Historical reconstruction of air pollution is important for understanding the long-term trends of air pollution and is useful for health studies of air pollution. This paper reconstructs the background air pollution over France for 2000-2015. This work is important. However, the major issue of the paper is lack of novelty. And the methodology used in this paper has not been compared with other models. Besides, the manuscript appears messy a little bit. I cannot stand the terrible typesetting.

The ambition of our paper is to present and document a new dataset. We believe that we implemented the most up to date and robust methodology, but we do not claim any novelty in producing such a type of historical reconstruction of air pollution. There has indeed been an earlier study on historical reconstruction of outdoor air pollution in France (Bentayeb et al., 2014). But their dataset is not public, which is precisely the gap we are trying to fill here by providing open and transparent access to air pollution exposure data for follow-up studies.

This is the first time that data on pollutant concentrations over France have been made available to the public at this resolution and over such a long time period. This dataset is made available under open access license since July 2020. We are already in close contact with 20 different scientific teams using extensively the dataset. The field of expertise of those teams ranges from epidemiology, to environmental economics and atmospheric science. In order to provide a solid basis for such downstram studies, it is very important that the dataset is clearly documented.

This is why we decided to write this paper and submit it in a journal whose primary aim is to make high quality data available and supported by thorough scientific description.

Finally, regarding the “terrible typesetting”, we can only apologies and explain to the reviewer that it is not always obvious for non-native English speakers to develop scientific expertise together with English fluency. We have completely revised the paper trying to improve the English and we hope this will not be regarded as a limitation for the editor and confirm that we would be willing to pay an additional fee for final editing if such a service would be offered by ESSD.

This paper used a kriging method. Currently, there are many cutting-edge statistical models used for historical reconstruction of air pollution, including many machine learning algorithms. From the results of this paper, the performance of the kriging method is not satisfactory (except for O3). I recommend the authors compare different models and select a model which performs best.

As a result of the reviewer's suggestion, we compared our cross-validation scores to those found in the literature for air pollution studies in Europe. The following discussion was added p16: The cross-validation scores can be compared with those obtained in Europe with other mapping methods. Chein et al. (2018) compared 16 algorithms to develop Europe-wide spatial models of PM2.5 and NO2, included linear stepwise regression, regularization techniques and machine learning methods. Those models were developed based on the 2010 routine monitoring data from the AIRBASE dataset, satellite observations, dispersion model estimates and land use variables as predictors. De Hoogh et al. (2018) also performed cross validation of their fine spatial scale land use regression models (also based on AIRBASE dataset, satellite observations, dispersion model estimates and land use variables as predictors) used in Europe for the year 2010. Results from their cross-validation are compared to our own cross-validation results (without distinction of station type) in Table 3.

Table 3: Validation scores for De Hoogh et al. (2018), Chein et al. (2019) and this study (Real et al. (2022)). The following scores are calculated by cross validation for the 3 studies: Pearson correlation coefficient R2, the bias, and the Root Mean Square Error (RMSE).  

The comparison of performance in these three studies is of course limited by the fact that the spatial coverage differs: in De Hoogh et al. (2018) and Chein et al. (2019), the cross validation is computed over the whole of Europe. In this study, the performances are assessed over France. For all pollutants the spatial correlation (R2) is better in our study. In the same time, higher RMSE are also found for our study. This may be due to a larger bias, but we also demonstrated in our paper that the bias was very small, except at rural NO2 stations. Snce the RMSE score also depends on the absolute concentrations, the different spatial coverage may also play a role. The lower RMSE over Europe could be an artifact of including areas where absolute concentrations of NO2, PM2.5 or O3 are lower than over France. The validation scores obtained, as well as the comparison with raw data and with other mapping method, allow us to be confident about the validity of the concentrations obtained and their good representativeness of background concentrations, in particular in urban areas. A point of vigilance appears however when it comes to the representativeness of rural NO2 concentrations which are overestimated in our results.

I don’t think it is appropriate to add a reference in the abstract (i.e., Real et al., 2021). (Line 18, P1)

This is required by the editor

There have been many studies about the historical reconstruction of air pollution. They should conduct a thorough literature review in the introduction section.

This following discussion was added L3P3 of the original manuscript:

“These choices are the results of successive studies that compared different kriging techniques (Malherbe and Ung, 2009, Beauchamp 2015a). A similar methodology was implemented for an earlier reconstruction of outdoor air pollution in Europe for the period 1989-2008 in (Bentayeb et al., 2014). There are also ambient air pollution maps produced at European scale at 1km resolution by the European Environment Agency, but only for selected annual indicators and without consistency for multi-year reconstructions (Horálek et al., 2012, 2020). The Copernicus Atmosphere Monitoring Service has also produced European analyses since 2015, but again there is no multi-year consistency as these European maps are produced on an annual basis with gradually improving methodologies (Marécal et al., 2015). At Global scale, the Global Burden of Disease also makes available air pollution exposure maps, a recent update of the methodology was presented in (Shaddick et al., 2017), but the resolution is 0.1 degrees or about 10km.”

