the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 03 Jan 2024
Global Emissions Inventory from Open Biomass Burning (GEIOBB): Utilizing Fengyun–3D global fire spot monitoring data
Abstract. Open biomass burning (OBB) significantly affects regional and global air quality, climate change, and human health. It is susceptible to fire types, including forests, shrublands, grasslands, peatlands, and croplands burning. Global high–resolution satellites can detect active fires, enabling a more accurate estimation of these emissions. In this study, we developed a global high–resolution (1×1 km) daily emission inventory associated with OBB emissions using the Chinese Fengyun–3D satellite’s global fire spot monitoring data, satellite and observational biomass data, vegetation index–derived spatiotemporal variable combustion efficiencies, and land–type–based emission factors. The average annual OBB emissions for 2020–2022 were 2,586.88 Tg C, 8841.45 Tg CO2, 382.96 Tg CO, 15.83 Tg CH4, 18.42 Tg NOX, 4.07 Tg SO2, 18.68 Tg OC, 3.77 Tg BC, 5.24 Tg NH3, 15.85 Tg NO2, 42.46 Tg PM2.5 and 56.03 Tg PM10. Specifically, taking carbon emissions as an example, the average annual OBB for 2020–2022 were 72.71 (BONA), 165.7 (TENA), 34.1 (CEAM), 42.9 (NHSA), 520.5 (Southern Hemisphere South America; SHSA), 13 (EURO), 8.4 (MIDE), 394.3 (Northern Hemisphere Africa; NHAF), 847 (Southern Hemisphere Africa; SHAF), 167.4 (BOAS), 27.9 (CEAS), 197.3 (Southeast Asia; SEAS), 13.2 (EQAS), and 82.4 (AUST) Tg. SHAF was identified as the region with the largest emissions. Notably, savanna grassland accounted for the lion’s share of total emissions, contributing to 46 %, followed by woody savanna/shrubs at 33 %. Moreover, notable seasonal variability characterizes the OBB carbon emissions, with marked increases observed in July and August. This surge in carbon emissions is chiefly attributed to fires in the savanna grasslands, woody savanna/shrubs, and tropical forests of SHAF, SHSA, and NHAF. Fires in savanna grasslands were predominant in the NHAF, contributing to 77 % of emissions during January–April, whereas in the SEAS, woody savanna/shrubs (52 %) and tropical forests (23 %) were the primary sources. Our comprehensive high–resolution inventory of OBB emissions provides valuable insights for enhancing the accuracy of air quality modelling, atmospheric transport and biogeochemical cycle studies. The GEIOBB dataset can be downloaded at http://figshare.com with the following identifier DOI: https://doi.org/10.6084/m9.figshare.24793623 (Liu et al., 2023).
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RC1: 'Comment on essd-2023-527', Anonymous Referee #1, 01 Feb 2024
General comments:
This study developed a global daily emission inventory of OBB with 1km×1km based on global fire point monitoring data from the Chinese Fengyun-3D satellite, fuel loading, combustion factor and emission factor. Considering the scientific impact of each study, several analysis is needed to substantiate the conclusions in your manuscript. Firstly, the manuscript emphasizes that the compared with MODIS, significant advantage of using the FY-3D fire detection product is the ability to enhance the detection of small fires, but the analysis of the results does not show how much the use of the FY-3D detection product has increased the emission estimates of small fires? Secondly, in the section about verification, the manuscript emphasizes the consistency with other datasets, but does not quantify the advantages of this study. Thirdly, the advance of activity data selected in this study needs to be verified, such as the quality and the resolution of the data. The manuscript can be considered for publication if the issues mentioned above and following specific comment could be addressed.
Specific comments：
P1 line23-25: The full name are not given for some regions (e.g., BONA), and them are given for some regions (e.g., SHSA).
P2 line64-65: The detection accuracy of MODIS and other related indexes should be clearly given to facilitate readers to compare directly. As well as the comparison with MODIS, other commonly used polar-orbiting satellite sensors (SNPP-VIIRS, Landsat-8, etc.) can be considered for comparison to highlight the advantages of FY-3D.
P4 line128: It is suggested that formulas could be transferred to the manuscript from SI, with the supplement of corresponding unit of the variable.
P5 line148: Source of the constant 0.013 in the formula? Empirical values should give literature. The fitted values should depict the fitting process and significance test results.
