Iodine Oxoacids in Atmospheric Aerosol Formation – from Chamber Simulations to Field Observations

REPORT SERIES IN AEROSOL SCIENCE No. 239 (2021)

IODINE OXOACIDS IN ATMOSPHERIC AEROSOL FORMATION – FROM CHAMBER SIMULATIONS TO FIELD OBSERVATIONS

XU-CHENG HE （何旭成）

Institute for Atmospheric and Earth System Research / Physics Faculty of Science, University of Helsinki, Helsinki, Finland

Academic dissertation

To be presented, with the permission of the Faculty of Science of the University of Helsinki, for public criticism in Exactum auditorium CK112, Pietari Kalmin Katu 5, on August 23rd, 2021, at 14 o'clock.

Helsinki 2021

Author’s Address Institute for Atmospheric and Earth System Research / Physics P.O. box 64 FI-00140 University of Helsinki [email protected]

Supervisors Academician, Professor Markku Kulmala, Ph.D. Institute for Atmospheric and Earth System Research / Physics University of Helsinki

Associate Professor Mikko Sipilä, Ph.D. Institute for Atmospheric and Earth System Research / Physics University of Helsinki

Assistant Professor Matti Petteri Rissanen, Ph.D. Aerosol Physics Laboratory Tampere University

Lecturer Theo Kurtén, Ph.D. Department of Chemistry University of Helsinki

Reviewers Research Professor Hannele Hakola, Ph.D. Air Quality Group Finnish Meteorological Institute

Assistant Professor Julia Schmale, Ph.D. Extreme Environments Research Laboratory École polytechnique fédérale de Lausanne

Opponent Professor Gordon McFiggans, Ph.D. Department of Earth and Environmental Sciences University of Manchester

ISBN 978-952-7276-59-4 (printed version) ISSN 0784-3496 Helsinki 2021 Unigrafia Oy

ISBN 978-952-7276-60-0 (pdf version) http://www.atm.helsinki.fi FAAR Helsinki 2021

Acknowledgements

The work for this thesis was carried out at the Division of Atmospheric Sciences at the Department of Physics and later at the Institute for Atmospheric and Earth System Research (INAR), University of Helsinki. I thank the heads of the department and the university for providing me with the state-of-art working facilities. I would like to express my gratitude to my thesis pre-examiners, Prof. Hannele Hakola and Dr. Julia Schmale, for taking time to review this thesis and for providing prompt reply. I’m also grateful to Prof. Gordon McFiggans for being my opponent.

I would like to share my deepest gratitude to my Custos, Acad. Prof. Markku Kulmala for accepting me into his research group which essentially changed my career. He once told me that the strength of the education in INAR is that students are allowed to speak what they believe. Indeed, he has been receptive toward my opinions, and allowed me to share them openly. He has always supported my projects and been open to my proposals and plans, despite he was not always positive toward them. “People shall make their own mistakes”, he once said to me. It’s exactly these mistakes from which I learned the most. My research would not have been possible without his open-minded education and leadership, and the best possible resources he and his group provided.

I wish to express my sincere appreciation for Professor Jasper Kirkby. Like all other children, I dreamed to be a scientist since I was young. Knowing him for almost five years, I realize that he was the imagined scientist in my childhood brain and is my lifetime role model in terms of scientific pursuit. His enthusiasm and persistence in science have overcome time and border. The CLOUD project, led by him, was the most important project in my doctoral studies. It was where I learned the most effectively in past years. He has always been replying to my questions in detail, even if it means writing extensive emails, and he has always responded to my requests both in terms of science and in terms of ways to communicate with people. “Shut up when you get what you want” was certainly one of the sentence I kept reminding myself. Without doubt, his contribution in the experiments, idea formulation, writing and presentation of my research was enormous and his advice in effective communication and academic ethics were beneficial.

During my doctoral studies, I received great support from senior researchers and my advisors. I thank Dr. Mikko Sipilä for his pioneering work in iodic acid particle formation which essentially formed the base of my follow-up research. I also thank him for the guidance in my master’s studies and for offering me the first job in my life (as a research assistant). I still remember the moment when he told me that we should continue the contract after my summer intern (2016) and let me participate the CLOUD project. I thank Dr. Matti Rissanen for helping me get started with mass spectrometry and gas kinetic experiments. His sophistication in chemistry and high standard in experiments have influenced me a lot and I wish to continue them in my future research. He has been more than just a supervisor to me but also a dear friend. I enjoyed the time spent with him and Dr. Siddharth Iyer when we were together, from Helsinki to Réunion island. I thank Dr. Theo Kurtén and Siddharth for teaching me the basics of quantum chemical calculations. It gave me the chance to combine laboratory experiments with theoretical calculations. Pursuing a consistency in experiment and theory has been the central task in my research and I also wish to continue that in the future. Theo, from whom I benefited a lot, has always been responsive to my requests and active in reviewing my research work. Prof. Douglas Worsnop is acknowledged for his scientific and emotional support throughout my studies. He was always the one who saved me from the breakdowns in CLOUD and highlighted the importance of my research even when there was not much attention on it. He was also the one who tried to bridge me with Prof. Rainer Volkamer, with whom we finally reached a semi-consistent opinion on iodine oxoacid particle formation. I also thank Rainer for acting as the tiger who chased me (the wild deer) and drove me running. The experiments and results would not have sparked without his consistent opposition in ideas and useful suggestions in experiments.

I’ll also take this chance to thank my dear colleagues in INAR and CLOUD. I thank Chao, Nina, Olga and Fede for the guidance in analyzing mass spectrometric data at the beginning of my doctoral studies. I thank Jiali, Birte, Henning, Mario, Martin, Wiebke and others for their accompany in difficult times at CLOUD. I thank Yonghong, Qiaozhi, Sainan, Juan, Putian and others for their help both in science and life (especially for feeding me authentic Chinese dishes). I also thank Prof. Aijun Ding and Dr. Wei Nie for keeping me in NJU and Prof. Yongchun Liu for keeping me in BUCT during COVID-19. I thank Lubna for her emotional support throughout my studies in Helsinki. I also learned a lot from her in how to communicate with people. I spent the most time with Mingyi at CLOUD discussing questions in science and beyond science. It was always a pleasure to find and spend time with a similar mind. Jun is acknowledged for his consistent help in my doctoral studies. We have spent hundreds of hours in the laboratory, days at CLOUD and months in field experiments. My journey in atmospheric iodine research would have been lonely without his accompany. Apart from science, I have spent precious time with Mao, Lu and Jinfeng on the road, from the theaters in London to the jacaranda in Lisbon. Although our careers have separated us apart into different continents, I wish we will continue our journey in the future. I truly enjoyed my time spent with my dear colleagues and friends. My doctoral study would not have been such a pleasure without them.

Finally, I’d like to thank my grandmother, who has passed away two years ago, for her caring and love. I regret very much that I did not have an opportunity to let her see my graduation and that I was only able to see her the last time through a cell phone. I thank my father for bringing me up and for providing me the best possible resources that he had. He is a very special Chinese father – he has never forced me to do anything but allowed me to make my own decision since I was 15. Because of these, I was able to come to Finland and enjoy my journey in science.

To my father (写给爸爸): 我想谢谢你一个人把我抚养长大。虽然我们家的条件并不宽裕，但是我现在回过头来想，好像除了特别小的时候，我也并没有感觉到过物质上有什么欠缺，这跟你的努力是分不开的。你也从来没对我的学业有过分的要求，即使是初三的时候因为沉迷电脑游戏而导致中考考砸只能去普高后，你也只是鼓励我不要放弃。不管是高中去哪里上，大学选什么专业，去哪里留学以及以后我想去哪里工作，你都尊重我自己的选择，也从来不催婚，不逼我去相亲。你对我唯二的要求好像是活着并且健康（可能还有好好念书），这也是我对你唯二的祝福。

In Beijing, Xu-Cheng He (何旭成) Iodine oxoacids in atmospheric aerosol formation – from chamber simulations to field observations Xu-Cheng He University of Helsinki, 2021 Abstract

New particle formation is estimated to contribute to around half of the cloud condensation nuclei (CCN) in the atmosphere which in turn have a cooling effect on Earth’s surface. Only a few gas species, including sulfuric acid, oxidized organic vapors and iodine species, are confirmed to contribute to new particle formation, which converts gases to aerosol particles. While new particle formation from sulfuric acid (with water or bases, such as ammonia and amines) is recognized globally, new particle formation solely induced by pure organic vapors only occurs under special conditions. Least is known about the coverage of iodine particle formation processes in the atmosphere.

Iodine species have widely been measured in marine and polar environments. However, most ambient measurements concentrated on molecular iodine (I2), iodine monoxide (IO), iodine dioxide (OIO) and organic iodine precursors. These measurements constrained the effect of iodine species in catalytic ozone loss processes, but far from enough to understand the particle formation processes. In this thesis, I utilized a bromide chemical ionization method to nearly comprehensively measure inorganic iodine species, including I2, iodine oxides and oxoacids. An unprecedented performance both in coverage and sensitivity is achieved.

We further deployed the bromide chemical ionization method for ambient observations at the Mace Head observatory on the Atlantic coast of Ireland. First successful online measurements of hypoiodous acid (HOI), bromoiodide (IBr) and chloroiodide (ICl) confirmed the heterogeneous uptake of HOI at ambient conditions which enhanced iodine atom production rate by 32% and accelerated ozone (O3) loss by 12%.

Comprehensive experiments were further carried out at the CLOUD (Cosmics Leaving OUtdoor Droplets) chamber to understand the iodine particle formation mechanisms. We found that the ion-induced (charged) and neutral nucleation proceed via distinct mechanisms. The ion-induced nucleation proceeds primarily by sequential addition of iodic acid (HIO3) which was measured to proceed at the kinetic limit. However, in contrast to earlier expectations, neutral nucleation additionally involves iodous acid (HIO2) to stabilize HIO3, replacing the role of the negative charge in the ion-induced nucleation. After passing the critical size of nucleation, the growth of iodine particles is essentially sustained by HIO3, with minor contributions from other species, which are present at much lower concentrations. Additionally, iodine oxoacids have much faster particle formation rates than the sulfuric acid – ammonia mixture at the same acid concentrations (when the ammonia mixing ratio is 100 parts per trillion by volume).

