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https://doi.org/10.5194/egusphere-2026-3428
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/egusphere-2026-3428
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
29 Jun 2026
| 29 Jun 2026
Status: this preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Evaluating the EPICC-Model for Regional Air Quality Simulation: A Comparative Study with CAMx and CMAQ
Abstract. This study presents a systematic evaluation of China’s independently developed EPICC-Model for regional PM2.5 and MDA8 O3 simulations against established international models, using WRF meteorological fields and a multi-source integrated emission inventory. Results highlight the strengths of the EPICC-Model in several aspects: it achieves relatively high spatial consistency with measurements for PM2.5, with an annual index of agreement (IOA) of 0.80, and accurately captures pollution patterns in heavily polluted North China. It also demonstrates improved performance in simulating summer O3 peaks, reducing maximum biases by more than 20 μg m-3, primarily through enhanced heterogeneous HONO formation and nitrate photolysis pathways that elevate OH concentrations, and it incorporates the CB6r5 mechanism to better represent biogenic VOC oxidation. The model exhibits the highest hit rate (45.6 %) for forecasting moderate PM2.5 and moderate O3 pollution events and successfully reproduces persistent pollution episodes. However, all models share common limitations, including insufficient capability in reproducing heavy pollution episodes, systematic underestimation of SO₄²⁻, and uncertainties in SOA-related OC simulations. Future improvements should focus on refining secondary aerosol chemistry, emission inventories, and boundary layer representations. This study has not only demonstrated the performance of the EPICC-Model against international models but also provides guidance for improving regional and global air quality models.
How to cite. Lou, M., Wu, Q., Wang, W., Chen, H., Cao, K., Fan, X., Liang, D., Yu, F. F., Zhang, J., Wang, W., and Wang, Z.: Evaluating the EPICC-Model for Regional Air Quality Simulation: A Comparative Study with CAMx and CMAQ, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2026-3428, 2026.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
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Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
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- RC1: 'Comment on egusphere-2026-3428', Anonymous Referee #1, 07 Jul 2026 reply
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RC2: 'Comment on egusphere-2026-3428', Anonymous Referee #2, 28 Jul 2026
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This manuscript provides a comparison of simulated regional PM2.5 and MDA8O3 fields generated by the Emission and Atmospheric Processes Integrated and Coupled Community Model (EPICC-Model) with results from the CAMx and CMAQ modeling systems. Operational model evaluation statistics for all three models, benchmarked against a set of surface observations, are presented to facilitate an assessment of their performance across average spatial and temporal scales. Comparison of EPICC performance results alongside those from established and widely used models such as CAMx and CMAQ is a primary motivation for this study. As expected, statistical metrics averaged over broad spatial (domain-wide) and temporal (seasonal) scales reveal comparable performance among the models, which are fundamentally based on similar process representations.
Establishing and documenting this baseline for EPICC performance relative to other models is valuable for prospective users – thus, a manuscript of this nature will be of interest to the journal readership. Equally critical for the adoption and advancement of a modeling system is the explicit demonstration of its unique strengths and a clear articulation of how its formulation and structural features support its intended applications – an aspect of the manuscript that could be improved further. This could be accomplished by enhancing the clarity of diagnostic evaluation for the EPICC model, thereby highlighting the influence of distinct attributes such as process representation, numerical schemes, and the integration with meteorology and emissions, on the simulation of PM2.5 and O3, relative to CAMx and CMAQ. The following comments and suggestions are offered that may help improve the uniqueness and usefulness of the manuscript.
- Throughout the manuscript the EPICC model is referred to as “independently developed”, but it is not apparent to me what that implies? Was the code development independent and if so, independent of what? Is the science in the model not based on other experiences and knowledge accumulated over decades of international research? Looking at the EPICC code, the overall structure of the model bears resemblance to other contemporary systems including CAMx and CMAQ, share similar names for routines, and often also borrows process routines and numerical approaches. Though the individual routines acknowledge derivative work and include some history of the code development, given the similarities between many aspects of the EPICC model structure and code with other existing open-source models, some clarity on what “independently developed” is intended to convey would be useful for the readers.
- L31: “maximum biases” should perhaps be “maximum bias”.
- L40: some indication of what specific aspect of boundary layer representation improvement is suggested would be useful. As written, the statement is too broad and does not convey much.
- L47: “As a typical secondary pollutant, PM2.5 is influenced by primary emissions” is contradictory and awkwardly stated. The sentence should be rewritten to convey that ambient PM2.5 is composed of constituents that have both primary emissions and are secondarily produced.
- L48-49: There are more seminal references that establish these underlying processes regulating PM2.5 accumulation and distributions in the troposphere and should probably be included.
- L53-54: Again, the dependence of O3 formation on NOx and VOC and on meteorological factors has been well established dating back several decades (e.g., Hagen Smit and Fox, 1956) and should be reflected by the citations.
- L54: This statement (use of "despite") implies some dependence on O3 and PM levels which may not always occur because of different trends in their precursors. Perhaps the statement should just convey that in many Chinese urban clusters PM2.5 levels have reduced but MDA8O3 has increased.
