1ERL, Institute of Nuclear & Radiological Sciences & Technology, Energy & Safety, National Centre for Scientific Research “Demokritos”, 15310 Ag. Paraskevi, Attiki, Greece
2Laboratory of Atmospheric Chemistry, Paul Scherrer Institute, Villigen-PSI, 5232, Switzerland
3Department of Nuclear Sciences and Engineering & C2TN, Instituto Superior Técnico, Universidade de Lisboa, Bobadela, Portugal
4Environmental Hygiene Unit, Institute for Medical Research and Occupational Health, Zagreb, 10000, Croatia
5Institute for Nuclear Research (ATOMKI), Bem tér 18/C, Debrecen, 4026, Hungary
6AGH University of Science and Technology, Faculty of Physics and Applied Computer Science, ul. Mickiewicza 30, 30-059, Krakow, Poland
1ERL, Institute of Nuclear & Radiological Sciences & Technology, Energy & Safety, National Centre for Scientific Research “Demokritos”, 15310 Ag. Paraskevi, Attiki, Greece
2Laboratory of Atmospheric Chemistry, Paul Scherrer Institute, Villigen-PSI, 5232, Switzerland
3Department of Nuclear Sciences and Engineering & C2TN, Instituto Superior Técnico, Universidade de Lisboa, Bobadela, Portugal
4Environmental Hygiene Unit, Institute for Medical Research and Occupational Health, Zagreb, 10000, Croatia
5Institute for Nuclear Research (ATOMKI), Bem tér 18/C, Debrecen, 4026, Hungary
6AGH University of Science and Technology, Faculty of Physics and Applied Computer Science, ul. Mickiewicza 30, 30-059, Krakow, Poland
Received: 16 Dec 2022 – Discussion started: 09 Jan 2023
Abstract. An inversion method has been developed in order to quantify the emission rate of certain aerosol pollution sources across a wide region in the Northern hemisphere, mainly in Europe and Western Asia. The data employed are the aerosol contribution factors (sources) deducted by Positive Matrix Factorization (PMF) on a PM2.5 chemical composition dataset from 16 European and Asian cities for the period 2014 to 2016. The spatial resolution of the method corresponds to the geographic grid cell size of the Lagrangian particle dispersion model (FLEXPART) which was utilized for the air mass backward simulations. The area covered is also related to the location of the 16 cities under study. Species with an aerodynamic geometric mean diameter of 400 nm and 3.1 μm and geometric standard deviation of 1.6 and 2.25 respectively, were used to model the Secondary Sulfate and Dust aerosol transport. PSCF analysis and Generalized Tikhonov regularization were applied so as to acquire potential source areas and quantify their emission rate. A significant source area for Secondary Sulfate on the East of the Caspian Sea is indicated, when data from all stations are used. The maximum emission rate in that area is as high as 10 g * m-2 * s-1. When Vilnius, Dushanbe and Kurchatov data were excluded, the areas with the highest emission factors were the Western and Central Balkans and South Poland. The results display many similarities to the SO2 emission map provided by ECLIPSE database. For Dust aerosol, measurements from Athens, Belgrade, Debrecen, Lisbon, Tirana and Zagreb are utilized. The west Sahara region is indicated as the most important source area and its contribution is quantified, with a maximum of 17.5 g * m-2 * s-1. When we apply the emission rates from every geographic grid cell (1º x 1º) for Secondary Sulfate aerosol deducted with the new method to air masses originating from Vilnius, a good approximation to the measured values is achieved.
The idea of the paper of obtaining fluxes of emission for dust and sulphate is very interesting. However, the presentation is poor and the support to results and conclusions is weak in my opinion.
I am in the interphase of major revision or rejection.
Change in tittle and all text ‘emission factors’ by ‘emission fluxes’. ‘Emission factor’ in ‘emission and projections’ has a very well-defined meaning, with kg or t/unit of activity. When referring to emission or deposition per area and time, the term ‘fluxes’ is used.
References for health studies are ok but old, please update at least with the most recent papers on the Global Burden of disease and 2021 WHO AQ guidelines
Very repetitive the paragraph below. Try to send the messages only once and add references in all cases:
‘The identification and quantification of sources and corresponding source areas of aerosols require significant effort by the Scientific Community. When these this information are is at hand, mitigation
measures can be applied and air quality can be improved. Source apportionment methods can support air quality planning activities, by providing information on the relationship between air pollutant sources and their concentrations. Reliable and quantitative information on the origin of pollution and on pollution sources is required in order to support the design of air quality plans and explain the origin of exceedances. This information regarding the quantification of the sources of air pollution, both in terms of their sectorial and spatial origins, constitutes an essential step of the air quality management process (Wesseling et al., 2019).’
Better justify that the method used can be applied for secondary PM components, such as sulphate. For dust it is clear but in different seasons the SO2 oxidation velocity might change and sulphate being formed faster or slowly and then the distance to the origin might change artificially for this. At least evaluate what effect it might have.
Not clear to me how regional from long range sulphate and dust can be distinguished.
You stated in text that 16 cities are studied and only 14 are indicated in the maps of Figure 1. You explain that only 14 were selected, but why 2 were excluded give reasons in methodology.
For a number of cities did you applied PMF with less than 50 samples. Is this right? I do not think so.
R143-147 you select and exclude sites without supporting reasons.
R161 cite Figure 3.
All this section on sulphate is very confuse, you select some cities, then you do not include because low samples and then use others. At the end the reader does not know what you have done and why you exclude and select ones or the others.
Why you did not use OMI or TROPOMI for SO2 concentrations in addition to Eclipse maps? These show much better the SO2 hotspots. Furthermore, I do not see properly that Eclipse and your maps show similar high SO2 regions.
Figure 4 you do not reach a good agreement for Vilnius but you do not mention for the other cities. You state that it might be precipitation the reason for the lack of this agreement but no support is given for this.
I am not able to identify in the result section on sulphate how and what are the emission fluxes you cited in the abstract.
Dust I am not able to see in the map that NW Africa is the main source, especially for Tirana. IN many cases is N Africa, but not the well known large sources from NW Africa, Central and S Argelia, Mauritania, Sahara,…. That are not covered by the map patches.
Summary: 16 or 14 cities?
Summary you give the quantitative emission fluxes for both dust and sulphate without showing results on it in the prior sections?????
Using a dataset from 16 European and Asian cities we develop a new method so as to quantify the emission rate from each geographic grid cell for aerosol contribution factors (sources) deducted by Positive Matrix Factorization (PMF). The application of the new method allowed us to identify and quantify the source areas and emission rates for Secondary Sulfate and Dust aerosol in Europe and Central Asia.
Using a dataset from 16 European and Asian cities we develop a new method so as to quantify the...
