Articles | Volume 19, issue 19
https://doi.org/10.5194/gmd-19-9651-2026
© 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/gmd-19-9651-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
12 Oct 2026
Model description paper |
| 12 Oct 2026
| 12 Oct 2026
Evaluating the EPICC-Model for regional air quality simulation: a comparative study with CAMx and CMAQ
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
Fenfen 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
Related authors
Mengjie Lou, Qizhong Wu, Yongli Wang, Baogang Zhang, Jinhua Tao, Pu Gan, Huaqiong Cheng, Jiating Zhang, Jiahuan He, and Meng Fan
EGUsphere, https://doi.org/10.5194/egusphere-2026-3195, https://doi.org/10.5194/egusphere-2026-3195, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Climate change worsens wildfires, but fire drivers are unclear and models run slowly. Simulating a 2023 Inner Mongolia grassland fire, we tested how weather and land cover affect simulation accuracy and found that wind controls burned area and spread distance, humidity drives spread rate and flame length, while fuel depth dominates burned area. In addition, computational optimizations cut runtime by 30%. These findings deepen understanding of fire mechanisms and improve model emergency response.
Nuohang Liu, Baozhu Ge, Danhui Xu, Nan Yang, Xueshun Chen, Qixin Tan, and Zifa Wang
EGUsphere, https://doi.org/10.5194/egusphere-2026-4057, https://doi.org/10.5194/egusphere-2026-4057, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
A physically-based below-cloud wet scavenging parameterization was developed and incorporated in the Nested Air Quality Prediction Model System. The scheme substantially improves simulations of nitrogen wet deposition across East Asia, particularly in polluted regions of China, by better capturing below-cloud scavenging coefficients and separating in-cloud and below-cloud processes, supporting more reliable assessments of pollutant transport and deposition.
Lianfang Wei, Xueshun Chen, Wenyi Yang, Zhe Wang, Jie Li, Di Liu, Huiyun Du, Xiaole Pan, Yafang Cheng, Pingqing Fu, and Zifa Wang
Atmos. Chem. Phys., 26, 11667–11682, https://doi.org/10.5194/acp-26-11667-2026, https://doi.org/10.5194/acp-26-11667-2026, 2026
Short summary
Short summary
Current models cannot track sulfur isotope changes during atmospheric transformations. We developed a new model that simulates these processes. It reproduces the isotope fractionation patterns, capturing both spatial and seasonal variations. This validation shows sulfate isotope signatures often resemble those of fuels despite chemical processing. Our results suggest combustion alters sulfur isotope ratios, counterbalancing chemical effects.
Mengjie Lou, Qizhong Wu, Yongli Wang, Baogang Zhang, Jinhua Tao, Pu Gan, Huaqiong Cheng, Jiating Zhang, Jiahuan He, and Meng Fan
EGUsphere, https://doi.org/10.5194/egusphere-2026-3195, https://doi.org/10.5194/egusphere-2026-3195, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Climate change worsens wildfires, but fire drivers are unclear and models run slowly. Simulating a 2023 Inner Mongolia grassland fire, we tested how weather and land cover affect simulation accuracy and found that wind controls burned area and spread distance, humidity drives spread rate and flame length, while fuel depth dominates burned area. In addition, computational optimizations cut runtime by 30%. These findings deepen understanding of fire mechanisms and improve model emergency response.
Yi Zhang, Wei Zhou, Weiqi Xu, Yan Li, Yu Zhang, Zijun Zhang, Bojiang Su, Ning Zhang, Eleonora Aruffo, Junfeng Wang, Piero Di Carlo, Lanzhong Liu, Xiaole Pan, Zifa Wang, Douglas Worsnop, and Yele Sun
EGUsphere, https://doi.org/10.5194/egusphere-2026-3643, https://doi.org/10.5194/egusphere-2026-3643, 2026
Short summary
Short summary
We tracked OOMs that form haze and clouds at a forested mountain in southeastern China. Warm weather boosted natural emissions from plants, making them the primary source of these compounds. Conversely, cooler weather amplified the impact of pollution blowing in from other regions. Clouds also played a major role by washing away certain chemicals while transforming others. These interactions between weather, nature, and pollution offer crucial insights for improving climate models.
Kai Cao, Qizhong Wu, Xiao Tang, Jinxi Li, Xueshun Chen, Huansheng Chen, Wending Wang, Huangjian Wu, Lei Kong, Jie Li, Jiang Zhu, and Zifa Wang
EGUsphere, https://doi.org/10.5194/egusphere-2026-3368, https://doi.org/10.5194/egusphere-2026-3368, 2026
Short summary
Short summary
This study achieves significant acceleration by developing an optimized advection module for Emission and atmospheric Processes Integrated and Coupled Community Model on Graphics Processing Unit. Through implementing thread-block indexing, minimizing Central Processing Unit and Graphics Processing Unit communication, and an parallel framework, we demonstrate speedups: 556.5× faster offline performance for the Heterogeneous Interface advection solver and 20.5× acceleration in coupled simulations.
Fei Zhang, Xingcai Li, Zifa Wang, Yunyun Wen, Xuyang Zhou, Zichen Wu, Zhuoran Wang, Huansheng Chen, Zhe Wang, and Xueshun Chen
Geosci. Model Dev., 19, 4999–5017, https://doi.org/10.5194/gmd-19-4999-2026, https://doi.org/10.5194/gmd-19-4999-2026, 2026
Short summary
Short summary
Solar power generation depends on weather conditions and photovoltaic modules, making accurate forecasts crucial for reliable grid operation. We combined weather prediction and artificial intelligence to improve the solar power prediction at different time scales for a plant. By improving sunlight predictions and incorporating physical constraints into the model, our approach reduced errors significantly. This can help integrate clean energy into power grids safely and efficiently.
Hongyi Li, Ting Yang, Lei Kong, Di Zhang, Guigang Tang, Xiao Tang, and Zifa Wang
Geosci. Model Dev., 19, 4835–4856, https://doi.org/10.5194/gmd-19-4835-2026, https://doi.org/10.5194/gmd-19-4835-2026, 2026
Short summary
Short summary
We develop a novel data assimilation system by integrating incremental machine learning with a localized ensemble Kalman filter to enhance the simulation and reanalysis of particulate matter < 2.5 µm chemical components. Compared to traditional chemical transport model-based data assimilation, our assimilation system has superior computational efficiency and simulation improvements. Comparisons with independent observations and reanalysis datasets validate the robust performance of our system.
Xiaofei Wu, Siyang Chen, Jinxi Li, Yu Zhang, Zifa Wang, Pu Gan, Jie Zheng, and Fangxin Fang
EGUsphere, https://doi.org/10.5194/egusphere-2026-1685, https://doi.org/10.5194/egusphere-2026-1685, 2026
Short summary
Short summary
Cities face a major challenge in tracking how wind and air pollution move through complex building clusters. Common numerical models often struggle to balance accuracy with calculating speed. We developed a new simulation system that automatically adjusts its focus to where the air is moving rapidly. By testing this against observations, it significantly improves predictions of wind and pollution. This tool helps urban planners design healthier cities by better identifying how pollutants travel.
Yutong Tian, Ting Yang, Zifa Wang, Linghan Zeng, Yining Tan, Weichun Liang, Qingqing Xia, Tong Wang, and Shitian Kou
EGUsphere, https://doi.org/10.5194/egusphere-2026-2017, https://doi.org/10.5194/egusphere-2026-2017, 2026
Preprint archived
Short summary
Short summary
This study introduces a novel algorithm using multiwavelength lidar to classify and quantify tropospheric aerosols at a mountain observatory. Using seven optical parameters, it separates smoke, pollution, pollen, and dust in complex mixtures. Validated across multiple platforms, the method decouples meteorologically driven optical enhancement from actual mass fluctuations, enabling high-precision aerosol typing and mass retrieval in high-altitude environments.
Yi Zhang, Weiqi Xu, Yan Li, Guohua Zhang, Dantong Liu, Ye Kuang, Yu Zhang, Wei Zhou, Xiaocong Peng, Bojiang Su, Weihong Huang, Zijun Zhang, Liu Yang, Yangzhou Wu, Siyuan Li, Shitong Zhao, Lanzhong Liu, Xiaole Pan, Zifa Wang, Xinhui Bi, Mikael Ehn, Douglas R. Worsnop, and Yele Sun
Atmos. Chem. Phys., 26, 5617–5634, https://doi.org/10.5194/acp-26-5617-2026, https://doi.org/10.5194/acp-26-5617-2026, 2026
Short summary
Short summary
This study explores how clouds influence the chemical composition of air particles through field research at a high-altitude station in southeastern China across different seasons. We found that different cloud types cause varying degrees of chemical changes in these particles. These findings enhance our understanding of the impact of clouds on air quality and contribute to improving climate models.
Hongyi Li, Ting Yang, Yele Sun, and Zifa Wang
Atmos. Meas. Tech., 19, 2225–2244, https://doi.org/10.5194/amt-19-2225-2026, https://doi.org/10.5194/amt-19-2225-2026, 2026
Short summary
Short summary
We develop a physics-constrained deep-learning framework to retrieve vertical profiles of particulate matter <2.5 µm chemical components from lidar measurements. Comparisons with surface, tower, and aircraft measurements validate the performance of the framework. A six-year vertical profile dataset generated for Beijing reveals that organic matter and nitrate are dominant chemical components. This work offers a new perspective on the lidar inversion of chemical component profiles.