P5 line152: There are other products (MODIS) of NDVI with a time interval of 8d. Why the products of 16d was selected in this study?
P6 Table 1: The EF for specific biomass (e.g., crop) is fixed value for different regions with various crop distribution characteristic. Regional or crop differences should be reflected in EF values.
P11 line269: What does “intensify both the frequency and frequency of fires in the area” mean?
P15 line352: Why the dataset is not include FINN (e.g., FINNv2.5)? The resolution of it is the same with the dataset developed in this study (1km, 1d).
P16 line377-379: There is a lack of clarity in the explanation of how FY-3D can capture small fires more effectively compared to MODIS, and how the difference in transit times between the two satellites affects the detection of agricultural small fires. More data analysis is needed to support this question.
P18 line410: The article should add a comparative analysis of how much the addition of FY-3D improves emission estimates for small fires, which is a key factor in determining the innovativeness of the study.
P19 line435: What is the difference between the 1° spatial resolution of FY-3D mentioned here and the 1km mentioned previously (line 106)?
Citation: https://doi.org/10.5194/essd-2023-527-RC1 - AC1: 'Reply on RC1', Yang Liu, 28 Feb 2024
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CC1: 'Comment on essd-2023-527', xiansheng zhou, 02 Feb 2024
In this study, the authors utilized data from the Chinese Fengyun-3D satellite, combined with multiple sources of data, to develop a high-resolution (1×1 km) daily emission inventory on a global scale. This was done to assess the impact of open biomass burning (OBB) on air quality, climate change, and human health. The research provided detailed emission data from 2020 to 2022 and analyzed emission characteristics in different regions and seasons. The article has four main strengths: integration of data and innovative methodology - the study comprehensively utilized satellite data, observed biomass data, and other relevant factors, providing a novel method for estimating OBB emissions; high spatial resolution - the 1×1 km resolution data offered is extremely valuable for understanding local and regional emission characteristics; comprehensive emission data - encompassing a range of gases and particulate matter, aiding in a more complete understanding of the environmental impact of OBB; regional and seasonal analysis - the study thoroughly examined emissions across different regions and seasons, providing a basis for related policies and intervention strategies. There are some suggestions: based on the research findings, specific policy recommendations or management strategies can be provided to help reduce OBB emissions and their environmental impact; the study could be compared with existing OBB emission research to highlight its innovative aspects and improvements. Overall, the paper is well-written, with a logical and coherent structure. I recommend that EESD accept this article for publication.
Citation: https://doi.org/10.5194/essd-2023-527-CC1 -
CC2: 'Comment on essd-2023-527', Yang Chen, 02 Feb 2024
As Terra and Aqua reach the end of their life cycles, alternative methods are needed to derive global emissions from biomass burning. In this manuscript, Liu et al. used the FY-3D active fire product as the main input to create a new global emissions inventory from open biomass burning. While this study is a valuable contribution to the field, I would not recommend that the manuscript be published on ESSD in its current form. The manuscript requires substantial revision to improve clarity, enhance data/method presentation, and provide a more robust and coherent narrative. In addition, addressing the methodological gaps (between this dataset and previous datasets) and providing more detailed explanations will contribute to the overall credibility of the research. Below, I have listed several major deficiencies in this manuscript.
- The authors claim that “the GFR product, which was integrated with the MERSI–2 instrument, exhibited superior judgment accuracy” (Line 102-103), “Consequently, our inventory yielded accurate assessment results and captured the spatial variation and heterogeneity of minor OBB emissions effectively” (Line 358-359), and “the accuracy of the OBB carbon emissions assessment significantly improved” (Line 417-418). In my opinion, these claims are not sufficiently justified in the manuscript. First, the superiority of MERSI-2 over MODIS in detecting active fires is not well explained. MERSI-2 on FY-3D has higher spatial resolution than MODIS in the visible and NIR bands. However, the active fire algorithm mainly uses the mid-infrared band, where both MERSI-2 and MODIS have a spatial resolution of 1km. The authors cited a number of previous papers (such as Dong et al. 2022 and Chen et al., 2022) to show better fire detection accuracy from MERSI-2 than from MODIS. However, these studies were mostly based on comparisons with limited data samples from manual inspection, and are not very convincing to me. Second, there are many limitations in the algorithm that are not mentioned in the manuscript. For example, this study used AGB as the fuel load, completely ignoring the emissions from soil organic matter burning. The omission error of active fires due to cloud cover/thick smoke is also not quantified. Third, this emissions dataset was derived from FY-3D active fires, but many MODIS products are still needed to generate GEIOBB. The use of MODIS products, which include MOD44B, MODIS NDVI, and MODIS land cover type data, may hinder the effect of quantifying global fire emissions after Terra and Aqua are gone. This potential problem should also be addressed in this paper.
