Institute for Environmental and Climate Research, Jinan University, Guangzhou, Guangdong 511443, China.
Institute for Environmental and Climate Research, Jinan University, Guangzhou, Guangdong 511443, China.
Sci Total Environ. 2020 Jun 1;719:137473. doi: 10.1016/j.scitotenv.2020.137473. Epub 2020 Feb 20.
Cloud condensation nuclei (CCN) play an important role in the formation and evolution of cloud droplets. However, the dataset of global CCN number concentration (N) is still scarce due to the lack of direct CCN measurements, hindering an accurate evaluation of its climate effects. Alternative approaches to determine N have thus been proposed to calculate N based on measurements of other aerosol properties, such as particle number size distribution, bulk aerosol chemical composition and aerosol optical properties. To better understand the interaction between haze pollution and climate, we performed direct CCN measurements in the winter of 2018 at the Gucheng site, a typical polluted suburban site in North China Plain (NCP). The results show that the average CCN concentrations were 3.81 × 10 cm, 5.35 × 10 cm, 9.74 × 10 cm, 1.27 × 10 cm, 1.44 × 10 cm at measured supersaturation levels of 0.114%, 0.148%, 0.273%, 0.492% and 0.864%, respectively. Based on these observational data, we have further investigated two methods of calculating N from: (1) bulk aerosol chemical composition and particle number size distribution; (2) bulk aerosol chemical composition and aerosol optical properties. Our results showed that both methods could well reproduce the observed concentration (R > 0.88) and variability of N with a 9% to 23% difference in the mean value. Further error analysis shows that the estimated N tends to be underestimated by about 20% during the daytime while overestimated by <10% at night compared with the measured N. These results provide quantitative instructions for the N prediction based on conventional aerosol measurements in the NCP.
云凝结核 (CCN) 在云滴的形成和演化中起着重要作用。然而，由于缺乏直接的 CCN 测量，全球 CCN 数浓度 (N) 的数据集仍然稀缺，这阻碍了对其气候影响的准确评估。因此，已经提出了替代方法来确定 N，以便根据其他气溶胶特性（例如粒子数大小分布、整体气溶胶化学成分和气溶胶光学特性）的测量来计算 N。为了更好地理解雾霾污染与气候之间的相互作用，我们于 2018 年冬季在典型的华北平原（NCP）污染郊区古城区进行了直接 CCN 测量。结果表明，在测量的过饱和度水平为 0.114%、0.148%、0.273%、0.492%和 0.864%时，CCN 浓度的平均值分别为 3.81×10cm、5.35×10cm、9.74×10cm、1.27×10cm和 1.44×10cm。基于这些观测数据，我们进一步研究了两种从以下方面计算 N 的方法：(1) 整体气溶胶化学成分和粒子数大小分布；(2) 整体气溶胶化学成分和气溶胶光学特性。我们的结果表明，这两种方法都可以很好地再现观测到的 N 的浓度（R>0.88）和变化性，均值差异在 9%到 23%之间。进一步的误差分析表明，与实测 N 相比，在白天估计的 N 往往低估了约 20%，而在夜间高估了<10%。这些结果为在 NCP 基于常规气溶胶测量的 N 预测提供了定量指导。