Wei Zhou, Liu Yang, Siqi Zeng, Yunping Kan, Lirong Yang, Weihong Zhang, Weijie Wang, Zijun Zhang, Yan Li, Weiqi Xu, Yucheng Gu, Yaozong Wang, Zhengyan Zuo, Jie Li, Zifa Wang, and Yele Sun
Atmos. Chem. Phys., 26, 2425–2441, https://doi.org/10.5194/acp-26-2425-2026, https://doi.org/10.5194/acp-26-2425-2026, 2026
Short summary
Short summary
Northwest China, situated in an arid and semi-arid climate region; air quality issues in this area have received less attention compared to other Chinese metropolitan clusters. This research identify a significant shift towards the coupling PM2.5 and O3 relationships over the past decade in northwest China, highlighting the great importance of urban terpenes and aromatic oxidation in secondary organic aerosol formation.
Weibin Zhu, Sai Shang, Jieqi Wang, Yunfei Wu, Zhaoze Deng, Liang Ran, Ye Kuang, Guiqian Tang, Xiangpeng Huang, Xiaole Pan, Lanzhong Liu, Weiqi Xu, Yele Sun, Bo Hu, Zifa Wang, and Zirui Liu
Atmos. Chem. Phys., 26, 1947–1965, https://doi.org/10.5194/acp-26-1947-2026, https://doi.org/10.5194/acp-26-1947-2026, 2026
Short summary
Short summary
NPF (new particle formation) is a key global CCN (cloud condensation nuclei) source, but its contribution at the polluted boundary-layer top remains unclear. Based on mountaintop observations in the Yangtze River Delta, we show that under polluted conditions, NPF at the boundary-layer top is enhanced and accelerates its conversion to CCN. Ammonia plays a key role, and a newly defined "Time Window" metric highlights the importance of oxidation-driven growth and regional transport in this process.
Yutong Tian, Ting Yang, Hongyi Li, Ping Tian, Yifan Song, Jiancun He, Yining Tan, Yele Sun, and Zifa Wang
Atmos. Chem. Phys., 25, 17581–17594, https://doi.org/10.5194/acp-25-17581-2025, https://doi.org/10.5194/acp-25-17581-2025, 2025
Short summary
Short summary
This study examines how nitrate pollution varies with height and season to combat urban haze. Nitrate levels peak near the ground in spring/winter due to humidity and temperature, while wind and sunlight drive high-altitude pollution in late autumn. Winter shows unique daytime peaks linked to sunlight and nighttime chemistry. Findings help cities design targeted strategies, like timing emissions cuts, to improve air quality by addressing pollution at specific heights and times.
Mengjie Lou, Qizhong Wu, Wending Wang, Huansheng Chen, Kai Cao, Xiaohan Fan, Dingyue Liang, Fenfen Yu, Jiating Zhang, Wei Wang, and Zifa Wang
EGUsphere, https://doi.org/10.5194/egusphere-2025-4441, https://doi.org/10.5194/egusphere-2025-4441, 2025
Preprint archived
Short summary
Short summary
This study compares the performance of the independently developed EPICC-Model with CAMx and CMAQ in simulating PM2.5 and O3 in China. It finds that EPICC-Model excels in simulating summer ozone peaks, accurately captures pollution characteristics in highly polluted areas, and better reproduces persistent compound pollution processes. Furthermore, this study reveals common issues among the models and directions for improvement, providing a basis for optimizing global air quality models.
Kai Cao, Qizhong Wu, Xiao Tang, Jinxi Li, Xueshun Chen, Huansheng Chen, Wending Wang, Huangjian Wu, Lei Kong, Jie Li, Jiang Zhu, and Zifa Wang
EGUsphere, https://doi.org/10.5194/egusphere-2025-2918, https://doi.org/10.5194/egusphere-2025-2918, 2025
Preprint archived
Short summary
Short summary
This study achieves significant acceleration by developing an optimized advection module for Emission and atmospheric Processes Integrated and Coupled Community Model on GPU-like accelerators. Through implementing thread-block coordinated indexing, minimizing CPU-GPU communication, and an hybrid parallelization framework, we demonstrate prominent speedups: 556.5× faster offline performance for the Heterogeneous Interface PPM solver and 20.5× acceleration in coupled simulations.
Xi Chen, Ke Li, Ting Yang, Xipeng Jin, Lei Chen, Yang Yang, Shuman Zhao, Bo Hu, Bin Zhu, Zifa Wang, and Hong Liao
Atmos. Chem. Phys., 25, 9151–9168, https://doi.org/10.5194/acp-25-9151-2025, https://doi.org/10.5194/acp-25-9151-2025, 2025
Short summary
Short summary
Aerosol vertical distribution that plays a crucial role in aerosol–photolysis interaction (API) remains underrepresented in chemical models. We integrated lidar and radiosonde observations to constrain the simulated aerosol profiles over North China and quantified the photochemical responses. The increased photolysis rates in the lower layers led to increased ozone and accounted for a 36 %–56 % reduction in API effects, resulting in enhanced atmospheric oxidizing capacity and aerosol formation.
Huiyun Du, Jie Li, Xueshun Chen, Gabriele Curci, Fangqun Yu, Yele Sun, Xu Dao, Song Guo, Zhe Wang, Wenyi Yang, Lianfang Wei, and Zifa Wang
Atmos. Chem. Phys., 25, 5665–5681, https://doi.org/10.5194/acp-25-5665-2025, https://doi.org/10.5194/acp-25-5665-2025, 2025
Short summary
Short summary
Inadequate consideration of mixing states and coatings on black carbon (BC) hinders aerosol radiation forcing quantification. Core–shell mixing aligns well with observations, but partial internal mixing is a more realistic representation. We used a microphysics module to determine the fraction of embedded BC and coating aerosols, constraining the mixing state. This reduced absorption enhancement by 30 %–43 % in northern China, offering insights into BC's radiative effects.
Jiayi Lai, Lanning Wang, Qizhong Wu, Yizhou Yang, and Fang Wang
Geosci. Model Dev., 18, 1089–1102, https://doi.org/10.5194/gmd-18-1089-2025, https://doi.org/10.5194/gmd-18-1089-2025, 2025
Short summary
Short summary
High-performance computing limitations often hinder numerical model development. Traditional models use double precision for accuracy, which is computationally expensive. Lower precision reduces costs but can introduce errors. The quasi-double-precision (QDP) algorithm helps mitigate these errors. This study applies the QDP algorithm to the Model for Prediction Across Scales – Atmosphere, showing reduced errors and computational time, making it an efficient solution for large-scale simulations.
Zichen Wu, Xueshun Chen, Zifa Wang, Huansheng Chen, Zhe Wang, Qing Mu, Lin Wu, Wending Wang, Xiao Tang, Jie Li, Ying Li, Qizhong Wu, Yang Wang, Zhiyin Zou, and Zijian Jiang
Geosci. Model Dev., 17, 8885–8907, https://doi.org/10.5194/gmd-17-8885-2024, https://doi.org/10.5194/gmd-17-8885-2024, 2024
Short summary
Short summary
We developed a model to simulate polycyclic aromatic hydrocarbons (PAHs) from global to regional scales. The model can reproduce PAH distribution well. The concentration of BaP (indicator species for PAHs) could exceed the target values of 1 ng m-3 over some areas (e.g., in central Europe, India, and eastern China). The change in BaP is lower than that in PM2.5 from 2013 to 2018. China still faces significant potential health risks posed by BaP although the Action Plan has been implemented.
Hongyi Li, Ting Yang, Lars Nerger, Dawei Zhang, Di Zhang, Guigang Tang, Haibo Wang, Yele Sun, Pingqing Fu, Hang Su, and Zifa Wang
Geosci. Model Dev., 17, 8495–8519, https://doi.org/10.5194/gmd-17-8495-2024, https://doi.org/10.5194/gmd-17-8495-2024, 2024
Short summary
Short summary
To accurately characterize the spatiotemporal distribution of particulate matter <2.5 µm chemical components, we developed the Nested Air Quality Prediction Model System with the Parallel Data Assimilation Framework (NAQPMS-PDAF) v2.0 for chemical components with non-Gaussian and nonlinear properties. NAQPMS-PDAF v2.0 has better computing efficiency, excels when used with a small ensemble size, and can significantly improve the simulation performance of chemical components.
Lei Kong, Xiao Tang, Zifa Wang, Jiang Zhu, Jianjun Li, Huangjian Wu, Qizhong Wu, Huansheng Chen, Lili Zhu, Wei Wang, Bing Liu, Qian Wang, Duohong Chen, Yuepeng Pan, Jie Li, Lin Wu, and Gregory R. Carmichael
Earth Syst. Sci. Data, 16, 4351–4387, https://doi.org/10.5194/essd-16-4351-2024, https://doi.org/10.5194/essd-16-4351-2024, 2024
Short summary
Short summary
A new long-term inversed emission inventory for Chinese air quality (CAQIEI) is developed in this study, which contains constrained monthly emissions of NOx, SO2, CO, PM2.5, PM10, and NMVOCs in China from 2013 to 2020 with a horizontal resolution of 15 km. Emissions of different air pollutants and their changes during 2013–2020 were investigated and compared with previous emission inventories, which sheds new light on the complex variations of air pollutant emissions in China.
Kai Cao, Qizhong Wu, Lingling Wang, Hengliang Guo, Nan Wang, Huaqiong Cheng, Xiao Tang, Dongxing Li, Lina Liu, Dongqing Li, Hao Wu, and Lanning Wang
Geosci. Model Dev., 17, 6887–6901, https://doi.org/10.5194/gmd-17-6887-2024, https://doi.org/10.5194/gmd-17-6887-2024, 2024
Short summary
Short summary
AMD’s heterogeneous-compute interface for portability was implemented to port the piecewise parabolic method solver from NVIDIA GPUs to China's GPU-like accelerators. The results show that the larger the model scale, the more acceleration effect on the GPU-like accelerator, up to 28.9 times. The multi-level parallelism achieves a speedup of 32.7 times on the heterogeneous cluster. By comparing the results, the GPU-like accelerators have more accuracy for the geoscience numerical models.