- Many statements are incorrect or lacking scientific support.
- The estimation method and the use of fuel loading (F) are not clearly described. While the authors mention in the manuscript that three data sources, NDVI, TC, and AGB are used for fuel loading, the approach for combining different data streams and forming the fuel loading is embedded in the supporting text only. This formula was presented without any scientific justification or explanation (there are also some errors in the description of this formula, e.g., 2020 should be corrected to 2010).
- The use of emission factors (EF) is also ambiguously described in the manuscript. In section 2.4, the authors simply listed a table of EFs without indicating the specific data sources. Although references to various studies and some locally measured data are cited, the specific methodology employed to construct Table 1 remains undisclosed.
- Line 27-29: “Moreover, notable seasonal variability characterizes the OBB carbon emissions, with marked increases observed in July and August. This surge in carbon emissions is chiefly attributed to fires in the savanna grasslands, woody savanna/shrubs, and tropical forests of SHAF, SHSA, and NHAF.” The peak burning month for NHAF is in boreal winter months. How can the burning in this region contribute to the surge in carbon emissions in July and August?
- Line 166: “EF denotes the amount of pollutants released during burning.” This seems not the correct definition or description of the emission factor (EF).
- Line 181-182: “significant spatial variations in the OBB carbon emissions were observed across Africa, and certain regions in the Americas and Asia.”. How do you define ‘significant’? Based on Figure 1, I think the spatial variations in all continents are big.
- Line 215: “According to GFED”. Which version of GFED data are you using? Please be more specific.
- Line 230: “This suggests relative homogeneity in the NHAF’s biomass–burning emission sources”. I don’t understand how did you get this conclusion based on the previous results “In the NHAF, the predominant source of OBB was savanna grasslands (Roberts et al., 2009), contributing 76.14% to the region’s total biomass–burning carbon emissions, averaging 300.21 Tg/year.”
- Line 233: “...leading to increased OBB and carbon emissions in this region”. In fact in this region (NHAF), the emissions from biomass burning have been decreasing during the past 2 decades.
- Line 257-258: “emissions from SHSA decreased at a rate of 105.22 Tg per year from 2020 to 2022, with peak monthly emissions over the 3 years reaching 184.63, 222.12, and 123.98, respectively, consistent with Griffin et al. (2023)”. Griffin et al. (2023) explored the wildfire CO emissions. But it’s unclear to me which part of your results is “consistent with” with that paper.
- Line 259: “NHAF also exhibited a decreasing trend in annual emissions, … over the 3 years”. 3 years are too short for deriving meaningful trends in annual emissions.
- Line 316-317: “The top three major emitting regions were SHAF, SHSA, and NHAF, which exhibited emission patterns that aligned closely with global emission trends over time”. The comparison between Figure 5 and Figure 6 does not seem to support this conclusion. NHAF emissions have a very different seasonal cycle than SHAF and SHSA. The interannual variability of emissions in these regions is also different.
- Line 379: “However, the use of FY–3D, which captures data at 14:00, was highly effective in capturing such events.” This is also a statement without supporting evidence. Similar to Terra and Aqua, FY-3D also records data twice a day for a given location and cannot detect short-lived fires. The local time difference between FY-3D and Aqua is only 30 minutes (13:30 vs 14:00), which won’t make much difference in the ability to detect agricultural fires.
- There are many citations in this manuscript that do not support the text before the citation. It seems that the authors didn’t really read and try to understand these references, but just made the citation based on some related keywords. Below is a partial list of inappropriate citations I have found. Please carefully double check the citations throughout the manuscript.
- Line 39: (Hussain and Reza, 2023) is not a good citation here; it studied the detrimental impact on global health by general environmental damages, not specifically from open biomass burning. There are many studies in literature about this topic which can be used for citation here.