While sulfuric acid – ammonia new particle formation has been confirmed to be an important mechanism in polar regions, the role of iodine new particle formation is usually considered to have a limited global reach. We carried out iodic acid measurements at ten boundary layer sites, ranging from the cleanest polar regions to polluted urban environments. The existence of iodic acid is ubiquitously confirmed, with concentrations comparable to sulfuric acid. This indicates a greater importance of iodine oxoacid particle formation processes than just a coastal phenomenon.

Keywords: bromide chemical ionization, iodine particle formation, iodic acid, new particle formation, iodine chemistry.

List of abbreviations

APi-TOF Atmospheric Pressure interface Time-Of-Fight mass spectrometer Br-MION Bromide Multi-scheme chemical IONization inlet CCN Cloud Condensation Nuclei CE-DOAS Cavity Enhanced Differential Optical Absorption Spectroscopy CHARMM Chemistry at HARvard Macromolecular Mechanics CIMS Chemical Ionization Mass Spectrometer CLOUD the Cosmics Leaving OUtdoor Droplets experiment FIGAERO Filter Inlet for Gases and AEROsols MMFF Merck molecular force field PANDA520 Polar ANd high-altituDe Atmospheric research model XTB Semiempirical Tight Binding

Contents

1 Introduction ...... 11 2 Methods ...... 15 2.1 Facilities ...... 15 2.1.1 The CLOUD chamber at CERN ...... 15 2.1.2 Mace Head observatory ...... 16 2.2 Instrumentation ...... 18 2.2.1 APi-TOF ...... 18 2.2.2 Nitrate-CIMS ...... 19 2.2.3 Br-CIMS ...... 19 2.2.3.1 Calibration of I2 and Cl2 using permeation tubes ...... 20 2.2.3.2 Calibration of I2 using CE-DOAS ...... 21 2.2.3.3 Calibration of HOI ...... 22 2.2.3.4 Calibration of H2SO4 ...... 22 2.2.3.5 Calibration of HNO3 ...... 23 2.2.3.6 Connecting sensitivity to binding enthalpy ...... 23 2.3 Quantum chemical calculations ...... 24 2.4 Kinetic model ...... 25 3 Advances in atmospheric iodine chemistry ...... 27 3.1 Formation of iodine oxoacids ...... 27 3.2 Enhanced iodine recycling from heterogeneous reactions ...... 28 4 Iodine oxoacid particle formation ...... 32 4.1 Ion-induced iodic acid nucleation ...... 32 4.1.1 Discovery of ion-induced iodic acid nucleation ...... 33 4.1.2 Experimental evidence from the CLOUD chamber at CERN ...... 33 4.1.3 Determination of ion-polar molecule collision rate coefficients ...... 35 4.1.3.1 Analytical derivation of the appearance time method ...... 35 4.1.3.2 Calculation of the apparent collision rate coefficient ...... 39 4.1.3.3 Correction of the apparent collision rate coefficient ...... 42 4.2 Neutral nucleation of iodine oxoacids ...... 42 4.2.1 Iodine oxoacid nucleation revealed at CLOUD ...... 42 4.2.2 Revisiting neutral iodine nucleation at Mace Head ...... 45 4.3 Growth of nucleation mode particles by iodic acid ...... 47 4.4 Nucleation and growth rates ...... 50

5 Global observation of HIO3 ...... 52

5.1 HIO3 in pristine environments ...... 52 5.2 HIO3 in polluted environments ...... 52 6 Conclusions and outlook ...... 54 7 Review of papers and the Authors contribution ...... 57 8 References ...... 59

List of publications

This thesis consists of an introductory review, followed by five research articles. The articles are cited according to their roman numbers in the introduction part. Papers I, III and V are reprinted with the approval of the publishers. Papers II and IV are reprinted under the Creative Commons Attribution 4.0 International license.

I. Wang, M., Kong, W., Marten, R., He, X.-C., Chen, D., Pfeifer, J., Heitto, A., Kontkanen, J., Dada, L., Kürten, A., Yli-Juuti, T., Manninen, H.E., Amanatidis, S., Amorim, A., Baalbaki, R., Baccarini, A., Bell, D.M., Bertozzi, B., Bräkling, S., Brilke, S., Murillo, L.C., Chiu, R., Chu, B., De Menezes, L.-P., Duplissy, J., Finkenzeller, H., Carracedo, L.G., Granzin, M., Guida, R., Hansel, A., Hofbauer, V., Krechmer, J., Lehtipalo, K., Lamkaddam, H., Lampimäki, M., Lee, C.P., Makhmutov, V., Marie, G., Mathot, S., Mauldin, R.L., Mentler, B., Müller, T., Onnela, A., Partoll, E., Petäjä, T., Philippov, M., Pospisilova, V., Ranjithkumar, A., Rissanen, M., Rörup, B., Scholz, W., Shen, J., Simon, M., Sipilä, M., Steiner, G., Stolzenburg, D., Tham, Y.J., Tomé, A., Wagner, A.C., Wang, D.S., Wang, Y., Weber, S.K., Winkler, P.M., Wlasits, P.J., Wu, Y., Xiao, M., Ye, Q., Zauner-Wieczorek, M., Zhou, X., Volkamer, R., Riipinen, I., Dommen, J., Curtius, J., Baltensperger, U., Kulmala, M., Worsnop, D.R., Kirkby, J., Seinfeld, J.H., El-Haddad, I., Flagan, R.C., and Donahue, N.M. (2020). Rapid growth of new atmospheric particles by nitric acid and ammonia condensation. Nature 581 (7807):184–189.

II. Wang, M.*, He, X.-C.*, Finkenzeller, H., Iyer, S., Chen, D., Shen, J., Simon, M., Hofbauer, V., Kirkby, J., Curtius, J., Maier, N., Kurtén, T., Worsnop, D.R., Kulmala, M., Rissanen, M., Volkamer, R., Tham, Y.J., Donahue, N.M., and Sipilä, M. (2021). Measurement of iodine species and sulfuric acid using bromide chemical ionization mass spectrometers. Atmospheric Measurement Techniques 14 (6):4187-4202. (*Authors with equal contribution)

III. Tham, Y.J., He, X.-C., Li, Q., Cuevas, C.A., Shen, J., Kalliokoski, J., Yan, C., Iyer, S., Lehmusjärvi, T., Jang, S., Thakur, R.C., Beck, L., Kemppainen, D., Olin, M., Sarnela, N., Mikkilä, J., Hakala, J., Marbouti, M., Yao, L., Li, H., Huang, W., Wang, Y., Wimmer, D., Zha, Q., Virkanen, J., Spain, T.G., O’Doherty, S., Jokinen, T., Bianchi, F., Petäjä, T., Worsnop, D.R., Mauldin, R.L., Ovadnevaite, J., Ceburnis, D., Maier, N.M., Kulmala, M., O’Dowd, C., Dal Maso, M., Saiz-Lopez, A., and Sipilä, M. (2021). Direct field evidence of autocatalytic iodine release from atmospheric aerosol. Proceedings of the National Academy of Sciences 118 (4): e2009951118.

IV. He, X.-C., Iyer, S., Sipilä, M., Ylisirniö, A., Peltola, M., Kontkanen, J., Baalbaki, R., Simon, M., Kürten, A., Tham, Y.J., Pesonen, J., Ahonen, L.R., Amanatidis, S., Amorim, A., Baccarini, A., Beck, L., Bianchi, F., Brilke, S., Chen, D., Chiu, R., Curtius, J., Dada, L., Dias, A., Dommen, J., Donahue, N.M., Duplissy, J., El Haddad, I., Finkenzeller, H., Fischer, L., Heinritzi, M., Hofbauer, V., Kangasluoma, J., Kim, C., Koenig, T.K., Kubečka, J., Kvashnin, A., Lamkaddam, H., Lee, C.P., Leiminger, M., Li, Z., Makhmutov, V., Xiao, M., Marten, R., Nie, W., Onnela, A., Partoll, E., Petäjä, T., Salo, V.-T., Schuchmann, S., Steiner, G., Stolzenburg, D., Stozhkov, Y., Tauber, C., Tomé, A., Väisänen, O., Vazquez-Pufleau, M., Volkamer, R., Wagner, A.C., Wang, M., Wang, Y., Wimmer, D., Winkler, P.M., Worsnop, D.R., Wu, Y., Yan, C., Ye, Q., Lehtinen, K., Nieminen, T., Manninen, H.E., Rissanen, M., Schobesberger, S., Lehtipalo, K., Baltensperger, U., Hansel, A., Kerminen, V.-M., Flagan, R.C., Kirkby, J., Kurtén, T., and Kulmala, M. (2020). Determination of the collision rate coefficient between charged iodic acid clusters and iodic acid using the appearance time method. Aerosol Science and Technology 55 (2):231-242.