- L64-68: References for these models should be provided here.
- L83: Structural differences in context of models is often used to describe differences in model structure - architecture, code, design, and not necessarily process differences. Suggest changing "structural differences" to "differences”.
- Table 1: Though based on Fast-J, Binkowski et al. (2007; https://doi.org/10.1175/JAM2531.1) is perhaps a more appropriate reference for the photolysis approach in CMAQ.
- L139-140: To my knowledge there is nothing that specifies 14 layers for CMAQ or CAMx. Why did the authors also not configure CMAQ and CAMx with the so-called "superior" 20-layer configuration or the default WRF layer structure?
- L142-143: Though not employed in the current study, it should be noted that for consistency, CMAQ applications typically employ WRF fields also simulated using ACM in WRF.
- L151-153: Given that models are continuously evolving, it is important to qualify these statements with the specific version of the models used in the inter-comparison. Though the CMAQ version used here may have been configured with CB6r3, newer versions do include CB6r5 and variants.
- L164-166: Default LBC profiles for CMAQ are only provided to facilitate model setup and initial testing. By definition, the LBCs are dependent on the geographical extent and location of the limited area model application. Thus, it is a bit surprising that the authors did not consider harmonizing the LBCs across the 3 modeling systems. Acknowledgement of this somewhat fundamental difference in configuration setting should be provided.
- L265: Is the Kz_min setting too low relative to other models or what is physically expected - if the latter what is the basis to support the value? What fraction of the time during the January-May period are stable conditions encountered?
- L278-279: This sentence is vague as it covers everything possible and thus not insightful – which process may be the dominant in driving the noted overestimation?
- Figure 1 caption: please clarify if daily PM2.5 implies daily-average PM2.5?
- L296-297: please clarify what “lower background concentrations” imply. Is the background low or is it that ventilation is strong in the summer and thus concentrations are lower?
- L299-301: Based on Figure 1 alone it is difficult to say whether compared to the other two, one model does better in striking better balance between accurately simulating extreme pollution and sustaining strong annual performance, since these are regional average values. Would the same conclusion also hold at individual locations?
- L309: Reduced reaction activity during the cooler months arises from both decreased photolysis as well as lower reaction rates associated with thermal chemistry.
- Do the scatter plots in Figure 2b show the same data as in 2a, i.e., average across all sites or is each point the MDA8O3 at individual observation location?
- L317-319: References should be provided for the heterogeneous HONO formation and nitrate photolysis pathways. To my knowledge, though uncertain, the nitrate photolysis primarily occurs in marine environments (e.g., Ye et al., 2017; Reed et al 2017; Kasibhatla et al., 2018; Shah et al., 2023; Sarwar et al., 2024; Sarwar et al., 2025) or environments impacted by marine air masses, since halide ions are highly surface active and promote the surface propensity of coexisting nitrate anions (DOI: 10.1039/d1ea00087j). How then does it influence inland environments, especially since the large scale O3 in EPICC is expected to be modulated by the MOZART LBCs? Did the MOZART simulations also include nitrate photolysis? How different are the OH concentrations across the 3 models?
- L325: Is the isoprene chemistry representation in cb6r3 and cb6r5 that different to impact the ozone forming potential as speculated? It would be useful to further discuss the differences in the isoprene chemistry leading to the noted O3 differences.
- L328: An overly deep PBL should result in more dilution and systematic low (negative) bias as opposed to positive bias. I am not sure I understand the rationale here?
- L336: “well-controlled absolute bias despite its systematic underestimation” is confusing and somewhat contradictory. If the model is systematically low biased, how can its absolute bias be well controlled?
- L345-346: How does one quantify the better balance between overestimation and underestimation. Based on the analyses presented, it is hard to conclude this in any compelling manner. At best the performance is comparable across 3 models with one model performing better than the other two under certain conditions.
- L404: It is not apparent to me what “multi-centered and scattered distribution pattern” implies? Is there a specific physical process that drives the Springtime distribution? This discussion would benefit from additional clarification.
- L408: Please elaborate on the reasons why coarse vertical resolution reduces mixing? Also, what altitude O3 are the near surface precursors being mixed with? Why does the EPICC layer structure facilitate "better" mixing representation and under what conditions? What prevented CMAQ and CAMx from also being configured with the same layer structure (and implicitly same meteorology fields) as EPICC?
- L414-417: The authors suggest that regional O3 levels and differences between models are primarily controlled by photochemical production. How does one rule out the role of large-scale transport as represented by the differing boundary conditions? Also, given that the layer structure of the native meteorological fields and that of the 3 models is different, and also different amongst the CTMs, I do not see how it can be claimed that identical meteorological fields were used? The starting WRF output is the same but it's translation to the grid structures of the individual CTMs (Table 1) is likely different, resulting in not "identical" meteorological fields driving the CTM calculations.
- L417: Perhaps "nation-wide O3" is better usage than "national O3”.
- L425: What shifts southward - peak values or just relative abundance? What processes drive this seasonal change in distribution and how does the ability of a model to capture it link back to its process formulation?
