the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 13 Mar 2026
Hyperlocal air quality monitoring and source apportionment of non-refractory PM2.5 at three urban sites using stationary van-based measurements: A Lucknow case study
Abstract. The present study addresses a key gap in characterizing hyperlocal air quality across three contrasting land-use settings during the peak pollution season in a central city in the Indo-Gangetic Plain (CIGP). We conducted ~744 hours of hyperlocal measurements in Lucknow city, spanning the post-monsoon to winter season over 31 days, using a mobile ambient air quality monitoring platform (MAAQMP). Measurements were conducted at three contrasting land-use settings—a background Site (“Site 1”), a traffic corridor (“Site 2”), and a major industrial cluster (“Site 3”). High-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) organic and inorganic mass spectra were subjected to Multilinear Engine-2, Positive Matrix Factorization (ME-2 PMF) source apportionment using a unified organic-inorganic aerosol (OA–IA) matrix. Across sites, NR-PM2.5 was dominated by secondary organic aerosols (SOA), with biomass burning, traffic, and inorganic-associated organic factors (sulphate, nitrate-rich OA). Unlike Sites 1–2 (sulphate/low-volatility oxygenated organic aerosol (OOA)-dominant), Site 3 showed heterogeneous OA from biomass burning, semi-volatile/nitrate OOA, solid fuel, and traffic. Particle growth events (PGEs) were predominantly nocturnal, occurring under stable boundary-layer and inversion conditions. Site 3 exhibited ~5 times more nocturnal PGEs than the background, which was attributed to a higher condensation sink (CS). Stack vapors drive these PGEs (5–15 nm/h), enhancing the growth of oxidized biomass-burning organic aerosols (O-BBOA) and low-volatility OOA under inversions (r = 0.24). These results highlight the potential of near-source stationary van-based measurements for resolving hyperlocal aerosol growth processes and source influences.
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Status: closed (peer review stopped)
- RC1: 'Comment on egusphere-2026-595', Anonymous Referee #2, 19 Apr 2026
-
RC2: 'Comment on egusphere-2026-595', Benjamin Werden, 04 May 2026
The authors deploy a well-instrumented mobile platform (HR-ToF-AMS, SMPS, Xact, micro-aethalometer, gas analyzers, Vaisala) at three sites in Lucknow and present a combined organic–inorganic PMF analysis with a discussion of particle growth events. The instrumentation deployment is impressive. Despite these strengths, the manuscript has structural problems that cannot be repaired through textual revision. My primary concerns are that the central inter-site comparison is confounded with season; that the PMF configurations differ across sites without justification undermining the comparison the manuscript is built around; and that there are claims and novelty framing ("hyperlocal," "first of its kind”) that the data do not support.
- The inter-site comparison is confounded by season
Sites 1 and 2 were sampled in October (post-monsoon) and Site 3 in November (winter), with roughly two weeks separating the Site 2 and Site 3 campaigns. Essentially every inter-site contrast highlighted in the manuscript: the ~7× higher C-PM₂.₅ at Site 3, the appearance of SFC-OA only at Site 3, the dominance of nocturnal PGEs at Site 3, the shift toward nitrate partitioning, and the "industrial influence" are all consistent with a shallower boundary layer, lower temperatures, increased domestic heating, and post-monsoon-to-winter biomass-burning evolution across the IGP, independent of any site-specific characteristic. The authors invoke winter chemistry to explain Site 3's nitrate fraction (lines 365–369) but do not confront the implication for the broader argument: in the absence of contemporaneous measurements, the manuscript cannot distinguish a site signal from a season signal. The "background vs. traffic vs. industrial" framing therefore does interpretive work that the design does not support. This is the dominant limitation of the study and needs to be acknowledged forthrightly throughout, in the abstract, conclusions, and every site-comparison claim, rather than addressed in passing for one species. I am not convinced the inter-site comparison as currently framed can be salvaged from the existing data alone.
