the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 07 Feb 2025
The HTAP_v3.1 emission mosaic: merging regional and global monthly emissions (2000–2020) to support air quality modelling and policies
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_v3.1, an ad-hoc global mosaic of anthropogenic inventories, is an update to the HTAP_v3 global mosaic inventory and has been developed by integrating official inventories over specific areas (North America, Europe, Asia including China, 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, NOx, CO, NMVOC, NH3, PM10, PM2.5, 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 2000–2020 (https://doi.org/10.5281/zenodo.14499440, https://edgar.jrc.ec.europa.eu/dataset_htap_v31). 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_v3.1 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.
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Status: final response (author comments only)
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RC1: 'Comment on essd-2024-601', Anonymous Referee #1, 02 Jun 2025
The manuscript described the data source, methodology, trend analysis, and uncertainty analysis for HTAP_v3.1 emission inventory very clearly. I only have several small suggestions:
- Page 16, Global NOx emissions: The authors described that “increased from 108.2 Mt in 2000 to 113.6 Mt in 2018 as a result of the increase in energy- and industry-related activities for most of the world regions (in particular over the Asian domain)”. However, from Figure 2, global NOx emissions increase from 2000 to around 2011, then decrease slightly. The Asian emissions also show similar trend. So, I think the description is inaccurate and may cause misunderstandings.
- Page 17, Particulate matter emissions: The authors only described PM10 emissions. I would also expect short descriptions of PM2.5, BC, and OC emissions.
- Page 17, line 26: “+56.8.0% for Africa”?
- Figure 3: As authors mentioned sectoral emissions changes several times in text, I would expect a figure like Figure 2 but shows the time series of sectoral emissions.
- Figure 5: numbers of subplots are lost.
- Page 18, line 41: Does Figure 6 show PM2.5 or PM10? Description here is inconsistent with the figure caption.
- Page 18, line 43-45: Please add brief descriptions of these two figures as well.
- Citations of figures in SI are wrong. Please check and correct carefully.
- Page 19, line 38: Is it “Figure 12 and 13”?
- Page 19, line 41-44: Add China as well?
- Page 19, line 46: I can’t find map for March in Figure 13.
- Page 27 in SI: “Table S2 provides the list of Global Emissions InitiAtive (GEIA) 25 NMVOC groups included in HTAP_v3.1 with the corresponding molecular formula.” should be Table S3.
Citation: https://doi.org/10.5194/essd-2024-601-RC1 -
RC2: 'Comment on essd-2024-601', Anonymous Referee #2, 24 Jun 2025
General comments:
The Global Air Pollution Mosaic Inventory HTAP_v3.1 proposed in this paper is an up-to-date database of seven regional inventories coordinated and blended, with gaps filled in using the latest version of EDGARv8. The results provide an information support to analyze the status and trends of air pollutants emission. There are still some issues need to be addressed before it can be accepted.
Firstly, it is recommended to highlight the differences in the results of HTAP_v3.1 and HTAP_v3 (The HTAP_v3 emission mosaic: merging regional and global monthly emissions (2000–2018) to support air quality modelling and policies) to further reflecting the advantages of HTAP_v3.1. For example, HTAP_v3.1 has added China's MEIC emission inventory, what is the difference in the results between HTAP_v3.1 and HTAP_v3? Secondly, has the HTAP-v3.1 result been validated and what about the accuracy of it? Thirdly, the time scale for HTAP_v3.1 has been extended from 2018(HTAP_v3) to 2020, but the results section has less analysis for 2020, does the results for those two years reflect the impact of the epidemic? It is necessary to analyze the emission inventory results in 2020 to understand the impact of the COVID-19 pandemic on the emission. Finally, why the results figures for different pollutants are presented in different time scales? For example, Figure 4 shows 2018 emissions for SO2; Figure 5 shows 2000 and 2018 emissions for NOx; Figures 6-8 show 2018 January emissions for different pollutants in different sectors.
In addition, there are some details need to be checked, such as the mismatch of the figure caption and the description in the text (e.g., Figure 6), the inconsistent use of “Fig. X” and “Figure. X”, and the missing units of the results. It is recommended that the authors check and verify the details.
Specific comments:
Abstract:It is important to highlight not only the improved features of HTAP_v3.1, but also the differences in the results between HTAP_v3.1 and HTAP_v3, such as the differences and changes in the results of the old and new databases. What are the emission result differences and changes of these two databases in the same year (e.g., 2018)? Has the HTAP-v3.1 result been validated?
P16 line38-42: “……while they declined to 103Mt as effect of the COID-19 pandemic.” Which year the 103Mt emission is for? What is the base year for comparison?
P17 line5: “……552.3 Mt in 2000 to 533.9 Mt in 2018 (and 515.5 in 2020).” What is the unit of 515.5 here?
P17 line8-11: “Road transport CO emissions halved over the past two decades (54.5%), while the emissions from all other sectors increased.” Why?
P17 line19-23: Global NH3 emissions in 2020 are higher than in 2018, why? Can this result reflect the impact of the epidemic? It can also demonstrate the necessity of analyzing the 2020 emission results.
P17 line24-43: The analysis of particulate emissions is mainly for PM10. I would suggest that PM2.5, BC, and OC emissions could be provided.
P17 line26: “……+56.8.0% for Africa.” Should this be 56.8 % here?
P18 line33 and 43: The expressions “Figs. 5-8” and “Figures 7 and 8” are inconsistent, and it is recommended that abbreviations or full names be used consistently.
P22 line24-26: “The largest variability is found domestic shipping emissions (CO and NMVOC), energy (OC, BC), agricultural crops (PM), road transport (PM, NMVOC) and industry (NH3, NMVOC)”. Here, it is mentioned that OC, BC emissions from energy have the largest variability. But there is no analysis of the OC and BC emission results in the results section.
Citation: https://doi.org/10.5194/essd-2024-601-RC2
Data sets
HTAP_v3.1 emission mosaic Diego Guizzardi, Monica Crippa, 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, Rachel Hoesly, Marilena Muntean, Manjola Banja, Edwin Schaaf, Federico Pagani, Jung-Hun Woo, Jinseok Kim, Enrico Pisoni, Junhua Zhang, David Niemi, Mourad Sassi, Annie Duhamel, Tabish Ansari, Kristen Foley, Guannan Geng, Yifei Chen, and Qiang Zhang https://doi.org/10.5281/zenodo.14499440
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