Zijun Zhang, Weiqi Xu, Yi Zhang, Wei Zhou, Xiangyu Xu, Aodong Du, Yinzhou Zhang, Hongqin Qiao, Ye Kuang, Xiaole Pan, Zifa Wang, Xueling Cheng, Lanzhong Liu, Qingyan Fu, Douglas R. Worsnop, Jie Li, and Yele Sun
Atmos. Chem. Phys., 24, 8473–8488, https://doi.org/10.5194/acp-24-8473-2024, https://doi.org/10.5194/acp-24-8473-2024, 2024
Short summary
Short summary
We investigated aerosol composition and sources and the interaction between secondary organic aerosol (SOA) and clouds at a regional mountain site in southeastern China. Clouds efficiently scavenge more oxidized SOA; however, cloud evaporation leads to the production of less oxidized SOA. The unexpectedly high presence of nitrate in aerosol particles indicates that nitrate formed in polluted areas has undergone interactions with clouds, significantly influencing the regional background site.
Zehua Bai, Qizhong Wu, Kai Cao, Yiming Sun, and Huaqiong Cheng
Geosci. Model Dev., 17, 4383–4399, https://doi.org/10.5194/gmd-17-4383-2024, https://doi.org/10.5194/gmd-17-4383-2024, 2024
Short summary
Short summary
There is relatively limited research on the application of scientific computing on RISC CPU platforms. The MIPS architecture CPUs, a type of RISC CPUs, have distinct advantages in energy efficiency and scalability. The air quality modeling system can run stably on the MIPS and LoongArch platforms, and the experiment results verify the stability of scientific computing on the platforms. The work provides a technical foundation for the scientific application based on MIPS and LoongArch.
Zhiqiang Zhang, Ying Li, Haiyan Ran, Junling An, Yu Qu, Wei Zhou, Weiqi Xu, Weiwei Hu, Hongbin Xie, Zifa Wang, Yele Sun, and Manabu Shiraiwa
Atmos. Chem. Phys., 24, 4809–4826, https://doi.org/10.5194/acp-24-4809-2024, https://doi.org/10.5194/acp-24-4809-2024, 2024
Short summary
Short summary
Secondary organic aerosols (SOAs) can exist in liquid, semi-solid, or amorphous solid states, which are rarely accounted for in current chemical transport models. We predict the phase state of SOA particles over China and find that in northwestern China SOA particles are mostly highly viscous or glassy solid. Our results indicate that the particle phase state should be considered in SOA formation in chemical transport models for more accurate prediction of SOA mass concentrations.
Yaqi Wang, Lanning Wang, Juan Feng, Zhenya Song, Qizhong Wu, and Huaqiong Cheng
Geosci. Model Dev., 16, 6857–6873, https://doi.org/10.5194/gmd-16-6857-2023, https://doi.org/10.5194/gmd-16-6857-2023, 2023
Short summary
Short summary
In this study, to noticeably improve precipitation simulation in steep mountains, we propose a sub-grid parameterization scheme for the topographic vertical motion in CAM5-SE to revise the original vertical velocity by adding the topographic vertical motion. The dynamic lifting effect of topography is extended from the lowest layer to multiple layers, thus improving the positive deviations of precipitation simulation in high-altitude regions and negative deviations in low-altitude regions.
Aodong Du, Jiaxing Sun, Hang Liu, Weiqi Xu, Wei Zhou, Yuting Zhang, Lei Li, Xubing Du, Yan Li, Xiaole Pan, Zifa Wang, and Yele Sun
Atmos. Chem. Phys., 23, 13597–13611, https://doi.org/10.5194/acp-23-13597-2023, https://doi.org/10.5194/acp-23-13597-2023, 2023
Short summary
Short summary
We characterized the impacts of emission controls on particle mixing state and density during the Beijing Olympic Winter Games using a SPAMS in tandem with a DMA and an AAC. OC and sulfate-containing particles increased, while those from primary emissions decreased. The effective particle densities increased and varied largely for different particles, highlighting the impacts of aging and formation processes on the changes of particle density and mixing state.
Tao Wang, Hang Liu, Jie Li, Shuai Wang, Youngseob Kim, Yele Sun, Wenyi Yang, Huiyun Du, Zhe Wang, and Zifa Wang
Geosci. Model Dev., 16, 5585–5599, https://doi.org/10.5194/gmd-16-5585-2023, https://doi.org/10.5194/gmd-16-5585-2023, 2023
Short summary
Short summary
This paper developed a two-way coupled module in a new version of a regional urban–street network model, IAQMS-street v2.0, in which the mass flux from streets to background is considered. Test cases are defined to evaluate the performance of IAQMS-street v2.0 in Beijing by comparing it with that simulated by IAQMS-street v1.0 and a regional model. The contribution of local emissions and the influence of on-road vehicle control measures on air quality are evaluated by using IAQMS-street v2.0.
Xi Chen, Ting Yang, Zifa Wang, Futing Wang, and Haibo Wang
Atmos. Meas. Tech., 16, 4289–4302, https://doi.org/10.5194/amt-16-4289-2023, https://doi.org/10.5194/amt-16-4289-2023, 2023
Short summary
Short summary
Uncertainties remain great in the planetary boundary layer height (PBLH) determination from radiosonde, especially during the transition period of different PBL regimes. We combine seven existing methods along with statistical modification on gradient-based methods. We find that the ensemble method can eliminate the overestimation of PBLH and reduce the inconsistency between individual methods. The ensemble method improves the effectiveness of PBLH determination to 62.6 %.
Kai Cao, Qizhong Wu, Lingling Wang, Nan Wang, Huaqiong Cheng, Xiao Tang, Dongqing Li, and Lanning Wang
Geosci. Model Dev., 16, 4367–4383, https://doi.org/10.5194/gmd-16-4367-2023, https://doi.org/10.5194/gmd-16-4367-2023, 2023
Short summary
Short summary
Offline performance experiment results show that the GPU-HADVPPM on a V100 GPU can achieve up to 1113.6 × speedups to its original version on an E5-2682 v4 CPU. A series of optimization measures are taken, and the CAMx-CUDA model improves the computing efficiency by 128.4 × on a single V100 GPU card. A parallel architecture with an MPI plus CUDA hybrid paradigm is presented, and it can achieve up to 4.5 × speedup when launching eight CPU cores and eight GPU cards.
Lichao Yang, Wansuo Duan, and Zifa Wang
Geosci. Model Dev., 16, 3827–3848, https://doi.org/10.5194/gmd-16-3827-2023, https://doi.org/10.5194/gmd-16-3827-2023, 2023
Short summary
Short summary
An approach is proposed to refine a ground meteorological observation network to improve the PM2.5 forecasts in the Beijing–Tianjin–Hebei region. A cost-effective observation network is obtained and makes the relevant PM2.5 forecasts assimilate fewer observations but achieve the forecasting skill comparable to or higher than that obtained by assimilating all ground station observations, suggesting that many of the current ground stations can be greatly scattered to avoid much unnecessary work.
Jinming Feng, Meng Luo, Jun Wang, Yuan Qiu, Qizhong Wu, and Ke Wang
EGUsphere, https://doi.org/10.5194/egusphere-2023-867, https://doi.org/10.5194/egusphere-2023-867, 2023
Preprint withdrawn
Short summary
Short summary
We modified the code of the Weather Research and Forecasting Model (WRF) v3.8.1 to include the forcing components more than the Greenhouse Gases and evaluate the impact of forcing configurations on the climate simulation results in China. It showed that different external forcing configurations in WRF could result in considerable impact on the annual temperature and precipitation trend, which was stronger than parameterization schemes but was weaker than spectral nudging.
Hang Liu, Xiaole Pan, Shandong Lei, Yuting Zhang, Aodong Du, Weijie Yao, Guiqian Tang, Tao Wang, Jinyuan Xin, Jie Li, Yele Sun, Junji Cao, and Zifa Wang
Atmos. Chem. Phys., 23, 7225–7239, https://doi.org/10.5194/acp-23-7225-2023, https://doi.org/10.5194/acp-23-7225-2023, 2023
Short summary
Short summary
We provide the average vertical profiles of black carbon (BC) concentration, size distribution and coating thickness at different times of the day in an urban area based on 112 vertical profiles. In addition, it is found that BC in the residual layer generally has a thicker coating, higher absorption enhancement and hygroscopicity than on the surface. Such aged BC could enter into the boundary layer and influence the BC properties in the early morning.
Wen Lu, Bin Zhu, Shuqi Yan, Jie Li, and Zifa Wang
EGUsphere, https://doi.org/10.5194/egusphere-2023-1089, https://doi.org/10.5194/egusphere-2023-1089, 2023
Preprint archived
Short summary
Short summary
Parameterized the minimum turbulent diffusivity (Kzmin) by sensible heat flux and latent heat flux and embedded it into the WRF-Chem model. New scheme improved the underestimation of turbulence diffusion underestimation and overestimation of surface PM2.5 under stable boundary layer simulation over eastern China. The physical relationship between Kzmin and two factors was discussed. Process analysis showed that vertical mixing is the key process to improve surface PM2.5 simulations.
Lei Kong, Xiao Tang, Jiang Zhu, Zifa Wang, Yele Sun, Pingqing Fu, Meng Gao, Huangjian Wu, Miaomiao Lu, Qian Wu, Shuyuan Huang, Wenxuan Sui, Jie Li, Xiaole Pan, Lin Wu, Hajime Akimoto, and Gregory R. Carmichael
Atmos. Chem. Phys., 23, 6217–6240, https://doi.org/10.5194/acp-23-6217-2023, https://doi.org/10.5194/acp-23-6217-2023, 2023
Short summary
Short summary
A multi-air-pollutant inversion system has been developed in this study to estimate emission changes in China during COVID-19 lockdown. The results demonstrate that the lockdown is largely a nationwide road traffic control measure with NOx emissions decreasing by ~40 %. Emissions of other species only decreased by ~10 % due to smaller effects of lockdown on other sectors. Assessment results further indicate that the lockdown only had limited effects on the control of PM2.5 and O3 in China.
