HTAP_v3 emission mosaic: a global effort to tackle air quality issues by quantifying global anthropogenic air pollutant sources

Crippa, Monica; Guizzardi, Diego; Butler, Tim; Keating, Terry; Wu, Rosa; Kaminski, Jacek; Kuenen, Jeroen; Kurokawa, Junichi; Chatani, Satoru; Morikawa, Tazuko; Pouliot, George; Racine, Jacinthe; Moran, Michael D.; Klimont, Zbigniew; Manseau, Patrick M.; Mashayekhi, Rabab; Henderson, Barron H.; Smith, Steven J.; Suchyta, Harrison; Muntean, Marilena; Solazzo, Efisio; Banja, Manjola; Schaaf, Edwin; Pagani, Federico; Woo, Jung-Hun; Kim, Jinseok; Monforti-Ferrario, Fabio; Pisoni, Enrico; Zhang, Junhua; Niemi, David; Sassi, Mourad; Ansari, Tabish; Foley, Kristen

doi:https://doi.org/10.5194/essd-2022-442

Preprints

https://doi.org/10.5194/essd-2022-442

Preprints

13 Jan 2023

| 13 Jan 2023

Status: a revised version of this preprint was accepted for the journal ESSD and is expected to appear here in due course.

HTAP_v3 emission mosaic: a global effort to tackle air quality issues by quantifying global anthropogenic air pollutant sources

Monica Crippa, Diego Guizzardi, Tim Butler, Terry Keating, Rosa Wu, Jacek Kaminski, Jeroen Kuenen, Junichi Kurokawa, Satoru Chatani, Tazuko Morikawa, George Pouliot, Jacinthe Racine, Michael D. Moran, Zbigniew Klimont, Patrick M. Manseau, Rabab Mashayekhi, Barron H. Henderson, Steven J. Smith, Harrison Suchyta, Marilena Muntean, Efisio Solazzo, Manjola Banja, Edwin Schaaf, Federico Pagani, Jung-Hun Woo, Jinseok Kim, Fabio Monforti-Ferrario, Enrico Pisoni, Junhua Zhang, David Niemi, Mourad Sassi, Tabish Ansari, and Kristen Foley

Abstract. This study, performed under the umbrella of the Task Force on Hemispheric Transport of Air Pollution (TF-HTAP), responds to the need of the global and regional atmospheric modelling community of having a mosaic emission inventory of air pollutants that conforms to specific requirements: global coverage, long time series, spatially distributed emissions with high time resolution, and a high sectoral resolution. The mosaic approach of integrating official regional emission inventories based on locally reported data, with a global inventory based on a globally consistent methodology, allows modellers to perform simulations of a high scientific quality while also ensuring that the results remain relevant to policymakers.

HTAP_v3, an ad-hoc global mosaic of anthropogenic inventories, has been developed by integrating official inventories over specific areas (North America, Europe, Asia including Japan and Korea) with the independent Emissions Database for Global Atmospheric Research (EDGAR) inventory for the remaining world regions. The results are spatially and temporally distributed emissions of SO₂, NO_x, CO, NMVOC, NH₃, PM₁₀, PM_2.5, Black Carbon (BC), and Organic Carbon (OC), with a spatial resolution of 0.1 x 0.1 degree and time intervals of months and years covering the period 2000–2018 (DOI 10.5281/zenodo.7516361, https://edgar.jrc.ec.europa.eu/dataset_htap_v3). The emissions are further disaggregated to 16 anthropogenic emitting sectors. This paper describes the methodology applied to develop such an emission mosaic, reports on source allocation, differences among existing inventories, and best practices for the mosaic compilation. One of the key strengths of the HTAP_v3 emission mosaic is its temporal coverage, enabling the analysis of emission trends over the past two decades. The development of a global emission mosaic over such long time series represents a unique product for global air quality modelling and for better-informed policy making, reflecting the community effort expended by the TF-HTAP to disentangle the complexity of transboundary transport of air pollution.

