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首页> 外文期刊>Atmospheric chemistry and physics >Hygroscopicity distribution concept for measurement data analysis and modeling of aerosol particle mixing state with regard to hygroscopic growth and CCN activation
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Hygroscopicity distribution concept for measurement data analysis and modeling of aerosol particle mixing state with regard to hygroscopic growth and CCN activation

机译:吸湿性分布概念,用于有关吸湿性生长和CCN活化的气溶胶颗粒混合状态的测量数据分析和建模

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摘要

This paper presents a general concept and mathematical framework of particle hygroscopicity distribution for the analysis and modeling of aerosol hygroscopic growth and cloud condensation nucleus (CCN) activity. The cumulative distribution function of particle hygroscopicity, H(k,D_d) is defined as the number fraction of particles with a given dry diameter, (Hk,D _d,) and with an effective hygroscopicity parameter smaller than the parameter.K From hygroscopicity tandem differential mobility analyzer (HTDMA) and size-resolved CCN measurement data, (Hk,D_d) can be derived by solving the k-K?hler model equation. Alternatively, H(k,_d) can be predicted from measurement or model data resolving the chemical composition of single particles. A range of model scenarios are used to explain and illustrate the concept, and exemplary practical applications are shown with HTDMA and CCN measurement data from polluted megacity and pristine rainforest air. Lognormal distribution functions are found to be suitable for approximately describing the hygroscopicity distributions of the investigated atmospheric aerosol samples. For detailed characterization of aerosol hygroscopicity distributions, including externally mixed particles of low hygroscopicity such as freshly emitted soot, we suggest that size-resolved CCN measurements with a wide range and high resolution of water vapor supersaturation and dry particle diameter should be combined with comprehensive HTDMA measurements and size-resolved or single-particle measurements of aerosol chemical composition, including refractory components. In field and laboratory experiments, hygroscopicity distribution data from HTDMA and CCN measurements can complement mixing state information from optical, chemical and volatility-based techniques. Moreover, we propose and intend to use hygroscopicity distribution functions in model studies investigating the influence of aerosol mixing state on the formation of cloud droplets.
机译:本文提出了颗粒吸湿性分布的一般概念和数学框架,用于分析和建模气溶胶吸湿性增长和云凝结核(CCN)活性。颗粒吸湿性的累积分布函数H(k,D_d)定义为具有给定干径(Hk,D _d,)且有效吸湿性参数小于该参数的颗粒的数量分数.K由吸湿性串联差分迁移率分析仪(HTDMA)和尺寸解析的CCN测量数据(Hk,D_d)可以通过求解kK?hler模型方程式得出。可替代地,可以从解析单个颗粒的化学组成的测量或模型数据中预测H(k,_d)。一系列模型场景用于解释和说明该概念,并显示了来自污染大城市和原始雨林空气的HTDMA和CCN测量数据的示例性实际应用。发现对数正态分布函数适合于大致描述所研究的大气气溶胶样品的吸湿性分布。为了详细表征气溶胶的吸湿性分布,包括低吸湿性的外部混合颗粒(例如新鲜散发的烟灰),我们建议将具有宽范围且高分辨率的水蒸气过饱和度和干粒径的尺寸分辨CCN测量与综合HTDMA结合使用测量和气溶胶化学成分(包括耐火成分)的尺寸分辨或单颗粒测量。在现场和实验室实验中,来自HTDMA和CCN测量的吸湿性分布数据可以补充基于光学,化学和挥发性技术的混合状态信息。此外,我们提出并打算在模型研究中使用吸湿性分布函数,以研究气溶胶混合状态对云滴形成的影响。

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