V. He, X.-C., Tham, Y.J., Dada, L., Wang, M., Finkenzeller, H., Stolzenburg, D., Iyer, S., Simon, M., Kürten, A., Shen, J., Rörup, B., Rissanen, M., Schobesberger, S., Baalbaki, R., Wang, D.S., Koenig, T.K., Jokinen, T., Sarnela, N., Beck, L.J., Almeida, J., Amanatidis, S., Amorim, A., Ataei, F., Baccarini, A., Bertozzi, B., Bianchi, F., Brilke, S., Caudillo, L., Chen, D., Chiu, R., Chu, B., Dias, A., Ding, A., Dommen, J., Duplissy, J., El Haddad, I., Gonzalez Carracedo, L., Granzin, M., Hansel, A., Heinritzi, M., Hofbauer, V., Junninen, H., Kangasluoma, J., Kemppainen, D., Kim, C., Kong, W., Krechmer, J.E., Kvashin, A., Laitinen, T., Lamkaddam, H., Lee, C.P., Lehtipalo, K., Leiminger, M., Li, Z., Makhmutov, V., Manninen, H.E., Marie, G., Marten, R., Mathot, S., Mauldin, R.L., Mentler, B., Möhler, O., Müller, T., Nie, W., Onnela, A., Petäjä, T., Pfeifer, J., Philippov, M., Ranjithkumar, A., Saiz-Lopez, A., Salma, I., Scholz, W., Schuchmann, S., Schulze, B., Steiner, G., Stozhkov, Y., Tauber, C., Tomé, A., Thakur, R.C., Väisänen, O., Vazquez-Pufleau, M., Wagner, A.C., Wang, Y., Weber, S.K., Winkler, P.M., Wu, Y., Xiao, M., Yan, C., Ye, Q., Ylisirniö, A., Zauner-Wieczorek, M., Zha, Q., Zhou, P., Flagan, R.C., Curtius, J., Baltensperger, U., Kulmala, M., Kerminen, V.-M., Kurtén, T., Donahue, N.M., Volkamer, R., Kirkby, J., Worsnop, D.R., and Sipilä, M. (2021). Role of iodine oxoacids in atmospheric aerosol nucleation. Science 371 (6529):589–595.

1 Introduction

Earth’s atmosphere is an essential part of the ecosystem which keeps the surface temperature relatively stable, provides essential gases for organisms and protects vulnerable species from ultraviolet solar radiation. It consists of numerous gas molecules and liquid or solid phase aerosol particles. There are around 2.4 × 1019 molecules per cubic centimeter (cm-3) in the dry air (excluding water vapors) at ground level, among which 78.09% are nitrogen, 20.95% are oxygen and 0.93% are argon. Astonishingly, among the remaining 0.03%, many trace level gases have huge impacts on the climate system. Chloroflurocarbons (CFCs), present in the atmosphere in the range of tens of parts per trillion by volume (pptv), are largely responsible for stratospheric ozone

depletion (Molina and Rowland 1974). Greenhouse gases (GHGs), such as carbon dioxide (CO2),

methane (CH4) and CFCs, warm the atmosphere despite their tiny fractions in the atmosphere ranging from tens of pptv to hundreds of parts per million by volume (ppmv). The radiative forcing from changes of all the GHGs was 2.83 W m-2 in 2011 (compared to 1750) and a record breaking

415 ppmv CO2 was recently detected at the Mauna Loa Observatory (Miller and Rice 2019). The continuing rising trend calls for unified efforts from the people across the world to tackle the problem.

Apart from gases, liquid droplets and solid particles (aerosols) are essential in the climate system. They affect the Earth’s radiative forcing both directly and indirectly. Aerosols, such as sulfate aerosols, can directly scatter solar radiation, thus cooling the Earth’s surface (Charlson and Wigley 1994). However, black carbon aerosols absorb solar radiation, and have a net warming effect (IPCC 2014). On the other hand, a population of relatively small aerosol particles, named cloud condensation nuclei (CCN), can indirectly affect the radiation balance by forming clouds with different properties (Hudson 1993). This indirect effect generally cools the Earth’s surface. Intergovermental Panel on Climate Change reported an effective radiative forcing of -0.9 [-1.9 to -0.1] W m-2 from aerosols.

Besides their climatic effects, aerosol particles influence human health. It was estimated that severe air pollution, associated with significant levels of particulate matter, was the fifth greatest health risk worldwide, ranked after high blood pressure, smoking, diabetes and obesity (Forouzanfar et al. 2015; Cohen et al. 2017). It was estimated that 400 000 deaths (7% of annual mortality) in Europe were attributed to exposure to particulate matter with a size below 2.5 micrometers (PM2.5) (Khomenko et al. 2021). One of the many ways to estimate the health effects of particulate matter is the oxidative potential of particulate matters (Bates et al. 2019) and the oxidative potential is proposed to be mainly contributed by residential biomass burning and non-exhaust vehicular emissions in Europe (Daellenbach et al. 2020). Aside from the chronic effect, fine particles can

11 cause acute respiratory health problems (Schwartz and Neas 2000). Aerosols are also hypothesized to contribute to the transmission of the on-going COVID19 pandemic (Anderson et al. 2020).

There are two major types of aerosol particles in the atmosphere: primary and secondary. Primary aerosols include, for instance, mineral dust, sea salt and volcanic dust of natural origins, as well as soot, industrial aerosol dust and combustion aerosols of anthropogenic origins. Secondary aerosols are formed from gas to particle conversion, or the so-called new particle formation process (NPF). NPF is commonly characterized by bursts of high concentrations of subnanometer-sized (1-3 nm) particles and it represents a major source of aerosol number concentration in the troposphere (Lee et al. 2019). Importantly, NPF processes exert crucial impacts on climate by introducing about half of the CCN in the atmosphere (Gordon et al. 2017). Atmospheric observations showed that around 10% to 60% percent of the observed events led to the production of CCN (both from natural and anthropogenic sources), depending on the location (Kerminen et al. 2018), which in turn reduced radiative forcing caused by aerosols.

Interestingly, only a few species can trigger new particle formation despite its importance in the atmosphere. The most common species that contributes to most of the particle formation events around the globe is sulfuric acid, and its particle formation mechanism has thus been studied for years. Binary sulfuric acid – water nucleation (a particle formation process that involves energy barriers) was first thought to be responsible for atmospheric particle formation (Kulmala et al. 1998; Doyle 1961). However, the predicted concentrations of sulfuric acid in the boundary layer (106 – 107 cm-3) were too low for this binary nucleation to proceed (Ball et al. 1999; Kulmala et al.

2004; Kerminen et al. 2010). Basic species, such as ammonia (NH3) and amines were therefore proposed to stabilize sulfuric acid clusters (Kurtén et al. 2008; Ball et al. 1999). While sulfuric acid – ammonia nucleation alone was experimentally found to be unable to explain all the observed new particle formation rates at ground level, recent studies suggested that it plays an important role in some high latitude sites (Beck et al. 2021; Sipilä et al. 2021; Jokinen et al. 2018). Dimethyl amine, a more effective stabilizer for sulfuric acid, was found to be able to enhance sulfuric acid particle formation, leading to much faster rates than ammonia (Kürten et al. 2014; Almeida et al. 2013), and sulfuric acid – dimethyl amine particle formation was recently found to be an important mechanism in polluted urban environments (Xiao et al. 2021; Yao et al. 2018). Oxidized organic vapors have long been known to contribute to the growth of secondary organic aerosols (Ehn et al. 2014) and their role in new particle formation in the absence of sulfuric acid was recently revealed in laboratory conditions (Kirkby et al. 2016) and in ambient measurements (Rose et al. 2018; Bianchi et al. 2016). Last but not the least, iodine has long been recognized of its rapid particle formation in coastal regions (Sipilä et al. 2016; O’Dowd et al. 2002; Hoffmann et al. 2001), and the exact mechanism of iodine particle formation has been studied for around 30 years (Kulmala et al. 1991; O’Dowd et al. 2002; Jimenez et al. 2003; Saunders and Plane 2005; Saunders et al. 2010; Gómez Martín et al. 2013; Sipilä et al. 2016; Gómez Martín et al. 2020). However, debates

12 remain on whether iodine oxoacids or iodine oxides are the key species that initiate the particle formation processes and whether iodine particle formation is solely a coastal phenomenon.

Aside from new particle formation, iodine species are essential nutrients for organisms. Iodine is an essential element for organisms including human beings. Lacking ingestion of iodine can cause iodine deficiency which affected about two billion people in 2007 (Ahmed 2008) which is the leading preventable cause of intellectual and development disabilities. In many countries, iodine deficiency is addressed by adding a small amount of iodine in sodium chloride salt which is daily used in cooking.

Atmospheric chemistry of iodine also has a significant impact on the oxidation capacity of the atmosphere (Saiz-Lopez et al. 2012). Various trace gases are emitted into the atmosphere both from natural and anthropogenic sources and they are removed by oxidizing chemical reactions initiated by, e.g., ozone and hydroxyl radicals. The oxidation capacity is primarily determined by the concentrations and production rates of ozone, hydroxyl radicals (OH) and other oxidizing gases, which act as cleaners for the atmosphere. In the daytime, the photolysis of nitrogen dioxide (NO2) 3 forms O( P) atoms, which in turn combines with oxygen (O2) to form O3. The photolysis of O3 1 can produce O( D) atoms, a small fraction of which reacts with water (H2O) to form OH radicals.

At the nighttime, the reaction between NO2 and O3 forms nitrate radicals (NO3), which acts as an important oxidizer. Atmospheric chemistry of iodine influences the oxidation capacity of the atmosphere through reactions with these oxidizing vapors. Among these reactions, the most well- known one is the catalytic ozone destruction process, a process that destroys ozone while preserving iodine. For example, iodine chemistry was proposed to account for 9% of the global tropospheric O3 sink and it disturbs the HOx and NOx circle (Sherwen et al. 2016; Saiz-Lopez et al. 2012). However, accurate estimation of the global iodine sources remains challenging as only a limited set of species could be measured at ambient conditions, which hinders a quantitative understanding of its role in changing atmospheric oxidizing capacity and particle formation.

Understanding atmospheric iodine chemistry and new particle formation requires an instrument with high time resolution, high sensitivity, and a capability of measuring a wide range of iodine species. It also requires sophisticated laboratory experiments to study the particle formation mechanisms and to obtain values of parameters that can be used in global simulations. These have long been challenging because of the low abundance of iodine species and their short lifetimes due to photolysis and oxidation processes in the atmosphere. Such an active nature further complicates laboratory explorations with elevated vapor concentrations since the extrapolation to atmospheric conditions can be challenging and problematic. Therefore, the aims of this thesis are

I. Develop a chemical ionization mass spectrometric method that can measure inorganic iodine species with high sensitivity and time resolution (papers I and II).