How to cite. Crippa, M., Guizzardi, D., Butler, T., Keating, T., Wu, R., Kaminski, J., Kuenen, J., Kurokawa, J., Chatani, S., Morikawa, T., Pouliot, G., Racine, J., Moran, M. D., Klimont, Z., Manseau, P. M., Mashayekhi, R., Henderson, B. H., Smith, S. J., Suchyta, H., Muntean, M., Solazzo, E., Banja, M., Schaaf, E., Pagani, F., Woo, J.-H., Kim, J., Monforti-Ferrario, F., Pisoni, E., Zhang, J., Niemi, D., Sassi, M., Ansari, T., and Foley, K.: HTAP_v3 emission mosaic: a global effort to tackle air quality issues by quantifying global anthropogenic air pollutant sources, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2022-442, in review, 2023.

Received: 14 Dec 2022 – Discussion started: 13 Jan 2023

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Monica Crippa et al.

Status: closed

RC1:
'Comment on essd-2022-442', Anonymous Referee #1, 15 Feb 2023

This manuscript presents a description of the HTAPv3 global mosaic of anthropogenic inventories. The dataset provides a consistent times series for almost two decades of air pollutant emissions at high spatial (0.1x0.1 degree) and temporal (monthly) resolution by incorporating the best available local information. As stated by the authors, HTAPv3 is a unique and state-of-the-art tool that will substantially contribute to support policy-relevant modelling studies at both regional and global levels. Therefore, the dataset presented in the manuscript if of interest. The paper is well written and structured, and its quality is very good, which makes it a good contribution to ESSD. I recommend the manuscript to be published once the following comments have been addressed:
General comments:
Despite the recent advances made in terms of emission inventory developments in Latin America and Africa - e.g., GEAA-AEIv3.0M for Argentina by Puliafito et al. (2021), INEMA for Chile by Álamos et al., (2022); DACCIWA for Africa by Keita et al., (2021) - the HTAP_v3 mosaic does not integrate regional inventories from any of these two regions. Could you clarify why you decided to cover these regions with EDGAR emissions instead of using more local information?
Following with the previous point, it is not clear to me why for China the authors decided to use REAS instead of MEIC, giving the fact that the later report emissions until a more recent year (2015 versus 2017) and that the information considered to spatially distribute emissions from industrial plants is more precise in MEIC.
According to the authors, "One key goal of the HTAP_v3 mosaic is to collate in one inventory the most accurate spatially-distributed emissions for all air pollutants at the global level, based on the best available local information". Recent studies have shown that the EDGAR inventory tends to significantly over allocate PM emissions from residential combustion processes in certain urban areas of Latin America (Huneeus et al., 2020). Coming back to my first point, should not HTAPv3 consider local available emissions for this region?
The HTAPv3 inventory includes NOx and NMVOC emissions from agricultural crops. These emissions could potentially be double counted if HTAPv3 is combined with a natural emission model such as MEGAN, which includes the estimation of NMVOC from crops and soil NOx emissions (including agricultural soils). It would be good if the authors can add a sentence mentioning that these emissions should be treated with careful.
The HTAPv3 inventory provides information and guidance for the speciation of NMVOC and PM emissions (PM2.5 is reported together with BC and OC). However, no information is provided concerning the speciation of NOx emissions (NO and NO2, and HONO for the specific case of road transport). Could you comment on this point?
Following with what happened with their predecessors, HTAP_v3 will quickly become a widely used emission dataset among the air quality modelling community. Having this in mind, I think it is important that in the conclusions sections the authors include a subsection listing the main limitations of the dataset and summarizing the considerations that users should take into account when using it (e.g., agricultural waste burning emissions should be treated with caution to avoid double counting when combined with existing biomass burning emission inventories). I would also recommend to further develop the part on future works (e.g., are there any knowing emission information needs from the modelling community that could not be covered with the present version of HTAP and may be tackled in future versions?)
Specific comments:
Adding a summary table that compares the main features of the different versions of HTAP (e.g., pollutants, number of sectors, temporal coverage, resolution), would help the reader to quickly spot the main improvements and added value of this new version
Change CAMS-REF-v5.1 to CAMS-REG-v5.1 (line 54)
References:
Puliafito, S. E., Bolaño-Ortiz, T. R., Fernandez, R. P., Berná, L. L., Pascual-Flores, R. M., Urquiza, J., López-Noreña, A. I., and Tames, M. F.: High-resolution seasonal and decadal inventory of anthropogenic gas-phase and particle emissions for Argentina, Earth Syst. Sci. Data, 13, 5027–5069, https://doi.org/10.5194/essd-13-5027-2021, 2021.
Keita, S., Liousse, C., Assamoi, E.-M., Doumbia, T., N'Datchoh, E. T., Gnamien, S., Elguindi, N., Granier, C., and Yoboué, V.: African anthropogenic emissions inventory for gases and particles from 1990 to 2015, Earth Syst. Sci. Data, 13, 3691–3705, https://doi.org/10.5194/essd-13-3691-2021, 2021.
Álamos, N., Huneeus, N., Opazo, M., Osses, M., Puja, S., Pantoja, N., Denier van der Gon, H., Schueftan, A., Reyes, R., and Calvo, R.: High-resolution inventory of atmospheric emissions from transport, industrial, energy, mining and residential activities in Chile, Earth Syst. Sci. Data, 14, 361–379, https://doi.org/10.5194/essd-14-361-2022, 2022.
Nicolas Huneeus, Hugo Denier van Der Gon, Paula Castesana, Camilo Menares, Claire Granier, et al.. Evaluation of anthropogenic air pollutant emission inventories for South America at national and city scale. Atmospheric Environment, 2020, 235, pp.117606. 10.1016/j.atmosenv.2020.117606.