Line 26, P1 – Line 13, P2: It is not necessary to describe the trends of air pollution trends coming from ground observations in detail in the Introduction section. These contents have little to do with the purpose of the historical reconstruction of air pollution in France. These contents can be moved to the Results and Discussion section. They can compare their results of the trends using the reconstruction data and the results from previous studies using ground observations.

We have reduced this part in the introduction and introduced elements of comparison in the different sections referring to the trend analysis.

Exemple 1:Section 4.1.1, p21: “Taking the year 2000 as the base year, this amounts to a 39% reduction. In a study conducted for France over the period 2000-2010, Malherbe et al. (2017) estimated a downward trend that was twice as small (0.4). This reflects the accelerated decline in concentrations in France in recent years.”

Exemple 2:

Section 4.1.4, p29:   “This downward trend is slightly stronger than that calculated in Malherbe et al. (2017) over the period 2000-2010 over France (-0.37 µg.m^-3.year^-1) and corresponds to a reduction of about 30% (taking 2020 as the base year).”

Line 22, P3. They exclude industrial and traffic stations. In this case, the reconstruction maps of air pollutants will miss many pollution hot spots. I know that they want to reconstruct the background air pollution. However, without these hot spots, the reconstruction of air pollution is not that useful. I think another reason they exclude these stations is that the kriging method cannot deal with these stations with higher pollution levels well, because these stations are much less than urban and rural stations. However, the machine learning algorithms with land use information as covariates can capture the high pollution hot spots. Of course, they also need to incorporate meteorological variables in the models.

As stated in the paper, and as specified by the reviewer, the data proposed here are intended to reproduce background concentrations in France. Given the resolution of our data (about 4km2), the simulated concentrations on a grid cell must be representative of the average of the real concentrations on this grid cell. However, traffic and industrial stations are representative of more local concentrations, which evolve rapidly when air masses move away from these sources. For the resolution proposed in this paper, it is therefore a sensible choice to use only background stations, which are further away from the sources and therefore more representative of the concentrations at the scale of the grid cell. It would of course be interesting to go down in resolution and propose maps on such time scales at the sub-kilometer scale. In this case, the addition of traffic and industrial stations would be justified. We have modified our discussion/conclusion in this sense.

Table 1, P3. Why the number of the stations of PM2.5 in 2007 are much fewer than that in 2006 and 2008?

We exchanged with our colleagues who are specialists in particle measurements. Before 2009, the data are based on a mix of TEOM and TEM-FDMS data, but with very few reference measurements (FDMS). In view of the discontinuity and low reliability of the data before 2009, they advised us not to use these data. This was already the case, but we have revised the text and the table accordingly:

Section 2.1: “Concerning PM2.5, given the few reference measurements available before 2009, the reliability of even annual measurements is low. It was therefore decided to apply the kriging methodology only from the year 2009 onwards, for which the change in measurement method had become widespread. ».

The number of PM2.5 stations is shown from the year 2009 onwards now.

Line 14, P5. Move “(particles with a radius < 10 μm)” and “(particles with a radius <2.5 μm)” to the places where PM2.5 and PM10 first appear.

OK

P6, ”3. Data validation”. I think the leave-one-station-out CV cannot capture the model overfitting issues well. Typically, 10-fold spatially CV (leave-10%-station-out CV) is commonly used in such kind of studies.

We added the following sentences at the beginning of section 3:

“Leave-one-out validation is a commonly used method in the air quality community (see for example ETC reports on air quality mapping (ETC, 2020)) which is presently recommended by FAIRMODE (FAIRMODE guidance, 2020).  However scores derived from the results of the leave-one-out validation might be influenced by areas where the density of sampling points is highest. For this reason, during the FAIRMODE project (Riviere et al., 2019), for which a kriging method similar to the one conducted here was conducted, a comparison has been performed between  cross-validation results obtained by the leave-one-out cross-validation and cross-validation results obtained by the 5-fold cross validation (leave-20%-station-out CV). Results and related scores were very similar. We therefore decided to keep to the leave-one-out cross-validation process for the validation of our kriging results.”

The chapter and section numbers are messy: “ Data validation”->” 3.1.4. PM10”->” 3.1.5. PM2.5”->” 3.1.6. O3”->” 3.1.7. NO2”->” 4. Results”->” 4.1 Concentration maps and trends”-> “3.1.1. PM10”->” 3.1.2. PM2.5”->” 3.1.3. Ozone”->” 3.1.8. NO2”->” 4.2 Exposure trends”->” 4. Data availability”.

OK, this has been corrected

The words in the figure are too small. (e.g., Figure 9, etc.)

Figures 9 to 15 have been enlarged

Change “4. Results” to ” 4. Results and Discussion”

OK

Incorporate the section “4 Data availability” into “Conclusion” section.

This section is required by the Editor

The figures and tables can be better-looking.

We have enlarged the figure 9 to 15 to make them more readable

First of all, we would like to thank the reviewer for his pertinent remarks which helped us to improve the new version of our paper. We would also like to apologise for the delay in responding.  

First of all, we would like to thank the reviewer for his pertinent remarks which helped us to improve the new version of our paper. We would also like to apologise for the delay in responding.  All responses can be found in the associated pdf.