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中国重点控制区细颗粒物与臭氧污染事件的伴随分析

Adjoint analysis of PM and O episodes in priority control zones in China.

作者信息

Zhang Ruixin, Chen Zhihong, Wu Xueyan, Liu Qiming, Mai Zelin, Zheng Zhiyu, Chen Yilin, Tao Shu, Hu Yongtao, Zhao Shunliu, Hakami Amir, Russell Armistead G, Shen Huizhong

机构信息

State Key Laboratory of Soil Pollution Control and Safety, Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China.

Coastal Atmosphere and Climate of the Greater Bay Area Observation and Research Station of Guangdong Province, Southern University of Science and Technology, Shenzhen, Guangdong, 518055, China.

出版信息

Environ Sci Ecotechnol. 2025 Aug 9;27:100612. doi: 10.1016/j.ese.2025.100612. eCollection 2025 Sep.

Abstract

Understanding and mitigating PM and ozone (O) pollution remains challenging due to the nonlinear atmospheric chemistry and spatially heterogeneous nature of pollutant emissions. Traditional forward modeling approaches suffer from high computational cost and limited diagnostic resolution to precisely attribute emissions sources at fine spatial, temporal, and chemical scales. Adjoint modeling has emerged as an efficient alternative, enabling high-resolution, multi-pollutant source attribution in a single integrated framework; however, its application to simultaneous PM-O pollution episodes is limited, particularly in densely populated regions experiencing complex co-pollutant interactions. Here we apply a newly developed multiphase adjoint of the Community Multiscale Air Quality (CMAQ) model to quantify the emission sensitivities of PM and O concentrations during pollution episodes in major urban agglomerations. Our results indicate that local emissions predominantly drive PM concentrations, contributing up to 79 μg m. In contrast, O episodes are largely initiated by regional transport (3.8-7.3 ppbv), surpassing local emission contributions during episode onset. The sensitivity analyses reveal distinct spatial emission signatures and pollutant-specific influences from critical precursors, including volatile organic compounds (VOCs; up to 15.9 ppbv O, 11.4 μg m PM), nitrogen oxides (NO ; 16.6 ppbv O, 13.8 μg m PM), and ammonia (NH; up to 8.7 μg m PM). This study demonstrates the diagnostic strength and predictive capabilities of adjoint modeling in unraveling complex source-receptor relationships. By offering detailed, pollutant-specific emission sensitivity information, our approach provides a robust foundation for precision-driven emission control strategies and improved cross-regional policy coordination, substantially advancing air quality management frameworks.

摘要

由于大气化学的非线性和污染物排放的空间异质性,理解和减轻颗粒物(PM)和臭氧(O)污染仍然具有挑战性。传统的正向建模方法存在计算成本高和诊断分辨率有限的问题,难以在精细空间、时间和化学尺度上精确确定排放源。伴随建模已成为一种有效的替代方法,能够在单一综合框架内进行高分辨率、多污染物源归因;然而,其在同时发生的PM-O污染事件中的应用有限,尤其是在经历复杂共污染物相互作用的人口密集地区。在此,我们应用新开发的社区多尺度空气质量(CMAQ)模型的多相伴随模型,来量化主要城市群污染事件期间PM和O浓度的排放敏感性。我们的结果表明,本地排放主要驱动PM浓度,贡献高达79 μg/m³。相比之下,O污染事件在很大程度上由区域传输引发(3.8 - 7.3 ppbv),在事件开始时超过了本地排放贡献。敏感性分析揭示了关键前体物(包括挥发性有机化合物(VOCs;高达15.9 ppbv O、11.4 μg/m³ PM)、氮氧化物(NOₓ;16.6 ppbv O、13.8 μg/m³ PM)和氨(NH₃;高达8.7 μg/m³ PM))不同的空间排放特征和特定污染物影响。本研究展示了伴随建模在揭示复杂源 - 受体关系方面的诊断优势和预测能力。通过提供详细的、特定污染物的排放敏感性信息,我们的方法为精准驱动的排放控制策略和改进的跨区域政策协调提供了坚实基础,极大地推进了空气质量管理框架。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d418/12362701/80e98bb59659/ga1.jpg

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