Citation: https://doi.org/10.5194/essd-2022-442-RC1
- AC1: 'Reply on RC1', Monica Crippa, 11 Apr 2023
  
  The comment was uploaded in the form of a supplement: https://essd.copernicus.org/preprints/essd-2022-442/essd-2022-442-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/essd-2022-442-AC1
RC2:
'Comment on essd-2022-442', Hugo Denier van der Gon, 19 Mar 2023

Review of MS No.: essd-2022-442: HTAP_v3 emission mosaic: a global effort to tackle air quality issues by quantifying global anthropogenic air pollutant sources
Author(s): Monica Crippa et al.
The paper describes the construction of an emission database using where possible national or regional inventories and using a global dataset (EDGARv6) for gap filling and achieving completeness. A major difference with previous efforts (e.g. HTAPv2) isa that a complete timeseries is provided and that much more recent years are covered. The dataset meets all the requirement of (global) AQ modellers and at the same time is highly policy relevant because generally speaking the regional inventories are closest to nationally-accepted emission levels. Moreover the authors made a great effort to accommodate potential users for example with sector-cross walk tables, tools to extract certain domains and a consisting mapping of VOC species. The paper is a valuable contribution to the scientific community and deserves to be published after a number of, mostly minor, corrections are made and a few issues are discussed in a bit more detail.
I only select major revision because I really like to EDGARv6 also in the figures S1-S4. (see the comments below)

Main concerns
Title: The title does not make clear that regional and global emission datasets are merged nor that it is a timeseries . I suggest changing the title. A suggestion could be (but not necessarily the best)
“The HTAP_v3 emission mosaic: merging regional and global emission time series (2000-2018) to support air quality modelling and policies”

A more complete discussion on the use of REAS vs MEIC for China should be provided. In the Supp material (page 13) it is stated that “CEDS is calibrated to MEIC, as is HTAP_v3 (indirectly via REAS)”. So are the MEIC emissions the same as REAS? In the paper this is not clear, as it says REAS is scaled to MEIC for the years after 2015. Scaling is more about trends, calibration is more about absolute levels. Since China is globally such a major contributor it would be good to be more explicit about why MEIC is not used directly (there can be very valid reasons) and what is meant with indirect calibration and what the scaling does..