Junhua Wang, Baozhu Ge, Xueshun Chen, Jie Li, Keding Lu, Yayuan Dong, Lei Kong, Zifa Wang, and Yuanhang Zhang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-22, https://doi.org/10.5194/gmd-2023-22, 2023
Revised manuscript not accepted
Short summary
Short summary
We developed a quantitative decoupling analysis (QDA) method to quantify the contributions of emissions, meteorology, chemical reactions, and their nonlinear interactions on PM2.5. We found the effects of adverse meteorological conditions and the importance of nonlinear interactions. This method can provide valuable information for understanding of key factors to heavy pollution, but also help the modelers to find out the sources of uncertainties in numerical models.
Shujun Zhong, Shuang Chen, Junjun Deng, Yanbing Fan, Qiang Zhang, Qiaorong Xie, Yulin Qi, Wei Hu, Libin Wu, Xiaodong Li, Chandra Mouli Pavuluri, Jialei Zhu, Xin Wang, Di Liu, Xiaole Pan, Yele Sun, Zifa Wang, Yisheng Xu, Haijie Tong, Hang Su, Yafang Cheng, Kimitaka Kawamura, and Pingqing Fu
Atmos. Chem. Phys., 23, 2061–2077, https://doi.org/10.5194/acp-23-2061-2023, https://doi.org/10.5194/acp-23-2061-2023, 2023
Short summary
Short summary
This study investigated the role of the secondary organic aerosol (SOA) loading on the molecular composition of wintertime urban aerosols by ultrahigh-resolution mass spectrometry. Results demonstrate that the SOA loading is an important factor associated with the oxidation degree, nitrate group content, and chemodiversity of nitrooxy–organosulfates. Our study also found that the hydrolysis of nitrooxy–organosulfates is a possible pathway for the formation of organosulfates.
Futing Wang, Ting Yang, Zifa Wang, Haibo Wang, Xi Chen, Yele Sun, Jianjun Li, Guigang Tang, and Wenxuan Chai
Atmos. Meas. Tech., 15, 6127–6144, https://doi.org/10.5194/amt-15-6127-2022, https://doi.org/10.5194/amt-15-6127-2022, 2022
Short summary
Short summary
We develop a new algorithm to get the vertical mass concentration profiles of fine aerosol components based on the synergy of ground-based remote sensing for the first time. The comparisons with in situ observations and chemistry transport models validate the performance of the algorithm. Uncertainties caused by input parameters are also assessed in this paper. We expected that the algorithm can provide a new idea for lidar inversion and promote the development of aerosol component profiles.
Lichao Yang, Wansuo Duan, Zifa Wang, and Wenyi Yang
Atmos. Chem. Phys., 22, 11429–11453, https://doi.org/10.5194/acp-22-11429-2022, https://doi.org/10.5194/acp-22-11429-2022, 2022
Short summary
Short summary
The initial meteorological state has a great impact on PM2.5 forecasts. Assimilating additional observations is an effective way to improve the accuracy of the initial meteorological state. Here we used an advanced optimization approach to identify where we should preferentially place the meteorological observations associated with PM2.5 forecasts in the Beijing–Tianjin–Hebei region of China. We provide evidence that the target observation strategy is effective for improving PM2.5 forecasts.
Zhiqiang Zhang, Yele Sun, Chun Chen, Bo You, Aodong Du, Weiqi Xu, Yan Li, Zhijie Li, Lu Lei, Wei Zhou, Jiaxing Sun, Yanmei Qiu, Lianfang Wei, Pingqing Fu, and Zifa Wang
Atmos. Chem. Phys., 22, 10409–10423, https://doi.org/10.5194/acp-22-10409-2022, https://doi.org/10.5194/acp-22-10409-2022, 2022
Short summary
Short summary
We present a comprehensive characterization of water-soluble organic aerosol and the first mass spectral characterization of water-insoluble organic aerosol in the cold season in Beijing by integrating online and offline aerosol mass spectrometer measurements. WSOA comprised dominantly secondary OA and showed large changes during the transition season from autumn to winter. WIOA was characterized by prominent hydrocarbon ions series, low oxidation states, and significant day–night differences.
Jiaxing Sun, Yele Sun, Conghui Xie, Weiqi Xu, Chun Chen, Zhe Wang, Lei Li, Xubing Du, Fugui Huang, Yan Li, Zhijie Li, Xiaole Pan, Nan Ma, Wanyun Xu, Pingqing Fu, and Zifa Wang
Atmos. Chem. Phys., 22, 7619–7630, https://doi.org/10.5194/acp-22-7619-2022, https://doi.org/10.5194/acp-22-7619-2022, 2022
Short summary
Short summary
We analyzed the chemical composition and mixing state of BC-containing particles at urban and rural sites in winter in the North China Plain and evaluated their impact on light absorption enhancement. BC was dominantly mixed with organic carbon, nitrate, and sulfate, and the mixing state evolved significantly as a function of relative humidity (RH) at both sites. The absorption enhancement depended strongly on coated secondary inorganic aerosol and was up to ~1.3–1.4 during aging processes.
Junjun Deng, Hao Ma, Xinfeng Wang, Shujun Zhong, Zhimin Zhang, Jialei Zhu, Yanbing Fan, Wei Hu, Libin Wu, Xiaodong Li, Lujie Ren, Chandra Mouli Pavuluri, Xiaole Pan, Yele Sun, Zifa Wang, Kimitaka Kawamura, and Pingqing Fu
Atmos. Chem. Phys., 22, 6449–6470, https://doi.org/10.5194/acp-22-6449-2022, https://doi.org/10.5194/acp-22-6449-2022, 2022
Short summary
Short summary
Light-absorbing brown carbon (BrC) plays an important role in climate change and atmospheric chemistry. Here we investigated the seasonal and diurnal variations in water-soluble BrC in PM2.5 in the megacity Tianjin in coastal China. Results of the source apportionments from the combination with organic molecular compositions and optical properties of water-soluble BrC reveal a large contribution from primary bioaerosol particles to BrC in the urban atmosphere.
Haibo Wang, Ting Yang, Zifa Wang, Jianjun Li, Wenxuan Chai, Guigang Tang, Lei Kong, and Xueshun Chen
Geosci. Model Dev., 15, 3555–3585, https://doi.org/10.5194/gmd-15-3555-2022, https://doi.org/10.5194/gmd-15-3555-2022, 2022
Short summary
Short summary
In this paper, we develop an online data coupled assimilation system (NAQPMS-PDAF) with the Eulerian atmospheric chemistry-transport model. NAQPMS-PDAF allows efficient use of large computational resources. The application and performance of the system are investigated by assimilating 1 month of vertical aerosol observations. The results show that NAQPMS-PDAF can significantly improve the performance of aerosol vertical structure simulation and reduce the uncertainty to a large extent.
Qian Ma, Kaicun Wang, Yanyi He, Liangyuan Su, Qizhong Wu, Han Liu, and Youren Zhang
Earth Syst. Sci. Data, 14, 463–477, https://doi.org/10.5194/essd-14-463-2022, https://doi.org/10.5194/essd-14-463-2022, 2022
Short summary
Short summary
Surface incident solar radiation plays a key role in atmospheric circulation, the water cycle, and ecological equilibrium on Earth. A homogenized century-long surface incident solar radiation dataset was obtained over Japan.
Jiaxing Sun, Zhe Wang, Wei Zhou, Conghui Xie, Cheng Wu, Chun Chen, Tingting Han, Qingqing Wang, Zhijie Li, Jie Li, Pingqing Fu, Zifa Wang, and Yele Sun
Atmos. Chem. Phys., 22, 561–575, https://doi.org/10.5194/acp-22-561-2022, https://doi.org/10.5194/acp-22-561-2022, 2022
Short summary
Short summary
We analyzed 9-year measurements of BC and aerosol optical properties from 2012 to 2020 in Beijing, China. Our results showed large reductions in BC and light extinction coefficient due to the Clean Air Action Plan. As a response, both SSA and mass extinction efficiency (MEE) showed considerable increases, demonstrating a future challenge in visibility improvement. The primary and secondary BrC was also separated and quantified, and the changes in radiative forcing of BC and BrC were estimated.
Qian Ye, Jie Li, Xueshun Chen, Huansheng Chen, Wenyi Yang, Huiyun Du, Xiaole Pan, Xiao Tang, Wei Wang, Lili Zhu, Jianjun Li, Zhe Wang, and Zifa Wang
Geosci. Model Dev., 14, 7573–7604, https://doi.org/10.5194/gmd-14-7573-2021, https://doi.org/10.5194/gmd-14-7573-2021, 2021
Short summary
Short summary
We developed a global tropospheric atmospheric chemistry source–receptor model. This model can quantify the contributions of multiple air pollutants from various source regions in one simulation without introducing the nonlinear error of atmospheric chemistry. The S-R relationships of PM2.5 and O3 from a global high-resolution (0.5° × 0.5°) simulation were given and compared with previous studies. This model will be useful for creating a link between the scientific community and policymakers.
Yuting Zhang, Hang Liu, Shandong Lei, Wanyun Xu, Yu Tian, Weijie Yao, Xiaoyong Liu, Qi Liao, Jie Li, Chun Chen, Yele Sun, Pingqing Fu, Jinyuan Xin, Junji Cao, Xiaole Pan, and Zifa Wang
Atmos. Chem. Phys., 21, 17631–17648, https://doi.org/10.5194/acp-21-17631-2021, https://doi.org/10.5194/acp-21-17631-2021, 2021
Short summary
Short summary
In this study, the authors used a single-particle soot photometer (SP2) to characterize the particle size, mixing state, and optical properties of black carbon aerosols in rural areas of the North China Plain in winter. Relatively warm and high-RH environments (RH > 50 %, −4° < T < 4 °) were more favorable to rBC aging than dry and cold environments (RH < 60 %, T < −8°). The paper emphasizes the importance of meteorological parameters in the mixing state of black carbon.
