This study looks at a unique dataset gathered by advanced aerosol composition measurement techniques from a region not as well studied by online aerosol chemistry and composition. The study adds to the wealth of knowledge of aerosol pollution and long-range transport, especially owing to the unique impacts of sulfate on Cyprus and impact of Power Generation Plants. Several established scientific methods are used to establish pollution sources impacting the site and the authors do a nice job of using multiple techniques to evaluate and come to some conclusions. Generally, a nice paper, although with little novel methods, applied to a new and distinctive dataset. General comments are below with detailed comments following.

Based on the back trajectory cluster analysis alone, it is not convincing that there are enough air masses advecting over the Middle East to draw the conclusions the title suggest. The authors may consider adding in a graph which shows all 72-hr air mass back trajectories during the campaign (the authors use 120 hrs but this is excessive giving the uncertainty associated with HYSPLIT) at 6-12 hr intervals. Perhaps this would give a better idea of the potential influence of the Middle East. Please also include the Log10(n+1) trajectory footprint graphs from Zefir for the PSCF in Figure S17.

While the authors use NWR and PSCF productively to understand the potential sources of pollutants impacting the Nicosia site, I wonder that there is no mention of shipping emission impact given the high levels of sulfate measured. The sulphate burden over the Mediterranean has been shown to be higher in summer than that over Europe owing to shipping activities (Marmer & Langmann, 2005). While source apportionment techniques have shown a range of effects from ship related sources, from OA fractions of 4.5% in the Mediteranean (Chazeau et al. 2021) to 25% of PM2.5 in Hong Kong (Yau et al. 2013), the ship related burden does not seem to be even considered in the text. The Mediterranean will not become a sulfur emission control area (SECA) (marine fuels S content below 0.1%) until 2025 (COP 2021 under MARPOL (Annex VI), decision 10 June 2022), and has only been under the 0.5% S fuel content since Jan 2020 (IMO-2021, Low Sulfur Regulation), which is after this data 2018-2019 was collected. Therefore, it is strongly suggested that the impact of marine shipping be discussed within the main text as a possible source or reasons given why it is not being considered.

Marmer, E., and Langmann, B.: Impact of ship emissions on the Mediterranean summertime pollution and climate: A regional model study, Atmospheric Environment, 39, 4659-4669, https://doi.org/10.1016/j.atmosenv.2005.04.014, 2005.

Chazeau, B., Temime-Roussel, B., Gille, G., Mesbah, B., D'Anna, B., Wortham, H., and Marchand, N.: Measurement report: Fourteen months of real-time characterisation of the submicronic aerosol and its atmospheric dynamics at the Marseille–Longchamp supersite, Atmos. Chem. Phys., 21, 7293-7319, 10.5194/acp-21-7293-2021, 2021.

Yau, P. S., Lee, S. C., Cheng, Y., Huang, Y., Lai, S. C., and Xu, X. H.: Contribution of ship emissions to the fine particulate in the community near an international port in Hong Kong, Atmospheric Research, 124, 61-72, https://doi.org/10.1016/j.atmosres.2012.12.009, 2013.

Title - Title reorder suggestion as LRT is not really impacting the sources but rather a large mixed source itself. ‘Carbonaceous aerosol over Cyprus impacted by long-range transported pollution from the Middle East’

Line 73 – insert of, ‘in terms of PM’.

Line 86 – consider cool rather than mild

Line 97 – use of ca. reconsider throughout text. Circa is not always appropriate (see line 166), and the term ‘about’ or ‘approximately’ is modern usage. Historically, it is most commonly used in reference to a date that is not accurately known.

Line 99 – c.a. or ca. please harmonise, should be ca. (see comment for line 97)

Line 127-138 – (i) SOP by the Cost Action COLOSSAL, that should be referenced. (ii) It is assumed that the Q-ACSM operated here had a PM1 lens as cyclone cutoff was 1.3 um, but it should be stated. If you used CDCE, it is a standard vaporiser, but this should also be stated. (iii) Mass concentrations are not calculated via the CDCE, but rather corrected by it. Concentrations are calculated based on signal intensity and Fragmentation Table. (iv) Please also include RIE and RF values used. (v) It is stated RH was below 30%, how was it monitored?

