@article {XU2025121124,
	title = {Development of an emission-driven box model to diagnose ozone formation sensitivity},
	journal = {Atmospheric Environment},
	volume = {348},
	year = {2025},
	pages = {121124},
	abstract = {Surface ozone (O3) pollution affects air quality, human health, and the ecosystem. Understanding the complex non-linear relationship between ozone formation and its precursors, nitrogen oxides (NOx), and volatile organic compounds (VOCs) is critical for policymakers to mitigate the pollution. The Empirical Kinetic Modeling Approach (EKMA) based on classical observation-constrained zero-dimension box model provides the sensitivity of ozone production to precursor concentrations instead of emissions. This makes the box-model EKMA hard to apply in a real emission reduction scenario. Here, we developed an alternative box model approach driven by localized emissions, which are derived from the field-observed concentrations. This model approach reproduced the O3 variations well by capturing the short-term changes of NOx and AVOCs emissions among different phases of pollution control during the 31st World University Games in Chengdu in 2023. The EKMA analysis based on this model approach showed a different O3 response to precursor reductions from the concentration-constrained approach, which overestimated the baseline of O3 concentration. The result from the EKMA analysis demonstrated that the O3 level was most sensitive to NOx due to stringent control strategies during the event and rapidly rebounded to almost VOC-limited regime after the event. The effects of VOCs reduction on O3 control examined by concentration-constrained model approach were less pronounced than those by emission-driven approach due to the lack of consideration of the emission-to-reaction process. Our findings suggest that the emission-driven box model is applicable for developing O3 control strategy in the local scale.},
	keywords = {ekma, Methodology, ozone pollution, Sensitivity regime},
	issn = {1352-2310},
	doi = {https://doi.org/10.1016/j.atmosenv.2025.121124},
	url = {https://www.sciencedirect.com/science/article/pii/S1352231025000998},
	author = {Xu, Xuezhen and Xiaorui Chen and Haichao Wang and Yuanjun Gong and Lu, Keding}
}