13 II. Deploy the chemical ionization mass spectrometer in the ambient marine atmosphere at the Mace Head observatory to study iodine chemistry (paper III). III. Carry out iodine particle formation experiments in the CLOUD (the Cosmics Leaving OUtdoor Droplets) chamber at CERN with a comprehensive set of instruments to study iodine particle formation mechanisms (papers IV and V, Figure 1). IV. Discuss the role of iodine oxoacid particle formation in the boundary layer based on data collected by chemical ionization mass spectrometers at ten ground-based sites (paper V).

Figure 1. A schematic of the cloud formation from iodine precursors in the Arctic and marine environments. Iodine species, emitted from the sea ice and sea surface, are rapidly photolyzed to form iodine oxoacids which form new particles and may grow to become new cloud condensation nuclei. We simulate these processes in the CLOUD chamber with artificial light sources to mimic the solar radiation and ion pair production by galactic cosmic rays and the beam from the proton synchrotron at CERN. Molecular iodine (I2) was directly injected into the chamber from the bottom of the chamber. This figure is contributed by Helen Cawley (https://www.helencawley.com).

14 2 Methods

Understanding atmospheric chemistry and new particle formation requires a combination of ambient observations, laboratory experiments and numerical calculations. Ambient observations are essential for revealing the composition of Earth’s atmosphere. Following the observations, dedicated laboratory experiments can aid in understanding the physical and chemical properties, and formation pathways of different gases and aerosols that are present in the atmosphere, since it is extremely difficult to disentangle these properties in ambient air with innumerable components. Finally, numerical calculations (e.g., quantum chemical calculations and numerical models from molecular to global scales) can help us to interpret both ambient observations and laboratory experiments, to trace back historical records, or even to predict future changes of the atmosphere. These methods are complementary, and a combination of all three is usually needed to answer any questions in atmospheric science. Therefore, we try to combine all the three aspects to investigate the role of iodine species in atmospheric chemistry and aerosol formation to obtain a consistent and meaningful picture.

2.1 Facilities

2.1.1 The CLOUD chamber at CERN

Flow reactors and smog chambers are the two main apparatus for simulating atmospheric chemistry. The flow reactor commonly has a much shorter reaction time than the smog chamber, thus it requires higher reactant concentrations to produce the same amount of products. The flow reactor is an effective way to study reaction kinetics of relatively fast reactions and the first steps of complex chemistry processes. However, lots of atmospheric processes are highly non-linear, so the atmospheric relevance of the results generated under elevated concentrations can be questionable. On the other hand, experiments with smog chambers usually employ more realistic precursor concentrations which can produce more relevant results to the atmosphere.

As the new particle formation processes are highly non-linear (Lee et al. 2019) and thus difficult to simulate with flow reactors, the majority of the experiments for this thesis were carried out in the CLOUD (the Cosmics Leaving OUtdoor Droplets) atmospheric simulation chamber located at CERN (European Center for Nuclear Research), Geneva, Switzerland. The volume of this stainless-steel chamber is 26.1 m3, with the inner surface being electropolished. We ensure the highest standard cleanness of the chamber with contaminant levels of sulfuric acid (< 5×104 cm- 3), total organic (< 150 parts per trillion by volume, pptv) and ammonia (below 4 pptv) by periodically rinsing the walls with ultra-pure water heated at 100 °C for at least 24 hours (Kirkby et al. 2011; Pfeifer et al. 2020).

Cryogenic oxygen (Messer, 99.999%) and nitrogen (Messer, 99.999%) were mixed and supplied to the chamber with a mixing ratio of 21:79, with total flow rates from 250-300 standard litre per minute (slpm). Humidity was supplied and controlled by passing a branch of the synthetic air through a temperature-controlled ultra-pure water bath. Ozone (O3) was generated in the synthetic air under UVC irradiation and it was added to the main flow. Precursor vapors were either injected into the chamber from gas bottles (NO2, SO2), or from evaporators containing either liquid (HNO3) or solid (I2) samples. Injection lines were temperature-stabilized and the lines for HNO3 and I2 injection were additionally sulfinert-coated to minimize line conditioning effects. Additionally,

HNO3 could also be produced by reactions between NO2 and OH radicals. Magnetically driven mixing fans at the top and the bottom enabled a fast establishment of near-homogeneous conditions in the chamber within several minutes. A typical wall loss rate for sulfuric acid at 5 °C with a typical fan setting was around 2.2×10-3 s-1 (Stolzenburg et al. 2020).

The chamber facilitated a set of light sources at different wavelengths. A green light source, consisting of 48 green LEDs was employed to photolyze I2. The produced iodine atoms were oxidized by O3 and H2O to form iodine oxides and oxoacids which in turn triggered new particle formation (papers IV, V). The maximum total optical power output was 153 W, centered at 528 -3 -1 nm. This represents roughly an I2 photolysis rate of 7×10 s , thus a lifetime of around 2.4 mins (though most experiments were carried out at roughly 10% of its full intensity). A 200 W Hamamatsu Hg-Xe lamps with ultraviolet fiber-optic system (wavelength between 250-450 nm) and a 4 W KrF exciplex ultraviolet laser (wavelength at 248 nm) were used to produce OH radicals from O3 photolysis.

The chamber has a unique control of ion concentrations. Electrodes installed in the chamber produced a 20 – 30 kV m-1 electric field which swept out ions within 1 s enabling experiments under neutral conditions (ion free conditions). When the field was turned off, ions could be produced by galactic cosmic rays (gcr conditions), allowing studies under typical sea level ion concentrations. Additionally, pion beam irradiation by the proton synchrotron at CERN enhanced ion production in the chamber which yielded ion concentrations typical of the tropopause (beam conditions).

2.1.2 Mace Head observatory

The oceans supply the majority of atmospheric iodine both in organic and inorganic forms (Saiz- Lopez et al. 2012). Many field observations have been carried out to study atmospheric iodine sources, chemistry, and particle formation processes. Organic iodine, such as iodomethane (CH3I) and diiodomethane (CH2I2), was first thought to contribute to the most of atmospheric iodine (Saiz- Lopez et al. 2012; Carpenter 2003; Gschwend et al. 1985). However, growing evidence suggests

16 that inorganic iodine, primarily I2 and hypoiodous acid (HOI) (Carpenter et al. 2013) also contribute significantly to iodine emissions to the atmosphere (Simpson et al. 2015).

Many of the key observations in iodine chemistry and particle formation are carried out in the Mace Head observatory, a well-known iodine hotspot (O’Dowd et al. 2002). For example, the first field evidence relating the tidal cycle and polyhalomethane emissions was obtained more than 20 years ago at Mace Head (Carpenter et al. 1999). At the same site, iodine species were found to account for the regional particle formation processes (O’Dowd et al. 2002; Hoffmann et al. 2001) and reactive iodine species were first detected, e.g., I2 (Saiz-Lopez and Plane 2004), iodine monoxide (IO) (Alicke et al. 1999), iodine dioxide (OIO) (Allan et al. 2001), iodic acid (HIO3) (Sipilä et al. 2016). With lots of background knowledge revealed and studied at the Mace Head observatory, related to iodine sources and chemistry, it naturally became the best choice for us to perform field observations to compare laboratory results and to measure additional iodine species which were previously un-measurable.

Intensive field observations were carried out at the Mace Head observatory, Ireland, from June 19 to July 19, 2018. The station is located in Galway County, on the western coast of Ireland (53°19’ N, 9°54’ W). The measurement station is surrounded by several small villages (e.g., Carna and Roundstone), but the nearest city is located roughly 90 km away, east of the station. Such an environment minimizes anthropogenic influence, although biomass burning from nearby farms was occasionally observed. The site commonly receives exceptionally clean air parcels sourced from as far as Azores in the south and Greenland in the north, and less frequently also polluted air parcels from continental Europe and North America (Heard et al. 2006). The coastline near the site has a strong tidal change, and is characterized by its iodine emitting macro-algae beds, such as kelps and other brown algae. Active iodine emissions during low tide events are associated with the formation of new particles (O’Dowd et al. 2002), and the observed particle growth rates can exceed few 100 nm h-1 (Ehn et al. 2010).

A suite of instruments that measured both gas-phase precursors, their oxidation products, and particle number size distributions was deployed. A chemical ionization time-of-flight mass spectrometer (CIMS) was used to measure selected vapors. It mainly measured sulfuric acid

(H2SO4), iodic acid (HIO3), methane sulfonic acid (MSA) and oxidized organic vapors when - coupled with a co-axial inlet with nitrate ion (NO3 ) as the reagent ion (referred to as nitrate-CIMS in this thesis; Jokinen et al. 2012). Alternatively, during most of the measurement campaign, the CIMS was coupled with a Multi-scheme chemical IONization inlet (referred to as Br-MION-CIMS in this thesis; Rissanen et al. 2019) which measured I2, HOI, IBr and ICl concentrations. Additionally, a nitrate ion based quadrupole-CIMS (referred to as nitrate-Q-CIMS; Eisele and

Tanner 1993) was used to measure constantly H2SO4, HIO3 and MSA, and was used as an online reference for these acid measurements from either the nitrate-CIMS or the Br-MION-CIMS.

17 Ozone (O3) and carbon monoxide (CO) were measured with an O3 monitor (Thermo, 49i) and a CO monitor (Trace Analytical RGA3), respectively. A SMPS (Scanning Mobility Particle Sizer) was used to measure particles between 20-500 nm (Vienna type DMA, TSI 3010 CPC). PM2.5 and

PM10 (particulate matter below 10 μm) were measured by a continuous ambient particulate monitor (TEOM model 1405) and an aerosol mass spectrometer (AMS, Aerodyne) was used to measure size-resolved non-refractory chemical composition of submicron aerosol particles.

2.2 Instrumentation

Due to the chemically and photochemically reactive nature of halogen species, optical spectroscopic methods have been widely used in iodine measurements. For example, the first OIO measurement was carried out by Allan et al. (2001) using a differential optical absorption spectrometer. A broadband cavity ringdown spectrometer was deployed for in situ point I2 and OIO measurements (Bitter et al. 2005; Leigh et al. 2010). A long-path differential optical absorption spectroscopy was used to measure I2 in a linear path (Leigh et al. 2010). Vaughan et al.