Line 139 – should read ‘…were conducted using an 7-wavelength aethalometer (AE33, Magee Scientific, USA) a…’

Line 139 -146 – For clarity, what tape was used for the AE33, was it the post 2017 tape (no. 8060) that had non-linearity issues with short wavelengths (https://mageesci.com/tape/Magee_Scientific_Filter_Aethalometer_AE_Tape_Replacement_discussion.pdf )? Not an issue for BC6 but an issue for BB fraction. What was the MAC used?

Using an AAE of 1 and 2 for FF and WB is the default settings. However, using an AAE of 2 for WB is risky in coastal sites unless WB is the only residential solid fuel burning source. For example if other fuel types such as turf or peat are used the AAE could be quite different due to the combined effects of combustion efficiency and fuel moisture content while the AAE can also be generally affected by photochemical aging and the mixing state of black carbon (Garg et al. 2016). Additionally the AAE value can be highly sensitive to small changes at coastal sites. If you apply the Zotter at al. 2017 values for FF and biomass burning (BB rather than WB) of 0.9 and 1.68 rather than 1 and 2, it can lead to a doubling of the BB percentage (Spohn 2021).

Can the authors please elaborate on the known fuel sources and why the default values were used rather than optimising the alpha values?

Garg, S., Chandra, B. P., Sinha, V., Sarda-Esteve, R., Gros, V., and Sinha, B.: Limitation of the Use of the Absorption Angstrom Exponent for Source Apportionment of Equivalent Black Carbon: a Case Study from the North West Indo-Gangetic Plain, Environmental Science & Technology, 50, 814-824, 10.1021/acs.est.5b03868, 2016.

Zotter, P., Herich, H., Gysel, M., El-Haddad, I., Zhang, Y., Močnik, G., Hüglin, C., Baltensperger, U., Szidat, S., and Prévôt, A. S. H.: Evaluation of the absorption Ångström exponents for traffic and wood burning in the Aethalometer-based source apportionment using radiocarbon measurements of ambient aerosol, Atmos. Chem. Phys., 17, 4229-4249, 10.5194/acp-17-4229-2017, 2017.

Spohn, T. K.: A study of black carbon and related measurements from Ireland's atmospheric composition and climate change network, Thesis (Ph.D.) : NUI Galway, Galway

Line 152 – 154 – Why convert volume concentrations to mass concentrations by assuming variable density? Why not convert ACSM measurements to volume as you know the bulk mass fraction of species? You are adding 1 extra unnecessary approximation. Please try by volume and see if there are any differences.

Line 142 – C = 1.39 is a correction to C=1.57 from Drinovec et al. 2015.

Drinovec, L., Močnik, G., Zotter, P., Prévôt, A. S. H., Ruckstuhl, C., Coz, E., Rupakheti, M., Sciare, J., Müller, T., Wiedensohler, A., and Hansen, A. D. A.: The “dual-spot” Aethalometer: An improved measurement of aerosol black carbon with real-time loading compensation, Atmos. Meas. Tech., 8, 1965–1979, https://doi.org/10.5194/amt-8-1965-2015, 2015.

Line 174 – please report in asl as you did for the stations rather than above ground.

Line 201 – m/z used for the first time and not defined.

Line 216 – consider adding literature for BBOA

Lin, C., Ceburnis, D., Hellebust, S., Buckley, P., Wenger, J., Canonaco, F., Prévôt, A. S. H., Huang, R.-J., O’Dowd, C., and Ovadnevaite, J.: Characterization of Primary Organic Aerosol from Domestic Wood, Peat, and Coal Burning in Ireland, Environmental Science & Technology, 51, 10624-10632, 10.1021/acs.est.7b01926, 2017.

Trubetskaya, A., Lin, C., Ovadnevaite, J., Ceburnis, D., O’Dowd, C., Leahy, J. J., Monaghan, R. F. D., Johnson, R., Layden, P., and Smith, W.: Study of Emissions from Domestic Solid-Fuel Stove Combustion in Ireland, Energy & Fuels, 35, 4966-4978, 10.1021/acs.energyfuels.0c04148, 2021.