- The site deployments are not contiguous, durations are unequal, and the Site 1 baseline is unstable
The campaign durations at each location are 6 days at Site 1, 11 days at Site 2, and 14 days at Site 3. The authors note (lines 352–356) that the final 1–2 days at Site 1 were a "Polluted Day" episode driven by a lemongrass distillation operation roughly 1.5 km from the van. This means the "background" baseline against which Sites 2 and 3 are compared rests on perhaps four ‘ideal’ days. The standard deviation at Site 1 (33.8 µg m⁻³) exceeds the mean (32.6 µg m⁻³), which the manuscript reports but does not flag. This shows that the Site 1 distribution is not well-characterized by its mean and that any ratios involving Site 1 should not be taken at face value. Please report medians and statistics alongside means, separately analyze the PD period, and provide a justification for treating these time windows as comparable.
- The AMS inlet configuration is ambiguous meaning the "NR-PM₂.₅" label is not justified by what is described
Section 2.2.1 does not state whether a PM₂.₅ or PM1 aerodynamic lens is in use in the AMS. The manuscript refers throughout to "NR-PM₂.₅," but standard HR-ToF-AMS configurations measure NR-PM₁ unless specifically modified. The validation against E-BAM PM₂.₅ (Fig. S1) does not resolve the question as a PM₁-vs-PM₂.₅ size cut difference could produce this same pattern. Please clarify the lens and either justify the NR-PM₂.₅ designation or report results as NR-PM₁.
- "Hyperlocal" is not demonstrated by this design
The manuscript leans heavily on the "hyperlocal" framing, including in the title, but the three sites are separated by 10–20 km. The mobile-measurement literature the authors cite shows pollutant variability on the 100 m to 1 km scale; three stationary points at 10–20 km spacing, sampled in different seasons, characterize neighborhood-scale contrasts at best. They do not constitute hyperlocal measurement in the sense in which that term is used in the literature the authors cite. Relatedly, the "first of its kind in the CIGP" claim (line 85) sits awkwardly given the extensive citation of Lakra et al. 2024 and 2025. Which is your same group, same VSAAQMP platform, same city, multi-site stationary measurements (lines 80, 146–147, 212, 565). The relationship between the present work and Lakra et al. 2024 and 2025 should be made explicit: what data overlap, what is genuinely new, and whether the incremental contribution warrants standalone publication. The novelty framing as currently written is overstated.
- The PMF configuration differs across sites in ways that prevent the inter-site comparison
The authors resolve 7 factors at Site 1, 6 at Site 2, and 7 at Site 3; constrain HOA with an a-value of 0.5 at Site 1, an a-value of 0.2 at Site 2, and use a fully unconstrained solution at Site 3; and resolve different factor sets at each site (BBOA-1 + BBOA-2 + SVOOA at Site 1; BBOA-1 + BBOA-2 at Site 2; O-BBOA + SVOOA + SFC at Site 3). An imprecise range of Q/Q_exp values are reported, but only for Site 1 (lines 277–278). The manuscript does not address why the factor number differs between sites, why HOA is anchored at two sites and free at the third with different a-values, whether the apparent absence of SFC at Sites 1–2 reflects a true source absence or an unresolved factor below the noise floor (especially given the much lower OA loading at Site 1), whether the apparent absence of SVOOA at Site 2 is real or a consequence of the 6-factor solution choice, or how n-factor solutions were compared and rejected at each site. Different PMF configurations yield different solutions this is at the core of the diagnostic literature the authors cite. The inter-site comparison of factor contributions in Section 3.3 and Figure 9 is therefore not interpretable as a comparison of physical sources. At minimum, please report Q/Q_exp, factor-number sweep diagnostics, and bootstrap stability for all three sites, and explain why a standard solution structure (consistent factor number, consistent constraints) was not adopted.