- Line 40-41: (Estrellan and Iino, 2010) reviewed toxic emissions from open burning. It did not provide evidence for “major fire types worldwide”. So it is also not a good citation.
- Line 42: (Manisalidis et al., 2020) is a review of environmental and health impacts of air pollution. It did not talk about the specific impacts from “open burning activities”.
- Line 44: (Ma et al., 2022) studied wildfires in Amazon during 2019 only. The paper does not support the claim “regions worldwide are experiencing a notable increase in fire incidents”.
- Line 45: (You and Xu, 2023) investigated how delayed wildfires in 2020 promote snowpack melting in the western US. Same as above, this paper does not support the ‘increase in fire accidents’.
- Line 56: (Lv et al., 2020) studies CO2 mixing ratio using satellite observations. They used the GFED dataset for CO2 emissions from biomass burning. This study does not support the previous sentence “Alternatively, a method based on the fire radiative power can effectively enhance the assessment of small fire events, thereby addressing this issue to a certain extent.”
- Line 128: (Spawn and Gibbs, sssss2020). Remove the sssss here.
- Line 255: (Russell-Smith et al., 2021) focus on opportunities and challenges for savanna burning emissions abatement. It did not provide sufficient evidence to support the conclusion “In August, specific meteorological conditions, such as high temperatures and low humidity facilitated the increased combustibility of biomass, resulting in a peak in carbon emissions”.
- Line 297: (Wiggins et al., 2020) presented estimates of fire emissions in the USA using data from the FIREX-AQ mission. It has little connection to the text preceding the citation.
- Line 308: (Thackeray et al., 2022) did study the precipitation change under global warming, but the main topic of this paper was precipitation extremes. It does not support the statement in this manuscript “an overall augmentation in annual precipitation played a key role”.
- There are also many cases where the presentation is poorly structured, vague, or inconsistent.
- Line 23-26: The presentations of region names within the parentheses are inconsistent; the full name is shown for some regions, but not shown for other regions.
- Line 27-28: “...notable seasonal variability characterizes the OBB carbon emissions, with marked increases observed in July and August.” Although I understand the meaning of this sentence, it is not well organized. For example, what is the object of comparison when you say ‘marked increase’?
- Line 41-42: “These open burning activities severely impact air quality and ecosystems and exacerbate climate change and air pollution issues.” In this sentence “severely impact air quality” and “exacerbate…air pollution” are basically referring to the same thing.
- Line 46-47: “These fires release substantial amounts of harmful particulate matter and organic pollutants, posing serious threats to air quality and potentially causing health problems”. I don’t understand why this sentence is here. Does it represent the same meaning as the first sentence in this paragraph?
- Line 51: “The burned area method…”. I believe most readers don’t know what the ‘burned area method’ is. A short definition or introduction to this method needs to be presented here.
- Line 52-53: “Shi et al. (2020) estimated OBB emissions in tropical continents from 2001 to 2017 using widely used inventory data, such as the Global Fire Emissions Database (GFED) and the Fire INventory from NCAR (FINN)”. I don’t think Shi et al. (2020) estimated OBB emissions using GFED and FINN, since GFED and FINN are themselves global emissions datasets.
- Line 103: “... exhibited superior judgment accuracy”. What is ‘judgment accuracy’ referring to?
- Line 117-118: “In contrast, satellite data cover the entire globe and provide surface parameters, thereby enabling biomass estimation.” This is a potentially confusing sentence; Ground observations can also “provide surface parameters and enables biomass estimation”.
- Line 126-128: “Global AGB for other years was generated based on the global aboveground and belowground biomass carbon density maps for the 2010 product”. While I now understand the method by reading the SI, the sentence is not very clear in its current form. It’s better to day that in 2010 the Spawn and Gibbs product was used and then say that in other years the AGB was estimated using a scalar based on TC and NDVI. BTW, AGB stands for “above ground biomass”; how did you derive the ‘below ground’ biomass?
- Line 136: “the subsurface condition” should mean the below ground condition, but I suspect that you are referring to ‘surface condition’ here.
- Line 171-172: “the EF for the following seven land types were updated”. It’s not clear to me what original EF data were used and what data were used to replace (update) them.
- Line 178-181: Please combine/simplify these three sentences.