P10 l41 – the emphasis on energy (+98%) is misleading. The total emission for this sector is not even visible in fig3a. So, here it is necessary to discuss % change in combination with absolute importance.
Fig 3a: BC and OC will be part of PM2.5. However, they make up less than 50% of PM2.5 – some discussion on what the other 50+ % is made of? (sulfates, mineral)?
Also on Fig 3 panel b- are there no CO emissions from int shipping? – seems strange giving the BC, NOx contributions. Or is it just not visible?
In the supp. Material page 12 in the section S2 – Comparison of HTAP_v3 emission mosaic vs. regional and global inventories it says
we compare HTAP_v3 against CEDS_v2021_04_21 (O'rourke, 2021), EDGARv5.0 (https://edgar.jrc.ec.europa.eu/dataset_ap50, (Oreggioni et al., 2022)), EDGARv6.1 (which is used in HTAP_v3 as gapfilling inventory, https://edgar.jrc.ec.europa.eu/dataset_ap61), etc…..
However, in the figures S1-S4 (and legend) EDGARv6 is not present. Only v5 is represented (in green). This should be amended because now the question remains open what the difference is between v6 and the regional inventories used. Are the discrepancies the same as EDGARv5 or? Next to showing this in these valuable figures S1 -S4; the discrepancy (if present) with EDGARv6 and the regional inventories should be discussed because v6 is also used for gap filling and/or replacing any region w/o a consistent regional emission timeseries.
The figures S1-S4 also raise a few additional questions.
EDGARv5 seems to show a very strange trend for the region “islands” (e.g. CO emissions around 2012). I assume the HTAPv3 here is in line with EDGARv6? Obviously the total emission level for this region is very small but the pattern is so strange that a brief comment would be helpful
Minor corrections
Throughout the paper (starting p2 l 34) HTAPv3 is called a tool. In my opinion it is an emission database, not a tool. I would favor correcting that throughout the MS. (e.g. a model is a tool, but an emission data base is a product in itself). Later it is said that it also contains tools for the extraction of certain domains. Yes, those are tools to be used on the emission database. (Not tools to be used on the tool.)
P2 l 42-43 This is duplication of what was said before
P2 l 50 allow
P3 l4 remove wide
P3 l8 baseline or start year?
P5 l 38 – constant appears 2x
P5 l39 submitted – what is status now?
P6 l5 2002 onwards
P6 l20 years 200 and 2001
P10 l5 replace can than consider with discuss
P10 l50 should be (-24.3%) not (24.3%)
P11 l3 remove “it” from it is
P12 l 7-16 the more detailed CAMS-TEMPO dataset by Guevara et al (2021) is made specifically to match, and support the use of, the CAMS-REG inventory. So after l13 “Further analysis has shown that for the European domain 14 regional rather than country-specific monthly profiles are applied.” It would be better to say: Therefore, for Europe new state-of-the-art profiles have been made available under the CAMS programme by Guevara et al.(2021)
P13 l37 derives from the = is made by
P14 l1 remove certain
P14 l2 pollutants = pollutant emissions . Moreover it would better to break l1-4 into 2 sentences.