- The scientific question driving the analysis is unclear
The paper would be substantially stronger if organized around a testable hypothesis. As written, Section 3 reads as three parallel measurement reports: what we found at Site 1, what we found at Site 2, what we found at Site 3, followed by a brief local-vs-regional contribution analysis (3.3) and a PGE case study (3.4). The parts do not integrate into a cohesive presentation. Most importantly there is no quantitative cross-site framework: no regression of factor contributions against site descriptors, no formal analysis of common vs. site-unique factor variability, no transferability test of one site's PMF basis at another. The Section 4 conclusions are a recitation of percentages. This dataset can support real scientific questions and I would urge the authors to identify the question they are answering and then revisit the analysis in that context.
- Several headline findings rest on correlations and comparisons asked to do too much work
Examples:
The "5× more nocturnal PGEs at Site 3" comparison (2 events at Site 1 over 6 days, 5 at Site 2 over 11 days, 10 at Site 3 over 14 days) is across different seasons, different campaign durations, and different background aerosol loadings. Normalizing per measurement-day helps but does not separate the seasonal driver from any site driver.
The SOR/NOR comparison across sites (Section 3.2.2.5) compares oxidation ratios across seasons. Both ratios depend strongly on temperature, RH, oxidant availability, and partitioning equilibria, precisely the variables that differ between post-monsoon and winter. The interpretation as a "site" effect is not separable from the interpretation as a season effect.
These should be addressed individually, but they share a common pattern: weak quantitative evidence carrying strong causal language.
Presentation
The title and abstract overstate what the design supports, particularly "hyperlocal" and the implied site-attributed conclusions. The structure is followable but descriptive; subsections proceed factor-by-factor with limited synthesis. The English is generally clear though imprecise in places. Several figures (Figs. 3–5, the mass-spectra panels) are dense and difficult to read at the printed size; the elemental-ratio insets are nearly illegible. Figure 9 uses non-comparable PMF bases across the three sites (see Major #5) and should not be presented as a direct comparison without that caveat.
Technical
The treatment of CO₂-related variables (line 269) refers to them being "calculated as constant fractions" without specifying the fraction or the reference; this is a non-trivial choice and should be stated.
References are generally appropriate, though the mobile-measurement framing would benefit from engagement with the broader literature on what "hyperlocal" means quantitatively.
Recommendation
I recommend rejection, with the suggestion that the authors consider resubmitting after the issues in the points above are addressed. The dataset has real value and the platform deployment deserves to appear in the literature; the current manuscript does not, in my view, present that value in a form that supports the claims it makes. A re-scoped paper organized around a single defensible question would in my judgment be a stronger contribution than the current attempt to do everything at once.
Citation: https://doi.org/10.5194/egusphere-2026-595-RC2
Status: closed (peer review stopped)
-
RC1: 'Comment on egusphere-2026-595', Anonymous Referee #2, 19 Apr 2026
The manuscript attempts to investigate organic aerosol sources, secondary formation, and particle growth processes using AMS and SMPS measurements across multiple sites in an urban setting. While the topic is relevant, the study suffers from serious conceptual, methodological, and presentation flaws that substantially limit its scientific credibility and contribution. The manuscript requires substantial rethinking of its objectives, analytical framework, and data interpretation, rather than incremental revision.
Specific comments:
- There is a lack of clear and defensible scientific objective. Several key claims are either poorly justified or internally inconsistent. For e.g., the inclusion of new particle formation (NPF) with AMS-based source apportionment is conceptually disconnected. It nowhere mentions why NPF is relevant to the stated objectives, which precursor vapors are/could be responsible for NPF at the sites, and how AMS-derived factors (e.g., BBOA, LVOOA) are linked to particle growth processes. The claim of heterogeneity in biomass burning organic aerosol (BBOA) is not supported with any evidence. The factor interpretation is inconsistent across sites, with similar O/C ratios assigned different factor names without justification (e.g., BBOA, LVOOA). Further, the assumption that LVOOA and SO4-OA are purely regional, and SVOOA and NO3-OA are local, is overly simplistic and contradicted by the authors’ own trajectory (CWT) analysis showing transboundary influence for NO3-OA.