- Line 261: “Cumulatively, these territories represent…”. What are “these territories”. Based on the previous paragraph, they should probably include SHAF and NHAF. But these should be explicitly stated.
- Line 317: “Over the past 3 years”. The ‘past 3 years’ can change depending on the reference year. This kind of description should be more specific.
- Line 427: What are “substrate types”?
- There are other minor issues, including potential errors or typos
- Line 60: “MEIRSI–2” should be “MERSI-2”
- Figure 2: If these geographical regions are the same to that in GFED, you probably need to acknowledge/cite the GFED group/paper.
- Line 269: “intensify both the frequency and frequency of fires in the area”. One ‘frequency’ should be removed or changed to other words.
- Line 280: “in the Tropical Eastern North America (TENA) region”. As shown in Figure 2, TENA should be ‘Temperate North America’.
- Line 435-436: “Although the FY–3D GFR dataset is reliable for most OBB events, its resolution of 1 degree…” Shouldn’t the resolution of FY-3D GFR dataset 1 km?
Citation: https://doi.org/10.5194/essd-2023-527-CC2 -
RC2: 'Comment on essd-2023-527', Anonymous Referee #2, 14 Feb 2024
As Terra and Aqua reach the end of their life cycles, alternative methods are needed to derive global emissions from biomass burning. In this manuscript, Liu et al. used the FY-3D active fire product as the main input to create a new global emissions inventory from open biomass burning. While this study is a valuable contribution to the field, I would not recommend that the manuscript be published on ESSD in its current form. The manuscript requires substantial revision to improve clarity, enhance data/method presentation, and provide a more robust and coherent narrative. In addition, addressing the methodological gaps (between this dataset and previous datasets) and providing more detailed explanations will contribute to the overall credibility of the research. Below, I have listed several major deficiencies in this manuscript.
- The authors claim that “the GFR product, which was integrated with the MERSI–2 instrument, exhibited superior judgment accuracy” (Line 102-103), “Consequently, our inventory yielded accurate assessment results and captured the spatial variation and heterogeneity of minor OBB emissions effectively” (Line 358-359), and “the accuracy of the OBB carbon emissions assessment significantly improved” (Line 417-418). In my opinion, these claims are not sufficiently justified in the manuscript. First, the superiority of MERSI-2 over MODIS in detecting active fires is not well explained. MERSI-2 on FY-3D has higher spatial resolution than MODIS in the visible and NIR bands. However, the active fire algorithm mainly uses the mid-infrared band, where both MERSI-2 and MODIS have a spatial resolution of 1km. The authors cited a number of previous papers (such as Dong et al. 2022 and Chen et al., 2022) to show better fire detection accuracy from MERSI-2 than from MODIS. However, these studies were mostly based on comparisons with limited data samples from manual inspection, and are not very convincing to me. Second, there are many limitations in the algorithm that are not mentioned in the manuscript. For example, this study used AGB as the fuel load, completely ignoring the emissions from soil organic matter burning. The omission error of active fires due to cloud cover/thick smoke is also not quantified. Third, this emissions dataset was derived from FY-3D active fires, but many MODIS products are still needed to generate GEIOBB. The use of MODIS products, which include MOD44B, MODIS NDVI, and MODIS land cover type data, may hinder the effect of quantifying global fire emissions after Terra and Aqua are gone. This potential problem should also be addressed in this paper.
- Many statements are incorrect or lacking scientific support.
- The estimation method and the use of fuel loading (F) are not clearly described. While the authors mention in the manuscript that three data sources, NDVI, TC, and AGB are used for fuel loading, the approach for combining different data streams and forming the fuel loading is embedded in the supporting text only. This formula was presented without any scientific justification or explanation (there are also some errors in the description of this formula, e.g., 2020 should be corrected to 2010).
- The use of emission factors (EF) is also ambiguously described in the manuscript. In section 2.4, the authors simply listed a table of EFs without indicating the specific data sources. Although references to various studies and some locally measured data are cited, the specific methodology employed to construct Table 1 remains undisclosed.
- Line 27-29: “Moreover, notable seasonal variability characterizes the OBB carbon emissions, with marked increases observed in July and August. This surge in carbon emissions is chiefly attributed to fires in the savanna grasslands, woody savanna/shrubs, and tropical forests of SHAF, SHSA, and NHAF.” The peak burning month for NHAF is in boreal winter months. How can the burning in this region contribute to the surge in carbon emissions in July and August?
