ORIGINAL RESEARCH article
Front. Environ. Sci.
Sec. Atmosphere and Climate
Unveiling the Critical Role of Tall-Stack Emissions in Winter Nitrate Episodes Over North China through Machine Learning and 3D Model Analysis
Provisionally accepted
- Institute of Atmospheric Physics Chinese Academy of Sciences, Beijing, China
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Particulate nitrate (pNO3-) pollution persists over the North China Plain despite emission controls. We unravel a key mechanism: tall industrial stacks (≥210 m) release ammonia which, under strong winter oxidizing conditions, forms ammonium nitrate aloft. Utilizing a novel 3D high-resolution nitrate assimilation dataset and machine learning (XGBoost-SHAP), we tracked a major pollution event. Nitrate formed above 200 m accumulated in the nocturnal residual layer. Morning boundary layer development mixed this pollution downward, elevating surface concentrations by up to 35.5 μg m-3 within hours. Crucially, the Taihang and Yanshan Mountains south-westerly winds channeled, transporting the plume ~ 400 km. Downwind urban heating and enhanced oxidants during winter (including COVID-19-period anomalies) further amplified nitrate production within the boundary layer. This study establishes a complete 3D picture of elevated nitrate formation, transport, and mixing, highlighting the need for targeted controls on elevated industrial sources and cross-regional strategies.
Keywords: Particulate nitrate, data assimilation, Elevated source, Three-dimensional transport, Atmospheric oxidation
Received: 03 Oct 2025; Accepted: 11 Nov 2025.
Copyright: © 2025 Yang, Tian and wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Ting Yang, tingyang@mail.iap.ac.cn
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