the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 28 Apr 2025
The role of dust mineral composition in atmospheric radiation and pollution in North China: new insights from EMIT and two-way coupled modeling
Abstract. Mineral dust is a major atmospheric aerosol influencing Earth’s energy balance through aerosol-radiation (ARI) and aerosol-cloud interactions (ACI). While homogeneous dust effects have been studied, the impact of mineralogical composition on regional meteorology and air quality remains underexplored, limiting accurate forecasting of dust storm impacts, especially in dust belt regions. In this study, we used a two-way coupled WRF-CHIMERE model with three mineralogical dust atlases (Nickovic et al. (2012) (N2012), Journet et al. (2014) (J2014), and a new dataset, Li et al. (2024) (L2024), from the Earth Surface Mineral Dust Source Investigation (EMIT)) to evaluate ARI effects during the March 2021 dust storm in North China. Results showed significant spatial variations in radiative forcing due to mineralogical differences. Bulk dust (without considering mineralogy) caused an average shortwave radiative forcing of −5.72 W/m², while mineral-specific forcings increased this by up to +0.10 W/m². Integrating EMIT data reduced PM10 biases by over 15 % in high-concentration regions and improved ozone predictions, with localized changes of −2.46 to +3.52 µg/m³. Hematite’s strong absorption and quartz’s reflective properties were key in altering radiative and air quality outcomes. Compared to scenarios of bulk dust, the consideration of ARI effects of mineralogical compositions can increase PM10 concentration by up to 1189.48 µg/m³ in dust source regions. Future research perspectives on the utilization of high-resolution EMIT data in two-way coupled meteorology and air quality models for investigating the ACI effects of mineralogical dust on cloud microphysics are proposed.
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2025-611', Anonymous Referee #1, 26 Aug 2025
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AC1: 'Reply on RC1', Zhang xuelei, 14 Oct 2025
We would like to express our sincere appreciation to the Reviewer #1 for the valuable and constructive suggestions, which have helped us improve the quality of this manuscript. We have addressed all these comments carefully and revised the manuscript accordingly. Following the Reviewer’ comments in black, please find our point-to-point responses in blue. Hereafter, all new added or modified sentences are marked in blue and italic in this response.
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AC1: 'Reply on RC1', Zhang xuelei, 14 Oct 2025
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RC2: 'Comment on egusphere-2025-611', Anonymous Referee #2, 22 Sep 2025
This manuscript used a 2-way coupled WRF-CHIMERE model to investigate how different mineralogical compositions of dust affect aerosol-radiation and aerosol-cloud interactions (ARI/ACI) and their subsequent air quality outcomes. The model was run on a synoptic scale over North China during a major dust storm in March 2021. The authors observed that using EMIT to enhance the mineralogical details has improved the model predictions of PM10, by revealing significant spatial differences in radioactive forcing and increased PM10 levels in source regions. These results are critical for a better prediction during dust storm periods when the level of PM10 is readily underestimated due to the lack of ARI/ACI consideration. Nevertheless, the manuscript could be improved by addressing the following concerns. I would suggest that paper be reconsidered after a major revision.
Major comments:
- In multiple positions (lines 37-38, 44-45, 72-73), the authors declared the importance of ACI effects on the Earth’s energy balance and can also be altered by the difference in mineral compositions. However, this research also stated that they did not consider ACI effects in their 2-way coupled model but did not clearly explain why this is not included and how it will affect the final predictions. This affected the rationale of adopting this 2-way coupled model and a justification should be better provided.
- In Section 2.1, The authors collected environmental data from various sources: meteorology data from CMA, PM concentrations from an online blog, and SSR data from a peer-reviewed paper. How do the authors ensure the fidelity of the data they obtained, and how do they maintain the integrity of them?
- Lines 134-145, the authors mentioned a lack of feldspar and quartz and the combination of illite and muscovite in EMIT. Their proportions were all estimated based on N2012 or J2014 data. This is ambiguous since there are no details and rationale about which database was chosen. A sensitivity test is suggested to show how different methods of filling and splitting cause the change of results and how the actual methods are selected for each mineral component.
- Lines 207-208, the authors mentioned a huge overestimation of SSR (>60%) from the model. This is an interesting finding, since this overestimation may lead to large errors in dust dispersion and hence change the PM prediction. An attribution of meteorological biases vs. mineralogical composition to the PM10 prediction would help clarify the conclusions of mineralogical effects. Comparing the simulation with the bias and after correcting the bias may provide insights into how much the actual ARI effect accounts for.