(2008) reported I2, IO and OIO concentrations using the combination of an incoherent broad-band cavity-enhanced absorption spectroscopy with a discharge-flow tube. Dillon et al. (2006) combined pulsed laser photolysis and laser induced fluorescence for the detection of IO. Resonance and off-resonance fluorescence by lamp excitation was used to measure iodine atoms and molecules (Gómez Martín et al. 2011). The spectroscopic methods provide accurate measurements of iodine species, but are commonly limited by their specificity, i.e., they can only measure a few species, and cannot measure species having congested or broad absorption cross sections, e.g., iodine oxoacids.

Various mass spectrometric methods have been also used to measure iodine species. For instance, photoionization mass spectrometer was used previously to detect a set of iodine oxides (IxOy) under laboratory conditions with elevated concentrations (Gómez Martín et al. 2013, 2020). - Superoxide (O2 ) chemical ionization was used previously to detect I2, Br2, Cl2, IBr, ICl and BrCl, using an atmospheric pressure chemical ionization tandem mass spectrometer (Finley and Saltzman 2008). Huang et al. (2010) used the combination of a diffusion denuder and a - chromatography mass spectrometer to measure I2. Sulfur hexafluoride ion (SF6 ) was also used previously to measure I2 (Raso et al. 2017). Most of the reported chemical ionization mass spectrometric methods were used to measure I2, while the measurements of subsequent oxidation products following I2 photolysis, e.g., iodine oxides and oxoacids remain challenging. Recently, - + nitrate (NO3 ) and hydronium (H3O ) ions were also employed to measure gas-phase HIO3 (Pfeifer et al. 2020; Sipilä et al. 2016).

2.2.1 APi-TOF

Atmospheric Pressure interface Time-Of-Flight mass spectrometer (APi-TOF, Tofwerk A.G., Switzerland and Aerodyne Research Inc, MA USA; Junninen et al. 2010) measures ions suspended in the atmosphere. It has been widely used in the investigation of ion-induced nucleation mechanisms of, e.g., H2SO4 – NH3 (Kirkby et al. 2011; Jokinen et al. 2018), H2SO4 – dimethyl amine (DMA) (Almeida et al. 2013), pure organic vapors (Kirkby et al. 2016; Rose et al. 2018) and HIO3 (Sipilä et al. 2016; papers IV and V).

2.2.2 Nitrate-CIMS

When coupled with chemical ionization inlets (CI-APi-TOF, or CIMS), APi-TOF is also able to detect neutral molecules by clustering of analytes with reagent ions, or by charge transfer from reagent ions to analytes. For example, nitrate-CIMS is widely used to detect H2SO4 and highly oxygenated organic molecules (Jokinen et al. 2012; Ehn et al. 2014) and it was recently deployed to detect HIO3 (Sipilä et al. 2016). In this thesis, nitrate-CIMS was mainly used to detect HIO3 and iodine oxoacid clusters (papers IV and V). We note that the nitrate-CIMS used in this thesis had a different inlet than the one used in Sipilä et al. (2016). The latter was described in detail in Jokinen et al. (2012) while the former was introduced in Kürten et al. (2014). The key advantage of the inlet utilized in this thesis (Kürten et al. 2014) was that the charging time in the ion-molecule reaction chamber (IMR) was around 50 ms, compared to approximately 300 ms in the Eisele design (Eisele and Tanner 1993) utilized by Jokinen et al. (2012). Chemical reactions induced in the charging processes are known to affect peak interpretation from CIMS measurements (Passananti et al. 2019). Thus, by reducing the charging time, we were able to keep a larger fraction of charged - ions in their original forms (either in deprotonated forms or forming analyte-NO3 clusters). Additionally, collision induced cluster fragmentation was also confirmed to affect peak interpretation as suggested by Passananti et al. (2019) and the references therein. To minimize this effect, we tuned our instrument to a maximum softness which reduced cluster fragmentation inside the low-pressure chambers of the nitrate-CIMS.

2.2.3 Br-CIMS

The very specificity of earlier instrumentations limits the understanding of iodine chemistry and particle formation since commonly only a rather limited set of iodine species were traced in laboratory or field observations. Moreover, online measurements for some important iodine species (e.g., hypoiodous acid, HOI and iodous acid, HIO2) were never reported. HOI was revealed as an important iodine precursor emitted from ocean using offline methods (Carpenter et al. 2013), while HIO2 was recently found to participate in neutral iodine cluster formation (paper V). A high time resolution, high sensitivity, and simultaneous measurements of iodine species are urgently needed to promote comprehensive studies of iodine chemistry and particle formation.

He 2017 demonstrated the concept of using a bromide chemical ionization mass spectrometer (Br- CIMS) to detect iodine containing species which included iodine oxides, oxoacids and interhalogen species. The technique was further developed and characterized in the papers I and II in which we demonstrate a bromide chemical ionization mass spectrometer coupled with a Multi-scheme chemical IONization inlet (Karsa Oy / Ltd. Finland) (Br-MION-CIMS), capable of detecting multiple iodine containing species with detection limits as low as a few tens of parts per quadrillion by volume (ppqv).

Additionally, Filter Inlet for Gases and AEROsols (FIGAERO, Aerodyne Research Inc., MA, USA) (Lopez-Hilfiker et al. 2014) was used to measure iodine particle composition. Although the FIGAERO inlet commonly uses iodide (I-) chemical ionization method, the iodine atoms from the reagent ion could interfere with the iodine vapors evaporated from the formed particles in our experiments. Therefore, we changed the reagent gas from methyl iodide (CH3I) to dibromomethane (CH2Br2) to measure iodine particle composition unambiguously.

Although mass spectrometric methods usually require dedicated calibration experiments to translate the measured electronic signals to absolute concentrations. Laboratory calibrations for species that are easily synthesized and stored are relatively easy but can be extremely difficult for other species which are difficult to standardize. In the latter case, it is very often the case that mass spectrometric methods can trace a species qualitatively but not quantitatively. However, to understand atmospheric iodine chemistry and particle formation processes, quantification is extremely important. Therefore, in addition to developing measurement methods, we dedicated a significant amount of time to calibrate a selected set of iodine species.

2.2.3.1 Calibration of I2 and Cl2 using permeation tubes

One way to quantify the measured signals from the Br-MION-CIMS is using permeation tubes which emit known amounts of vapors. We calibrated I2 and Cl2 using a I2 permeation tube and a

Cl2 permeation tube (VICI Metronic). The permeation tubes were isolated in an electronically controlled heating mantle which was able to control temperature from 80 to 140 (±2) ºC, or at room temperature, to allow adjustable permeation rates. The permeation devices were flushed with the same flow rates continuously for at least 72 (for I2) or 12 (for Cl2) hours before any calibration experiments to ensure a complete equilibrium in the system. Different levels of calibrants were permeated and measured by the Br-MION-CIMS for at least 24 hours to reach and ensure a stable signal.

After an ensured constant permeation rate, the I2 was flushed at 50 mlpm N2 at 140 ºC oven temperature and was trapped in n-hexane solvent at -80 ºC following the method described in

20 Chance et al. (2010). An absorption glass vessel was filled with 20 ml n-hexane which was then immersed into a wide-necked Dewar vessel, filled with an acetone/dry ice mixture. The collection continued for 5 hours with a flushing flow rate of 50 mlpm N2 passing the permeation device. The absorption vessel was then allowed to warm up to room temperatures before disassembling the setup to prevent any loss of iodine. The weight of the absorption vessel showed less than 2% difference before and after the collection, indicating a negligible loss of n-hexane during the trapping process. The I2 / n-hexane solutions were stored at 4 ºC for 14 hours before being subjected to further analysis. The collected I2 / n-hexane solutions were measured by a UV/Vis spectrophotometer (Shimadzu Model UV2450) and the calibration curve was established via measurements with a set of I2 / n-hexane solutions ranging from 270 to 5300 nmol. Repetition of the same analysis yielded similar results after 2 and 7 days, suggesting that the solutions were - - stable when stored at 4 ºC. The slope of I2 concentration versus normalized I2 signal (I2·Br / (Br - 10 -3 + H2O·Br )) in the Br-MION-CIMS gives a calibration coefficient of 6.3×10 molec cm per normalized signal (cps cps-1) with an overall uncertainty of ±45% (1σ).

The Cl2 permeation device was maintained at the room temperature and was flushed with 20 mlpm

N2 which carried out sufficient amount of Cl2 for calibration purpose. Finley and Saltzman (Finley and Saltzman 2008) proposed a method to quantify the permeation rate of Cl2 which was adopted in this study. A buffered aqueous potassium iodide solution (2.0 % KI (m/v)) was prepared in 1.00 mM aqueous phosphate buffer, pH = 7.0 which was further used to fill an all glass two-stage serial absorption apparatus (stage 1 = 100 ml and stage 2 = 50 ml). A 20 mlpm flush flow through the

Cl2 permeation device was introduced to the absorption apparatus for 3 hours at room temperature. - - The Cl2 in the solution oxidized iodide (I ) into iodine (I2) which in turn reacted with the excess I - anions to form I3 . An UV/Vis spectrophotometer (Shimadzu Model UV-1800) was used to - - quantify the I3 using 1 cm cells at 352 cm. No I3 was detected in the solution from the second stage suggesting that a full absorption of Cl2 in the first stage was achieved. We established the - -6 calibration curve by preparing I3 standards in the range of 5 to 68×10 M by dilution of a stock obtained by dissolving 174 mg iodine in 200 ml, the same buffered 2% KI solution. The calibration coefficient was determined to be 3.5×1011 molec cm-3 cps cps-1 with an overall uncertainty of ±30% (1σ).

