Dynamics of Urban Atmospheric Conditions under Emission Mitigation Pathways

Authors

  • Siying Wang Choate Rosemary Hall, Wallingford, CT, 06492, USA Author
  • Yuejia Wu Wuhan Britain-China School, Wuhan, Hubei, 430033, China Author
  • Tianjia Zhang Ningbo Foreign Language School, Ningbo, Zhejiang, 315121, China Author
  • Jiashun Hui The Experimental High School Attached to Beijing Normal University, Beijing, 100032, China Author
  • Chuanzhe Lin Hamden Hall Country Day School, Hamden, CT, 06517, United Sates Author
  • Wanran Tu The High School Affiliated to Renmin University of China, Beijing, China Author
  • Kewei Feng International Education Division, The Experimental High School Attached to BNU, Beijing 100032, China Author
  • Patrick Qi Beijing National Day School, Beijing 100011, China Author

DOI:

https://doi.org/10.71222/ce2cay39

Keywords:

WRF-CMAQ, deep decarbonization, non-linear response, synergistic control, Yangtze River Delta

Abstract

Rapid urbanization and industrialization have led to complex atmospheric pollution characterized by coincident high levels of PM2.5 and ozone (O3) . This study establishes a high-resolution "Emission-Meteorology-Chemistry" simulation system using the WRF-CMAQ model to evaluate the air quality response to three mitigation pathways—structural adjustment (S1), end-of-pipe control (S2), and deep decarbonization (S3)—in the Yangtze River Delta (YRD) region. The results indicate that while all scenarios reduce PM2.5, the Deep Decarbonization (S3) pathway provides the most significant co-benefits, reducing urban PM2.5 by 58.5% to 32.1μg/m3. In contrast, O3 exhibits a strong non-linear response; the S2 scenario triggers a "Chemical Penalty," increasing peak MDA8 ozone by +5.2% due to the weakened  NOx titration effect in VOC-limited urban cores. Integrated Process Rate (IPR) and EKMA analysis reveal that only the S3 pathway, through synchronous NOx and VOCs reductions (>60%), successfully crosses the "EKMA Ridgeline" and suppresses the atmospheric oxidizing capacity (AOC) by slowing the HOx radical propagation rate by 84.8%. The findings suggest that achieving carbon neutrality goals is a prerequisite for overcoming the ozone bottleneck. This study proposes a "Spatially Differentiated and Temporally Dynamic" control strategy, prioritizing VOCs abatement in urban cores and strict management of industrial emissions during peak photochemical hours to achieve synergistic pollution and carbon reduction.

References

1. A. Mazzeo, J. Zhong, C. Hood, S. Smith, J. Stocker, X. Cai, and W. J. Bloss, “Modelling the impact of national vs. local emission reduction on PM2.5 in the West Midlands, UK using WRF-CMAQ,” Atmosphere, vol. 13, no. 3, p. 377, 2022.

2. C. Fang, J. Qiu, J. Li, and J. Wang, “Analysis of the meteorological impact on PM2.5 pollution in Changchun based on KZ filter and WRF-CMAQ,” Atmospheric Environment, vol. 271, p. 118924, 2022.

3. J. Li, S. Yu, X. Chen, Y. Zhang, M. Li, Z. Li, et al., “Evaluation of the WRF-CMAQ model performances on air quality in China with the impacts of the observation nudging on meteorology,” Aerosol and Air Quality Research, vol. 22, no. 4, p. 220023, 2022.

4. X. Dou, S. Yu, J. Li, Y. Sun, Z. Song, N. Yao, and P. Li, “The WRF-CMAQ simulation of a complex pollution episode with high-level O3 and PM2.5 over the North China Plain: Pollution characteristics and causes,” Atmosphere, vol. 15, no. 2, p. 198, 2024.

5. C. Gao, X. Zhang, A. Xiu, Q. Tong, H. Zhao, S. Zhang, et al., “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,” Geoscientific Model Development, vol. 17, no. 6, pp. 2471–2492, 2024.

6. N. N. L. M. Napi, M. C. G. Ooi, M. T. Latif, L. Juneng, M. S. M. Nadzir, W. Cheah, et al., “Sensitivity analysis of WRF-CMAQ model in predicting PM2.5 and O3 concentration in Peninsular Malaysia: 2019 transboundary burning smoke case study,” Atmospheric Environment, vol. 362, p. 121496, 2025.

7. P. C. Huang, H. M. Hung, H. C. Lai, and C. C. K. Chou, “Assessing the effectiveness of SO2, NOx, and NH3 emission reductions in mitigating winter PM2.5 in Taiwan using CMAQ,” Atmospheric Chemistry and Physics, vol. 24, no. 18, pp. 10759–10772, 2024.

8. W. Duan, X. Wang, S. Cheng, R. Wang, and J. Zhu, “Influencing factors of PM2.5 and O3 from 2016 to 2020 based on DLNM and WRF-CMAQ,” Environmental Pollution, vol. 285, p. 117512, 2021.

9. S. Zhang, Z. Zhang, Y. Li, X. Du, L. Qu, W. Tang, et al., “Formation processes and source contributions of ground-level ozone in urban and suburban Beijing using the WRF-CMAQ modelling system,” Journal of Environmental Sciences, vol. 127, pp. 753–766, 2023.

10. J. Wang, Y. Cai, S. Zou, X. Zhou, and C. Fang, “Source attribution analysis of an ozone concentration increase event in the main urban area of Xi’an using the WRF-CMAQ model,” Atmosphere, vol. 15, no. 10, p. 1208, 2024.

11. J. Wang, W. Zhang, W. Shi, X. Li, and C. Fang, “Analysis of the causes of an O3 pollution event in Suqian on 18–21 June 2020 based on the WRF-CMAQ model,” Atmosphere, vol. 15, no. 7, p. 831, 2024.

Downloads

Published

25 December 2025

Issue

Section

Article

How to Cite

[1]
S. Wang , Trans., “Dynamics of Urban Atmospheric Conditions under Emission Mitigation Pathways”, Eur. J. Public Health Environ. Res., vol. 1, no. 2, pp. 100–113, Dec. 2025, doi: 10.71222/ce2cay39.