Cited articles
Amnuaylojaroen, T., Barth, M. C., Emmons, L. K., Carmichael, G. R., Kreasuwun, J., Prasitwattanaseree, S., and Chantara, S.: Effect of different emission inventories on modeled ozone and carbon monoxide in Southeast Asia, Atmos. Chem. Phys., 14, 12983–13012, https://doi.org/10.5194/acp-14-12983-2014, 2014.
Appel, K. W., Napelenok, S. L., Foley, K. M., Pye, H. O. T., Hogrefe, C., Luecken, D. J., Bash, J. O., Roselle, S. J., Pleim, J. E., Foroutan, H., Hutzell, W. T., Pouliot, G. A., Sarwar, G., Fahey, K. M., Gantt, B., Gilliam, R. C., Heath, N. K., Kang, D., Mathur, R., Schwede, D. B., Spero, T. L., Wong, D. C., and Young, J. O.: Description and evaluation of the Community Multiscale Air Quality (CMAQ) modeling system version 5.1, Geosci. Model Dev., 10, 1703–1732, https://doi.org/10.5194/gmd-10-1703-2017, 2017.
Appel, K. W., Bash, J. O., Fahey, K. M., Foley, K. M., Gilliam, R. C., Hogrefe, C., Hutzell, W. T., Kang, D., Mathur, R., Murphy, B. N., Napelenok, S. L., Nolte, C. G., Pleim, J. E., Pouliot, G. A., Pye, H. O. T., Ran, L., Roselle, S. J., Sarwar, G., Schwede, D. B., Sidi, F. I., Spero, T. L., and Wong, D. C.: The Community Multiscale Air Quality (CMAQ) model versions 5.3 and 5.3.1: system updates and evaluation, Geosci. Model Dev., 14, 2867–2897, https://doi.org/10.5194/gmd-14-2867-2021, 2021.
Bao, F., Li, M., Zhang, Y., Chen, C., and Zhao, J.: Photochemical aging of Beijing urban PM2.5: HONO production, Environ. Sci. Technol., 52, 6309–6316, https://doi.org/10.1021/acs.est.8b00538, 2018.
Bao, Z., Zhang, X., Li, Q., Zhou, J., Shi, G., Zhou, L., Yang, F., Xie, S., Zhang, D., Zhai, C., Li, Z., Peng, C., and Chen, Y.: Measurement report: Intensive biomass burning emissions and rapid nitrate formation drive severe haze formation in the Sichuan Basin, China – insights from aerosol mass spectrometry, Atmos. Chem. Phys., 23, 1147–1167, https://doi.org/10.5194/acp-23-1147-2023, 2023.
Bessagnet, B., Pirovano, G., Mircea, M., Cuvelier, C., Aulinger, A., Calori, G., Ciarelli, G., Manders, A., Stern, R., Tsyro, S., García Vivanco, M., Thunis, P., Pay, M.-T., Colette, A., Couvidat, F., Meleux, F., Rouíl, L., Ung, A., Aksoyoglu, S., Baldasano, J. M., Bieser, J., Briganti, G., Cappelletti, A., D'Isidoro, M., Finardi, S., Kranenburg, R., Silibello, C., Carnevale, C., Aas, W., Dupont, J.-C., Fagerli, H., Gonzalez, L., Menut, L., Prévôt, A. S. H., Roberts, P., and White, L.: Presentation of the EURODELTA III intercomparison exercise – evaluation of the chemistry transport models' performance on criteria pollutants and joint analysis with meteorology, Atmos. Chem. Phys., 16, 12667–12701, https://doi.org/10.5194/acp-16-12667-2016, 2016.
Binkowski, F. S., Arunachalam, S., Adelman, Z., and Pinto, J.: Examining photolysis rates with a prototype on-line photolysis module in CMAQ, J. Appl. Meteorol., 46, 1252–1256, https://doi.org/10.1175/JAM2531.1, 2007.
Byun, D. and Schere, K. L.: Review of the governing equations, computational algorithms, and other components of the Models-3 Community Multiscale Air Quality (CMAQ) modeling system, Appl. Mech. Rev., 59, 51–77, https://doi.org/10.1115/1.2128636, 2006.
Carmichael, G. R., Sakurai, T., Streets, D., Hozumi, Y., Ueda, H., Park, S. U., Fung, C., Han, Z., Kajino, M., Engardt, M., Bennet, C., Hayami, H., Sartelet, K., Holloway, T., Wang, Z., Kannari, A., Fu, J., Matsuda, K., Thongboonchoo, N., and Amann, M.: MICS-Asia II: The model intercomparison study for Asia Phase II methodology and overview of findings, Atmos. Environ., 42, 3468–3490, https://doi.org/10.1016/j.atmosenv.2007.04.007, 2008.
Chen, L., Gao, Y., Zhang, M., Fu, J. S., Zhu, J., Liao, H., Li, J., Huang, K., Ge, B., Wang, X., Lam, Y. F., Lin, C.-Y., Itahashi, S., Nagashima, T., Kajino, M., Yamaji, K., Wang, Z., and Kurokawa, J.: MICS-Asia III: multi-model comparison and evaluation of aerosol over East Asia, Atmos. Chem. Phys., 19, 11911–11937, https://doi.org/10.5194/acp-19-11911-2019, 2019.
CNEMC (China National Environmental Monitoring Centre): Technical Regulation for the Assessment of Urban Ambient Air Quality Index (AQI) Forecasts (Trial), Beijing, China, 2021.
Colella, P. and Woodward, P. R.: The Piecewise Parabolic Method (PPM) for gas-dynamical simulations, J. Comput. Phys., 54, 174–201, https://doi.org/10.1016/0021-9991(84)90143-8, 1984.
Crippa, M., Solazzo, E., Huang, G., Guizzardi, D., Koffi, E., Muntean, M., Schieberle, C., Friedrich, R., and Janssens-Maenhout, G.: High resolution temporal profiles in the Emissions Database for Global Atmospheric Research, Sci. Data, 7, 121, https://doi.org/10.1038/s41597-020-0462-2, 2020.
Eastham, S. D., Long, M. S., Keller, C. A., Lundgren, E., Yantosca, R. M., Zhuang, J., Li, C., Lee, C. J., Yannetti, M., Auer, B. M., Clune, T. L., Kouatchou, J., Putman, W. M., Thompson, M. A., Trayanov, A. L., Molod, A. M., Martin, R. V., and Jacob, D. J.: GEOS-Chem High Performance (GCHP v11-02c): a next-generation implementation of the GEOS-Chem chemical transport model for massively parallel applications, Geosci. Model Dev., 11, 2941–2953, https://doi.org/10.5194/gmd-11-2941-2018, 2018.
Emery, C., Jung, J., Koo, B., and Yarwood, G.: Improvements to CAMx Snow Cover Treatments and Carbon Bond Chemical Mechanism for Winter Ozone, Final Report, Ramboll Environ, Novato, CA, USA, https://www.camx.com/files/udaq_snowchem_final_6aug15.pdf (last access: 29 September 2026), 2015.
Emery, C., Baker, K., Wilson, G., and Yarwood, G.: Comprehensive air quality model with extensions: formulation and evaluation for ozone and particulate matter over the US, Atmosphere-Basel, 15, 1158, https://doi.org/10.3390/atmos15101158, 2024.
EPA: CMAQv5.3.3 User's Guide, U. S. Environmental Protection Agency, Research Triangle Park, NC, USA, Zenodo [code], https://doi.org/10.5281/zenodo.5213949, 2021.
EPICC-Model Working Group: Description and evaluation of the Emission and atmospheric Processes Integrated and Coupled Community (EPICC) Model version 1.0, Adv. Atmospheric Sci., https://doi.org/10.1007/s00376-025-4384-y, 2026a.
EPICC-Model Working Group: Emission and atmospheric Processes Integrated and Coupled Community Model (EPICC-Model) (Version 1.6.0), Zenodo [code], https://doi.org/10.5281/zenodo.20303367, 2026b.
Foley, K. M., Roselle, S. J., Appel, K. W., Bhave, P. V., Pleim, J. E., Otte, T. L., Mathur, R., Sarwar, G., Young, J. O., Gilliam, R. C., Nolte, C. G., Kelly, J. T., Gilliland, A. B., and Bash, J. O.: Incremental testing of the Community Multiscale Air Quality (CMAQ) modeling system version 4.7, Geosci. Model Dev., 3, 205–226, https://doi.org/10.5194/gmd-3-205-2010, 2010.
Fountoukis, C. and Nenes, A.: ISORROPIA II: a computationally efficient thermodynamic equilibrium model for
Gao, C., Zhang, X., Xiu, A., Tong, Q., Zhao, H., Zhang, S., Yang, G., Zhang, M., and Xie, S.: Intercomparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1–CMAQ v5.3.1, WRF–Chem v4.1.1, and WRF v3.7.1–CHIMERE v2020r1) in eastern China, Geosci. Model Dev., 17, 2471–2492, https://doi.org/10.5194/gmd-17-2471-2024, 2024.