- Line 166: “EF denotes the amount of pollutants released during burning.” This seems not the correct definition or description of the emission factor (EF).
- Line 181-182: “significant spatial variations in the OBB carbon emissions were observed across Africa, and certain regions in the Americas and Asia.”. How do you define ‘significant’? Based on Figure 1, I think the spatial variations in all continents are big.
- Line 215: “According to GFED”. Which version of GFED data are you using? Please be more specific.
- Line 230: “This suggests relative homogeneity in the NHAF’s biomass–burning emission sources”. I don’t understand how did you get this conclusion based on the previous results “In the NHAF, the predominant source of OBB was savanna grasslands (Roberts et al., 2009), contributing 76.14% to the region’s total biomass–burning carbon emissions, averaging 300.21 Tg/year.”
- Line 233: “...leading to increased OBB and carbon emissions in this region”. In fact in this region (NHAF), the emissions from biomass burning have been decreasing during the past 2 decades.
- Line 257-258: “emissions from SHSA decreased at a rate of 105.22 Tg per year from 2020 to 2022, with peak monthly emissions over the 3 years reaching 184.63, 222.12, and 123.98, respectively, consistent with Griffin et al. (2023)”. Griffin et al. (2023) explored the wildfire CO emissions. But it’s unclear to me which part of your results is “consistent with” with that paper.
- Line 259: “NHAF also exhibited a decreasing trend in annual emissions, … over the 3 years”. 3 years are too short for deriving meaningful trends in annual emissions.
- Line 316-317: “The top three major emitting regions were SHAF, SHSA, and NHAF, which exhibited emission patterns that aligned closely with global emission trends over time”. The comparison between Figure 5 and Figure 6 does not seem to support this conclusion. NHAF emissions have a very different seasonal cycle than SHAF and SHSA. The interannual variability of emissions in these regions is also different.
- Line 379: “However, the use of FY–3D, which captures data at 14:00, was highly effective in capturing such events.” This is also a statement without supporting evidence. Similar to Terra and Aqua, FY-3D also records data twice a day for a given location and cannot detect short-lived fires. The local time difference between FY-3D and Aqua is only 30 minutes (13:30 vs 14:00), which won’t make much difference in the ability to detect agricultural fires.
- There are many citations in this manuscript that do not support the text before the citation. It seems that the authors didn’t really read and try to understand these references, but just made the citation based on some related keywords. Below is a partial list of inappropriate citations I have found. Please carefully double check the citations throughout the manuscript.
- Line 39: (Hussain and Reza, 2023) is not a good citation here; it studied the detrimental impact on global health by general environmental damages, not specifically from open biomass burning. There are many studies in literature about this topic which can be used for citation here.
- Line 40-41: (Estrellan and Iino, 2010) reviewed toxic emissions from open burning. It did not provide evidence for “major fire types worldwide”. So it is also not a good citation.
- Line 42: (Manisalidis et al., 2020) is a review of environmental and health impacts of air pollution. It did not talk about the specific impacts from “open burning activities”.
- Line 44: (Ma et al., 2022) studied wildfires in Amazon during 2019 only. The paper does not support the claim “regions worldwide are experiencing a notable increase in fire incidents”.
- Line 45: (You and Xu, 2023) investigated how delayed wildfires in 2020 promote snowpack melting in the western US. Same as above, this paper does not support the ‘increase in fire accidents’.
- Line 56: (Lv et al., 2020) studies CO2 mixing ratio using satellite observations. They used the GFED dataset for CO2 emissions from biomass burning. This study does not support the previous sentence “Alternatively, a method based on the fire radiative power can effectively enhance the assessment of small fire events, thereby addressing this issue to a certain extent.”
- Line 128: (Spawn and Gibbs, sssss2020). Remove the sssss here.
- Line 255: (Russell-Smith et al., 2021) focus on opportunities and challenges for savanna burning emissions abatement. It did not provide sufficient evidence to support the conclusion “In August, specific meteorological conditions, such as high temperatures and low humidity facilitated the increased combustibility of biomass, resulting in a peak in carbon emissions”.
