General comments

In this study by Di Antonio et al. the aerosol load variability and trends are investigated at different scales in Europe (from continental to urban scale for 21 cities), relying on a very fine aerosol optical depth product. The objectives of the study are quite straightforward and are addressed by a thorough analysis. The AOD climatology and trends that the authors provide here for Europe is of significance for assessing the local emissions and transported aerosols affecting cities’ air quality. I consider the topic and results of this manuscript to fit the scope of ACP and it could be published if the minor comments listed below are addressed.

Specific comments

Lines 132-133: This minimum limit is for both local and regional scale, right?

Lines 132-135: Before this calculation, which type of aggregation was performed to the daily values of the two scales considered here? Inside the local and inside the regional scale?

Lines 149-153:  To my opinion the MAIAC AOD retrievals uncertainty could be discuss better when you first introduce the MAIAC product in the first paragraph of the 2.1 Section.

Lines 160: Please check again formula 2, in the numerator you mean 550nm?

Line 165: I don't follow here why the area from the MAIAC product is expressed in deg x deg and not only in km x km? As has already been mentioned in the previous section. 

Line 186: Could you please provide this equation before this sentence, elaborating a little how it is extracted?

Lines 199 & 201: The “EE is …%”? could you please rephrase according to lines 189-199?

Line 200: Could you provide any relevant reference for the limit use here for marine and dust dominated aerosol scenes?

Lines 203-205: Could you elaborate or rephrase this sentence because it is a little bit confusing?

Line 278: Instead of “the extra” better “the different AOD levels” since lower values of AOD have been found for the local scale?

Line 293: Could you please quantify here most of the time?

Lines 301-302: I would recommend changing the order of factors explaining the negative LTRR values, starting from inhomogeneity of AOD at the regional domain.

Lines 310-313: Another recommendation is to add here as reason of inhomogeneity in AOD at the regional domain the spatial extent of the city (also related to topography, but not always), different location of sources like industrial areas etc. which in combination with the meteorological conditions could lead to different spatial patterns inside the 100 x 100km^2 domain.

Lines 338-339: Repetition with what is inside parenthesis in lines 337-338

Line 360: The 0.83 value here is the coefficient of determination R^2 of the regression and this value is different from Pearson correlation coefficient r=0.89 shown in figure 5? 

Line 1021: I recommend here to add “… cities (not metropolitan regions) with more than 1 million…”. It is something that is also different in the Eurostat database.

Technical corrections

Line 986: In Fig.2 explain red lines and dashed green lines in a, c,d, e.

Line 990: In Fig.2 “times x”? Expressed in percentage you mean?

Line 990: In Fig.2 “EE” instead of “EE5”

Line 1009: In Fig 5. please explain solid and dashed lines and it is messing AOD nest to local versus regional.   

Line 1006: Perhaps “mean AOD” better than “mean of the boxplot”?

A review of manuscript “Aerosol optical depth climatology from the high–resolution MAIAC product over Europe: differences between major European cities and their surrounding environments” by L. Di Antonio et al.

In this study, a long-term MAIAC MODIS record for 2000-2021 was analyzed over Europe. The MAIAC AOD was, first, validated against AERONET, and then used to analyze annual and seasonal AOD patterns and trends at a local (city), regional (100x100 km2) and European sub-continent scales.  The obtained results, e.g., decreasing AOD trend over Europe, generally agree and complement previous studies. Novel is analysis of AOD level in the city with respect to the regional background performed for 21 selected cities with population over 1 million. In most cases, cities are found to be sources of aerosol emission, whereas over three cities (Amsterdam, Brussels and Berlin) the regional AOD was found higher than that over the city.

Overall, the study is well-designed and results are well presented and clear. My only reservation is language – editing is required throughout the paper.  I suggest some editing below (up to line 135). My final evaluation is “accept with minor revision”.

Alexei.

Specific suggestions:

• Remove “one” from the last sentence of the Abstract.

Ln. 35: Replace “still huge” with “large remaining”

Ln. 38: Replace “are everyday exposed to significant aerosol levels” with “experience a significant particulate matter exposure”

Ln. 42: Correct to “ and last for several consecutive days”

Ln. 43: Delete “If such episodes occur frequently”. Visibility and air quality deteriorate even if it happens once.

Ln. 48: “to potentially”

Ln. 51: “and is a matter”

Ln. 53 Re-phrase “properties”. AOD alone does not give much information about aerosol properties.

Ln. 68: Please, add reference to Hammer et al., 2020 (Global Estimates and Long-Term Trends of Fine Particulate Matter Concentrations (1998–2018) Ln. 72: remove “aerosol”

Ln. 84: “and distinguish between smoke and dust scenes.” Add reference to Lyapustin et al., 2012 (Lyapustin, A., S. Korkin, Y. Wang, B. Quayle, and I. Laszlo, 2012b: Discrimination of biomass burning smoke and clouds in MAIAC algorithm, Atmos. Chem. Phys., 12, 9679–9686, doi:10.5194/acp-12-9679-2012.)

Ln. 86: I recommend adding the latest reference to most comprehensive DB, DT, MAIAC and NOAA AOD comparison and validation analysis (Su, X., M. Cao, L. Wang, X. Gui, M. Zhang, Y. Huang, Y. Zhao, Validation, inter-comparison, and usage recommendation of six latest VIIRS and MODIS aerosol products over the ocean and land on the global and regional scales, Science of The Total Environment, v. 884, 2023, 163794, https://doi.org/10.1016/j.scitotenv.2023.163794)

Ln. 90: I recommend adding two important missed references: a) van Donkelaar, A., et al. (2021). Monthly Global Estimates of Fine Particulate Matter and Their Uncertainty. Environmental science & technology, doi: 10.1021/acs.est.1c05309, and b) Wei, J. et al., 2021: Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications. Remote Sensing of Environment, 2021, 252, 112136. https://doi.org/10.1016/j.rse.2020.112136

Ln. 95: “In the Paris area”

Ln. 96: Change to “aims to achieve”

Ln. 122: Replace “taking into account available observations in the day” with “using available cloud-free observations”

Ln. 130: Replace “was chosen large enough in order to avoid effects due to the city and its plume” with “was chosen large enough to minimize effects of city’s pollution”

Ln. 205: A similar analysis and conclusion over Israel was just published in (Irina Rogozovsky, Kevin Ohneiser, Alexei Lyapustin, Albert Ansmann, Alexandra Chudnovsky, The impact of different aerosol layering conditions on the high-resolution MODIS/MAIAC AOD retrieval bias: The uncertainty analysis, Atmospheric Environment, V. 309, 2023, 119930, https://doi.org/10.1016/j.atmosenv.2023.119930.

…

Ln. 435: “indicates an average local–to– regional ratio of 39%,”   Please add “local-to-regional excess ratio” to indicate that the ratio >1.