Caption fig 3 delete 2018 as first word and add behind HTAP_v3 “for the year 2018”
Table 1; 2^nd row US EPA (and ECCC) is country inventory not inventories

Citation: https://doi.org/10.5194/essd-2022-442-RC2
- AC2: 'Reply on RC2', Monica Crippa, 11 Apr 2023
  
  The comment was uploaded in the form of a supplement: https://essd.copernicus.org/preprints/essd-2022-442/essd-2022-442-AC2-supplement.pdf
  
  Citation: https://doi.org/10.5194/essd-2022-442-AC2

Status: closed

RC1:
'Comment on essd-2022-442', Anonymous Referee #1, 15 Feb 2023

This manuscript presents a description of the HTAPv3 global mosaic of anthropogenic inventories. The dataset provides a consistent times series for almost two decades of air pollutant emissions at high spatial (0.1x0.1 degree) and temporal (monthly) resolution by incorporating the best available local information. As stated by the authors, HTAPv3 is a unique and state-of-the-art tool that will substantially contribute to support policy-relevant modelling studies at both regional and global levels. Therefore, the dataset presented in the manuscript if of interest. The paper is well written and structured, and its quality is very good, which makes it a good contribution to ESSD. I recommend the manuscript to be published once the following comments have been addressed:
General comments:
Despite the recent advances made in terms of emission inventory developments in Latin America and Africa - e.g., GEAA-AEIv3.0M for Argentina by Puliafito et al. (2021), INEMA for Chile by Álamos et al., (2022); DACCIWA for Africa by Keita et al., (2021) - the HTAP_v3 mosaic does not integrate regional inventories from any of these two regions. Could you clarify why you decided to cover these regions with EDGAR emissions instead of using more local information?
Following with the previous point, it is not clear to me why for China the authors decided to use REAS instead of MEIC, giving the fact that the later report emissions until a more recent year (2015 versus 2017) and that the information considered to spatially distribute emissions from industrial plants is more precise in MEIC.
According to the authors, "One key goal of the HTAP_v3 mosaic is to collate in one inventory the most accurate spatially-distributed emissions for all air pollutants at the global level, based on the best available local information". Recent studies have shown that the EDGAR inventory tends to significantly over allocate PM emissions from residential combustion processes in certain urban areas of Latin America (Huneeus et al., 2020). Coming back to my first point, should not HTAPv3 consider local available emissions for this region?
The HTAPv3 inventory includes NOx and NMVOC emissions from agricultural crops. These emissions could potentially be double counted if HTAPv3 is combined with a natural emission model such as MEGAN, which includes the estimation of NMVOC from crops and soil NOx emissions (including agricultural soils). It would be good if the authors can add a sentence mentioning that these emissions should be treated with careful.
The HTAPv3 inventory provides information and guidance for the speciation of NMVOC and PM emissions (PM2.5 is reported together with BC and OC). However, no information is provided concerning the speciation of NOx emissions (NO and NO2, and HONO for the specific case of road transport). Could you comment on this point?
Following with what happened with their predecessors, HTAP_v3 will quickly become a widely used emission dataset among the air quality modelling community. Having this in mind, I think it is important that in the conclusions sections the authors include a subsection listing the main limitations of the dataset and summarizing the considerations that users should take into account when using it (e.g., agricultural waste burning emissions should be treated with caution to avoid double counting when combined with existing biomass burning emission inventories). I would also recommend to further develop the part on future works (e.g., are there any knowing emission information needs from the modelling community that could not be covered with the present version of HTAP and may be tackled in future versions?)
Specific comments:
Adding a summary table that compares the main features of the different versions of HTAP (e.g., pollutants, number of sectors, temporal coverage, resolution), would help the reader to quickly spot the main improvements and added value of this new version
Change CAMS-REF-v5.1 to CAMS-REG-v5.1 (line 54)
References:
Puliafito, S. E., Bolaño-Ortiz, T. R., Fernandez, R. P., Berná, L. L., Pascual-Flores, R. M., Urquiza, J., López-Noreña, A. I., and Tames, M. F.: High-resolution seasonal and decadal inventory of anthropogenic gas-phase and particle emissions for Argentina, Earth Syst. Sci. Data, 13, 5027–5069, https://doi.org/10.5194/essd-13-5027-2021, 2021.
Keita, S., Liousse, C., Assamoi, E.-M., Doumbia, T., N'Datchoh, E. T., Gnamien, S., Elguindi, N., Granier, C., and Yoboué, V.: African anthropogenic emissions inventory for gases and particles from 1990 to 2015, Earth Syst. Sci. Data, 13, 3691–3705, https://doi.org/10.5194/essd-13-3691-2021, 2021.
Álamos, N., Huneeus, N., Opazo, M., Osses, M., Puja, S., Pantoja, N., Denier van der Gon, H., Schueftan, A., Reyes, R., and Calvo, R.: High-resolution inventory of atmospheric emissions from transport, industrial, energy, mining and residential activities in Chile, Earth Syst. Sci. Data, 14, 361–379, https://doi.org/10.5194/essd-14-361-2022, 2022.
Nicolas Huneeus, Hugo Denier van Der Gon, Paula Castesana, Camilo Menares, Claire Granier, et al.. Evaluation of anthropogenic air pollutant emission inventories for South America at national and city scale. Atmospheric Environment, 2020, 235, pp.117606. 10.1016/j.atmosenv.2020.117606.

