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Cloud water composition during HCCT-2010: Scavenging efficiencies, solute concentrations, and droplet size dependence of inorganic ions and dissolved organic carbon

机译:HCCT-2010期间的云水组成:清除效率,溶质浓度和无机离子的液滴尺寸依赖性和溶解有机碳

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Cloud water samples were taken in September/October 2010 at Mt. Schmücke in a rural, forested area in Germany during the Lagrange-type Hill Cap Cloud Thuringia 2010 (HCCT-2010) cloud experiment. Besides bulk collectors, a three-stage and a five-stage collector were applied and samples were analysed for inorganic ions (SO42?,NO3?, NH4+, Cl?, Na+, Mg2+, Ca2+, K+), H2O2 (aq), S(IV), and dissolved organic carbon (DOC). Campaign volume-weighted mean concentrations were 191, 142, and 39 μmol L?1 for ammonium, nitrate, and sulfate respectively, between 4 and 27 μmol L?1 for minor ions, 5.4 μmol L?1 for H2O2 (aq), 1.9 μmol L?1 for S(IV), and 3.9 mgC L?1 for DOC. The concentrations compare well to more recent European cloud water data from similar sites. On a mass basis, organic material (as DOC × 1.8) contributed 20–40 % (event means) to total solute concentrations and was found to have non-negligible impact on cloud water acidity. Relative standard deviations of major ions were 60–66 % for solute concentrations and 52–80 % for cloud water loadings (CWLs). The similar variability of solute concentrations and CWLs together with the results of back-trajectory analysis and principal component analysis, suggests that concentrations in incoming air masses (i.e. air mass history), rather than cloud liquid water content (LWC), were the main factor controlling bulk solute concentrations for the cloud studied. Droplet effective radius was found to be a somewhat better predictor for cloud water total ionic content (TIC) than LWC, even though no single explanatory variable can fully describe TIC (or solute concentration) variations in a simple functional relation due to the complex processes involved. Bulk concentrations typically agreed within a factor of 2 with co-located measurements of residual particle concentrations sampled by a counterflow virtual impactor (CVI) and analysed by an aerosol mass spectrometer (AMS), with the deviations being mainly caused by systematic differences and limitations of the approaches (such as outgassing of dissolved gases during residual particle sampling). Scavenging efficiencies (SEs) of aerosol constituents were 0.56–0.94, 0.79–0.99, 0.71–98, and 0.67–0.92 for SO42?, NO3?, NH4+, and DOC respectively when calculated as event means with in-cloud data only. SEs estimated using data from an upwind site were substantially different in many cases, revealing the impact of gas-phase uptake (for volatile constituents) and mass losses across Mt. Schmücke likely due to physical processes such as droplet scavenging by trees and/or entrainment. Drop size-resolved cloud water concentrations of major ions SO42?, NO3?, and NH4+ revealed two main profiles: decreasing concentrations with increasing droplet size and “U” shapes. In contrast, profiles of typical coarse particle mode minor ions were often increasing with increasing drop size, highlighting the importance of a species' particle concentration size distribution for the development of size-resolved solute concentration patterns. Concentration differences between droplet size classes were typically? 2 for major ions from the three-stage collector and somewhat more pronounced from the five-stage collector, while they were much larger for minor ions. Due to a better separation of droplet populations, the five-stage collector was capable of resolving some features of solute size dependencies not seen in the three-stage data, especially sharp concentration increases (up to a factor of 5–10) in the smallest droplets for many solutes.
机译:云水样本于2010年9月/ 10月在2010年9月/ 10月在德国的农村森林地区,在拉格朗兰型山顶云图林根州2010年(HCCT-2010)云实验期间。除了散装收集器外,施用三级和五级收集器,对无机离子进行分析样品(SO42〜,NO 3?,NH 4 +,Cl 2,Na +,Mg2 +,Ca2 +,K +),H 2 O 2(AQ),S (iv)和溶解的有机碳(DOC)。竞选体积加权平均浓度为191,142和39μmol,铵,硝酸铵和硫酸盐,少量离子4至27μmol,4.4μmol,1.4μm(aq),1.9 S(iv)的μmoll≤1,以及用于doc的3.9 mgc l?1。浓度比类似地点的更近期欧洲云水数据相比。在质量基础上,有机材料(作为DOC×1.8)导致20-40%(事件手段)到总溶质浓度,并发现对云水酸度的影响不可或缺。主离子的相对标准偏差为溶质浓度的60-66%,云水载荷(CWLS)的52-80%。溶质浓度和CWL的相似可变性以及后轨迹分析和主成分分析的结果表明,在进入空气质量(即空气质量历史)中的浓度,而不是云液体含水量(LWC)是主要因素研究云的散装溶质浓度。发现液滴有效半径是云水总离子含量(TIC)的稍微预测因子,而不是LWC,即使没有单一的解释变量可以完全描述由于所涉及的复杂过程而在简单的功能关系中的TIC(或溶质浓度)变化。堆积浓度通常在2倍的时间内同意,共同定位测量由逆流虚拟撞击器(CVI)采样的残留颗粒浓度,并由气溶胶质谱仪(AMS)分析,偏差主要由系统差异和限制引起这种方法(例如在残留颗粒抽样期间除去溶解气体)。气溶胶成分的清除效率(SES)为0.56-0.94,0.79-0.99,0.71-98和0.67-0.92,用于SO42?,NO3?,NH4 +,并且在仅用云内数据的事件手段计算时分别计算。在许多情况下,使用来自上风地理位置的数据估计的SE在许多情况下显着不同,揭示了气相摄取(对于挥发性成分)的影响和跨越MT的大规模损失可能由于树木和/或夹带等物理过程,例如液滴清除。滴尺寸分辨云水浓度的主要离子SO42?,NO3?,NH4 +揭示了两个主要曲线:随着液滴尺寸的增加和“U”形状降低浓度。相反,典型的粗颗粒模式次要离子的谱越随滴尺寸的增加而增加,突出了物种颗粒浓度尺寸分布的重要性,用于显影尺寸分辨的溶质浓度图案。液滴尺寸类之间的浓度差异通常是? 2对于三级收集器的主要离子,从五阶段收集器中稍微明显,而微离子的稍微较大。由于液滴种群的更好分离,五级收集器能够解决在三阶段数据中未见的溶质大小依赖性的一些特征,特别是最小的浓度增加(高达5-10倍)液滴很多溶质。

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