- Line 297: (Wiggins et al., 2020) presented estimates of fire emissions in the USA using data from the FIREX-AQ mission. It has little connection to the text preceding the citation.
- Line 308: (Thackeray et al., 2022) did study the precipitation change under global warming, but the main topic of this paper was precipitation extremes. It does not support the statement in this manuscript “an overall augmentation in annual precipitation played a key role”.
- There are also many cases where the presentation is poorly structured, vague, or inconsistent.
- Line 23-26: The presentations of region names within the parentheses are inconsistent; the full name is shown for some regions, but not shown for other regions.
- Line 27-28: “...notable seasonal variability characterizes the OBB carbon emissions, with marked increases observed in July and August.” Although I understand the meaning of this sentence, it is not well organized. For example, what is the object of comparison when you say ‘marked increase’?
- Line 41-42: “These open burning activities severely impact air quality and ecosystems and exacerbate climate change and air pollution issues.” In this sentence “severely impact air quality” and “exacerbate…air pollution” are basically referring to the same thing.
- Line 46-47: “These fires release substantial amounts of harmful particulate matter and organic pollutants, posing serious threats to air quality and potentially causing health problems”. I don’t understand why this sentence is here. Does it represent the same meaning as the first sentence in this paragraph?
- Line 51: “The burned area method…”. I believe most readers don’t know what the ‘burned area method’ is. A short definition or introduction to this method needs to be presented here.
- Line 52-53: “Shi et al. (2020) estimated OBB emissions in tropical continents from 2001 to 2017 using widely used inventory data, such as the Global Fire Emissions Database (GFED) and the Fire INventory from NCAR (FINN)”. I don’t think Shi et al. (2020) estimated OBB emissions using GFED and FINN, since GFED and FINN are themselves global emissions datasets.
- Line 103: “... exhibited superior judgment accuracy”. What is ‘judgment accuracy’ referring to?
- Line 117-118: “In contrast, satellite data cover the entire globe and provide surface parameters, thereby enabling biomass estimation.” This is a potentially confusing sentence; Ground observations can also “provide surface parameters and enables biomass estimation”.
- Line 126-128: “Global AGB for other years was generated based on the global aboveground and belowground biomass carbon density maps for the 2010 product”. While I now understand the method by reading the SI, the sentence is not very clear in its current form. It’s better to day that in 2010 the Spawn and Gibbs product was used and then say that in other years the AGB was estimated using a scalar based on TC and NDVI. BTW, AGB stands for “above ground biomass”; how did you derive the ‘below ground’ biomass?
- Line 136: “the subsurface condition” should mean the below ground condition, but I suspect that you are referring to ‘surface condition’ here.
- Line 171-172: “the EF for the following seven land types were updated”. It’s not clear to me what original EF data were used and what data were used to replace (update) them.
- Line 178-181: Please combine/simplify these three sentences.
- Line 261: “Cumulatively, these territories represent…”. What are “these territories”. Based on the previous paragraph, they should probably include SHAF and NHAF. But these should be explicitly stated.
- Line 317: “Over the past 3 years”. The ‘past 3 years’ can change depending on the reference year. This kind of description should be more specific.
- Line 427: What are “substrate types”?
- There are other minor issues, including potential errors or typos
- Line 60: “MEIRSI–2” should be “MERSI-2”
- Figure 2: If these geographical regions are the same to that in GFED, you probably need to acknowledge/cite the GFED group/paper.
- Line 269: “intensify both the frequency and frequency of fires in the area”. One ‘frequency’ should be removed or changed to other words.
- Line 280: “in the Tropical Eastern North America (TENA) region”. As shown in Figure 2, TENA should be ‘Temperate North America’.
- Line 435-436: “Although the FY–3D GFR dataset is reliable for most OBB events, its resolution of 1 degree…” Shouldn’t the resolution of FY-3D GFR dataset 1 km?
Citation: https://doi.org/10.5194/essd-2023-527-RC2 - AC2: 'Reply on RC2', Yang Liu, 28 Feb 2024
Data sets
Global Emissions Inventory from Open Biomass Burning (GEIOBB): Utilizing Fengyun-3D global fire spot monitoring data Yang Liu, Jie Chen, Yusheng Shi, Wei Zheng, Tianchan Shan, and Gang Wang https://doi.org/10.6084/m9.figshare.24793623.v2
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