REVIEW

Veratti et al., 

Source attribution and estimation of black carbon levels in an urban hotspot of the central Po Valley: An integrated approach combining high-resolution dispersion modelling and microaethalometers

ACP

STAGE 2

General comments

Veratti et al. describe an interesting combination of in-situ monitoring and modelling application to a mid-sized city in the Po valley. The results clearly demonstrate the need to treat the contribution to black carbon (BC) of biomass burning (BB) and traffic (the sole source of fossil fuel – FF) on different spatial scales.

The manuscript is well written (even if somewhat long-ish) and deserves publication after the comments below are addressed. It may become a tool for many municipalities to analyze the sources of primary air pollution and measure the efficiency of abatement.

The especially important comments are the ones that need to be addressed at:

L 148-151

L 491-495

L 587-647

L 634-636

L 638-642

Supplement, Fig. S10

Specific comments

Lines 20-26: The terminology on BC and EC can be shortened and Petzold et al. (2013) cited.

L 38-51: When discussing the climate effects of BC, cite the latest IPCC report. 

L 75, “This approach…”: This sentence needs to be replaced by a longer explanation of how filter absorption photometers work. There is a difference between the true absorption measurement (such as photoacoustics or photothermal interferometry) and proxy measurements with filter photometers. Parametrization of corrections needs to be addressed only in the context of the multiple scattering parameter – value 1.3 is used later on.

L 85: Add the revision of the EU Air Quality Directive, as it requires BC measurements to be taken.

L 109-111, “… or attempted to apportion the sector-specific contribution”: This is unclear. Apportionment means ascribing a measured parameter to sources. I think the authors mean: the contribution of sources in the model to ambient concentrations at a site. Please reword.

L 131 and elsewhere: The temperature inversions are mentioned – please add plots demonstrating this in the Supplement.

L 142: The inlet temperature of 30 C may lead to losses in BrC and/or coatings from aerosol. There needs to be some explanation on the use of such a high temperature for winter measurements. 

L 148-151: The “additional” correction factor of 1.3 needs a thorough explanation – see comment above (L 75). The MAC values, as reported in the Supplement are quite high when compared to the ones in the literature and the ACTRIS recommended ones (~10 m2/g at 635 nm). An explanation on the choice of the multiple scattering parameter 1.3 and the MAC values, and at least a comparison with the published ones (Zanatta et al., 2016; Savadkoohi et al., 2023) is required.

L 152-158: Was the aggregation of the data achieved by recalculation or averaging? The Bigi et al. (2023a) paper is not clear on that. Additionally, Bigi et al (2023b) was in the meantime accepted in ACP.

L 177, ”… by multi-wavelength fitting of 1 …”: ”… by multi-wavelength fitting of Eq. 1 …”?

L 187, Table 1: Is the “sensor height” meant above ground? If so, please mention. 

L 201-202 and elsewhere: BC was treated as inert in GRAMM-GRAL and NINFA. While this is true for BC, it is not true for the coatings that might accumulate on the BC particles. These coatings increase the MAC and the response of filter photometers overestimated the BC mass concentration (Kalbermatter et al., 2022). The Po valley is a location where BC is coated fast. Modelling the coatings is extremely difficult, but the authors should estimate the uncertainty induced by potential coatings. 

L 222, “… 10 nm to 40 m.”: I suspect that the authors did not mean spectacularly giant aerosols – µm?

L 287, “City emissions” subsection: The description of how EFs were obtained is fairly detailed which is to be commended. The Supplement lists EF diurnal profiles in Fig. S10, but without the unit. It would be wise to report the EF values somewhere, especially in light of comments below. 

L 438, “Meteorology” subsection: This section is very detailed and lacks the parameter which is mentioned as being important later on: the PBL height. Consider moving some of it in the Supplement and adding PBL comparison here.

L 491-495, Fig. 4, Fig. 5, L577: The explanation about the thermal inversions and nearby sources causing high BC concentrations at the traffic site sounds overly simplistic. There is an obvious spike in 1-hour averages (!) also at the background station on 2 Jan 2021 and other periods of high concentrations appear at both sites as well. This seems like a meteorological effect, possibly non entirely linear. It may be that the models simply do not capture well the extreme events, thi sis known to happen with (more or less) linear models (see also comment below on the EFs). The discussion needs to be expanded, taking advantage of suggestions below.