- L430-431: Can just examination of a single IOA metric be regarded as "comprehensive evaluation”? Suggest replacing “comprehensive evaluation” with just “evaluation”.
- L486: The underestimation of SO4 may also contribute to the overestimation of NO3 assuming NH3 emissions are well constrained.
- L510-511: Since one would expect artificial dilution of emissions over grid cell volumes to likely result in underestimation of concentrations of a primary species, does the BC overestimation imply overestimation of BC emissions or could the representativeness of the monitor locations also influence the noted overestimation?
- L570-571: It is not clear to me what either "consistency in species simulations" or "stable performance of RMSE and NMB" implies? Are the authors suggesting that EPICC simulated PM composition is similar to the other models? Both RMSE and NMB in Figure 7 exhibit some spread across models for several species - it is not apparent to me what stable performance of these metrics implies?
- L576-579: Full references to the documents mentioned here should be provided.
- L582: If heavy and severe AQI are dictated by significant wind-blown dust (WBD) events and if WBD emissions are not included in the models, what is the value of conducting this AQI comparison, since it should be expected that lower accuracy is expected at higher observed AQI. In other words, if the models are not configured for an application, what is the value in "evaluating the applicability" for graded air quality forecasts. Do the measurement locations examined in Figure 6 also report crustal contribution?
- L591-596: This is a potentially useful finding but based on the results presented it is somewhat speculative. Based on comparisons of concentrations of secondary inorganic aerosol constituents by the 3 models for selected such episodes, can the authors demonstrate that EPICC indeed predicts different and more accurate "rapid growth" of secondary inorganic aerosols?
- L696-700: How does one quantify spatial progression from central to south China to assess which model simulated this progression "best". Figure 11 only enables a qualitative and subjective assessment.
- L700: Would be useful to elaborate on how excessively strong aerosol-radiation feedback led to O3 overestimation in southeastern China and on which day?
- L701: Visually while CAMx and CMAQ appear to underestimate relative to the EPICC fields, both CAMx and CMAQ also appear to better capture the gradients in O3 - high O3 at monitors interspersed with monitors with low O3 while EPICC depicts more regionally elevated O3.
- L706-708: In my assessment Figure 11 in its current form does not conclusively support the claim conveyed in this statement that EPICC outperformed the other two models in capturing the spatiotemporal patterns.
- L725-726: I am not sure what "unified WRF fields" implies. Also as noted earlier, given the different layer structures, it is also difficult to ascertain whether the 3D fields are identical or even consistent across models. Thus, probably should be reworded to: "using meteorological fields derived from the same WRF simulation”.
- L731: “seasonal turning point” is vague – sentence should be reworded.
- L745-746: No comparisons with observed boundary layer heights were presented to support the statement that ACM2/YSU schemes underestimate mixing layer heights. The statement should be adequately qualified.
- L800: It is stated that this work “provides a clear pathway for improving next generation of atmospheric CTMs”. I feel this is a somewhat lofty claim as no new or specific process or structural deficiency was identified. There may certainly be specific enhancements identified for the EPICC system, but I am not sure the analysis identified a “clear path” for next generation of models. Suggest deleting this sentence as the study and results do not substantiate it.
- L802-804: In my assessment this study at best provides a comparison of operational evaluation metrics of 3 models. There is no specific analysis presented to support air pollution control or policy making. Thus, I feel this statement should be reworded or deleted.
Citation: https://doi.org/10.5194/egusphere-2026-3428-RC2
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Mengjie Lou
Institute of Earth System Science, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
Institute of Earth System Science, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
Wending Wang
CORRESPONDING AUTHOR
State Key Laboratory of Atmospheric Environment and Extreme Meteorology, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
University of Chinese Academy of Sciences, Beijing, 100049, China
Huansheng Chen
State Key Laboratory of Atmospheric Environment and Extreme Meteorology, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
University of Chinese Academy of Sciences, Beijing, 100049, China
Kai Cao
State Key Laboratory of Atmospheric Environment and Extreme Meteorology, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
Xiaohan Fan
3Clear Technology Co., Ltd., Beijing, 100029, China
Dingyue Liang
3Clear Technology Co., Ltd., Beijing, 100029, China
Fen Fen Yu
3Clear Technology Co., Ltd., Beijing, 100029, China
Jiating Zhang
Institute of Earth System Science, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
Wei Wang
China National Environmental Monitoring Centre, Beijing, 100012, China
Zifa Wang
State Key Laboratory of Atmospheric Environment and Extreme Meteorology, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
University of Chinese Academy of Sciences, Beijing, 100049, China
Short summary
This study compares the model performance of the Emission and atmospheric Processes Integrated and Coupled Community Model with the two air quality models in China. The EPICC-Model excels in simulating summer ozone peaks, accurately captures pollution characteristics in highly polluted areas, and better reproduces persistent compound pollution processes. The study reveals common issues among the models and directions for improvement, providing a basis for optimizing global air quality models.
This study compares the model performance of the Emission and atmospheric Processes Integrated...
Please see attached review.