Key Laboratory of Atmospheric Chemistry of CMA, Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, Beijing, 100081, China.
College of Earth Science, University of Chinese Academy of Sciences, Beijing, 100049, China.
Sci Rep. 2017 Jul 19;7(1):5819. doi: 10.1038/s41598-017-05998-3.
Atmospheric aerosol particles acting as cloud condensation nuclei (CCN) are key elements in the hydrological cycle and climate. To improve our understanding of the activation characteristics of CCN and to obtain accurate predictions of their concentrations, a long-term field campaign was carried out in the Yangtze River Delta, China. The results indicated that the CCN were easier to activate in this relatively polluted rural station than in clean (e.g., the Amazon region) or dusty (e.g., Kanpur-spring) locations, but were harder to activate than in more polluted urban areas (e.g., Beijing). An improved method, using two additional parameters-the maximum activation fraction and the degree of heterogeneity, is proposed to predict the accurate, size-resolved concentration of CCN. The value ranges and prediction uncertainties of these parameters were evaluated. The CCN predicted using this improved method with size-resolved chemical compositions under an assumption that all particles were internally mixed showed the best agreement with the long-term field measurements.
大气气溶胶粒子作为云凝结核(CCN)是水文循环和气候的关键要素。为了提高我们对 CCN 激活特性的理解,并获得其浓度的准确预测,在中国长江三角洲地区进行了一项长期的野外考察。结果表明,与清洁(例如亚马逊地区)或尘土飞扬(例如坎普尔春季)的地点相比,CCN 在这个相对污染的农村站更容易被激活,但比污染更严重的城市地区(例如北京)更难被激活。提出了一种改进的方法,使用两个附加参数——最大激活分数和异质性程度,来预测准确的、按大小分辨的 CCN 浓度。评估了这些参数的取值范围和预测不确定性。在假设所有粒子都是内部混合的情况下,使用这种改进的方法和按大小分辨的化学成分预测 CCN,与长期野外测量的结果最为吻合。