Line 226 – 46% variation is quite high, are you sure BBOA should be related to BCwb, as discussed wood burning may not be the main source of fuel for home heating. This may also change the AAE value for the non-ff BC.

Line 237 – S4 order – a should come before b

Line 236-239 – Why compare PM and not Volume from the SMPS? (see comment for line 152-154)

Line 246-249 – Why does the low ratio clearly denote a major contribution of long-chain hydrocarbon OA, is there the possibility that the Org RIE used is incorrect and the OA concentrations are a bit too low?

Line 279-280 – That is not clear from the figure. It seems the reverse, that in the warm period Cluster 5 originates over Middle East/Asia. Also, several clusters pass over Turkey. Please specifically name the clusters and refer to figure in the main text.

Figure 3 – no need for the 100^th place decimal place on the pie charts. Are the data stacked onto each other in the time series, or overlaid? In other words, are the 6-hr average peaks greater than 50 ug/m3 or do the peaks show total NR-PM1?

Line 304-310 – Significant figures are incorrect. All averages are only as accurate as the standard deviation. E.g. 10.30 ± 7.92 should be 10 ± 8. Check significant figures in the numbers throughout the text (E.g. Table 1).

Line 317 – omit the word much. SO4 is 3 ± 2 ug/m3 which is within the range reported in other studies shown in Table 2.

Figure 4 – These are diurnal averages, the ranges should be shown. Left and right axis should be the same scale. Correct for all average plots.

Line 386-387 – First definition of MO-OOA. While MO-OOA is synonymous with low-volatile OOA, it should be first defined here as More Oxidised OOA. Same argument for the first mention of LO-OOA. Suggest changing the text to read more like, ‘… namely the low-volatile MO-OOA (More-Oxidized Oxygenated Organic Aerosol) and semi-volatile LO-OOA (Less-Oxidized Oxygenated Organic Aerosol)…’

Figure 10 – Shows PSCF 75^th Percentile (should represent the probability of an air parcel to be responsible for measured concentrations at the receptor site above the 75^th percentile) - please amend the figure text to reflect what 75^th Percentile means in this figure 

Line 659 – Figure S19 shows LV-OOA, should it show MO-OOA as the figure caption and text suggest? Rather than abbreviated as low-volatility

Line 702 – insert the word of, ‘… of the acquired data’

Figure S4 – a) should be BC AE33 rather than ACSM

Figure S6 – a) cluster 1 and 7 are difficult to differentiate – why has 7 clusters been chosen rather than another number? Is warm and cold switched in the text – refer here to comment about line 279-280.

Figure S9 – it would be good to insert the correlations graphs between the factors in warm and cold periods. E.g. HOA-1 (cold) vs HOA-1 (warm).

Figure S17 – Also include the Log10(n+1) trajectory footprint graphs from Zefir for the PSCF.

Figure S19 – rename MO-OOA rather than LV-OOA

This study is a very nice and detailed work that indeed was lacking for the specific area. The manuscript contains a lot of interesting information, and it should be published. However, it is necessary to polish some parts of it.

1. The title denotes that the main subject of this paper is the transported pollution from Middle East to Cyprus. However, backtrajectories indicated that only a small contribution was from Middle East and that the main origin of the air parcels arriving in Nicosia were originated from Europe. Thus, regional pollution from Cyprus and long-range transported pollution from Europe is much more important. Middle East had rather a minor contribution to the PM₁ levels measured in Cyprus. Thus, the corresponding parts in the manuscript should be revised.

2. As a consequence, the title of the paper should be changed.

3. Regarding the inorganic species: using the mass fractions in Figure 3 or the concentrations in Table 1 in order to check the mass balance of SO₄^-2, NH₄⁺ and NO₃^- one will expect to see neutralized ammonium sulfate ((NH₄)₂SO₄) particles plus ammonium nitrate particles (NH₃NO₄).