- The manuscript claims a “hyperlocal” measurement approach, which is not justified as the sites are separated by only 10–20 km, which does not qualify as hyperlocal (typically <1–2 km where microenvironmental variability is resolved). All sites are within the same city; thus, boundary layer dynamics are expected to be similar, undermining claims of distinct atmospheric behavior without supporting evidence on chemical composition. The study design is equivalent to standard multi-site stationary monitoring, which has already been reported in previous works, including by the authors themselves. The novelty claim is therefore overstated.
- The PMF/ME-2 analysis is insufficiently described and poorly justified with no clear criteria for selecting the optimal factor solution. It is unclear whether PMF was conducted separately for each site or as a combined multi-site analysis. However, the latter would be more appropriate. Often, the factor interpretation is confusing and inconsistent, for e.g., similar O/C ratios are assigned to differently named factors across sites, multiple BBOA factors within and across sites are not meaningfully distinguished, and the same factor names are mentioned from their previous studies without demonstrating new insights. Overall, the physical meaning and added value of resolving site-specific factors is not established, especially given the proximity of the sites.
- There are several serious gaps in experimental details and data QA, e.g., AMS calibration, relative ionization efficiency (RIE) for Chloride is missing and for different sites RIE calibration is not done. Given instrument shutdown and relocation between sites, recalibration of both IE and RIE is essential but not addressed. Further, the instrument operation details are incomplete, e.g., there is no explanation of power supply for the stationary van (e.g., DG set, UPS) and potential contamination from self-emissions (if DG set used) is not addressed. For the microaethalometer, claims of “regular monitoring” are vague and unsupported, no correction for relative humidity effects, which are critical in winter and overloading mitigation is mentioned, but data quality procedure is missing. Similarly, no calibration details for BAM, insufficient operational details for SMPS (which is critical for PGE determination) and no source or description of PBLH data. Further, inclusion of unused information (e.g., PToF calibration when not used) reflects lack of focus of the study.
- The manuscript demonstrates incorrect citation practice where several references are inappropriate or misrepresented, e.g.: Aktypis et al. 2024 is incorrectly used to support global aerosol context, and an appropriate one would be Kanakidou et al. 2005, Liu et al. 2024 does not address aerosol transformation as claimed. The introduction section seems to be biased toward the authors’ own prior work, with limited engagement with broader South Asian or global literature. Also, the manuscript is poorly written with a lot of grammatical errors and unclear sentences and lacks basic clarity.
Citation: https://doi.org/10.5194/egusphere-2026-595-RC1 -
RC2: 'Comment on egusphere-2026-595', Benjamin Werden, 04 May 2026
The authors deploy a well-instrumented mobile platform (HR-ToF-AMS, SMPS, Xact, micro-aethalometer, gas analyzers, Vaisala) at three sites in Lucknow and present a combined organic–inorganic PMF analysis with a discussion of particle growth events. The instrumentation deployment is impressive. Despite these strengths, the manuscript has structural problems that cannot be repaired through textual revision. My primary concerns are that the central inter-site comparison is confounded with season; that the PMF configurations differ across sites without justification undermining the comparison the manuscript is built around; and that there are claims and novelty framing ("hyperlocal," "first of its kind”) that the data do not support.