Gao, M., Han, Z., Liu, Z., Li, M., Xin, J., Tao, Z., Li, J., Kang, J.-E., Huang, K., Dong, X., Zhuang, B., Li, S., Ge, B., Wu, Q., Cheng, Y., Wang, Y., Lee, H.-J., Kim, C.-H., Fu, J. S., Wang, T., Chin, M., Woo, J.-H., Zhang, Q., Wang, Z., and Carmichael, G. R.: Air quality and climate change, Topic 3 of the Model Inter-Comparison Study for Asia Phase III (MICS-Asia III) – Part 1: Overview and model evaluation, Atmos. Chem. Phys., 18, 4859–4884, https://doi.org/10.5194/acp-18-4859-2018, 2018.
Gao, Y., Yan, F., Ma, M., Ding, A., Liao, H., Wang, S., Wang, X., Zhao, B., Cai, W., Su, H., Yao, X., and Gao, H.: Unveiling the dipole synergic effect of biogenic and anthropogenic emissions on ozone concentrations, Sci. Total Environ., 818, 151722, https://doi.org/10.1016/j.scitotenv.2021.151722, 2022.
Gao, Z. and Zhou, X.: A review of the CAMx, CMAQ, WRF-Chem and NAQPMS models: application, evaluation and uncertainty factors, Environ. Pollut., 343, 123183, https://doi.org/10.1016/j.envpol.2023.123183, 2024.
Geng, G., Liu, Y., Liu, Y., Liu, S., Cheng, J., Yan, L., Wu, N., Hu, H., Tong, D., Zheng, B., Yin, Z., He, K., and Zhang, Q.: Efficacy of China's clean air actions to tackle PM2.5 pollution between 2013 and 2020, Nat. Geosci., 17, 987–994, https://doi.org/10.1038/s41561-024-01540-z, 2024.
Gong, C. and Liao, H.: A typical weather pattern for ozone pollution events in North China, Atmos. Chem. Phys., 19, 13725–13740, https://doi.org/10.5194/acp-19-13725-2019, 2019.
Grell, G. A., Peckham, S. E., Schmitz, R., McKeen, S. A., Frost, G., Skamarock, W. C., and Eder, B.: Fully coupled “online” chemistry within the WRF model, Atmos. Environ., 39, 6957–6975, https://doi.org/10.1016/j.atmosenv.2005.04.027, 2005.
Guenther, A.: MEGANv3.21.tar.gz emission calculator code (Version 3.21), Zenodo [code], https://doi.org/10.5281/zenodo.10526206, 2024.
Guenther, A. B., Jiang, X., Heald, C. L., Sakulyanontvittaya, T., Duhl, T., Emmons, L. K., and Wang, X.: The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1): an extended and updated framework for modeling biogenic emissions, Geosci. Model Dev., 5, 1471–1492, https://doi.org/10.5194/gmd-5-1471-2012, 2012.
Haagen-Smit, A. J. and Fox, M. M.: Ozone formation in photochemical oxidation of organic substances, Ind. Eng. Chem., 48, 1484–1487, https://doi.org/10.1021/ie51400a033, 1956.
Henry, W.: III. Experiments on the quantity of gases absorbed by water, at different temperatures, and under different pressures, Philos. Trans. R. Soc. Lond., 93, 29–274, https://doi.org/10.1098/rstl.1803.0004, 1803.
Hogrefe, C., Liu, P., Pouliot, G., Mathur, R., Roselle, S., Flemming, J., Lin, M., and Park, R. J.: Impacts of different characterizations of large-scale background on simulated regional-scale ozone over the continental United States, Atmos. Chem. Phys., 18, 3839–3864, https://doi.org/10.5194/acp-18-3839-2018, 2018.
Hong, C., Zhang, Q., He, K., Guan, D., Li, M., Liu, F., and Zheng, B.: Variations of China's emission estimates: response to uncertainties in energy statistics, Atmos. Chem. Phys., 17, 1227–1239, https://doi.org/10.5194/acp-17-1227-2017, 2017.
Hong, S.-Y., Noh, Y., and Dudhia, J.: A new vertical diffusion package with an explicit treatment of entrainment processes, Mon. Weather Rev., 134, 2318–2341, https://doi.org/10.1175/MWR3199.1, 2006.
Huang, L., Zhu, Y., Zhai, H., Xue, S., Zhu, T., Shao, Y., Liu, Z., Emery, C., Yarwood, G., Wang, Y., Fu, J., Zhang, K., and Li, L.: Recommendations on benchmarks for numerical air quality model applications in China – Part 1: PM2.5 and chemical species, Atmos. Chem. Phys., 21, 2725–2743, https://doi.org/10.5194/acp-21-2725-2021, 2021.
Huang, L., Liu, H., Yarwood, G., Wilson, G., Tao, J., Han, Z., Ji, D., Wang, Y., and Li, L.: Modeling of secondary organic aerosols (SOA) based on two commonly used air quality models in China: consistent S/IVOCs contribution but large differences in SOA aging, Sci. Total Environ., 903, 166162, https://doi.org/10.1016/j.scitotenv.2023.166162, 2023.
Huang, X., Song, Y., Li, M., Li, J., Huo, Q., Cai, X., Zhu, T., Hu, M., and Zhang, H.: A high-resolution ammonia emission inventory in China, Global Biogeochem. Cy., 26, GB1030, https://doi.org/10.1029/2011GB004161, 2012a.
Huang, X., Li, M., Li, J., and Song, Y.: A high-resolution emission inventory of crop burning in fields in China based on MODIS Thermal Anomalies/Fire products, Atmos. Environ., 50, 9–15, https://doi.org/10.1016/j.atmosenv.2012.01.017, 2012b.
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A., and Collins, W. D.: Radiative forcing by long-lived greenhouse gases: calculations with the AER radiative transfer models, J. Geophys. Res., 113, D13103, https://doi.org/10.1029/2008JD009944, 2008.
Itahashi, S., Ge, B., Sato, K., Fu, J. S., Wang, X., Yamaji, K., Nagashima, T., Li, J., Kajino, M., Liao, H., Zhang, M., Wang, Z., Li, M., Kurokawa, J., Carmichael, G. R., and Wang, Z.: MICS-Asia III: overview of model intercomparison and evaluation of acid deposition over Asia, Atmos. Chem. Phys., 20, 2667–2693, https://doi.org/10.5194/acp-20-2667-2020, 2020.
Jia, W., Zhang, X., Wang, H., Wang, Y., Wang, D., Zhong, J., Zhang, W., Zhang, L., Guo, L., Lei, Y., Wang, J., Yang, Y., and Lin, Y.: Comprehensive evaluation of typical planetary boundary layer (PBL) parameterization schemes in China – Part 1: Understanding expressiveness of schemes for different regions from the mechanism perspective, Geosci. Model Dev., 16, 6635–6670, https://doi.org/10.5194/gmd-16-6635-2023, 2023.
Jiang, F., Guo, H., Wang, T. J., Cheng, H. R., Wang, X. M., Simpson, I. J., Ding, A. J., Saunders, S. M., Lam, S. H. M., and Blake, D. R.: An ozone episode in the Pearl River Delta: field observation and model simulation, J. Geophys. Res.-Atmos., 115, 2009JD013583, https://doi.org/10.1029/2009JD013583, 2010.
Jiménez, P. A. and Dudhia, J.: On the ability of the WRF model to reproduce the surface wind direction over complex terrain, J. Appl. Meteorol., 52, 1610–1617, https://doi.org/10.1175/JAMC-D-12-0266.1, 2013.
Jing, Z., Liu, P., Wang, T., Song, H., Lee, J., Xu, T., and Xing, Y.: Effects of meteorological factors and anthropogenic precursors on PM2.5 concentrations in cities in China, Sustainability, 12, 3550, https://doi.org/10.3390/su12093550, 2020.
Kain, J. S.: The Kain–Fritsch convective parameterization: an update, J. Appl. Meteorol., 43, 170–181, https://doi.org/10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2, 2004.
Kang, Y., Liu, M., Song, Y., Huang, X., Yao, H., Cai, X., Zhang, H., Kang, L., Liu, X., Yan, X., He, H., Zhang, Q., Shao, M., and Zhu, T.: High-resolution ammonia emissions inventories in China from 1980 to 2012, Atmos. Chem. Phys., 16, 2043–2058, https://doi.org/10.5194/acp-16-2043-2016, 2016.
Kim, D.-J., Kim, T.-H., Choi, J.-Y., Lee, J., Kim, R.-H., Son, J.-S., and Lee, D.: The impact of vertical eddy diffusivity changes in the CMAQ Model on PM2.5 concentration variations in northeast Asia: focusing on the Seoul Metropolitan Area, Atmosphere-Basel, 15, 376, https://doi.org/10.3390/atmos15030376, 2024.
Kong, L., Tang, X., Zhu, J., Wang, Z., Pan, Y., Wu, H., Wu, L., Wu, Q., He, Y., Tian, S., Xie, Y., Liu, Z., Sui, W., Han, L., and Carmichael, G.: Improved inversion of monthly ammonia emissions in China based on the Chinese ammonia monitoring network and ensemble Kalman filter, Environ. Sci. Technol., 53, 12529–12538, https://doi.org/10.1021/acs.est.9b02701, 2019.
Li, B., Gao, J., Chen, C., Wen, L., Zhang, Y., Li, J., Zhang, Y., Du, X., Zhang, K., and Wang, J.: Exploring HONO production from particulate nitrate photolysis in representative regions of China: characteristics, influencing factors, and environmental implications, Atmos. Chem. Phys., 24, 13183–13198, https://doi.org/10.5194/acp-24-13183-2024, 2024.
Li, J., Nagashima, T., Kong, L., Ge, B., Yamaji, K., Fu, J. S., Wang, X., Fan, Q., Itahashi, S., Lee, H.-J., Kim, C.-H., Lin, C.-Y., Zhang, M., Tao, Z., Kajino, M., Liao, H., Li, M., Woo, J.-H., Kurokawa, J., Wang, Z., Wu, Q., Akimoto, H., Carmichael, G. R., and Wang, Z.: Model evaluation and intercomparison of surface-level ozone and relevant species in East Asia in the context of MICS-Asia Phase III – Part 1: Overview, Atmos. Chem. Phys., 19, 12993–13015, https://doi.org/10.5194/acp-19-12993-2019, 2019a.
