the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Global high-resolution fire-sourced PM2.5 concentrations for 2000–2023
Abstract. Fires are a significant disturbance in Earth’s systems. Smoke aerosols emitted from fires can cause environmental degradation and climatic perturbations, leading to exacerbated air pollution and posing hazards to public health. However, research on the climatic and health impacts of fire emissions is severely limited by the scarcity of air pollution data directly attributed to these emissions. Here, we develop a global daily fire-sourced PM2.5 concentration ([PM2.5]) dataset at a spatial resolution of 0.25° for the period 2000–2023, using the GEOS-Chem chemical transport model driven with two fire emission inventories, the Global Fire Emissions Database version 4.1 with small fires (GFED4.1s) and the Quick Fire Emission Dataset version 2.5r1 (QFED2.5) . Simulated all-source [PM2.5] are bias-corrected using a machine learning algorithm, which incorporates ground observations from over 9000 monitoring sites worldwide. Then the simulated ratios between fire- and all-source [PM2.5] at individual grids are applied to derive fire-sourced [PM2.5]. Globally, the average fire-sourced [PM2.5] is estimated to be 1.94 μg m-3 with GFED4.1s and 3.74 μg m-3 with QFED2.5. Both datasets show consistent spatial distributions with regional hotspots in central Africa and widespread decreasing trends over most areas. While the mean levels of fire-sourced [PM2.5] are much larger at low latitudes, fire episodes at the boreal regions can cause comparable PM2.5 levels as in the tropics. This dataset serves as a valuable tool for exploring the impacts of fire-related air pollutants on climate, ecosystems, and public health, enabling accurate assessments and supports for decision-making in environmental management and policy.
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Status: open (until 07 Dec 2024)
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RC1: 'Comment on essd-2024-414', Anonymous Referee #1, 18 Nov 2024
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I cannot support the acceptance of this paper at its present form due to the following major concerns:
1) The method and interpretation are very similar (nearly identical) to a recent publication (Xu et al., Nature, s41586-023-06398-6, 2023). There are essentially no new developments after I examined the whole paper, except for the slightly extended time coverage (by including three additional years). If the authors intended to revise the manuscript, they should extensively discuss how and why their method and results are different from the Xu et al. study.
2) There appears to be very limited discussion about uncertainties in the derived datasets. The Zenodo archive only presents absolute concentrations, while no information about the expected error was included in the data or discussed in the paper. Especially considering that the paper presented strong dependence of the fire-induced PM2.5 on the specific fire inventory, what uncertainty envelope do you recommend in each of the dataset? After all, these datasets are expected to be used by the community for various applications, and such information is vital.
3) The paper only provided evaluation of the total PM2.5 using ground-based measurements, which is insufficient and partially reflected by the fact that the GFED- and QFED-derived products both agree well in terms of total PM2.5 while fire-PM2.5 are different systematically. Many recent products of fire-PM2.5 have been developed in North America (e.g., 10.1021/acs.est.2c02934, 10.1038/s41586-023-06522-6). The manuscript should use these critical data sources to inter-compare with the modeled fire fraction and the final estimates of fire-PM2.5.
Other comments:
1) I downloaded one example data, and found that negative values occur in occasional pixels. What are the physical meanings of them?
2) Line 44, fire PM2.5 aerosols can be larger in size than urban PM2.5, see e.g., https://acp.copernicus.org/articles/19/6561/2019/
3) Line 71-81: These uncertainties seem not narrowed in this new dataset compared to the previous studies? Even the Xu et al. 2023 study itself has indicated similar differences in the derived fire-PM2.5 using four inventories. So what new insights/constraints have this work provided?
4) Line 93-94: I do not think computational cost is a major obstacle of machine learning approach.
5) Line 108-110: Is it necessary/critical to do this specifically for China? Many other regions also bear with incomplete time series. If the ML method is very sensitive to the availability of data over 2000-2013 in China, how uncertain are your predictions for e.g., India before ~2010 when observation data is available?
6) Line 117-118: Please provide references of the method to convert AQI to PM2.5.
7) Figure 1b: It appears that log-scale color scheme is needed.
8) Figure 3: It looks abnormal to me that the cross-validation R2 (Panel b) values are stronger than the direct R2 (Panel c) in many years? Also, please do not use "simulated" for ML-corrected PM2.5. Could use "estimated".
9) Figure 6: I do not understand the "green slashes". Why are they so regularly distributed?
Citation: https://doi.org/10.5194/essd-2024-414-RC1
Data sets
GFED&QFED: Fire-sourced PM2.5 concentrations dataset (2018-2022) Yonghang Hu, Chenguang Tian, and Xu Yue https://doi.org/10.5281/zenodo.13380164
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