Efficiency of cloud condensation nuclei formation from ultrafine particles
Efficiency of cloud condensation nuclei formation from ultrafine particles
复制标题
DOI：
10.5194/acp-7-1367-2007
复制
发表时间：
2007-02-27
影响因子：
6.3
通讯作者：
Adams, P. J.
中科院分区：
文献类型：
--
作者：
Pierce, J. R.;Adams, P. J.
在评估人为气溶胶对云和气候的影响时，大气云凝结核(CCN)浓度是一个关键的不确定性因素。新的超细粒子成长为CCN的能力在整个大气层中各不相同，必须了解才能理解CCN的形成。我们发展了超细粒子增长概率(PUG)模型，以回答关于哪些增长和汇机制控制这种增长，增长如何在大气不同部分之间变化，以及与超细排放的大小和尺寸分布有关的不确定性如何转化为CCN生成的不确定性。PUG模型的输入是可凝结气体的浓度、环境气溶胶的尺寸分布、颗粒沉积时间尺度以及颗粒和可凝结气体的物理性质。研究发现，在大多数情况下，凝聚是超细颗粒的主要生长机制，而与大颗粒的凝聚是超细颗粒的主要下沉机制。在这项工作中，我们发现一个新的超细粒子产生CCN的概率不同于
Atmospheric cloud condensation nuclei (CCN) concentrations are a key uncertainty in the assessment of the effect of anthropogenic aerosol on clouds and climate. The ability of new ultrafine particles to grow to become CCN varies throughout the atmosphere and must be understood in order to understand CCN formation. We have developed the Probability of Ultrafine particle Growth (PUG) model to answer questions regarding which growth and sink mechanisms control this growth, how the growth varies between different parts of the atmosphere and how uncertainties with respect to the magnitude and size distribution of ultrafine emissions translates into uncertainty in CCN generation. The inputs to the PUG model are the concentrations of condensable gases, the size distribution of ambient aerosol, particle deposition timescales and physical properties of the particles and condensable gases. It was found in most cases that condensation is the dominant growth mechanism and coagulation with larger particles is the dominant sink mechanism for ultrafine particles. In this work we found that the probability of a new ultrafine particle generating a CCN varies from