Li, K., Jacob, D. J., Liao, H., Zhu, J., Shah, V., Shen, L., Bates, K. H., Zhang, Q., and Zhai, S.: A two-pollutant strategy for improving ozone and particulate air quality in China, Nat. Geosci., 12, 906–910, https://doi.org/10.1038/s41561-019-0464-x, 2019b.
Li, R., Ma, X., Xiong, F., Jia, H., Sha, T., and Tian, R.: Comparisons and evaluation of aerosol burden and optical depth in CMIP5 simulations over East Asia, J. Atmospheric Sol.-Terr. Phys., 206, 105315, https://doi.org/10.1016/j.jastp.2020.105315, 2020.
Li, X., Hussain, S. A., Sobri, S., and Md Said, M. S.: Overviewing the air quality models on air pollution in Sichuan Basin, China, Chemosphere, 271, 129502, https://doi.org/10.1016/j.chemosphere.2020.129502, 2021.
Liu, Z., Zhou, M., Chen, Y., Chen, D., Pan, Y., Song, T., Ji, D., Chen, Q., and Zhang, L.: The nonlinear response of fine particulate matter pollution to ammonia emission reductions in North China, Environ. Res. Lett., 16, 034014, https://doi.org/10.1088/1748-9326/abdf86, 2021.
Lou, M. and Wu, Q.: The dataset of the manuscript “Evaluating the EPICC-Model for Regional Air Quality Simulation: A Comparative Study with CAMx and CMAQ”, Zenodo [data set], https://doi.org/10.5281/zenodo.21017233, 2026.
Lou, M., Wu, Q., Li, J., Cheng, H., Zhu, J., Wang, L., and Wang, Z.: The amplification effect of El Niño on winter heavy pollution frequency in Beijing, Environ. Res. Lett., 20, 074043, https://doi.org/10.1088/1748-9326/ade1ee, 2025.
Lyu, Y., Wu, D., Han, F., Zhang, H., Lv, F., Kang, A., Hu, Y., and Pang, X.: Co-occurring extremes of PM2.5 and ozone in warm seasons of the Yangtze River Delta of China: insights from explainable machine learning, ACS EST Air, 2, 578–588, https://doi.org/10.1021/acsestair.4c00314, 2025.
Mathur, R., Xing, J., Gilliam, R., Sarwar, G., Hogrefe, C., Pleim, J., Pouliot, G., Roselle, S., Spero, T. L., Wong, D. C., and Young, J.: Extending the Community Multiscale Air Quality (CMAQ) modeling system to hemispheric scales: overview of process considerations and initial applications, Atmos. Chem. Phys., 17, 12449–12474, https://doi.org/10.5194/acp-17-12449-2017, 2017.
MEE (Ministry of Ecology and Environment of the People's Republic of China): Technical Guideline for Numerical Forecasting of Ambient Air Quality (HJ 1130–2020), Beijing, China, 2020, https://www.mee.gov.cn/ywgz/fgbz/bz/bzwb/jcffbz/202005/t20200518_779714.shtml, last access: 8 August 2026.
Miao, R., Chen, Q., Zheng, Y., Cheng, X., Sun, Y., Palmer, P. I., Shrivastava, M., Guo, J., Zhang, Q., Liu, Y., Tan, Z., Ma, X., Chen, S., Zeng, L., Lu, K., and Zhang, Y.: Model bias in simulating major chemical components of PM2.5 in China, Atmos. Chem. Phys., 20, 12265–12284, https://doi.org/10.5194/acp-20-12265-2020, 2020.
Murphy, B. N., Woody, M. C., Jimenez, J. L., Carlton, A. M. G., Hayes, P. L., Liu, S., Ng, N. L., Russell, L. M., Setyan, A., Xu, L., Young, J., Zaveri, R. A., Zhang, Q., and Pye, H. O. T.: Semivolatile POA and parameterized total combustion SOA in CMAQv5.2: impacts on source strength and partitioning, Atmos. Chem. Phys., 17, 11107–11133, https://doi.org/10.5194/acp-17-11107-2017, 2017.
NCEP: NCEP FNL Operational Model Global Tropospheric Analyses, continuing from July 1999, National Centers for Environmental Prediction [data set], https://doi.org/10.5065/D6M043C6, 2000.
Nenes, A., Pandis, S. N., and Pilinis, C.: ISORROPIA: a new thermodynamic equilibrium model for multiphase multicomponent inorganic aerosols, Aquat. Geochem., 4, 123–152, https://doi.org/10.1023/A:1009604003981, 1998.
Odum, J. R., Jungkamp, T. P. W., Griffin, R. J., Forstner, H. J. L., Flagan, R. C., and Seinfeld, J. H.: The atmospheric aerosol-forming potential of whole gasoline vapor, Science, 276, 96–99, https://doi.org/10.1126/science.276.5309.96, 1997.
Oyegbile, O., Chan, A., Ooi, M., Anwar, P., Mohamed, A. A., and Li, L.: Evaluation of WRF model performance with different microphysics schemes for extreme rainfall prediction in Lagos, Nigeria: implications for urban flood risk management, Bull. Atmospheric Sci. Technol., 5, 19, https://doi.org/10.1007/s42865-024-00081-y, 2024.
Pandis, S. N., Harley, R. A., Cass, G. R., and Seinfeld, J. H.: Secondary organic aerosol formation and transport, Atmos. Environ., 26, 2269–2282, https://doi.org/10.1016/0960-1686(92)90358-r, 1992.
Pennington, E. A., Seltzer, K. M., Murphy, B. N., Qin, M., Seinfeld, J. H., and Pye, H. O. T.: Modeling secondary organic aerosol formation from volatile chemical products, Atmos. Chem. Phys., 21, 18247–18261, https://doi.org/10.5194/acp-21-18247-2021, 2021.
Pleim, J. E.: A combined local and nonlocal closure model for the atmospheric boundary layer. Part I: Model description and testing, J. Appl. Meteorol., 46, 1383–1395, https://doi.org/10.1175/JAM2539.1, 2007.
Ramboll: CAMx User's Guide, version 7.00, Ramboll, Novato, CA, USA, https://www.camx.com/Files/CAMxUsersGuide_v7.00.pdf (last access: 29 September 2026), 2020.
Ren, F., Lin, J., Xu, C., Adeniran, J. A., Wang, J., Martin, R. V., van Donkelaar, A., Hammer, M. S., Horowitz, L. W., Turnock, S. T., Oshima, N., Zhang, J., Bauer, S., Tsigaridis, K., Seland, Ø., Nabat, P., Neubauer, D., Strand, G., van Noije, T., Le Sager, P., and Takemura, T.: Evaluation of CMIP6 model simulations of PM2.5 and its components over China, Geosci. Model Dev., 17, 4821–4836, https://doi.org/10.5194/gmd-17-4821-2024, 2024.
Ren, J., Guo, F., and Xie, S.: Diagnosing ozone–NOx–VOC sensitivity and revealing causes of ozone increases in China based on 2013–2021 satellite retrievals, Atmos. Chem. Phys., 22, 15035–15047, https://doi.org/10.5194/acp-22-15035-2022, 2022.
Seinfeld, J. H. and Pandis, S. N.: Atmospheric Chemistry and Physics: From Air Pollution to Climate Change, 3rd edn., John Wiley and Sons, Hoboken, NJ, USA, ISBN 9781118947401, 2016.
Shao, J., Chen, Q., Wang, Y., Lu, X., He, P., Sun, Y., Shah, V., Martin, R. V., Philip, S., Song, S., Zhao, Y., Xie, Z., Zhang, L., and Alexander, B.: Heterogeneous sulfate aerosol formation mechanisms during wintertime Chinese haze events: air quality model assessment using observations of sulfate oxygen isotopes in Beijing, Atmos. Chem. Phys., 19, 6107–6123, https://doi.org/10.5194/acp-19-6107-2019, 2019.
Sillman, S.: The relation between ozone, NOx and hydrocarbons in urban and polluted rural environments, Atmos. Environ., 33, 1821–1845, https://doi.org/10.1016/S1352-2310(98)00345-8, 1999.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D., Duda, M. G., Huang, X. Y., Wang, W., and Powers, J. G.: A description of the Advanced Research WRF version 3, NCAR Technical note-475+STR, https://doi.org/10.5065/D68S4MVH, 2008.
Song, Y., Liu, B., Miao, W., Chang, D., and Zhang, Y.: Spatiotemporal variation in nonagricultural open fire emissions in China from 2000 to 2007, Global Biogeochem. Cy., 23, 2008GB003344, https://doi.org/10.1029/2008GB003344, 2009.
Tang, W., Cohan, D. S., Morris, G. A., Byun, D. W., and Luke, W. T.: Influence of vertical mixing uncertainties on ozone simulation in CMAQ, Atmos. Environ., 45, 2898–2909, https://doi.org/10.1016/j.atmosenv.2011.01.057, 2011.
Tewari, M., Chen, F., Wang, W., Dudhia, J., LeMone, M. A., Mitchell, K., Ek, M., Gayno, G., Wegiel, J., and Cuenca, R. H.: Implementation and verification of the unified Noah land-surface model in the WRF model, 20th Conference on Weather Analysis and Forecasting/16th Conference on Numerical Weather Prediction, Seattle, WA, USA, 11–15 January 2004, https://ams.confex.com/ams/pdfpapers/69061.pdf (last access: 29 September 2026), 2004.
The International GEOS-Chem User Community: geoschem/geos-chem: GEOS-Chem 14.5.1, Zenodo [code], https://doi.org/10.5281/zenodo.14629792, 2025.