2.2.3.2 Calibration of I2 using CE-DOAS

Aside from the permeation tube calibration, we carried out online I2 calibration at the CLOUD chamber using a Cavity Enhanced – Differential Optical Absorption Spectroscopy instrument (CE-

DOAS; Meinen et al. 2010). The I2 concentration in the chamber spanned from around 30 pptv to 2 parts per billion by volume (ppbv). The calibration gave a calibration coefficient of 2.7×1010 molec cm-3 cps cps-1 with an overall systematic error of 20% (1σ). Due to limitations of available equipment, the offline permeation tube calibration and the online CE-DOAS calibration utilized

21 two different Br-MION-CIMS with different tuning and setting. In this context, we regard the agreement within a factor of three as reasonable.

2.2.3.3 Calibration of HOI

The HOI was generated using a continuous HOI source from the reaction of I2 and OH radicals.

The calibration device was using the same principle as the H2SO4 calibrator as described in Kürten et al. (2012). Briefly, the OH was produced by photolyzing H2O with a mercury lamp, coupled with a filter which filters out other wavelengths but preserving the 184.9 nm. The intensity of the lamp was estimated by carrying out N2O photolysis experiments, which produced NO. To rule out

HOI production from I2 alone or wall reactions, we tested the calibration system by either removing

I2 or turning off the mercury lamp. No HOI was observed in these two test schemes, confirming that the I2 + OH reaction as the main source of HOI in the calibration experiments. A numerical model was developed to calculate the HOI concentration entering the pinhole of the instrument, considering chemical reactions, photolysis, diffusion and wall losses, which is analogous to the model used for H2SO4 calibration (Kürten et al. 2012).

There are two reactions that can form HOI in the calibration experiments: 1) I2 + OH à HOI + I and 2) IO + HO2 à HOI + O2. The formation of IO requires the involvement of O3 which itself is -20 2 produced from the slow photolysis of O2 in the system (photolysis rate at the order of 10 cm molec-1). Therefore, IO presents at a negligible concentration in the calibration device.

Additionally, the OH radicals are quickly scavenged by I2 in the reaction 1) which inhibits the HO2 formation in the system. Overall, reaction 1) dominates the HOI formation while reaction 2) contributes only a negligible fraction. Therefore, for simplicity only the first reaction was included in the numerical model. A range of HOI was produced by varying OH concentrations in the calibration device and was compared with model simulations. It yielded a final calibration factor of 3.3×1011 molec. cm-3 cps cps-1 with an uncertainty of 55% (1σ).

2.2.3.4 Calibration of H2SO4

Gaseous H2SO4 is commonly measured by the nitrate chemical ionization mass spectrometers (nitrate-CIMS) as described in Kürten et al. (2012). In this study, we deployed both the Br-MION- CIMS and nitrate-CIMS at the CLOUD chamber and carried out online calibration by comparing normalized H2SO4 signals from Br-MION-CIMS to the calibrated H2SO4 trace from the nitrate- 4 CIMS. The calibrated H2SO4 trace covered a wide range from less than 5.0×10 (detection limit) 7 -3 - to 6.0×10 molec cm . H2SO4 was commonly detected in two forms: HSO4 (96.96 m/z) and - H2SO4·Br (176.89 m/z). However, only the later was used to calculate the concentration of H2SO4 - - since HSO4 was very close to H2O·Br (96.93 m/z), one of the reagent ions, which significantly - interferes with the quantification of HSO4 . The calculated H2SO4 calibration coefficient was

22 10 -3 -1 4.1×10 molec cm cps cps . The systematic 3σ accuracy is +50 / -33 % for H2SO4 calibration using a nitrate-CIMS.

2.2.3.5 Calibration of HNO3

The bromide chemical ionization was also deployed to detect nitric acid (HNO3; paper I), but with a different inlet system. The inlet system utilized was the same as the commonly used nitrate- - CIMS inlet (Jokinen et al. 2012). When colliding with Br ion, HNO3 either deprotonates to form - - 79 81 - NO3 or forms HNO3·Br ( Br and Br) clusters. In this thesis, only the NO3 was taken into - - account to calculate HNO3 concentration as the ratio between HNO3·Br / NO3 generally remained constant.

The calibration factor was carried out by pathing 2 slpm N2 through a HNO3 permeation tube kept at 40 ºC. The same flow was also passed into deionized water, which was later analysed by spectrophotometric method (paper I). The final calibration factor was determined to be 1.77×1011 molec cm-3 cps cps-1.

2.2.3.6 Connecting sensitivity to binding enthalpy

Although we have carried out calibrations for several selected species, there are many more species that cannot be directly calibrated because of the lack of authentic standards or generation methods. However, earlier studies suggested that the binding enthalpies of the reagent ion – analyte clusters correlate well with the instrument sensitivity for the iodide chemical ionization method (Iyer et al. 2016). This should also apply for the bromide chemical ionization method. The fragmentation of the formed analyte – Br- clusters generally proceed by two ways:

1) Reversion to Br- and analyte

X − H ∙ Br → X − H + Br (1)

2) Proton transferred from the analyte to Br-

X − H ∙ Br → X + HBr (2) where the X-H is a hydrogen bond donor. In case an analyte does not have a hydrogen atom, replace the “H” in the equations by another atom. Only the reversion reaction affects the sensitivity of the Br-CIMS instrument since reaction (2) produces the X- ion which still is interpretated as originating from the X-H. Therefore, if reaction (2) is the dominant fragmentation pathway, i.e., if it requires much less energy to proceed than reaction (1), the sensitivity of Br-CIMS will not be

23 affected, and the detection proceeds at the kinetic limit (e.g., H2SO4 and HIO3 detection; paper II). However, if reaction (1) is the dominant fragmentation pathway, the weaker the binding between X-H and Br-, the higher the chance that the reversion reaction occurs. In contrast, if the fragmentation reaction enthalpy is higher than a (instrument-dependent) critical value, cluster fragmentation does not have time to occur (in a given instrument setting), and the analyte can also be detected at the kinetic limit. Therefore, we calculated the fragmentation reaction enthalpies between Br- and a series of analytes in the paper II and concluded that the critical enthalpy for our Br-MION-CIMS was below 33.7 kcal mol-1. We further inferred that both ICl and IBr were detected at the kinetic limit since the fragmentation reaction enthalpies are 33.8 and 36.7 kcal mol- 1, respectively, both higher than 33.7 kcal mol-1. On the other hand, the fragmentation reaction - - -1 enthalpies of HOI·Br and Cl2·Br are 26.9 and 22.3 kcal mol , respectively, both much lower than -1 33.7 kcal mol , suggesting that the fragmentation interferes with the detection of HOI and Cl2. 11 11 Indeed, the calibration coefficients for HOI and Cl2 were determined to be 3.3×10 and 3.5×10 -3 -1 10 molec cm cps cps , respectively, much lower than the calibration factor for I2 of 6.3×10 molec cm-3 cps cps-1.

2.3 Quantum chemical calculations

Quantum chemical calculations focus on applying quantum mechanics (in practice, the Schrödinger equation) to understand chemical systems. Solving the Schrödinger equation yields the wave function of a molecule (or system of molecules), which essentially determines all its chemical properties. However, an exact solution for the Schrödinger’s equation can only be obtained for hydrogen-like (one-electron) atoms (Heitler and London 1927), and several approximations have to be made for all other atomic or molecular systems. The approximations can both be made to the Hamiltonian term, which are defined as “methods” in quantum chemical calculations, and to the wave function (or electron density). The “methods” are further grouped into wave function – based methods, and widely used density functional theory (DFT) methods, which simplify the problem by solving for the electron density instead of the wave function. The approximations made to the wave function or electron density are usually defined in terms of a “basis set”, usually a collection of atom-centered gaussian functions resembling the atomic orbitals of hydrogen-like atoms. For heavy elements such as iodine, the innermost electrons are often replaced by a simple potential, the so-called pseudopotential which nicely accounts for most of the relativistic effects that would otherwise be very expensive and difficult to treat. Different combinations of methods and basis sets can achieve different accuracy for a specific problem. The commonly used notation is method/basis set, e.g., B3LYP/6-31+G(d) implies the B3LYP method with the 6-31+G(d) basis set. A rule of thumb is that a higher accuracy commonly requires heavier calculations and a longer computational time.

24 In this thesis, in order to be efficient in calculations while preserving accuracy, I combine a set of methods including one semi-empirical method (XTB; semiempirical Tight Binding), two methods based on density functional theory (B3LYP and ωB97xD) and one high-level method based on wave function theory (DLPNO-CCSD(T)). These methods are combined to predict the binding energies of charged and neutral clusters and to help explain particle formation mechanisms. Spartan 18 program was used to do the initial conformer sampling of monomers using the MMFF (Merck molecular force field). We calculated the partial charges of different atoms in the monomers at the ωB97X-D/aug-cc-pVTZ-PP level of theory by running single-point calculation with the pop=MKUFF keyword using the Gaussian 09/16 programs (Frisch et al. 2009). The monomer geometries and partial charges were used as inputs by the ABCluster program (Zhang and Dolg 2015, 2016) to generate initial structures. 200 to 500 initial structures were generated depending on the complexity of each cluster and the best 100 to 200 out of them were selected for further analysis. Configurational sampling of the dimer clusters during the ABCluster procedure was performed using molecular mechanisms and the intermolecular interactions defined by the CHARMM (Chemistry at Harvard Macromolecular Mechanics) force field and the computed partial charges (Vanommeslaeghe et al. 2010). Single point XTB calculations were used to calculate the electronic energy and conformers within 7 kcal mol-1 with respect to the lowest- energy conformer which were selected for further calculations. The iodine atoms in the selected conformers were further replaced by bromine atoms, which were later optimized at the B3LYP/6- 31+G* level using Gaussian. This procedure was done since the 6-31+G* basis set has not been optimized for iodine atoms, and the replacement of iodine to bromine was computationally efficient. Extensive test calculations suggested that such replacement is reasonable since these two atoms are rather similar in physical properties (Hyttinen et al. 2018). Optimized structures were within 3 – 6 kcal mol-1 in relative electronic energy compared to the lowest-energy structures which were selected for higher level analysis and the bromine atoms in the conformers were replaced back to iodine atoms. The structures were then optimized, and vibrational frequencies were calculated at the ωB97xD//aug-cc-pVTZ-PP level (Feller 1996; Kendall et al. 1992) with the iodine pseudopotential definitions taken from the EMSL basis set library (Feller 1996). In order to calculate cluster evaporation rates, an additional coupled-cluster single-point energy correction was carried out on the lowest-energy structure calculated at the ωB97xD//aug-cc-pVTZ- PP level. The coupled-cluster calculation was done using the DLPNO-CCSD(T)/def2-QZVPP method with the ORCA program ver. 4.1.1 (Riplinger and Neese 2013; Neese 2012).

