Role of iodine oxoacids in atmospheric aerosol nucleation

He, Xu‐Cheng; Tham, Yee Jun; Dada, Lubna; Wang, Mingyi; Finkenzeller, Henning; Stolzenburg, Dominik; Iyer, Siddharth; Simon, Mario; Kürten, Andreas; Shen, Jiali; Rörup, Birte; Rissanen, Matti; Schobesberger, Siegfried; Baalbaki, Rima; Wang, Dongyu; Koenig, Theodore K.; Jokinen, Tuija; Sarnela, Nina; Beck, Lisa; Almeida, João; Amanatidis, Stavros; Amorim, A.; Ataei, Farnoush; Baccarini, Andrea; Bertozzi, Barbara; Bianchi, Federico; Brilke, Sophia; Caudillo, Lucía; Chen, Dexian; Chiu, Randall; Chu, Biwu; Dias, António; Ding, Aijun; Dommen, Josef; Duplissy, Jonathan; Haddad, Imad El; Carracedo, Loïc Gonzalez; Granzin, Manuel; Hansel, Armin; Heinritzi, Martin; Hofbauer, Victoria; Junninen, Heikki; Kangasluoma, Juha; Kemppainen, Deniz; Kim, Changhyuk; Kong, Weimeng; Krechmer, Jordan E.; Kvashin, Aleksander; Laitinen, T. V.; Lamkaddam, Houssni; Lee, Chuan Ping; Lehtipalo, Katrianne; Leiminger, Markus; Li, Zijun; Махмутов, В. С.; Manninen, Hanna E.; Marie, Guillaume; Marten, Ruby; Mathot, S.; Mauldin, R. L.; Mentler, Bernhard; Möhler, Ottmar; Müller, Tatjana; Nie, Wei; Onnela, A.; Petäj̈ä, Tuukka; Pfeifer, Joschka; Philippov, Maxim; Ranjithkumar, Ananth; Saiz‐Lopez, Alfonso; Salma, Imre; Scholz, Wiebke; Schuchmann, S.; Schulze, Benjamin C.; Steiner, Gerhard; Stozhkov, Yuri; Tauber, Christian; Tomé, António; Thakur, Roseline C.; Väisänen, Olli; Vazquez‐Pufleau, Miguel; Wagner, Andrea C.; Wang, Yonghong; Weber, Stefan K.; Winkler, Paul M.; Wu, Yusheng; Xiao, Mao; Yan, Chao; Ye, Qing; Ylisirniö, Arttu; Zauner-Wieczorek, Marcel; Zha, Qiaozhi; Zhou, Putian; Flagan, Richard C.; Curtius, Joachim; Baltensperger, Urs; Kulmala, Markku; Kerminen, Veli‐Matti; Kurtén, Theo; Donahue, Neil M.; Volkamer, Rainer; Kirkby, J.; Worsnop, Douglas R.; Sipilä, Mikko

doi:10.1126/science.abe0298

Cited by 142 publications

(244 citation statements)

References 84 publications

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“…Thus, as shown in Fig. 5b, the branch ratios of IA-MSA and IA self-nucleation pathways were calculated based on field data (He et al, 2021) S3. Due to the lack of field data for MSA at multiple sites, a median of [MSA] (10 7 molecules cm -3 ) was chosen in this study.…”

Section: Cluster Growth Pathwaysmentioning

confidence: 99%

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Molecular-level evidence for marine aerosol nucleation of iodic acid and methanesulfonic acid

Liu

et al. 2021

Preprint

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Abstract. Both iodic acid (HIO3, IA) and methanesulfonic acid (CH3S(O)2OH, MSA) have been identified by field studies as important precursors of new particle formation (NPF) in marine areas. However, the mechanism of NPF in which IA and MSA are jointly involved is still unclear. Hence, we investigated the IA-MSA nucleation system under different atmospheric conditions and uncovered the corresponding nucleating mechanism at a molecular level for the first time using quantum chemical approach and Atmospheric Cluster Dynamics Code (ACDC). The findings showed that MSA can stabilize IA clusters via both hydrogen and halogen bonds. Moreover, the joint nucleation rate of IA-MSA system is significantly higher than that of IA self-nucleation, particularly in relatively cold marine regions with sparse IA and rich MSA. For the IA-MSA nucleation mechanism, in addition to self-nucleation of IA, the IA-MSA-involved clusters can also directly participate in the nucleation process, and their contribution is particularly prominent in the polar regions with rich MSA and sparse IA. The IA-MSA nucleation mechanism revealed in this work may help to elucidate some missing sources of marine NPF.

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Section: Cluster Growth Pathwaysmentioning

confidence: 99%

“…In addition to the above sulfur precursors, recent experimental and theoretical studies (He et al, 2021;Martí n et al, 2020;Xia et al, 2020;Rong et al, 2020) have also recognized the critical role of iodine compounds in marine NPF process.…”

Section: Introductionmentioning

confidence: 99%

Molecular-level evidence for marine aerosol nucleation of iodic acid and methanesulfonic acid