The timeseries in Figs. 4 and 5 should include eBC separated into BC_ff and BC_bb. Makle the figures larger in y-direction for transparency.

In addition, the regressions between BC, BC_ff, BC_bb should be shown. They have been performed as results are discussed in the text later on.

L 525: What is the “inherent underestimation of the traffic flows”. Please elaborate. I understand that traffic counts are available. 

L 548-549, Fig 6: The reason for the 1-hour delay in the model relative to measurements needs to be investigated in more depth. 

L 551-554: The “divergent results” should be investigated and the analysis repeated for the time period shared between both stations. Do the differences remain?

L 567: The reference to Figs. S1 and S2 is wrong. Please correct.

L 587-647, “4.2.3 Dispersion modelling based source apportionment”: The Snakey plots are an important visualization tool. It is unclear what are the sources of the uncertainty (for example 52%+/-10% for city contribution to BC). Some of the uncertainties are very small. Please elaborate.

L 595-596: Please add an online movie of the maps sowing modelled spatial distributions of diurnal profiles for BC_ff and BC_bb. This is super interesting.

L 634-636: I am very skeptical about the attribution of 50% of BC to non-exhaust emissions. This requires at least a paragraph of explanation, not a single sentence. This result is extreme and highly unexpected. At least street cleaning schedules need to be used to semi-quantitatively explain this with measurements.

L638-642: Similarly, the attribution of 19% of BC to Euro 4 vehicles (15% of the fleet) is simplification that does not hold. The authors cire a paper (Jezek et al., 2018) which has shown that 2/3 of the BC emissions are caused by ¼ of the vehicles. Treating the emissions linearly is blatantly wrong. There are super-emitters in the fleet which contribute disproportionately and using the fleet composition as the argument to attribute emissions to Euro 4 vehicles is wrong. Additionally, this is an important conclusion (as noted by the authors – it is included in the abstract) and therefore requires an extended explanation. 

Supplement, Fig. S10: What is the source of the diurnal profiles? Were they measured? How?

Supplement, Fig. S10: What is the source of the fleet composition? Please cite a reference. 

References

Bigi, et al.: Aerosol absorption by in-situ filter-based photometer and ground-based sun-photometer in an urban atmosphere, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-174, 2023a. 

Bigi, et al.: Aerosol absorption using in situ filter-based photometers and ground-based sun photometry in the Po Valley urban atmosphere, Atmos. Chem. Phys., 23, 14841–14869, https://doi.org/10.5194/acp-23-14841-2023, 2023b.

Crova F., et al.: Effectiveness of airborne radon progeny assessment for atmospheric studies, Atmos. Res. 250, 105390, https://doi.org/10.1016/j.atmosres.2020.105390

2021.

Gregorič, et al.: The determination of highly time-resolved and source-separated black carbon emission rates using radon as a tracer of atmospheric dynamics, Atmos. Chem. Phys., 20, 14139–14162, https://doi.org/10.5194/acp-20-14139-2020, 2020. 

Ježek,et al.: The traffic emission-dispersion model for a Central-European city agrees with measured black carbon apportioned to traffic, Atmospheric Environment, 184, 177–190, https://doi.org/10.1016/j.atmosenv.2018.04.028, 2018.

Kalbermatter, et al.: Comparing black-carbon- and aerosol-absorption-measuring instruments – a new system using lab-generated soot coated with controlled amounts of secondary organic matter, Atmos. Meas. Tech., 15, 561–572, https://doi.org/10.5194/amt-15-561-2022, 2022.

Petzold, et al.: Recommendations for reporting "black carbon" measurements, Atmos. Chem. Phys., 13, 8365–8379, https://doi.org/10.5194/acp-13-8365-2013, 2013.

Savadkoohi, et al.: The variability of mass concentrations and source apportionment analysis of equivalent black carbon across urban Europe, Environ. Intl., 178, 108081, https://doi.org/10.1016/j.envint.2023.108081, 2023.

Vecchi, et al.: Radon-based estimates of equivalent mixing layer heights: a long-term assessment, Atmos. Environ., 197, 150-158, https://doi.org/10.1016/j.atmosenv.2018.10.020, 2019.