- The inter-site comparison is confounded by season
Sites 1 and 2 were sampled in October (post-monsoon) and Site 3 in November (winter), with roughly two weeks separating the Site 2 and Site 3 campaigns. Essentially every inter-site contrast highlighted in the manuscript: the ~7× higher C-PM₂.₅ at Site 3, the appearance of SFC-OA only at Site 3, the dominance of nocturnal PGEs at Site 3, the shift toward nitrate partitioning, and the "industrial influence" are all consistent with a shallower boundary layer, lower temperatures, increased domestic heating, and post-monsoon-to-winter biomass-burning evolution across the IGP, independent of any site-specific characteristic. The authors invoke winter chemistry to explain Site 3's nitrate fraction (lines 365–369) but do not confront the implication for the broader argument: in the absence of contemporaneous measurements, the manuscript cannot distinguish a site signal from a season signal. The "background vs. traffic vs. industrial" framing therefore does interpretive work that the design does not support. This is the dominant limitation of the study and needs to be acknowledged forthrightly throughout, in the abstract, conclusions, and every site-comparison claim, rather than addressed in passing for one species. I am not convinced the inter-site comparison as currently framed can be salvaged from the existing data alone.
- The site deployments are not contiguous, durations are unequal, and the Site 1 baseline is unstable
The campaign durations at each location are 6 days at Site 1, 11 days at Site 2, and 14 days at Site 3. The authors note (lines 352–356) that the final 1–2 days at Site 1 were a "Polluted Day" episode driven by a lemongrass distillation operation roughly 1.5 km from the van. This means the "background" baseline against which Sites 2 and 3 are compared rests on perhaps four ‘ideal’ days. The standard deviation at Site 1 (33.8 µg m⁻³) exceeds the mean (32.6 µg m⁻³), which the manuscript reports but does not flag. This shows that the Site 1 distribution is not well-characterized by its mean and that any ratios involving Site 1 should not be taken at face value. Please report medians and statistics alongside means, separately analyze the PD period, and provide a justification for treating these time windows as comparable.
- The AMS inlet configuration is ambiguous meaning the "NR-PM₂.₅" label is not justified by what is described
Section 2.2.1 does not state whether a PM₂.₅ or PM1 aerodynamic lens is in use in the AMS. The manuscript refers throughout to "NR-PM₂.₅," but standard HR-ToF-AMS configurations measure NR-PM₁ unless specifically modified. The validation against E-BAM PM₂.₅ (Fig. S1) does not resolve the question as a PM₁-vs-PM₂.₅ size cut difference could produce this same pattern. Please clarify the lens and either justify the NR-PM₂.₅ designation or report results as NR-PM₁.
- "Hyperlocal" is not demonstrated by this design
The manuscript leans heavily on the "hyperlocal" framing, including in the title, but the three sites are separated by 10–20 km. The mobile-measurement literature the authors cite shows pollutant variability on the 100 m to 1 km scale; three stationary points at 10–20 km spacing, sampled in different seasons, characterize neighborhood-scale contrasts at best. They do not constitute hyperlocal measurement in the sense in which that term is used in the literature the authors cite. Relatedly, the "first of its kind in the CIGP" claim (line 85) sits awkwardly given the extensive citation of Lakra et al. 2024 and 2025. Which is your same group, same VSAAQMP platform, same city, multi-site stationary measurements (lines 80, 146–147, 212, 565). The relationship between the present work and Lakra et al. 2024 and 2025 should be made explicit: what data overlap, what is genuinely new, and whether the incremental contribution warrants standalone publication. The novelty framing as currently written is overstated.