Liu

et al. 2021

Preprint

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“…We use experiments performed with the differential mobility analyser-train (DMA-train; Stolzenburg et al, 2017) measuring new particle formation and growth at the CERN CLOUD chamber (Duplissy et al, 2016;Kirkby et al, 2011). In addition to testing the method with synthetic DMA-train data (in which the 'correct' results are known), we estimate formation and growth rates from three different formation and growth experiments: sulphuric-ammonia (Stolzenburg et al, 2020), alpha-pinene ozonolysis (Heinritzi et al, 2020) and iodic acid (He et al, 2021;Sipilä et al, 2016). We compare the formation rates with results obtained by using the methodology by Dada et al (2020) based on particle size magnifier (PSM) measurements and the growth rates with the results obtained by the INSIDE method (Pichelstorfer et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Aerosol formation and growth rates from chamber experiments using Kalman smoothing

et al. 2021

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Abstract. Bayesian state estimation in the form of Kalman smoothing was applied to differential mobility analyser train (DMA-train) measurements of aerosol size distribution dynamics. Four experiments were analysed in order to estimate the aerosol size distribution, formation rate, and size-dependent growth rate, as functions of time. The first analysed case was a synthetic one, generated by a detailed aerosol dynamics model and the other three chamber experiments performed at the CERN CLOUD facility. The estimated formation and growth rates were compared with other methods used earlier for the CLOUD data and with the true values for the computer-generated synthetic experiment. The agreement in the growth rates was very good for all studied cases: estimations with an earlier method fell within the uncertainty limits of the Kalman smoother results. The formation rates also matched well, within roughly a factor of 2.5 in all cases, which can be considered very good considering the fact that they were estimated from data given by two different instruments, the other being the particle size magnifier (PSM), which is known to have large uncertainties close to its detection limit. The presented fixed interval Kalman smoother (FIKS) method has clear advantages compared with earlier methods that have been applied to this kind of data. First, FIKS can reconstruct the size distribution between possible size gaps in the measurement in such a way that it is consistent with aerosol size distribution dynamics theory, and second, the method gives rise to direct and reliable estimation of size distribution and process rate uncertainties if the uncertainties in the kernel functions and numerical models are known.

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“…Some precursor compounds and their oxidation products such as SO 2 and H 2 SO 4 , volatile organic compounds (VOCs) and highly oxygenated molecules (HOMs), NH 3 or amines, iodine oxides, and HIO 3 were shown to influence NPF events (O'Dowd et al, 2002;Sipilä et al, 2010Sipilä et al, , 2016Metzger et al, 2010;Kirkby et al, 2011Kirkby et al, , 2016Riipinen et al, 2011;Almeida et al, 2013;Donahue et al, 2013;Schobesberger et al, 2013;Ehn et al, 2014;Riccobono et al, 2014;Jokinen et al, 2015;Bianchi et al, 2016;Tröstl et al, 2016;Yao et al, 2018;Kürten, 2019;He et al, 2021). Further chemical species such as isoprene or NO 2 showed inhibiting effects (Kiendler-Scharr et al, 2009;Lehtipalo et al, 2016;Heinritzi et al, 2020;. It was pointed out that NPF can proceed from HOMs alone when it is assisted by air ions (Kirkby et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Influence of vegetation on occurrence and time distributions of regional new aerosol particle formation and growth

et al. 2021

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Abstract. The occurrence frequency of regional atmospheric new aerosol particle formation and consecutive growth events (fNPF) were studied with respect to vegetation activity, aerosol properties, air pollutants and meteorological data in Budapest over the time interval from 2008 to 2018. The data set evaluated contained results of in situ measurements on the land surface that were mostly performed at the Budapest platform for Aerosol Research and Training Laboratory, of satellite-based products recorded by MODIS on Terra and of modelled vegetation emission-related properties from an advanced regional biogeochemical model. The annual mean relative occurrence frequencies were considerable (with an overall mean of 21 %), remained at a constant level (with an overall SD of 5 %) and did not exhibit tendentious change over the years. The shape of the distributions of monthly mean fNPF exhibited large variability from year to year, while the overall average distribution already possessed a characteristic pattern. The structure of the new particle formation (NPF) occurrence distributions was compared to those of environmental variables including concentrations of gas-phase H2SO4, SO2, O3, NO, NO2, CO, PM10 mass and NH3; particle numbers in the size fractions of 6–1000, 6–100 and 100–1000 nm; condensation sink; air temperature (T); relative humidity (RH); wind speed (WS); atmospheric pressure (P); global solar radiation (GRad); gross primary production (GPP) of vegetation; leaf area index (LAI); and stomatal conductance (SCT). There were no evident systematic similarities between fNPF on the one hand and all of the variables studied on the other hand, except for H2SO4 and perhaps NH3. The spring maximum in the NPF occurrence frequency distribution often overlapped with the time intervals of positive T anomaly in vegetated territories. The link between the potential heat stress exerted on plants in sultry summer intervals and the summer fNPF minimum could not be proven. The relevance of environmental variables was assessed by their ratios on NPF event days and on non-event days. The gas-phase H2SO4 concentration showed the largest monthly ratios, followed by O3. The WS, biogenic precursor gases and SO2 can generally favour NPF events, although their influence seemed to be constrained. An association between the fNPF and vegetation growth dynamics was clearly identified.

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Role of iodine oxoacids in atmospheric aerosol nucleation

Cited by 142 publications

References 84 publications

Molecular-level evidence for marine aerosol nucleation of iodic acid and methanesulfonic acid

Molecular-level evidence for marine aerosol nucleation of iodic acid and methanesulfonic acid

Aerosol formation and growth rates from chamber experiments using Kalman smoothing

Influence of vegetation on occurrence and time distributions of regional new aerosol particle formation and growth

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