However, for the cold period even if we assume that the sulfate particles are not fully neutralized (existing in the form of NH₄HSO₄ or NH₄HSO₄ + (NH₄)₂SO₄), the remaining NH₄ is quite low (one order of magnitude lower compared to the NO₃ amount) and only a very small amount of NH₄NO₃ could be justified. As a consequence, there is not enough NH₄ to form NH₄Cl. In what form could be the rest of NO₃ and the Cl^-? The diurnal profile of NO₃ (Figure 4) seems very similar to the organics. Could it be organonitrates? Could it originate from BBOA? Could it be KNO₃? Is it possible for the Cl^- to be in the form of KCl? Could part of the SO₄^-2 be in the form of K₂SO₄? According to the manuscript, the 24 h filters were analyzed for K⁺ and Cl^- (among other ions). How well does K⁺ correlate to NO₃^-? Or to Cl^-?

During the warm period ammonium is not enough to neutralize the sulfate. Again, if the sulfate particles are not fully neutralized (existing in the form of NH₄HSO₄ or NH₄HSO₄ + (NH₄)₂SO₄) the remaining NH₄ could make just a small amount of NH₄NO₃. In what form could be the rest amount of NO₃ given the fact that during this period there is no biomass burning sources?

Please explain, discuss and revise the above in the manuscript (e.g., lines 302-303, 370-378, 457).

ACSM tends to overestimate NO₃^- concentrations in comparison to the filters (according to Figure S4d), but NH₄⁺ has much better correlation, so it cannot be NH₄NO_3­ that evaporates from filter (please revise lines 242-243).

4. MO-OOA during the cold months: It is very difficult to transform fresh OA into that highly OOA within 4 hours during nighttime. Kodros et al. (2021) showed that it is possible to transform fresh BBOA in OOA (in dark aging experiments) within 3 hours resulting in OOA with a m/z 44 around 0.13. According to Figure S9a the MO-OOA factor has an m/z 44 around 0.3, which corresponds to much more oxygenated compounds. It would be better to remove the sentence in lines 490-491 unless it can be supported by a reference from literature. Nevertheless, there should be an explanation in the manuscript for the increase of the MO-OOA during the night.

In addition, MO-OOA seems highly oxidized (m/z 44 ~0.3) even though it refers to cold months. OOA factors during the winter usually are less oxidized (m/z 0.1-0.15) (e.g., Crippa et al.,2013; Florou et al., 2017). How is this behavior explained? Is it real or is it due to the way the PMF was run (i.e., two factors were constrained). If you use as a constrain solution a winter OOA mass spectrum, how do the rest mass spectra (LO-OOA and HOA-2) change? Is there still the increase of the MO-OOA during the night?

5. Mass spectra comparison (Figure S10): ASCM and Q-AMS mass spectra are usually manipulated using the old fragmentation table of Allan et al. (2007), while the HR-AMS has incorporated the new fragmentation table of Aiken et al. (2009). This means that m/z 18 and 28 are calculated differently. In Figure S10 some of the compared mass spectra have been treated with the old fragmentation table and some of them with the new fragmentation table. When comparing mass spectra derived by different fragmentation tables, were m/z’s 18 and 28 excluded or not? This must be clarified in the text.

6. Figure S9 is important, and I suggest it should be transferred to the main paper.

References:

Aiken, A. C., et al.: Mexico City aerosol analysis during MILAGRO using high resolution aerosol mass spectrometry at the urban supersite (T0) – Part 1: Fine particle composition and organic source apportionment, Atmos. Chem. Phys., 9, 6633–6653, doi:10.5194/acp-9-6633-2009, 2009.

Allan, J. D., et al.: A generalised method for the extraction of chemically resolved mass spectra from aerodyne aerosol mass spectrometer data, J. Aerosol. Sci., 35, 909–922, 2004.

Crippa, M., et al.: Wintertime aerosol chemical composition and source apportionment of the organic fraction in the metropolitan area of Paris, Atmos. Chem. Phys., 13, 961–981, doi:10.5194/acp-13-961-2013, 2013b.

Florou, K., et al.: The contribution of wood burning and other pollution sources to wintertime organic aerosol levels in two Greek cities, Atmos. Chem. Phys., 17, 3145–3163, https://doi.org/10.5194/acp-17-3145-2017, 2017.

Kodros, J. K., et al: Rapid dark aging of biomass burning as an overlooked source of oxidized organic aerosol, Proc. Natl. Acad. Sci. U. S. A., 117, 33028–33033, https://doi.org/10.1073/PNAS.2010365117, 2020.