant precursor sources of the OOA factors are. Specifically, if the major source of VOCs is from biomass burning, then reductions in primary aerosol will also reduce the secondary aerosol loading. This has very different implications than if the OOA is thought to be from natural sources, such as biogenic VOCs. This topic is brought up near the end of the paper (line 797), but if the authors have hypotheses on this, I would suggest adding it to the PMF discussion, such as in section 3.1.
Similar to above, is there any evidence that either of the OOA factors is directly related to combustion processes? This may be difficult to determine with ACSM measurements, however, there has been other work that has found OOA related to processed biomass burning aerosol appears spectrally similar to OOA (Such as Vasilakopoulou et al., 2023). It may also be important to note that f60 is lower when using a capture vaporizer (Zheng et al., 2020)
I would suggest that the authors consider changing the name of SV-OOA and LV-OOA as no volatility measurements were conducted. Instead, terminology such as more oxidized OOA (MO-OOA) and less oxidized OOA (LO-OOA) may be more appropriate.
How was the O/C and H/C for the PMF factors parameterized?
Can the authors expand on what type of fuel is typically used in this region for solid fuel combustion. Is there evidence that crop burning and SFC have equivalent angstrom exponents?
Line-by-line Comments
Line 34: “This region…” This sentence is missing a word
Line 161: empty parentheses
Line 178: SMPS has not been defined
Line 230 “..including air beam corrections..” this was already stated previously
Line 322: I’m confused by this sentence – at line 322, it says the five factor solution was obtained using unconstrained PMF, but then that one of the solutions was constrained. Can this be clarified?
Line 366. Section numbering is incorrect
Line 571: The statement about the increase in BBOA concentration is repeated, it is also stated at line 560
Line 578: The authors claim that the elevated SV-OOA during H2 is due to the shorter event duration, however, it is unclear how they came to that reasoning. For example, could it also be due to elevated local emissions, rather than long-range transport. Specifically, if it was the duration of the event that was the primary factor, I would expect the fraction of LVOOA to build up over the course of the events. Instead the opposite trend is observed in H1, with very little SVOOA at the start of the event. This should be reconciled, or additional evidence should be provided.
Line 677-682: HOA and eBCff was already discussed in the section above.
Line 761 and line 749: Cl- appears to be included in both primary and secondary?
Figure S1b – can BBOA be added to the diurnal profile here? Also, do the authors have any explanations of the diurnal shift between SFCOA and eBCbb?
References:
Vasilakopoulou, C.N., Matrali, A., Skyllakou, K. etl al., Rapid transformation of wildfire emissions to harmful background aerosol. npj Clim Atmos Sci 6, 218 (2023). https://doi.org/10.1038/s41612-023-00544-7
Zheng, Y., Cheng, X., Liao, K., Li, Y., Li, Y. J., Huang, R.-J., Hu, W., Liu, Y., Zhu, T., Chen, S., Zeng, L., Worsnop, D. R., and Chen, Q.: Characterization of anthropogenic organic aerosols by TOF-ACSM with the new capture vaporizer, Atmos. Meas. Tech., 13, 2457–2472, https://doi.org/10.5194/amt-13-2457-2020, 2020.
Citation: https://doi.org/10.5194/egusphere-2026-1428-RC2
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- 1
The manuscript is a 54-page Egusphere preprint posted on 15 April 2026 and presents high-time-resolution ToF-ACSM and Aethalometer measurements from Sonipat during 25 October–15 November 2023, with PMF, Aethalometer-model BC apportionment, and trajectory/wind analyses. Its central claim is that severe post-monsoon haze reaching Delhi-NCR is already strongly carbonaceous and regionally processed before entering the urban core.
The study is timely, the site is strategically important, and the dataset appears valuable. The paper’s strongest contribution is its framing of Sonipat as an upwind receptor that can separate regional transport from Delhi-core emissions, and the reported dominance of OA and biomass-related BC is potentially important for air-quality policy in the Indo-Gangetic Plain. However, several key methodological choices and interpretive steps need to be tightened before publication, especially around PMF constraints, uncertainty quantification, BC source apportionment assumptions, and the degree to which some conclusions go beyond what the evidence directly supports.
Major comments
The methods say SoFi was used, with FPEAK exploration plus displacement and bootstrap analyses, and the results mention a five-factor solution with a constrained HOA profile using a randomized a-value between 0 and 0.5. But the manuscript text shown here does not give the reader enough quantitative evidence to judge whether the chosen solution is truly robust: for example, how many factor numbers were tested, how Q changed, how often bootstrap runs reproduced the selected factors, whether DISP indicated swaps, or how sensitive source contributions were to the HOA constraint. That is especially important because the paper’s interpretation depends heavily on separating HOA vs SFCOA and fresh vs aged oxygenated OA. I would ask the authors to add a compact but explicit robustness section in the main paper, not only in supplementary material.
The manuscript uses fixed absorption Ångström exponents of 1.0 for fossil fuel and 2.0 for biomass burning. That is common practice, but it is also a known source of uncertainty, especially during complex mixed-source periods such as post-monsoon haze and Diwali. The reported result that eBCbb accounts for about 78% of BC overall and that eBCff falls to 0.03 µg m⁻³ during H2 is striking. Because such values can be quite sensitive to the assumed exponents, the paper should include a sensitivity analysis showing how the biomass/fossil split changes across a plausible AAE range. Without that, the qualitative conclusion may still be reasonable, but the quantitative confidence appears overstated.
The paper argues that haze over Delhi-NCR “largely forms outside the urban core” and that city-level controls alone cannot effectively mitigate peak PM. The transport evidence is strong enough to support an important regional component, but the phrase “largely forms outside the urban core” is stronger than what a single upwind receptor plus PMF/CWT can fully establish. The results show that Sonipat receives a major regional combustion plume before mixing with Delhi emissions, which is already a valuable conclusion. I would suggest recasting policy claims to emphasize that regional controls are necessary in addition to urban controls, rather than appearing to diminish the relevance of urban sources altogether.
I did not find a real limitations discussion in the main text. This paper especially needs one. Relevant limitations include: ACSM measures non-refractory PM and misses refractory species; the source apportionment pertains to one site and one season; PMF factor labels are not unique; Aethalometer source apportionment depends on fixed AAEs; and CWT identifies likely source regions but not emissions inventories or source strengths directly. A frank limitations section would strengthen the paper, not weaken it.
Minor comments
The manuscript briefly shifts into first person in a way that reads unpolished for a research article: “I have tried to answer some of those questions.” This should be rewritten in standard scientific style.
The methods would also read better with a clearer separation between what is reproduced from the prior Rathore et al. study and what is newly done here. Right now, several instrumental details are deferred to Rathore et al. (2025), which is understandable, but the present paper should still be as self-contained as possible for readers and reviewers.