The size-resolved cloud condensation nuclei (CCN) activity and its prediction based on aerosol hygroscopicity and composition in the Pearl Delta River (PRD) region during wintertime 2014
The size-resolved cloud condensation nuclei (CCN) activity and its prediction based on aerosol hygroscopicity and composition in the Pearl Delta River (PRD) region during wintertime 2014
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2014年冬季珠三角地区云凝结核尺寸分辨活动及其基于气溶胶吸湿性和成分的预测
DOI：
10.5194/acp-18-16419-2018
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发表时间：
2018
影响因子：
6.3
通讯作者：
Zhao Jun
中科院分区：
文献类型：
--
作者：
Cai Mingfu;Tan Haobo;Chan Chak K.;Qin Yiming;Xu Hanbing;Li Fei;Schurman Misha I.;Liu Li;Zhao Jun
抽象。一种吸湿串联微分迁移率分析仪（HTDMA），
扫描移动云凝结核（CCN）分析仪（SMCA）和Aerodyne高分辨率
飞行时间气溶胶质谱仪（HR-ToF-AMS）用于，
分别测量吸湿性、凝结核活化和
珠江三角洲番禺站气溶胶粒子的化学组成
2014年冬季的三角洲地区。尺寸分解的分布
在四个过饱和度下的CCN（0.1%的SS，
0.2%、0.4%和0.7%）和气溶胶颗粒尺寸分布
由SMCA提供。吸湿性参数κ（κCCN、κHTDMA和κAMS）为，
分别基于SMCA、HTDMA和AMS测量计算。结果
显示κHTDMA值略小于
所有直径的κCCN均为一个，且颗粒大于
在100 nm处，κAMS值显著小于其他两种
（κCCN和κHTDMA），这可能归因于
低估了有机物的吸湿性（κorg）。的
根据生长因子-概率计算的活化率（AR）
密度函数（Gf-PDF）没有表面张力校正被发现是
低于CCN测量值，很可能是由于
未校正的表面张力（σs scina），不考虑
有机化合物的表面活性剂效应。我们更好地证明了这一点
计算的AR和测量的AR之间的一致性可以通过以下方式获得：
调整σs闪烁。提出了各种方案来预测
基于HTDMA和AMS的CCN数浓度（NCCN）
测量.总的来说，预测的NCCN相当吻合，
对应的测量值使用不同的方案。对于HTDMA
测量，NCCN值预测从实时AR
测量值略小于（1.66%），
活化直径（D50）法，由于假定内部混合
D50预测从散装预测的NCCN值
PM 1的化学组成高于（11.5%），
那些来自AMS测量的尺寸分辨组合物的，因为
PM 1中无机物含量较高。的
根据AMS测量计算的NCCN值在
0.1%和0.2%过饱和，这可能是由于低估了
κorg和σs scina的高估。对于0.4%的SS值
和0.7%，发现NCCN值略有高估，因为
内部混合假设。我们的研究结果强调，
准确评估有机物对吸湿和吸湿的影响，
参数κ和表面张力σ，以便准确地
预测CCN活动。
Abstract. A hygroscopic tandem differential mobility analyzer (HTDMA), a
scanning mobility cloud condensation nuclei (CCN) analyzer (SMCA), and an Aerodyne high-resolution
time-of-flight aerosol mass spectrometer (HR-ToF-AMS) were used to,
respectively, measure the hygroscopicity, condensation nuclei activation, and
chemical composition of aerosol particles at the Panyu site in the Pearl
River Delta region during wintertime 2014. The distribution of the size-resolved
CCN at four supersaturations (SSs of 0.1 %,
0.2 %, 0.4 %, and 0.7 %) and the aerosol particle size distribution
were obtained by the SMCA. The hygroscopicity parameter κ (κCCN, κHTDMA, and κAMS) was,
respectively, calculated based upon the SMCA, HTDMA, and AMS measurements. The results
showed that the κHTDMA value was slightly smaller than the
κCCN one at all diameters and for particles larger than
100 nm, and the κAMS value was significantly smaller than the others
(κCCN and κHTDMA), which could be attributed to
the underestimated hygroscopicity of the organics (κorg). The
activation ratio (AR) calculated from the growth factor – probability
density function (Gf-PDF) without surface tension correction was found to be
lower than that from the CCN measurements, due most likely to the
uncorrected surface tension (σs∕a) that did not consider the
surfactant effects of the organic compounds. We demonstrated that better
agreement between the calculated and measured ARs could be obtained by
adjusting σs∕a. Various schemes were proposed to predict the
CCN number concentration (NCCN) based on the HTDMA and AMS
measurements. In general, the predicted NCCN agreed reasonably well
with the corresponding measured ones using different schemes. For the HTDMA
measurements, the NCCN value predicted from the real-time AR
measurements was slightly smaller (∼6.8 %) than that from
the activation diameter (D50) method due to the assumed internal mixing
in the D50 prediction. The NCCN values predicted from bulk
chemical composition of PM1 were higher (∼11.5 %) than
those from size-resolved composition measured by the AMS because a
significant fraction of PM1 was composed of inorganic matter. The
NCCN values calculated from AMS measurement were underpredicted at
0.1 % and 0.2 % supersaturations, which could be due to underestimation of
κorg and overestimation of σs∕a. For SS values of 0.4 %
and 0.7 %, slight overpredicted NCCN values were found because of
the internal mixing assumption. Our results highlight the need for
accurately evaluating the effects of organics on both the hygroscopic
parameter κ and the surface tension σ in order to accurately
predict CCN activity.