Citation: https://doi.org/10.5194/essd-2022-442-RC1
- AC1: 'Reply on RC1', Monica Crippa, 11 Apr 2023
  
  The comment was uploaded in the form of a supplement: https://essd.copernicus.org/preprints/essd-2022-442/essd-2022-442-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/essd-2022-442-AC1
RC2:
'Comment on essd-2022-442', Hugo Denier van der Gon, 19 Mar 2023

Review of MS No.: essd-2022-442: HTAP_v3 emission mosaic: a global effort to tackle air quality issues by quantifying global anthropogenic air pollutant sources
Author(s): Monica Crippa et al.
The paper describes the construction of an emission database using where possible national or regional inventories and using a global dataset (EDGARv6) for gap filling and achieving completeness. A major difference with previous efforts (e.g. HTAPv2) isa that a complete timeseries is provided and that much more recent years are covered. The dataset meets all the requirement of (global) AQ modellers and at the same time is highly policy relevant because generally speaking the regional inventories are closest to nationally-accepted emission levels. Moreover the authors made a great effort to accommodate potential users for example with sector-cross walk tables, tools to extract certain domains and a consisting mapping of VOC species. The paper is a valuable contribution to the scientific community and deserves to be published after a number of, mostly minor, corrections are made and a few issues are discussed in a bit more detail.
I only select major revision because I really like to EDGARv6 also in the figures S1-S4. (see the comments below)

Main concerns
Title: The title does not make clear that regional and global emission datasets are merged nor that it is a timeseries . I suggest changing the title. A suggestion could be (but not necessarily the best)
“The HTAP_v3 emission mosaic: merging regional and global emission time series (2000-2018) to support air quality modelling and policies”

A more complete discussion on the use of REAS vs MEIC for China should be provided. In the Supp material (page 13) it is stated that “CEDS is calibrated to MEIC, as is HTAP_v3 (indirectly via REAS)”. So are the MEIC emissions the same as REAS? In the paper this is not clear, as it says REAS is scaled to MEIC for the years after 2015. Scaling is more about trends, calibration is more about absolute levels. Since China is globally such a major contributor it would be good to be more explicit about why MEIC is not used directly (there can be very valid reasons) and what is meant with indirect calibration and what the scaling does..

