Ultrafine particles (UFPs) pose a significant health risk, making comprehensive assessment essential. The influence of emission sources on particle concentrations is not only constrained by meteorological conditions but often intertwined with them, making it challenging to separate these effects. This study utilized valuable long-term particle number and size distribution (PNSD) data from 2018 to 2023 to develop a tree-based machine learning model enhanced with an interpretable component, incorporating temporal markers to characterize background or time series residuals. Our results demonstrated that, differing from PM_2.5, which is significantly shaped by planetary boundary layer height, wind speed plays a crucial role in determining the particle number concentration (PNC), showing strong regional specificity. Furthermore, we systematically identified and analyzed anthropogenically influenced periodic trends. Notably, while Aitken mode observations are initially linked to traffic-related peaks, both Aitken and nucleation modes contribute to concentration peaks during rush hour periods on short-term impacts after deweather adjustment. Pollutant baseline concentrations are largely driven by human activities, with meteorological factors modulating their variability, and the secondary formation of UFPs is likely reflected in temporal residuals. This study provides a flexible framework for isolating meteorological effects, allowing more accurate assessment of anthropogenic impacts and targeted management strategies for UFP and PNC.

中文翻译：

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超细粒子观测的挑战：解决估计误判和时间趋势的必要性


超细颗粒物 （UFP） 对健康构成重大风险，因此必须进行全面评估。发射源对颗粒物浓度的影响不仅受到气象条件的限制，而且往往与气象条件交织在一起，这使得区分这些影响变得具有挑战性。本研究利用 2018 年至 2023 年有价值的长期颗粒数量和大小分布 （PNSD） 数据开发了一个基于树的机器学习模型，该模型使用可解释组件进行增强，结合时间标记来表征背景或时间序列残差。我们的结果表明，与 PM_{2.5 不同，PM 2.5} 受行星边界层高度显着影响，风速在决定粒子数浓度 （PNC） 方面起着至关重要的作用，表现出很强的区域特异性。此外，我们系统地识别和分析了受人为影响的周期性趋势。值得注意的是，虽然 Aitken 模式观测最初与交通相关的峰值有关，但 Aitken 模式和成核模式都有助于在天气调整后对高峰时段的短期影响产生集中峰值。污染物基线浓度主要由人类活动驱动，气象因素调节其可变性，UFP 的二次形成可能反映在时间残差中。本研究为隔离气象影响提供了一个灵活的框架，可以更准确地评估人为影响和 UFP 和 PNC 的针对性管理策略。