- The PMF configuration differs across sites in ways that prevent the inter-site comparison
The authors resolve 7 factors at Site 1, 6 at Site 2, and 7 at Site 3; constrain HOA with an a-value of 0.5 at Site 1, an a-value of 0.2 at Site 2, and use a fully unconstrained solution at Site 3; and resolve different factor sets at each site (BBOA-1 + BBOA-2 + SVOOA at Site 1; BBOA-1 + BBOA-2 at Site 2; O-BBOA + SVOOA + SFC at Site 3). An imprecise range of Q/Q_exp values are reported, but only for Site 1 (lines 277–278). The manuscript does not address why the factor number differs between sites, why HOA is anchored at two sites and free at the third with different a-values, whether the apparent absence of SFC at Sites 1–2 reflects a true source absence or an unresolved factor below the noise floor (especially given the much lower OA loading at Site 1), whether the apparent absence of SVOOA at Site 2 is real or a consequence of the 6-factor solution choice, or how n-factor solutions were compared and rejected at each site. Different PMF configurations yield different solutions this is at the core of the diagnostic literature the authors cite. The inter-site comparison of factor contributions in Section 3.3 and Figure 9 is therefore not interpretable as a comparison of physical sources. At minimum, please report Q/Q_exp, factor-number sweep diagnostics, and bootstrap stability for all three sites, and explain why a standard solution structure (consistent factor number, consistent constraints) was not adopted.
- The scientific question driving the analysis is unclear
The paper would be substantially stronger if organized around a testable hypothesis. As written, Section 3 reads as three parallel measurement reports: what we found at Site 1, what we found at Site 2, what we found at Site 3, followed by a brief local-vs-regional contribution analysis (3.3) and a PGE case study (3.4). The parts do not integrate into a cohesive presentation. Most importantly there is no quantitative cross-site framework: no regression of factor contributions against site descriptors, no formal analysis of common vs. site-unique factor variability, no transferability test of one site's PMF basis at another. The Section 4 conclusions are a recitation of percentages. This dataset can support real scientific questions and I would urge the authors to identify the question they are answering and then revisit the analysis in that context.
- Several headline findings rest on correlations and comparisons asked to do too much work
Examples:
The "5× more nocturnal PGEs at Site 3" comparison (2 events at Site 1 over 6 days, 5 at Site 2 over 11 days, 10 at Site 3 over 14 days) is across different seasons, different campaign durations, and different background aerosol loadings. Normalizing per measurement-day helps but does not separate the seasonal driver from any site driver.
The SOR/NOR comparison across sites (Section 3.2.2.5) compares oxidation ratios across seasons. Both ratios depend strongly on temperature, RH, oxidant availability, and partitioning equilibria, precisely the variables that differ between post-monsoon and winter. The interpretation as a "site" effect is not separable from the interpretation as a season effect.
These should be addressed individually, but they share a common pattern: weak quantitative evidence carrying strong causal language.
Presentation
The title and abstract overstate what the design supports, particularly "hyperlocal" and the implied site-attributed conclusions. The structure is followable but descriptive; subsections proceed factor-by-factor with limited synthesis. The English is generally clear though imprecise in places. Several figures (Figs. 3–5, the mass-spectra panels) are dense and difficult to read at the printed size; the elemental-ratio insets are nearly illegible. Figure 9 uses non-comparable PMF bases across the three sites (see Major #5) and should not be presented as a direct comparison without that caveat.
Technical
The treatment of CO₂-related variables (line 269) refers to them being "calculated as constant fractions" without specifying the fraction or the reference; this is a non-trivial choice and should be stated.
References are generally appropriate, though the mobile-measurement framing would benefit from engagement with the broader literature on what "hyperlocal" means quantitatively.
Recommendation
I recommend rejection, with the suggestion that the authors consider resubmitting after the issues in the points above are addressed. The dataset has real value and the platform deployment deserves to appear in the literature; the current manuscript does not, in my view, present that value in a form that supports the claims it makes. A re-scoped paper organized around a single defensible question would in my judgment be a stronger contribution than the current attempt to do everything at once.
Citation: https://doi.org/10.5194/egusphere-2026-595-RC2
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The manuscript attempts to investigate organic aerosol sources, secondary formation, and particle growth processes using AMS and SMPS measurements across multiple sites in an urban setting. While the topic is relevant, the study suffers from serious conceptual, methodological, and presentation flaws that substantially limit its scientific credibility and contribution. The manuscript requires substantial rethinking of its objectives, analytical framework, and data interpretation, rather than incremental revision.
Specific comments: