General comment

The paper reports a study of maritime contributions to PM10 in a rural coastal area of northern France. The topic is of interest and suitable for the Journal. The approach used is quite standard, however, the extensive dataset, the detailed statistical analysis, and the limited availability of data for this area make the paper useful to the scientific community. I would like to see it published after a revision step addressing my minor specific comments.

Specific comments

Please remove all instances of etc. in the paper. If something should be added, please do it explicitly.

Line 67. Please remove the “+”. In addition, this value seems to be very large compared to the measured ones and to the values published in other studies. What it represents Annual average or other short-term contribution?

Lines 77-80. It would be worth to mention here that the use of low-sulphur fuels actually reduce also primary PM emissions from ships how it has been demonstrated in several study and, of course, also secondary sulphate. So that the regulation was not done only for SO2.

Line 101. Are these quartz filters?

Line 172. Are you referring to secondary organic aerosol here?

Section 3.2. Considering that the receptor model used is still not widely applied, it should be useful to mention that it has comparable performances to the PMF model investigated in the recent work of Belis et al. (Atmospheric Environment: X, 5, 2020, 100053).

Lines 247-256. For the discussion on the V/Ni ratio, I suggest to have a look and mention the work of Gregoris et al (Environ Sci Pollut Res (2016) 23, 6951–6959) that shows ratios lower than expected in coastal areas with relevant impact of shipping as well as a strong spatial variability of this ratio.

Lines 293-295. I would not say that it is underestimated. The pint is that it was analysed here only the contribution to primary PM10 and that there would be also a contribution to secondary aerosol, mainly sulphate, that could be even larger than the primary one.

Lines 322-325. Please mention how and where these gases are measured.

Figure 4. Please use the same acronym as in the text (i.e. CBPF). To be honest, I do not understand why the CBPF of sea salt and aged sea salt are so different. A better interpretation of this aspect would be useful.

Figure S4. The use of three different level of blue is not a good choice because make the figure hardly readable generating confusions among the different sources. Please make a different choice for these colours.

The manuscript reports a standard source apportionment study with a focus on the maritime source. Beyond the original results and the use of a specific receptor site to achieve the goals, the rest of the study presents only the trivial application of well-established source apportionment techniques (PMF-like, CWT, CPF). Another weakness of this study is the "age" of the data, which were collected 10 years ago. However, since the lack of papers focusing on maritime sources, I am in favour of publication. The authors are requested to improve the manuscript by adding more information about the outcomes of the CW-NMF model (in particular about the number of factors extracted).

Specific comments.

Lines 76-79 “Additionally, despite the IMO regulation for global sulfur limit of 0.5% from ship’s fuel oil applied starting January 2020, different countries are still adopting higher sulfur limits. It is worth noting that these limits were only set for sulfur content in marine fuels in order to reduce SO2 emissions but no regulations for PM components neither in the sea nor at ports were issued”. Not clear why this consideration is reported within the objectives of the study. Is this topic addressed in the current study? Please clarify.

Lines 66-80. Please comment on the choice of sampling PM2.5 to quantify the maritime contribution. Combustion emissions are expected to be finer than other sources. On the contrary, other sources in the coastal areas (e.g., sea salt) may emit large particles. Thus, it would be better to analyze PM2.5. Please comment to support your choice.

Line 90. “The sampling site is far from major continental pollution sources and can be considered as a background site.” Which background? Rural, urban, suburban? If rural, are you sure the site has the characteristics to be classified in this way? Please comment.

Section 2.2. The sampling campaign is 1 year long (1st of January to 31st of December 2013) and samples were collected at daily frequency, however only 122 samples were analyzed. Why? How the samples to be analyzed have been selected? Randomly? 1 day over 3? Please explain.

Line 100. Was the beta monitor tested against the gravimetric measurement? Which calibration audit? Please add info.

Line 168 “The yearly-mean PM10 concentration obtained for the set of sampling days was of 24.3 μg/m3”. Is this the real yearly average (365 days in a year possibly measured with the beta monitor) or the average over the 122 analyzed samples? It would be great to see both values to understand if the sample selection is consistent with (indicative of, or similar to) the year average.

Figure 2. Please report the error bars relative to the bootstrap results. This would clarify some source profiles.

Section 3.2. The description of the model setup lacks many details. In addition, it looks like you pushed the model beyond the limits of what it is capable of. It is hard to believe that you can see so many factors with just 122 samples and 28 variables without overfitting the data. For instance, the presence of a “metal-rich” factor may be due to the overfit. Thus, I would ask you to add the scaled residual plots to the SI material file along with all the diagnostics returned by the model. This is to better investigate the goodness of the selected model setup.

Line 227. “with an average Cl--to-Na+ ratio of 1.8 which is commonly observed for fresh sea salts (Seinfeld and Pandis, 2016).” Is the seawater composition also valid for other fresh sea salt ionic species such as K+, Mg2+, Ca2+, and SO42-?

Line 230. Is the cations/anions ratio balanced in this factor?

Line 240-255. The authors report some literature data in support of their findings. However, most of these studies refer to finer PM (PM2.5 or even PM1). For example, the paper by Khan et al (2021) reports the EC to levoglucosan ratio in PM1. The paper by Salameh et al (2018) refers to the OC-to-EC ratio of PM2.5. It is not reliable that the ratios between different variables (chemical species) remain unchanged on PM10, PM2.5 or PM1. At least, not all. Please comment or change the references.

Figure 3. Please provide the uncertainty associated with the results.

Section 3.5. It appears that most of the factors come from the same area of origin. This again pinpoints the possible data overfit. However, the results for HFO appear to come from a more "marine" source area than the two secondary factors. It would be better to zoom in on Figure 5 to better visualize the differences. After all, we are not interested in results where the model returns too low a contribution (thus, please cut the more distant areas).