the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development of high spatial resolution annual emission inventory of greenhouse gases from open straw burning in Northeast China from 2001 to 2020
Abstract. Open straw burning has been widely recognized as a significant source of greenhouse gases (GHGs), posing critical risks to atmospheric integrity and potentially exacerbating global warming. In this study, we proposed a novel method that integrates crop cycle information into extraction and classification of fire spots from open straw burning in Northeast China from 2001 to 2020. By synergizing the extracted fire spots with the modified Fire Radiative Power (FRP) algorithm, we developed high spatial resolution emission inventories of GHGs, including carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). Results showed that the northern Sanjiang Plain, eastern Songnen Plain, and eastern Liao River Plain were areas with high intensity of open straw burning. The number of fire spots was elevated during 2013–2017, accounting for 58.0 % of the total fire spots observed during 2001–2020. The prevalent season for open straw burning shifted from autumn (pre-2016) to spring (post-2016), accompanied by a more dispersed pattern in burning dates. The two-decade cumulative emissions of CO2, CH4, and N2O were quantified at 202 Tg, 568 Gg, and 16.0 Gg, respectively, amounting to 221 Tg of CO2-eq. Significant correlations were identified between GHGs emissions and both straw yields and straw utilization (p < 0.01). The enforcement of straw burning bans since 2018 has played a pivotal role in curbing open straw burning, and reduced fire spots by 50.7 % on annual basis compared to 2013–2017. The novel method proposed in this study considerably enhanced the accuracy in characterizing spatiotemporal distributions of fire spots from open straw burning and quantifying associated pollutants emissions.
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RC1: 'Comment on egusphere-2024-980', Anonymous Referee #1, 03 Jul 2024
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Overall, this is an interesting paper, however, there are some technical issues that need to be addressed to fully support the results and conclusions.
Major Comments:
Section 3.1 and Figure 2 – The authors have not described how the errors were calculated. Furthermore, the justification of some of the coefficients needs to be further detailed and not simply justified by a previous citation. For example, line 185.
Figure 3c – This is a very interesting plot and a unique way to show the fire activity. However, in Spring 2014, there was a very distinct cut-off with a yellow section at the start of the cut-off. This tells me that this could be a data artifact, and the authors should re-download the data to see if there was a disruption with the data download. The science quality MCD14ML data downloaded from the University of Maryland's FUOCO SFTP site is the more robust data and contains the "type" column which the authors should use to filter out any non-vegetation fire activity within the cropland area (see User Guide section 5.5 and others - https://modis-fire.umd.edu/files/MODIS_C6_C6.1_Fire_User_Guide_1.0.pdf).
2001 - 2003 data: The authors should consider only analyzing data from 2003 (or 2004 for a full year) onwards since both Aqua and Terra satellites were available. The lower number of fires in 2001 – 2003 is due to the Terra-only time period. All the statistics (especially the ones related to fire counts) and the trend analysis will be skewed because the first few years have significantly fewer fires purely based on the Terra-only period. Also, there was a 2-week window in August 2020 where MODIS Aqua failed, and therefore, you will be missing ~ 2 weeks' worth of fire counts within that peak burning timeperiod (Section 8.4 - /https://modis-fire.umd.edu/files/MODIS_C61_BA_User_Guide_1.1.pdf). The authors need to add this as a limitation to the text.
Figure 5 and text - All trend analysis will be skewed because of 2001 – 2003 data. Trends should just be considered from 2004 onward since Aqua data was only available from July 2003.
Limitations and caveats: The authors need to add a section outlining the above-mentioned limitations and including the limitations of mapping crop residue burning using current remote sensing technology.
Validation or product intercomparison: The authors need to include a more formal section on validating their results and comparing them against other products. Are there other remote sensing sensors or on-the-ground station data that the authors can use to include a more high-resolution comparison?
Minor Comments:
Line 36: change “elevated” to “evaluated”
Line 61: change "remains to be prevalent” to “remains prevalent”
Line 132: change to “China”
Line 422 – change to “Period”
Figure 3 (and others) - Make a note in the caption that the y-axis is different for each crop or that the scales are different.
Citation: https://doi.org/10.5194/egusphere-2024-980-RC1 -
RC2: 'Comment on egusphere-2024-980', Anonymous Referee #2, 25 Jul 2024
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The manuscript entitled “Development of high spatial resolution annual 2 emission inventory of greenhouse gases from open 3 straw burning in Northeast China from 2001 to 2020” is generally well written but requires major revision before it can be accepted for publication. I have the following major concerns:
Introduction Lines 82-100:
1) The authors selectively represent the literature on fire-count based crop residue burning emission inventories and fail to discuss their shortcomings. Most notably they fail to discuss the work of Lui et al. 2019 and 2020 (Tianjia Liu et al 2019 Res. Commun. 1 011007DOI 10.1088/2515-7620/ab056c , Tianjia Liu et al 2020 Atmospheric Environment X https://doi.org/10.1016/j.aeaoa.2020.100091) which showed relatively high omission error which can be more than 95% of the total ignitions in particular in scenarios where farmers deploy partial burns (row burns or heap burns) instead of burning the full field to avoid fire detection. Even in the case of full burns farmers avoid detection via shift the timing of the burn to avoid the satellite overpasses, or preferably burn when the sky is overcast/very hazy to avoid detection. These papers have also dealt with algorithms to correct for the under detection.
