the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 31 Aug 2023
Global Anthropogenic Emissions (CAMS-GLOB-ANT) for the Copernicus Atmosphere Monitoring Service Simulations of Air Quality Forecasts and Reanalyses
Abstract. Anthropogenic emissions are the result of many different activities, related to transportation, power generation, industrial, residential and commercial activities, waste treatment and agriculture practices. Air quality models are used to forecast the atmospheric composition, analyse observations and reconstruct the chemical composition of the atmosphere during the previous decades. In order to drive these models, gridded emissions of all compounds emitted at the surface need to be provided. This paper describes a new global inventory of emissions called CAMS-GLOB-ANT, developed as part of the Copernicus Atmosphere Monitoring Service (CAMS). The inventory provides monthly averages of the global emissions of 36 compounds, including the main air pollutants and greenhouse gases, at a spatial resolution of 0.1x0.1 degree in latitude and longitude, for 17 emission sectors. The methodology to generate the emissions for the 2000–2023 period is explained, and the datasets are analysed and compared with publicly available global and regional inventories for selected world regions.
- Preprint
(5360 KB) - Metadata XML
-
Supplement
(4606 KB) - BibTeX
- EndNote
Status: closed
-
RC1: 'Comment on essd-2023-306', Anonymous Referee #1, 01 Oct 2023
I read this document with great interest. Generally the data look useful and credible. However, it seems the authors did not think thoroughly how to present the data. During reading, things sometimes remain unclear. In some instances, clarification came later (e.g. on the used scaling of emissions). Sometimes, things remained unclear (e.g. short cycle co2 emissions).
There is one issue with the NOx emissions. As far as I am aware, "normal" units are in mass NO2. Now, the authors use mass NO, which might lead to confusion. This is maybe an issue to check with the other inventories, although this information is normally difficult to find! (at least the authors mention the unit now clearly).
For the rest, I include an annotated PDF, which contains my remarks.
-
AC1: 'Reply on RC1', Claire Granier, 11 Jan 2024
The comment was uploaded in the form of a supplement: https://essd.copernicus.org/preprints/essd-2023-306/essd-2023-306-AC1-supplement.pdf
-
AC1: 'Reply on RC1', Claire Granier, 11 Jan 2024
-
RC2: 'Comment on essd-2023-306', Anonymous Referee #2, 21 Nov 2023
The authors document here an inventory an anthropogenic emissions (CAMS-GLOB-ANT) developed for the Copernicus Atmosphere Monitoring Service Simulations of Air Quality Forecasts and Reanalyses. This could be a useful paper especially providing provenance for the emissions dataset used in an operational system, however, in its current form, the paper reads as an informal technical report and leaves out many details/assumptions that are relevant for tracking the emissions dataset used in CAMS. Most importantly, it is not clear to me how authors have derived a dataset for the 4 months of 2023 without the actual activity data. I have provided specific comments on the attached pdf which the authors would hopefully consider for further improvement of the paper.
- AC2: 'Reply on RC2', Claire Granier, 11 Jan 2024
-
RC3: 'Comment on essd-2023-306', Anonymous Referee #3, 09 Dec 2023
Re: Global Anthropogenic Emissions (CAMS-GLOB-ANT) for the Copernicus Atmosphere Monitoring Service Simulations of Air Quality Forecasts and Reanalyses
Summary
=======This manuscript details a global inventory prepared for modeling of air pollutants. It describes the underlying inventories, their synthesis, auxiliary data, and compares the results to other relevant datasets.
Review
======The documentation of this dataset will be extremely useful to the community. The manuscript is in good shape, but I have a few minor suggestions.
Line-by-Line
============* 26, Rephrase "emitted at the surface". Currently, it suggests that aloft emissions (point sources, aircraft etc) are not necessary by omission.
* 45, Same as 26.
* 48, Might be worth referring to an overview paper of emission needs that talk about this (e.g, 10.1080/10962247.2019.1629363). Also, worth highlighting near-term top-down papers as another solution.
* 60, "most recent years" What level of fidelity is needed in the "most recent years"? Can you show that simple persistence from a previous year is not sufficient? Right now, this reads as an assumption that some readers may not yet have. Also, can you define the what is acceptable latency? Do you need this year now? One month lag? One year lag?