P10 l41 – the emphasis on energy (+98%) is misleading. The total emission for this sector is not even visible in fig3a. So, here it is necessary to discuss % change in combination with absolute importance.
Fig 3a: BC and OC will be part of PM2.5. However, they make up less than 50% of PM2.5 – some discussion on what the other 50+ % is made of? (sulfates, mineral)?
Also on Fig 3 panel b- are there no CO emissions from int shipping? – seems strange giving the BC, NOx contributions. Or is it just not visible?
In the supp. Material page 12 in the section S2 – Comparison of HTAP_v3 emission mosaic vs. regional and global inventories it says
we compare HTAP_v3 against CEDS_v2021_04_21 (O'rourke, 2021), EDGARv5.0 (https://edgar.jrc.ec.europa.eu/dataset_ap50, (Oreggioni et al., 2022)), EDGARv6.1 (which is used in HTAP_v3 as gapfilling inventory, https://edgar.jrc.ec.europa.eu/dataset_ap61), etc…..
However, in the figures S1-S4 (and legend) EDGARv6 is not present. Only v5 is represented (in green). This should be amended because now the question remains open what the difference is between v6 and the regional inventories used. Are the discrepancies the same as EDGARv5 or? Next to showing this in these valuable figures S1 -S4; the discrepancy (if present) with EDGARv6 and the regional inventories should be discussed because v6 is also used for gap filling and/or replacing any region w/o a consistent regional emission timeseries.
The figures S1-S4 also raise a few additional questions.
EDGARv5 seems to show a very strange trend for the region “islands” (e.g. CO emissions around 2012). I assume the HTAPv3 here is in line with EDGARv6? Obviously the total emission level for this region is very small but the pattern is so strange that a brief comment would be helpful
Minor corrections
Throughout the paper (starting p2 l 34) HTAPv3 is called a tool. In my opinion it is an emission database, not a tool. I would favor correcting that throughout the MS. (e.g. a model is a tool, but an emission data base is a product in itself). Later it is said that it also contains tools for the extraction of certain domains. Yes, those are tools to be used on the emission database. (Not tools to be used on the tool.)
P2 l 42-43 This is duplication of what was said before
P2 l 50 allow
P3 l4 remove wide
P3 l8 baseline or start year?
P5 l 38 – constant appears 2x
P5 l39 submitted – what is status now?
P6 l5 2002 onwards
P6 l20 years 200 and 2001
P10 l5 replace can than consider with discuss
P10 l50 should be (-24.3%) not (24.3%)
P11 l3 remove “it” from it is
P12 l 7-16 the more detailed CAMS-TEMPO dataset by Guevara et al (2021) is made specifically to match, and support the use of, the CAMS-REG inventory. So after l13 “Further analysis has shown that for the European domain 14 regional rather than country-specific monthly profiles are applied.” It would be better to say: Therefore, for Europe new state-of-the-art profiles have been made available under the CAMS programme by Guevara et al.(2021)
P13 l37 derives from the = is made by
P14 l1 remove certain
P14 l2 pollutants = pollutant emissions . Moreover it would better to break l1-4 into 2 sentences.

Caption fig 3 delete 2018 as first word and add behind HTAP_v3 “for the year 2018”
Table 1; 2^nd row US EPA (and ECCC) is country inventory not inventories

Citation: https://doi.org/10.5194/essd-2022-442-RC2
- AC2: 'Reply on RC2', Monica Crippa, 11 Apr 2023
  
  The comment was uploaded in the form of a supplement: https://essd.copernicus.org/preprints/essd-2022-442/essd-2022-442-AC2-supplement.pdf
  
  Citation: https://doi.org/10.5194/essd-2022-442-AC2

Monica Crippa et al.

Supplement

https://doi.org/10.5194/essd-2022-442-supplement

Data sets

HTAP_v3 mosaic Monica Crippa, Diego Guizzardi, Marilena Muntean, Edwin Schaaf, Fabio Monforti-Ferrario, Manjola Banja, Federico Pagani, Efisio Solazzo, Enrico Pisoni https://edgar.jrc.ec.europa.eu/dataset_htap_v3

HTAP_v3 emission mosaic The EDGAR data providers https://doi.org/10.5281/zenodo.7516361

Monica Crippa et al.

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Short summary

This study responds to the need of the global and regional atmospheric modelling community of having a mosaic of air pollutant emissions with global coverage, long time series, spatially distributed data at high time resolution, and high sectoral resolution to enhance the understanding on transboundary air pollution. The mosaic approach of integrating official regional emission inventories with a global inventory based on a consistent methodology ensures policy relevant results.


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