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

Abstract

Abstract. 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.

Chemical composition and droplet size distribution of cloud at the summit of Mount Tai, China

“…Oxalic acid was significantly correlated with formic acid (r = 0.667, p ≤ 0.01) and acetic acid (r = 0.638, p ≤ 0.01). This implied that formic acid, acetic acid and oxalic acid were probably emitted from the same sources and/or accumulated under similar physical conditions (Tanner and Law, 2003). No significant correlations were found between lactic acid and the other three carboxylic acids; no significant correlations were found between lactic acid and the other water-soluble ions in the cloud samples.…”

“…The lower pH values were likely to occur at higher concentrations of PM 2.5 . Generally, changes to the solute concentrations in cloud water can be caused by a combination of factors such as the microphysical conditions, the CCN properties, the chemical reactions in the cloud droplets and the gas-liquid phase equilibrium (van Pinxteren et al, 2016). Our data emphasize the crucial effect of PM 2.5 on the changes in ion concentrations.…”

“…Based on the sources or source strengths of formic acid and acetic acid, the formicto-acetic acid ratio (F / A) could be used as an indicator to determine the sources of organic acids (Sun et al, 2016;Tan et al, 2010). A low ratio indicated the important role of direct emissions (such as biomass emission, combustion activities and automobile exhaust) whereas a high ratio indicated the in situ photochemical generation of formic acid (Talbot et al, 1988;Tanner and Law, 2003). In the collected cloud samples, formic acid and acetic acid were highly correlated (r = 0.758, p ≤ 0.01).…”

Uptake of nitric acid, ammonia, and organics in orographic clouds: mass spectrometric analyses of droplet residual and interstitial aerosol particles

“…The measurements were conducted during the HCCT-2010 (Hill Cap Cloud Thuringia 2010) experiment between 13 September and 25 October 2010 (Tilgner et al, 2014). The experiment was carried out in the mountainous Thuringian Forest region in central Germany.…”

“…For the comparison of cloud residuals and interstitial aerosol, we have chosen the full cloud events (FCEs) defined in Tilgner et al (2014) and listed in Table 1. For comparison of cloud residuals and out-of-cloud aerosol, we tried to find appropriate cloud-free comparison periods with similar air mass origin and close as possible in time to the cloud measure- Table 1.…”