- Lines 446-453, this paragraph is not well discussed. By comparing the subfigures in Fig 8, we can see that the PM10 levels predicted using different database show substantial disparities. Suggest reducing the tone of the limited effect of mineral composition to PM10 concentration.
- Overall, this research did not include uncertainties in many of their reported values, such as predicted PM10 levels, changes in PM10 and ozone by including ARI effects, and different PM10 concentrations considering dust mineralogy atlases. It is important to quantify these uncertainties for a study with an improved modeling design, thus statistical measures are suggested to include.
Minor comments:
- Table 1, the last mineral was written as mica while the note mentioned it as muscovite. Although it is known that muscovite is a mica, it is suggested to make their name consistent to avoid confusion.
- Table 3, the digits of values should be uniform: suggest to retain a digit of 2.
- Figure 7, the picture for ozone is dazzling. The max/min/mean values (top left corner) cannot be seen clearly because of the color. Suggest reducing the hue of palette and move the text or change the color of the text.
- Figure 8, N2012_EMIT-bulk dust is exactly the same with J2014_default-Bulk dust. Suggest recheck the pictures.
- Figure 9, the texts in the violet region of quartz are not visible. Suggest changing its color to white. Also, the bars outside the pie look protruding; suggest using arrows to denote minimal values.
Citation: https://doi.org/10.5194/egusphere-2025-611-RC2 -
AC2: 'Reply on RC2', Zhang xuelei, 14 Oct 2025
We would like to express our sincere appreciation to the Reviewer #2 for the valuable and constructive suggestions, which have helped us improve the quality of this manuscript. We have addressed all these comments carefully and revised the manuscript accordingly. Following the Reviewer’ comments in black, please find our point-to-point responses in blue. Hereafter, all new added or modified sentences are marked in blue and italic in this response.
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General comments:
The manuscript titled "The role of dust mineral composition in atmospheric radiation and pollution in North China: new insights from EMIT and two-way coupled modeling" presents a novel and comprehensive investigation of mineral dust impacts using multiple dust atlases and a two-way coupled WRF-CHIMERE model. The integration of EMIT satellite-derived data is particularly innovative and demonstrates significant potential for improving model accuracy in historical dust storm simulations and future forecasting works. Overall, the paper is clearly written and methodologically sound. However, if the following comments are thoroughly addressed within this review process I would suggest publishing this paper in ACP.
Major comments:
While the paper demonstrates the benefit of using EMIT data in methodology, it would be helpful to provide a quantitative assessment of uncertainties introduced by the interpolation and assumptions in EMIT data processing (e.g., feldspar/quartz filling).
The manuscript often mentions ACI (aerosol-cloud interaction), yet the modeling focuses on ARI only. Please clarify this distinction earlier in the Introduction and reduce any ambiguity about what has or has not been included.
The SSR and PM10 comparisons are robust, but more details on the performance metrics (bias, RMSE, etc.) across multiple sites and time periods would strengthen the validation claims.
The influence of mineralogy on PM10 and O3 is clearly demonstrated, but more discussion of the physical mechanisms (e.g., specific reactions, photolysis suppression) would help interpret the observed changes.
The results show that quartz and feldspar dominate dust mass, while hematite dominates radiative effects. This contrast deserves more discussion in both the Results and Conclusion sections.
The model bias discussion (Section 3.1) is helpful but could be deepened by exploring possible reasons for the underestimation of PM10 at high dust sites.
Minor comments:
Line 137: Please specify how missing EMIT data (quartz/feldspar) are estimated — a numeric assumption or spatial filling?
Line 187–198: The bias in SSR is discussed, but no mention is made of possible causes (e.g., aerosol loading or model radiation scheme limitations).
Line 194: The overestimation of SSR and WS10 could be more quantitatively discussed. Is this bias consistent with other dust studies in this region?
Line 213–214: “minimizing the negative biases in T2” — perhaps “reducing the magnitude of negative biases” is clearer.
Line 250: “Positive O3 biases increased” is unclear — do you mean O3 concentrations were overestimated?
Line 305: “−900 W m−2” seems unusually large for surface shortwave cooling. Please double-check this value.
Line 584: Suggest shortening this part of the conclusion and moving satellite technical details into Data/Methods.
Figure 1: Please include a scale bar and clear region names to help interpret mineral distributions.
Figure 2: Consider including error bars or confidence intervals for observed values, “Statatiscal metrices” → should be “Statistical metrics” in its caption.
Figure quality could be improved — e.g., Figures 2 and 7 would benefit from enhanced color contrast and labeled axes for clarity.
Reference format is mostly consistent, but some recent references (e.g., Panta et al., 2023) are missing DOIs.