Zanatta, et al.: A European aerosol phenomenology-5: Climatology of black carbon optical properties at 9 regional background sites across Europe, Atmo. Environ., 145, https://doi.org/10.1016/j.atmosenv.2016.09.035, 2016.

Veratti et al., presented a study on the source apportionment of black carbon levels in the city of Modena (Po Valley, Italy). By combining multi-wavelength micro-aethalometer measurements and a hybrid Eulerian-Lagrangian modelling system, the authors decoupled both the emissions sources (i.e., residential biomass burning and fossil fuels) as well as the “geographical” contribution (i.e., inner city versus background) of black carbon.

The paper is well organized and easy to follow. The analysis is clearly presented with an appropriated level of details, and both the methodology and the statistical analysis are sound. Additionally, the study confirms the importance of residential wood combustion to the air quality levels in the Po Valley, which also goes along with previous modelling source apportionment studies (Jiang et al., 2019).

I only have few minor comments before the paper can be accepted in its final form, mainly to enrich and further corroborate the analysis and to make this study more useful to the scientific community, especially for what the chemical transport models are concerned:

Minor comments:

• What is currently missing in the manuscript is a more detailed comparison of the results presented here against other European sites. The authors claims that the site is “a representative example of a medium-sized urban area in Europe”, but it would be nice to provide more comparison with literature data in order to further strength this claim.
• The authors made use of a high-resolution emission inventory derived from local agencies. Those kinds of datasets are usually more challenging to obtain compared to already compiled global anthropogenic emissions data sets, such as, e.g., CAMS (and related ones). A brief comparison of the datasets used here (e.g., only the total emissions) against coarser datasets, could provide precious information to the modelling community about the levels of agreement with other emissions data, and therefore on future modelling application on the Po Valley.
• Since the Eulerian approach is built on the CHIMERE model, I would suggest spending some more work on the previous evaluation studies performed on CHIMERE. The model has actively participated in numerous model intercomparisons exercises e.g., EURODELTAIII (Bessagnet et al., 2016) and AQMEII, and I think it would be beneficial to this study, and to the past modelling intercomparison efforts, to briefly comments on previous modelling results.
• I think there is no mention in the manuscript regarding how the BC mass is distributed over the size distribution. As far as I am aware, CHIMERE centres the distribution of BC at 200nm with a 1.2 sigma, which is a proper guess for background BC concentrations. Is it the case (and representative) also for this study? Or does the size distribution of BC differ between what is applied in the city and what is considered as background?

References

Bessagnet, B., Pirovano, G., Mircea, M., Cuvelier, C., Aulinger, A., Calori, G., Ciarelli, G., Manders, A., Stern, R., Tsyro, S., García Vivanco, M., Thunis, P., Pay, M.-T., Colette, A., Couvidat, F., Meleux, F., Rouïl, L., Ung, A., Aksoyoglu, S., Baldasano, J. M., Bieser, J., Briganti, G., Cappelletti, A., D’Isidoro, M., Finardi, S., Kranenburg, R., Silibello, C., Carnevale, C., Aas, W., Dupont, J.-C., Fagerli, H., Gonzalez, L., Menut, L., Prévôt, A. S. H., Roberts, P., and White, L.: Presentation of the EURODELTA III intercomparison exercise – evaluation of the chemistry transport models’ performance on criteria pollutants and joint analysis with meteorology, Atmos. Chem. Phys., 16, 12667–12701, https://doi.org/10.5194/acp-16-12667-2016, 2016.

Jiang, J., Aksoyoglu, S., El-Haddad, I., Ciarelli, G., Denier van der Gon, H. A. C., Canonaco, F., Gilardoni, S., Paglione, M., Minguillón, M. C., Favez, O., Zhang, Y., Marchand, N., Hao, L., Virtanen, A., Florou, K., O’Dowd, C., Ovadnevaite, J., Baltensperger, U., and Prévôt, A. S. H.: Sources of organic aerosols in Europe: a modeling study using CAMx with modified volatility basis set scheme, Atmos. Chem. Phys., 19, 15247–15270, https://doi.org/10.5194/acp-19-15247-2019, 2019.