Thompson, G., Field, P. R., Rasmussen, R. M., and Hall, W. D.: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization, Mon. Weather Rev., 136, 5095–5115, https://doi.org/10.1175/2008MWR2387.1, 2008.
US EPA Office of Research and Development: CMAQ (Version 5.3.3), Zenodo [code], https://doi.org/10.5281/zenodo.5213949, 2021.
Walcek, C. J. and Aleksic, N. M.: A simple but accurate mass conservative, peak-preserving, mixing ratio bounded advection algorithm with FORTRAN code, Atmos. Environ., 32, 3863–3880, https://doi.org/10.1016/S1352-2310(98)00099-5, 1998.
Walcek, C. J. and Taylor, G. R.: A theoretical method for computing vertical distributions of acidity and sulfate production within cumulus clouds, J. Atmos. Sci., 43, 339–355, https://doi.org/10.1175/1520-0469(1986)043<0339:ATMFCV>2.0.CO;2, 1986.
Wang, H., Lu, X., Palmer, P. I., Zhang, L., Lu, K., Li, K., Nagashima, T., Koo, J.-H., Tanimoto, H., Wang, H., Gao, M., He, C., Wu, K., Fan, S., and Zhang, Y.: Deciphering decadal urban ozone trends from historical records since 1980, Natl. Sci. Rev., 11, nwae369, https://doi.org/10.1093/nsr/nwae369, 2024.
Wang, W., Li, X., Shao, M., Hu, M., Zeng, L., Wu, Y., and Tan, T.: The impact of aerosols on photolysis frequencies and ozone production in Beijing during the 4-year period 2012–2015, Atmos. Chem. Phys., 19, 9413–9429, https://doi.org/10.5194/acp-19-9413-2019, 2019.
Wang, W., Liu, M., Wang, T., Song, Y., Zhou, L., Cao, J., Hu, J., Tang, G., Chen, Z., Li, Z., Xu, Z., Peng, C., Lian, C., Chen, Y., Pan, Y., Zhang, Y., Sun, Y., Li, W., Zhu, T., Tian, H., and Ge, M.: Sulfate formation is dominated by manganese-catalyzed oxidation of SO2 on aerosol surfaces during haze events, Nat. Commun., 12, 1993, https://doi.org/10.1038/s41467-021-22091-6, 2021.
Wang, W., Chen, H., Wang, Z., Li, J., Chen, X., Yu, F., Fan, X., Zhao, S., Hu, B., Wang, W., Tang, X., Wang, Z., Ge, B., and Wu, J.: Development and evaluation of photolysis and gas-phase reaction scheme in EPICC-model: impacts on tropospheric ozone simulation, Atmos. Environ., 359, 121373, https://doi.org/10.1016/j.atmosenv.2025.121373, 2025.
Wang, X., Zhang, Y., Hu, Y., Zhou, W., Lu, K., Zhong, L., Zeng, L., Shao, M., Hu, M., and Russell, A. G.: Process analysis and sensitivity study of regional ozone formation over the Pearl River Delta, China, during the PRIDE-PRD2004 campaign using the Community Multiscale Air Quality modeling system, Atmos. Chem. Phys., 10, 4423–4437, https://doi.org/10.5194/acp-10-4423-2010, 2010.
Wang, Y., Jiang, S., Huang, L., Lu, G., Kasemsan, M., Yaluk, E. A., Liu, H., Liao, J., Bian, J., Zhang, K., Chen, H., and Li, L.: Differences between VOCs and NOx transport contributions, their impacts on O3, and implications for O3 pollution mitigation based on CMAQ simulation over the Yangtze River Delta, China, Sci. Total Environ., 872, 162118, https://doi.org/10.1016/j.scitotenv.2023.162118, 2023.
Wen, W., Shen, S., Liu, L., Ma, X., Wei, Y., Wang, J., Xing, Y., and Su, W.: Comparative analysis of PM2.5 and O3 source in Beijing using a chemical transport model, Remote Sens.-Basel, 13, 3457, https://doi.org/10.3390/rs13173457, 2021.
Wu, Q., Wang, Z., Chen, H., Zhou, W., and Wenig, M.: An evaluation of air quality modeling over the Pearl River Delta during November 2006, Meteorol. Atmospheric Phys., 116, 113–132, https://doi.org/10.1007/s00703-011-0179-z, 2012.
Xiao, Q., Geng, G., Xue, T., Liu, S., Cai, C., He, K., and Zhang, Q.: Tracking PM2.5 and O3 pollution and the related health burden in China 2013–2020, Environ. Sci. Technol., 56, 6922–6932, https://doi.org/10.1021/acs.est.1c04548, 2022.
Xu, W., Ning, L., and Luo, Y.: Applying satellite data assimilation to wind simulation of coastal wind farms in Guangdong, China, Remote Sens.-Basel, 12, 973, https://doi.org/10.3390/rs12060973, 2020.
Yang, J., Qu, Y., Chen, Y., Zhang, J., Liu, X., Niu, H., and An, J.: Dominant physical and chemical processes impacting nitrate in Shandong of the North China Plain during winter haze events, Sci. Total Environ., 912, 169065, https://doi.org/10.1016/j.scitotenv.2023.169065, 2024.
Yarwood, G., Rao, S., Yocke, M., and Whitten, G. Z.: Updates to the Carbon Bond Chemical Mechanism: CB05, Final Report RT-04-00675, U. S. Environmental Protection Agency, Research Triangle Park, NC, USA, https://www.camx.com/Files/CB05_Final_Report_120805.pdf (last access: 29 September 2026), 2005.
Yarwood, G., Shi, Y., and Beardsley, R.: Impact of CB6r5 mechanism changes on air pollutant modeling in Texas, Final Report, Work Order No. 582-20-11221-014, Texas Commission on Environmental Quality, Austin, TX, USA, https://wayback.archive-it.org/414/20210529064250/https://www.tceq.texas.gov/assets/public/implementation/air/am/contracts/reports/pm/5822011221014-20200730-Ramboll-CB6r5MechanismChanges.pdf (last access: 8 October 2026), 2020.
Yu, E., Ma, J., and Sun, J.: Developing a climate prediction system over Southwest China using the 8-km Weather Research and Forecasting (WRF) model: system design, model calibration, and performance evaluation, Weather Forecast., 37, 1703–1719, https://doi.org/10.1175/WAF-D-21-0188.1, 2022a.
Yu, E., Bai, R., Chen, X., and Shao, L.: Impact of physical parameterizations on wind simulation with WRF V3.9.1.1 under stable conditions at planetary boundary layer gray-zone resolution: a case study over the coastal regions of North China, Geosci. Model Dev., 15, 8111–8134, https://doi.org/10.5194/gmd-15-8111-2022, 2022b.
Zhai, S., Jacob, D. J., Wang, X., Liu, Z., Wen, T., Shah, V., Li, K., Moch, J. M., Bates, K. H., Song, S., Shen, L., Zhang, Y., Luo, G., Yu, F., Sun, Y., Wang, L., Qi, M., Tao, J., Gui, K., Xu, H., Zhang, Q., Zhao, T., Wang, Y., Lee, H. C., Choi, H., and Liao, H.: Control of particulate nitrate air pollution in China, Nat. Geosci., 14, 389–395, https://doi.org/10.1038/s41561-021-00726-z, 2021.
Zhang, J., Lian, C., Wang, W., Ge, M., Guo, Y., Ran, H., Zhang, Y., Zheng, F., Fan, X., Yan, C., Daellenbach, K. R., Liu, Y., Kulmala, M., and An, J.: Amplified role of potential HONO sources in O3 formation in North China Plain during autumn haze aggravating processes, Atmos. Chem. Phys., 22, 3275–3302, https://doi.org/10.5194/acp-22-3275-2022, 2022.
Zhang, J. L., Wang, W. D., Zhu, K. Y., Huang, Z. J., Sheng, L., Liao, S. D., Yuan, X., Hu, Y. N., Liu, J. P., Tang, M. X., Huang, X. F., Li, J., Wang, Z. F., and Zheng, J. Y.: The potentials of uncertainty analysis and Bayesian optimization in HONO source modeling diagnosis and improvement, Environ. Res., 276, 121494, https://doi.org/10.1016/j.envres.2025.121494, 2025.
Zhang, L., Gong, S., Padro, J., and Barrie, L.: A size-segregated particle dry deposition scheme for an atmospheric aerosol module, Atmos. Environ., 35, 549–560, https://doi.org/10.1016/S1352-2310(00)00326-5, 2001.
Zhang, L., Brook, J. R., and Vet, R.: A revised parameterization for gaseous dry deposition in air-quality models, Atmos. Chem. Phys., 3, 2067–2082, https://doi.org/10.5194/acp-3-2067-2003, 2003.
Zhang, X. Y., Wang, Y. Q., Niu, T., Zhang, X. C., Gong, S. L., Zhang, Y. M., and Sun, J. Y.: Atmospheric aerosol compositions in China: spatial/temporal variability, chemical signature, regional haze distribution and comparisons with global aerosols, Atmos. Chem. Phys., 12, 779–799, https://doi.org/10.5194/acp-12-779-2012, 2012.
Zhang, Y. and Arunachalam, S.: A call for an aloft air quality monitoring network: need, feasibility, and potential value, Environ. Sci. Technol., 52, 10903–10908, https://doi.org/10.1021/acs.est.8b02496, 2018.
Zhu, Q., Yu, Y., Gong, H., Wang, Y., Wang, H., Wang, W., Xu, B., and Cheng, T.: Spatio-temporal characteristics of PM2.5 and O3 synergic pollutions and influence factors in the Yangtze River Delta, Front. Environ. Sci., 10, 1104013, https://doi.org/10.3389/fenvs.2022.1104013, 2022.
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...