2.4 Kinetic model

New particle formation processes are governed by many sink and production processes for precursors vapors and clusters. It can be extremely difficult for a human brain to comprehend and predict the outcome if some of the parameters controlling either sinks or production are changed.

25 Kinetic models are therefore essential tools which simplify kinetic problems and help us to predict the behavior of a system, based on known parameters. Additionally, kinetic models can expand the results obtained in laboratory experiments by including the experimentally derived parameters, thus expanding the parameter space.

In this thesis, the Polar ANd high-altituDe Atmospheric research model (PANDA520) was developed (paper IV) to understand ion-induced iodic acid cluster formation processes. This model includes charged clusters with up to 15 monomers (including the core ion) and neutral clusters with 10 monomers. The model treats larger clusters (charged / neutral) the same as the largest considered cluster (charged / neutral). Such simplification was done since we only studied charged clusters with up to 11 monomers and further growth was not required for calculating the ion polar molecule collision rate coefficient (see discussions in the following sections; hereafter simplified as “collision rate coefficient”). The data used in the paper IV were time series of charged clusters from the start of the experiment to the time when the concentration of the 11-mers reached its maximum, so the charged clusters larger than the 11-mers played a minor role. The same also applied to the neutral clusters, which were shown to form at a much lower rates than the charged clusters because of lower collision rate coefficients and higher evaporation rates (paper IV).

Additionally, the PANDA520 model includes a full set of parameterizations that describes the cluster dynamics in the CLOUD chamber. It includes processes that affect the whole cluster population (condensation, coagulation, wall losses, dilution and evaporation) and processes that only influence charged clusters (ion production in the chamber and ion-ion recombination). Finally, the model considers the finite time resolution of the measurements of both physical and chemical properties to study the effect of the finite time resolution on the accuracy of the measured values. The details of the parameterization can be found in the supplementary information of the paper IV.

26 3 Advances in atmospheric iodine chemistry

3.1 Formation of iodine oxoacids

The formation of iodine oxoacids is commonly thought to involve reactions of reactive iodine

species (I, IO and OIO radicals) with HOx radicals (Khanniche et al. 2017; Plane et al. 2006;

Drougas and Kosmas 2005). However, due to limited production rates of HOx radicals, such

mechanisms would not explain the extremely rapid particle formation events and the high HIO3 concentrations observed at the Mace Head observatory (Sipilä et al. 2016; O’Dowd et al. 1999). Therefore, additional sources should account at least partially for the observed high iodine oxoacid concentrations.

To test this hypothesis, we used the green light source (528 nm) to initiate the experiment. Green 1 light is able to photolyze I2 into iodine atoms but it does not photolyze O3 to form O( D) and thus

OH radicals and so HOx were negligible. The iodine oxoacid concentrations and the time when we switched the green light on are shown in Figure 2. Surprisingly, we found that iodine atoms were

oxidized in the presence of water vapor and ozone, producing significant amount of both HIO3 and

HIO2. Since ozone and water vapor are present throughout the troposphere, our results imply that molecular iodine can produce iodine oxoacids even under cloudy daylight conditions with

negligible ultraviolet irradiation. Despite that the exact formation mechanisms of HIO3 and HIO2 remain uncertain, our results indicate that hydrated iodine atoms or oxide radicals may react with ozone to form iodine oxoacids. Another possibility is that the iodine oxoacids are formed from the

hydrolysis of IxOy (Gómez Martín et al. 2020).

Figure 2. Formation of iodine oxoacids without HOx radicals. Gas-phase HIO3 and HIO2 measured in the CLOUD chamber before and after switching on green light (528 nm). The experimental conditions are 7 pptv I2, I atom 4 -3 -1 production rate of 6.2×10 cm s , 40 ppbv ozone, 69% RH and −10°C. Green light photolyzes I2 to iodine atoms but

27 does not photolyze ozone and so HOx is absent. This demonstrates that HIOx can be produced by oxidation with ozone in the absence of HOx. The measured HIO2 concentration assumes the same mass-spectrometer calibration factor as for HIO3, and so represents a lower limit.

3.2 Enhanced iodine recycling from heterogeneous reactions

The production of interhalogen species (e.g., iodine monochloride [ICl] and iodine monobromide [IBr]) through heterogeneous uptake of hypoiodous acid (HOI) was proposed to accelerate atmospheric halogen recycling over 20 years ago (Vogt et al. 1996, 1999). Despite that multiple laboratory studies were carried out and confirmed the heterogeneous uptake of HOI (Mössinger and Cox 2001; Braban et al. 2007; O’Sullivan and Sodeau 2010), this hypothesis has not been confirmed in ambient measurements.

One of the key problems to study the heterogeneous uptake of HOI and the subsequent products is the incapability of measuring HOI and interhalogen species. However, the development of the bromide-CIMS enabled us to measure these species in a quantitative manner (paper II). Therefore, a field observation was carried out in the Mace Head observatory in summer 2018 to measure HOI and interhalogen species and to study the heterogeneous uptake of HOI.

The measured I2, HOI, IBr and ICl are shown in the Figure 3. We find that HOI has a clear diurnal and tidal pattern similar to that of I2, suggesting that the source of HOI at Mace Head is primarily the photolysis of I2. If the heterogeneous production of HOI from the deposition of O3 on sea surface was the main HOI source at Mace Head (Carpenter et al. 2013), a tidal pattern of HOI would not be expected. The maximum HOI concentration was around 67 pptv, while commonly it was close to or below 10 pptv during the daytime low tide event. In the meantime, significant amounts of IBr and ICl were constantly measured throughout the campaign. The mean daily maxima of IBr and ICl were 3.0 and 4.3 pptv, respectively. Both peak during the low tide events, similar to I2 and HOI. The correlation between these two interhalogen species and HOI is therefore interesting.

Figure 3. Time series of (A) HOI and the tidal heights and (B) I2 and solar radiation at Mace Head Observatory from June 19 to July 19, 2018 (1-min average data). (C) The ratio of HOI to I2 mixing ratios (5-min average) in relation to the O3 concentration. The I2 and HOI mixing ratios below the detection and quantification were omitted. The ozone data are 1-h average. (D) Time series of ICl and IBr observed during the campaign (1-min average). The time presented in this study is in Coordinated Universal Time (+1 in local time). The figure is adopted from the paper III.

In order to find out whether IBr and ICl were produced by heterogeneous uptake of HOI on aerosols, we calculated the aerosol surface area and compared it with the ratio of IBr or ICl to HOI. The aerosol surface area was found to be positively correlated to the ratio of the interhalogen

29 species to HOI, indicating that heterogeneous uptake of HOI indeed contribute to the formation of IBr and ICl.

The implication of the heterogeneous uptake of HOI and the production of IBr and ICl is remarkable. One of the major production pathways of HOI is shown by equations (3) to (5) (Saiz-

Lopez et al. 2012). The formation of one HOI molecule consumes one O3 molecule through this route. We show in paper III that the formed HOI can react on the aerosol surface to produce IBr and ICl, which in turn photolyze in the atmosphere to recycle back reactive halogen atoms (e.g., I atoms). These reactive halogen atoms will again react with O3 to produce HOI, which further accelerates this cycle. The oxidation of iodine atoms also produces HIO2/HIO3, which are important precursors for iodine oxoacid particle formation.

I + ℎ� → 2I (3)

I + O → IO + O (4)

IO + HO → HOI + O (5)

To investigate quantitatively the effect of the heterogeneous uptake of HOI on O3 loss and reactive iodine atom production rate, we carried out two model simulations, with or without considering the measured IBr and ICl. We find that the simulation including the heterogeneous uptake of HOI yields a daytime average of 32% more atomic I production and about 12% enhancement in the O3 loss rate.

Despite Mace Head being a known iodine hotspot, our findings indicate a wider role of the heterogeneous uptake of HOI. HOI is known to be emitted from the sea water together with I2 (Carpenter et al. 2013). Model simulations considering such emission mechanisms reported a considerable amount of HOI (~2 to 15 pptv) spreading in the marine boundary layer (Prados- Roman et al. 2015; Sherwen et al. 2016; Mahajan et al. 2019), which is comparable to our HOI measurement at Mace Head. Our results therefore hint that the rapid activation of IBr and ICl is likely to be ubiquitous in global marine atmosphere. This rapid activation not only accelerates the ozone loss but potentially also enhances the iodine oxoacid particle formation processes.

Figure 4. (A) Changes in atomic I production rates between the simulations with and without constraining ICl and IBr. The black solid line is the mean value and the gray shaded area is the standard deviation. (B) The daytime average atomic I production rates from photolysis of different iodine species under conditions with and without ICl and IBr. (C) The O3 loss rates modeled with and without ICl and IBr. Note that this figure is based on data from June 26 to July 14 of 2018 as indicated in the model description (Supplementary Information, paper III). The data are averaged over 10-min intervals. The figure is adopted from the paper III.

31 4 Iodine oxoacid particle formation

Despite iodine particle formation having been discovered over 20 years ago at a coastal site (Hoffmann et al. 2001; O’Dowd et al. 2002), the exact particle formation mechanisms are still

under debate. Earlier studies have suggested HI (hydrogen iodide) + H2O (Kulmala et al. 1991),

OIO (iodine dioxide, Jimenez et al. 2003), I2O3 (diiodine trioxide, Saunders et al. 2010), I2O4

(iodine tetroxide, Saunders et al. 2010; Gómez Martín et al. 2013, 2020) and I2O5 (iodine pentoxide, Saunders and Plane 2005) as the potential nucleating species for neutral iodine particle formation. However, most of earlier experimental laboratory investigations utilized very high concentrations of iodine precursors, which resulted in concentrations of iodine oxidation products that were orders of magnitude higher than the observed values in ambient conditions. It is therefore questionable whether they can precisely describe the particle formation processes that occur in the marine boundary layer. Moreover, earlier laboratory studies were commonly unable to measure iodine oxoacids, which left their role in particle formation unexplored.

