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The formation of particles from precursor vapors is an important source of atmospheric aerosol. Research at the Cosmics Leaving OUtdoor Droplets (CLOUD) facility at CERN tries to elucidate which vapors are responsible for this new particle formation, and how in detail it proceeds. Initial measurement campaigns at the CLOUD stainless-steel aerosol chamber focused on investigating particle formation from ammonia (NH3) and sulfuric acid (H2SO4). Experiments were conducted in the presence of water, ozone and sulfur dioxide. Contaminant trace gases were suppressed at the technological limit. For this study, we mapped out the compositions of small NH3-H2SO4 clusters over a wide range of atmospherically relevant environmental conditions. We covered [NH3] in the range from <2 to 1400 pptv, [H2SO4] from 3.3 × 106 to 1.4 × 109 cm−3, and a temperature range from −25 to +20 °C. Negatively and positively charged clusters were directly measured by an atmospheric pressure interface time-of-flight (APi-TOF) mass spectrometer, as they initially formed from gas-phase NH3 and H2SO4, and then grew to larger clusters containing more than 50 molecules of NH3 and H2SO4, corresponding to mobility-equivalent diameters greater than 2 nm. Water molecules evaporate from these clusters during sampling and are not observed. We found that the composition of the NH3-H2SO4 clusters is primarily determined by the ratio of gas-phase concentrations [NH3] / [H2SO4], as well as by temperature. Pure binary H2O-H2SO4 clusters (observed as clusters of only H2SO4) only form at [NH3] / [H2SO4]<0.1 to 1. For larger values of [NH3] / [H2SO4], the composition of NH3-H2SO4 clusters was characterized by the number of NH3 molecules m added for each added H2SO4 molecule n (Δm / Δn), where n is in the range 4–18 (negatively charged clusters) or 1–17 (positively charged clusters). For negatively charged clusters, Δm / Δn saturated between 1 and 1.4 for [NH3] / [H2SO4]>10. Positively charged clusters grew on average by Δm / Δn = 1.05 and were only observed at sufficiently high [NH3] / [H2SO4]. The H2SO4 molecules of these clusters are partially neutralized by NH3, in close resemblance to the acid-base bindings of ammonium bisulfate. Supported by model simulations, we substantiate previous evidence for acid-base reactions being the essential mechanism behind the formation of these clusters under atmospheric conditions and up to sizes of at least 2 nm. Our results also suggest that yet unobservable electrically neutral NH3-H2SO4 clusters grow by generally the same mechanism as ionic clusters, particularly for [NH3] / [H2SO4]>10. We expect that NH3-H2SO4 clusters form and grow also mostly by Δm / Δn>1 in the atmosphere's boundary layer, as [NH3] / [H2SO4] is mostly larger than 10. We compared our results from CLOUD with APi-TOF measurements of NH3-H2SO4 anion clusters during new particle formation in the Finnish boreal forest. However, the exact role of NH3-H2SO4 clusters in boundary layer particle formation remains to be resolved.
Immersion freezing is the most relevant heterogeneous ice nucleation mechanism through which ice crystals are formed in mixed-phase clouds. In recent years, an increasing number of laboratory experiments utilizing a variety of instruments have examined immersion freezing activity of atmospherically relevant ice nucleating particles (INPs). However, an inter-comparison of these laboratory results is a difficult task because investigators have used different ice nucleation (IN) measurement methods to produce these results. A remaining challenge is to explore the sensitivity and accuracy of these techniques and to understand how the IN results are potentially influenced or biased by experimental parameters associated with these techniques.
Within the framework of INUIT (Ice Nucleation research UnIT), we distributed an illite rich sample (illite NX) as a representative surrogate for atmospheric mineral dust particles to investigators to perform immersion freezing experiments using different IN measurement methods and to obtain IN data as a function of particle concentration, temperature (T), cooling rate and nucleation time. Seventeen measurement methods were involved in the data inter-comparison. Experiments with seven instruments started with the test sample pre-suspended in water before cooling, while ten other instruments employed water vapor condensation onto dry-dispersed particles followed by immersion freezing. The resulting comprehensive immersion freezing dataset was evaluated using the ice nucleation active surface-site density (ns) to develop a representative ns(T) spectrum that spans a wide temperature range (−37 °C < T < −11 °C) and covers nine orders of magnitude in ns.
Our inter-comparison results revealed a discrepancy between suspension and dry-dispersed particle measurements for this mineral dust. While the agreement was good below ~ −26 °C, the ice nucleation activity, expressed in ns, was smaller for the wet suspended samples and higher for the dry-dispersed aerosol samples between about −26 and −18 °C. Only instruments making measurement techniques with wet suspended samples were able to measure ice nucleation above −18 °C. A possible explanation for the deviation between −26 and −18 °C is discussed. In general, the seventeen immersion freezing measurement techniques deviate, within the range of about 7 °C in terms of temperature, by three orders of magnitude with respect to ns. In addition, we show evidence that the immersion freezing efficiency (i.e., ns) of illite NX particles is relatively independent on droplet size, particle mass in suspension, particle size and cooling rate during freezing. A strong temperature-dependence and weak time- and size-dependence of immersion freezing efficiency of illite-rich clay mineral particles enabled the ns parameterization solely as a function of temperature. We also characterized the ns (T) spectra, and identified a section with a steep slope between −20 and −27 °C, where a large fraction of active sites of our test dust may trigger immersion freezing. This slope was followed by a region with a gentler slope at temperatures below −27 °C. A multiple exponential distribution fit is expressed as ns(T) = exp(23.82 × exp(−exp(0.16 × (T + 17.49))) + 1.39) based on the specific surface area and ns(T) = exp(25.75 × exp(−exp(0.13 × (T + 17.17))) + 3.34) based on the geometric area (ns and T in m−2 and °C, respectively). These new fits, constrained by using an identical reference samples, will help to compare IN measurement methods that are not included in the present study and, thereby, IN data from future IN instruments.