Journal
ATMOSPHERIC CHEMISTRY AND PHYSICS
Volume 21, Issue 6, Pages 4541-4560Publisher
COPERNICUS GESELLSCHAFT MBH
DOI: 10.5194/acp-21-4541-2021
Keywords
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Funding
- European Union's Horizon 2020 Research and Innovation programme (ACTRIS TNA) [654109]
- Academy of Finland [299574, 326948, 331207]
- Agence Nationale de la Recherche [ANR-14-CE03-0004-04]
- French Government Laboratory of Excellence initiative [ANR-10-LABX-0006]
- Region Auvergne
- Austrian Science Fund (FWF) [J3951-N36]
- Academy of Finland (AKA) [326948, 326948, 331207, 331207] Funding Source: Academy of Finland (AKA)
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Sulfuric acid is a key precursor for atmospheric new particle formation, and while direct measurements of [H2SO4] remain challenging, proxies have been developed to predict it, with newly developed proxies showing better performance under volcanic plume conditions.
Sulfuric acid (H2SO4) is commonly accepted as a key precursor for atmospheric new particle formation (NPF). However, direct measurements of [H2SO4] remain challenging, thereby preventing the determination of this important quantity, and, consequently, a complete understanding of its contribution to the NPF process. Several proxies have been developed to bridge the gaps, but their ability to predict [H2SO4] under very specific conditions, such as those encountered in volcanic plumes (including, in particular, high sulfur dioxide mixing ratios), has not been evaluated so far. In this context, the main objective of the present study was to develop new proxies for daytime [H2SO4] under volcanic plume conditions and compare their performance to that of the proxies available in the literature. Specifically, the data collected at Maido during the OCTAVE (Oxygenated organic Compounds in the Tropical Atmosphere: variability and atmosphere-biosphere Exchanges) 2018 campaign, in the volcanic eruption plume of the Piton de la Fournaise, were first used to derive seven proxies based on knowledge of the sulfur dioxide (SO2) mixing ratio, global radiation, condensation sink (CS) and relative humidity (RH). A specific combination of some or all of these variables was tested in each of the seven proxies. In three of them (F1-F3), all considered variables were given equal weight in the prediction of [H2SO4], whereas adjusted powers were allowed (and determined during the fitting procedure) for the different variables in the other four proxies (A1-A4). Overall, proxies A1-A4 were found to perform better than proxies F1-F3, with, in particular, improved predictive ability for [H2SO4] > 2 x 10(8) cm(-3). The CS was observed to play an important role in regulating [H2SO4], whereas the inclusion of RH did not improve the predictions. A last expression accounting for an additional sink term related to cluster formation, S1, was also tested and showed a very good predictive ability over the whole range of measured [H2SO4]. In a second step, the newly developed proxies were further evaluated using airborne measurements performed in the passive degassing plume of Etna during the STRAP (Synergie Transdisciplinaire pour Repondre aux Aleas lies aux Panaches volcaniques) 2016 campaign. Increased correlations between observed and predicted [H2SO4] were obtained when the dependence of predicted [H2SO4] on the CS was the lowest and when the dependence on [SO2] was concurrently the highest. The best predictions were finally retrieved by the simple formulation of F2 (in which [SO2] and radiation alone were assumed to explain the variations in [H2SO4] with equal contributions), with a pre-factor adapted to the STRAP data. All in all, our results illustrate the fairly good capacity of the proxies available in the literature to describe [H2SO4] under volcanic plume conditions, but they concurrently highlight the benefit of the newly developed proxies for the prediction of the highest concentrations ([H2SO4] > 2-3 x 10(8) cm(-3)). Moreover, the contrasting behaviours of the new proxies in the two investigated datasets indicate that in volcanic plumes, like in other environments, the relevance of a proxy can be affected by changes in environmental conditions and that location-specific coefficients do logically improve the predictions.
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