* 69-74, I think the omission of non BC/OC primary PM25 is not good. In my experience, some models go to extreme efforts to infer the non OC/EC primary PM25 emissions that are omitted from the model. For example, many models make gross assumptions about all OC having the same OC:OM ratio. Obviously, this ratio is specific to the emission activity generating it. What other species like crustal components? This paragraph is glossing over a need by saying that models deal with it.
* 79, Because EDGAR and CEDS are overlapping, it would be nice to put up front some comment about how they are both used. You make it clear later, but it isn't for quite some time.
* 82, Is it true that this dataset does not include HTAP? If so, you should state why this was not a good starting place for CAMS.
* 124-125, That seems inconsistent with the statement that activity is based on data from years 201-2018. COVID happened after the data. If it really is addressed, you'd need some clarity on the scope of the activity data to better match the statement.
* 136-138, You should remove "monthly" given that you have daily profiles. Maybe month-specific? Also, do you have diurnal profiles?
* 138, Consider species separately? Or at sectoral level giving rise to variability in primary pollutants? Or sector-species specific?
* 155, Given that 2020 was the COVID year, this seems like a mistake. Why not use 2019?
* 175-190, The methodology for geometric projections seems unnecessarily complex for the 2016-2019 period. Why not use the actual ratio change in CEDS for those years and then use the mean factor for future years (i.e, outside of both CEDS and EDGAR)?
* 199, Figure 3: caption "growth factor for the industry factor"? Maybe "growth factor for the industry sector"?
* 301, Seems weird that you left out NMVOC, but kept in BC. This is not consistent. I would also recommend adding NMVOC to the list of significant decrease ("CO, NOx, NMVOC, SO2, and BC have")changing "other species" to a "NH3 and OC".
* 493-497, This seems like a small list. Perhaps expand on key other uncertainties.
Citation: https://doi.org/10.5194/essd-2023-306-RC3 - AC3: 'Reply on RC3', Claire Granier, 11 Jan 2024
Status: closed
-
RC1: 'Comment on essd-2023-306', Anonymous Referee #1, 01 Oct 2023
I read this document with great interest. Generally the data look useful and credible. However, it seems the authors did not think thoroughly how to present the data. During reading, things sometimes remain unclear. In some instances, clarification came later (e.g. on the used scaling of emissions). Sometimes, things remained unclear (e.g. short cycle co2 emissions).
There is one issue with the NOx emissions. As far as I am aware, "normal" units are in mass NO2. Now, the authors use mass NO, which might lead to confusion. This is maybe an issue to check with the other inventories, although this information is normally difficult to find! (at least the authors mention the unit now clearly).
For the rest, I include an annotated PDF, which contains my remarks.
-
AC1: 'Reply on RC1', Claire Granier, 11 Jan 2024
The comment was uploaded in the form of a supplement: https://essd.copernicus.org/preprints/essd-2023-306/essd-2023-306-AC1-supplement.pdf
-
AC1: 'Reply on RC1', Claire Granier, 11 Jan 2024
-
RC2: 'Comment on essd-2023-306', Anonymous Referee #2, 21 Nov 2023
The authors document here an inventory an anthropogenic emissions (CAMS-GLOB-ANT) developed for the Copernicus Atmosphere Monitoring Service Simulations of Air Quality Forecasts and Reanalyses. This could be a useful paper especially providing provenance for the emissions dataset used in an operational system, however, in its current form, the paper reads as an informal technical report and leaves out many details/assumptions that are relevant for tracking the emissions dataset used in CAMS. Most importantly, it is not clear to me how authors have derived a dataset for the 4 months of 2023 without the actual activity data. I have provided specific comments on the attached pdf which the authors would hopefully consider for further improvement of the paper.
- AC2: 'Reply on RC2', Claire Granier, 11 Jan 2024
-
RC3: 'Comment on essd-2023-306', Anonymous Referee #3, 09 Dec 2023
Re: Global Anthropogenic Emissions (CAMS-GLOB-ANT) for the Copernicus Atmosphere Monitoring Service Simulations of Air Quality Forecasts and Reanalyses
Summary
=======This manuscript details a global inventory prepared for modeling of air pollutants. It describes the underlying inventories, their synthesis, auxiliary data, and compares the results to other relevant datasets.