2) The authors fail to discuss the strength and weaknesses of various fire detection satellite products available particularly with respect to crop residue burning fires which in the case of China include a geostationary satellite (Chen et al. 2022 https://doi.org/10.1016/j.atmosenv.2021.118838 , hang, T., de Jong, M. C., Wooster, M. J., Xu, W., and Wang, L.: Trends in eastern China agricultural fire emissions derived from a combination of geostationary (Himawari) and polar (VIIRS) orbiter fire radiative power products, Atmos. Chem. Phys., 20, 10687–10705, https://doi.org/10.5194/acp-20-10687-2020, 2020.). It needs to be noted that the selected product MODIS has the lowest detection efficiency of al all available products, but even the most high resolution product VIIRs still under-detects crop residue fires compared to actual ignitions .
3) The authors also fail to discuss the strategy of developing hybrid inventories (Kumar et al. 2021 https://doi.org/10.1016/j.scitotenv.2021.148064) that use the more accurate bottom-up data from field surveys to estimate the total amount of crop residue burned and the resulting emissions but use the spatio-temporal patterns in fire counts to distribute these emissions in space and time.
Methods:
4) While the authors discuss both VIIRS and MODIS fire counts in their introduction they only use MODIS fire counts in their work. Crop residue burning fires are both small and transient and the relatively large 1 x 1 km footprint of the MODIS satellite leads to high under detection. Shifting from using MODIS towards using VIIRS typically doubles the crop residue burning estimates. For robust estimates the authors need to at the very least use both these satellites and the geostationary satellite and contrast the results (see: Tianjia Liu et al2020 Atmospheric Environment X https://doi.org/10.1016/j.aeaoa.2020.100091 for a case study from India)
Results and Discussions:
5) The authors need to contrast their MODIS based estimates with VIIRS based estimates arrived at using the same methodology. The 5% overestimation due to the wrong inclusion of off-season fire pales in comparison to the likely underestimation that the usage of such a coarse fire product causes. The number of detected crop residue fires more than double with the shift to VIIRS.
6) While comparing their data with existing emission inventories such as FINN the authors use the old MODIS based version of the inventory instead of the current VIIRS base FINNversion of the inventory v2.5 the authors need to shift their comparison to the current version (Wiedinmyer, C., Kimura, Y., McDonald-Buller, E. C., Emmons, L. K., Buchholz, R. R., Tang, W., Seto, K., Joseph, M. B., Barsanti, K. C., Carlton, A. G., and Yokelson, R.: The Fire Inventory from NCAR version 2.5: an updated global fire emissions model for climate and chemistry applications, Geosci. Model Dev., 16, 3873–3891, https://doi.org/10.5194/gmd-16-3873-2023, 2023). It is important to note that FINNv2.5 estimates are significantly higher than FINNv1.5 estimates and that the authors are making their own estimate look better by cherry picking the old version to compare their estimate with.
Also the authors need to compare with other inventories e.g. GFED, and VFEIv0 Ferrada, G. A., Zhou, M., Wang, J., Lyapustin, A., Wang, Y., Freitas, S. R., and Carmichael, G. R.: Introducing the VIIRS-based Fire Emission Inventory version 0 (VFEIv0), Geosci. Model Dev., 15, 8085–8109, https://doi.org/10.5194/gmd-15-8085-2022, 2022
The authors need to compare with other regional estimates e.g. Zhang, T., de Jong, M. C., Wooster, M. J., Xu, W., and Wang, L.: Trends in eastern China agricultural fire emissions derived from a combination of geostationary (Himawari) and polar (VIIRS) orbiter fire radiative power products, Atmos. Chem. Phys., 20, 10687–10705, https://doi.org/10.5194/acp-20-10687-2020, 2020.
7) The authors need to discuss whether increase in detection avoidance strategies (e.g. shifting of preferred hour of the day for burning in comparison to the MODIS /VIIRS overpasses and or the office hours of officials tasked with enforcing the burning ban) played a role in the “reduction” in crop residue burning cases. This can be done by contrasting between the local time of the detected crop residue burns from the geostationary satellite with the overpasses of the other satellites (See Figure 9 Chen et al) such Figures can be drawn for different years to detect time shifts.
8) The authors need to discuss to which degree the shift to smaller fires and/or dispersed fires may have contribute towards the drop in fire counts and whether the drop is due to actual reductions in the scale of the activity or an increasing under detection of crop residue burning fires. Synchronized burns of several neighboring fields within the same satellite footprint are much easier to detect with satellites than small dispersed fires. Is the drop in detected fires actually matched by air quality improvements of the same scale? If there is a shift in the fire detection efficiency then hybrid inventories that combine bottom up estimates of the amount burnt with satellite tools that help distributing the emission with the correct spatio-temporal patterns may actually be superior.
Citation: https://doi.org/10.5194/egusphere-2024-980-RC2
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