Review
======The documentation of this dataset will be extremely useful to the community. The manuscript is in good shape, but I have a few minor suggestions.
Line-by-Line
============* 26, Rephrase "emitted at the surface". Currently, it suggests that aloft emissions (point sources, aircraft etc) are not necessary by omission.
* 45, Same as 26.
* 48, Might be worth referring to an overview paper of emission needs that talk about this (e.g, 10.1080/10962247.2019.1629363). Also, worth highlighting near-term top-down papers as another solution.
* 60, "most recent years" What level of fidelity is needed in the "most recent years"? Can you show that simple persistence from a previous year is not sufficient? Right now, this reads as an assumption that some readers may not yet have. Also, can you define the what is acceptable latency? Do you need this year now? One month lag? One year lag?
* 69-74, I think the omission of non BC/OC primary PM25 is not good. In my experience, some models go to extreme efforts to infer the non OC/EC primary PM25 emissions that are omitted from the model. For example, many models make gross assumptions about all OC having the same OC:OM ratio. Obviously, this ratio is specific to the emission activity generating it. What other species like crustal components? This paragraph is glossing over a need by saying that models deal with it.
* 79, Because EDGAR and CEDS are overlapping, it would be nice to put up front some comment about how they are both used. You make it clear later, but it isn't for quite some time.
* 82, Is it true that this dataset does not include HTAP? If so, you should state why this was not a good starting place for CAMS.
* 124-125, That seems inconsistent with the statement that activity is based on data from years 201-2018. COVID happened after the data. If it really is addressed, you'd need some clarity on the scope of the activity data to better match the statement.
* 136-138, You should remove "monthly" given that you have daily profiles. Maybe month-specific? Also, do you have diurnal profiles?
* 138, Consider species separately? Or at sectoral level giving rise to variability in primary pollutants? Or sector-species specific?
* 155, Given that 2020 was the COVID year, this seems like a mistake. Why not use 2019?
* 175-190, The methodology for geometric projections seems unnecessarily complex for the 2016-2019 period. Why not use the actual ratio change in CEDS for those years and then use the mean factor for future years (i.e, outside of both CEDS and EDGAR)?
* 199, Figure 3: caption "growth factor for the industry factor"? Maybe "growth factor for the industry sector"?
* 301, Seems weird that you left out NMVOC, but kept in BC. This is not consistent. I would also recommend adding NMVOC to the list of significant decrease ("CO, NOx, NMVOC, SO2, and BC have")changing "other species" to a "NH3 and OC".
* 493-497, This seems like a small list. Perhaps expand on key other uncertainties.
Citation: https://doi.org/10.5194/essd-2023-306-RC3 - AC3: 'Reply on RC3', Claire Granier, 11 Jan 2024
Data sets
CAMS-GLOB-ANT_v5.3 anthropogenic emissions Antonin Soulie, Claire Granier, Sabine Darras, Nicolas Zilbermann, Thierno Doumbia, Marc Guevara, Jukka-Pekka Jalkanen, Sekou Keita, Cathy Liousse, Monica Crippa, Diego Guizzardi, Rachel Hoesly, Steven J. Smith https://eccad.aeris-data.fr/essd-surf-emis-cams-ant/
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 831 | 385 | 45 | 1,261 | 80 | 41 | 43 |
- HTML: 831
- PDF: 385
- XML: 45
- Total: 1,261
- Supplement: 80
- BibTeX: 41
- EndNote: 43
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
Cited
3 citations as recorded by crossref.
- Application of Machine Learning to Estimate Ammonia Atmospheric Emissions and Concentrations A. Marongiu et al. 10.3390/air2010003
- Global Scale Inversions from MOPITT CO and MODIS AOD B. Gaubert et al. 10.3390/rs15194813
- Comparison of Air Pollution–Mortality Associations Using Observed Particulate Matter Concentrations and Reanalysis Data in 33 Spanish Cities D. Royé et al. 10.1021/envhealth.3c00128