Recent field observations by Sipilä et al. (2016) reported the critical role of HIO3 in iodine particle formation for the first time based on observations at the Mace Head observatory. The ion-induced (charged) and neutral nucleation mechanisms were proposed to be the same in their study. However, neutral cluster composition measurements by the nitrate-CIMS can suffer from the interference from natural ions suspended in the atmosphere if a functional ion filter is not applied. The measured charged clusters can thus sometimes falsely be interpretated as neutral clusters in such scenario, resulting in an apparently consistent mechanism for both neutral and ion-induced mechanisms. Additionally, Passananti et al. (2019) suggests that collision induced cluster fragmentation and charging induced reactions can bias the interpretation of the mass spectra produced by mass spectrometers. All of the above-mentioned factors need careful treatment. While it remains difficult to optimize measurements in field observations, especially considering variabilities in environmental conditions, laboratory experiments are better equipped to pin down the exact mechanisms of iodine particle formation processes.

In this thesis, iodine particle formation experiments were carried out with vapor concentrations at atmospherically relevant conditions in the CLOUD chamber at CERN. More importantly, the deployed nitrate-CIMS was carefully turned to reduce collision induced cluster fragmentation (softness) and the reaction time in the ion-molecule reaction chamber was around 50 ms, four times faster than that utilized in Sipilä et al. (2016), to reduce charging induced reactions. Finally, an ion filter, which scavenged naturally charged ions prior to entering the ion molecule reaction chamber, was utilized to get a clean neutral cluster spectrum.

4.1 Ion-induced iodic acid nucleation

4.1.1 Discovery of ion-induced iodic acid nucleation

While earlier studies focused on the neutral particle formation mechanism, the ion-induced iodine particle formation mechanism was recently discovered to involve iodic acid (HIO3) based on an ambient observation at Mace Head, Ireland (Sipilä et al. 2016). The authors proposed a two-step mechanism suitable both for neutral and ion-induced nucleation: First, two HIO3 molecules condense on a neutral cluster or a charged cluster in a sequence (example for neutral nucleation case: equations (6) and (7)). Second, the two HIO3 molecules dehydrate to form an I2O5 and H2O molecules (equation (8)).

HIO + (IO) → HIO(IO) (6)

HIO + HIO(IO) → HIOHIO(IO) (7) HIOHIO(IO) → (IO) (8)

The proposed mechanism was primarily based on the fact that the observed cluster composition in the form of (HIO3)0-1(I2O5)n, i.e., at maximum one HIO3 was present in the clusters with the rest of the iodine found in the form of I2O5 molecules, and to lesser extent, less oxygenated iodine oxides. Laboratory experiments suggested that [HIO3] was proportional to the square root of [I2O5] with varying I2O5 concentrations, indicating that two HIO3 form I2O5, instead of the other way around (Sipilä et al. 2016).

4.1.2 Experimental evidence from the CLOUD chamber at CERN

Sipilä et al. (2016) speculated that the HIO3·HIO3 (dimer) dehydrates to form an I2O5. Whether this process also occurs in larger iodine clusters is still an open question. For example, I2O5 molecules may condense on small HIO3 clusters which can result in the same cluster composition

(in the form of (HIO3)0-1(I2O5)n). Therefore, it is not clear whether HIO3 alone drives the ion- induced iodine nucleation.

In order to examine the mechanism proposed by Sipilä et al. (2016), we carried out ion-induced iodine nucleation experiments in the CLOUD chamber at CERN. Humidified synthetic air at 34- 40 % relative humidity and 10 °C was injected into the chamber with 40 ppbv ozone and 0.4 to

3.5 pptv I2. The green light source was turned on, which was recorded as the t (time) = 0, after a steady state was achieved in the chamber. Rapid I2 photolysis was triggered, followed by fast oxidation processes which eventually led to the formation of HIO3 in the chamber. Galactic cosmic rays ionized the air in the chamber and produced positive and negative ion pairs. Due to the low - proton affinity of HIO3, negative charges were captured to form IO3 ion as the primary negative ion in the chamber, which in turn served as the seed for the ion-induced iodine nucleation processes.

33 An example experiment of such processes is shown in Figure 5 (from paper IV). At the start of - - the experiment, before turning on the green light, only IO3 was present in the chamber. This IO3 was the product of a small amount of residue HIO3 in the chamber. After turning on the green light, significant amounts of HIO3 were produced in the chamber, and ion-induced iodine nucleation was triggered. We measured a sequential appearance of individual charged iodine clusters with a - molecular composition of (HIO3)0-1(I2O5)n·IO3 , which is in perfect agreement with the observation by Sipilä et al. (2016). Furthermore, the appearance of each charged cluster clearly took place after that of a smaller charged cluster with one less iodine atom. This is direct evidence that the growth has to be contributed by a candidate molecule with one iodine atom and thus, I2O5 does not play a significant role in ion-induced iodine nucleation. Therefore, we define this process as ion-induced iodic acid nucleation.

1 a) 1 - IO- 3 0.9 2 - HIO IO- 0.8 3 3 3 - I O IO- 0.7 2 5 3 4 - HIO I O IO- 0.6 3 2 5 3 5 - I O IO- 0.5 4 10 3 6 - HIO I O IO- 0.4 3 4 10 3 7 - I O IO- 0.3 6 15 3 Normalized concentration 8 - HIO I O IO- 0.2 3 6 15 3

) 9 - I O IO- -3 0.1 8 20 3 10 - HIO I O IO- 0 3 8 20 3 11 - I O IO- b) 10 25 3 107

106 concentration (cm

3 0 100 200 300 400 500 Time (s)

HIO Figure 5. Example evolution of the time sequence of a single ion-induced nucleation experiment in the CLOUD chamber. The experimental conditions are 11 °C, 34% RH, 40 ppbv ozone and 8 (±6) pptv I2 with green light on. a) The concentrations of charged clusters are measured by a negative APi-TOF (circles joined by lines); model predictions are shown by smooth curves. They are normalized by the maximum and minimum of individual time series. The colors indicate the number of iodine atoms in the charged cluster. The chemical composition and number of iodine atoms in the charged clusters are listed in the legend with respect to colors. b) HIO3 concentration during the experiment. Time represents the elapsed time from the starting of the experiment in seconds. The figure is adopted from the paper IV.

On the other hand, despite a clear sequential cluster formation process being observed in the negative ion channel, no cluster formation was observed in the positive ion channel (Figures. S5 and S6, paper V). Therefore, the ion-induced iodic acid nucleation is a pure negative ion-induced nucleation process.

34 4.1.3 Determination of ion-polar molecule collision rate coefficients

4.1.3.1 Analytical derivation of the appearance time method

The sequential appearance of charged iodic acid clusters in Figure 5 mimics the particle growth processes observed in chamber studies and in field observations. Indeed, charged cluster growth and particle growth are similar processes at a different scale. The presence of a charge in a molecular cluster significantly increases its survival probability against losses by enhancing both cluster stability and the collision rate coefficients between colliding counterparts (Kirkby et al. 2011). The latter is a critical factor that allows the rate of charged cluster formation overrun neutral cluster formation. Accurate (ion-molecule) collision rate coefficients can help models to better describe charged iodic acid cluster formation processes. One question therefore arises: can we apply the appearance time method, which is commonly used in growth rate calculations (Lehtipalo et al. 2014), in the calculation of the ion-polar molecule collision rate coefficients?

The appearance time method was first proposed as a conceptual model to calculate particle growth rates (Lehtipalo et al. 2014). However, no analytical derivations have been made to assess its validity for computing collision rates. Therefore, in this thesis (paper IV), I examine the possibility of applying the appearance time method from the basic numerical expression of the cluster growth processes.

In order to derive an analytical solution, a number of assumptions have to be made to simplify the cluster growth processes. These assumptions are listed below (paper IV):

1) The HIO3 concentration is constant during an experiment 2) The ion production rate is constant during an experiment

3) At t = 0, the carrier air is assumed to be clean, thus without any ions or neutral HIO3 4) The collision rate coefficients are the same for all the charged clusters regardless of size

5) Only the HIO3 monomer condenses on the charged clusters, while the neutral clusters do not coagulate with the charged clusters 6) Cluster scavenging processes including wall loss, dilution loss, ion recombination, condensation sink and thermal evaporation are assumed to be negligible

We show in the supplementary information of the paper IV that the assumptions are reasonable. With these assumptions, the charged cluster growth processes can be simplified as a set of differential equations described as below:

35 ì de[]- ï =Qa-´[] e- ï dt 1 ï ï du[]1 - = ae111´´[]- au [] ï dt ï du[] ï 2 =au´´[]- au [] (9) í dt 11 12 ï ï du[]3 =au12´´[]- au 13 [] ï dt ï ï ...

ïdu[]i ï =au111´´[]ii- - au [] î dt

- where the e is the primary negative ion, Q is the ion production rate, [ui] is the concentration of the charged cluster i-mer, a1 is [HIO3] × k1 where [HIO3] is the concentration of iodic acid and k1 is the ion-polar molecule collision rate coefficient which is assumed the same for all charged clusters.

Solving the equation set (9), we find that the charged cluster population can be expressed by

ì i-1 i --Qe-at11[()] e atii!!++ ann t m+ a ii t ï å 11Õ ï[]u = n=1 mn=+1 i ï a1i! ï ii -at (10) ï du[] Qa t e 1 í i = 1 ï dt i! 2 ii-1 -at1 ï du[]i Qa1 t e( a1 t- i) ï 2 =- ï dt i! îï

Since the parameter a1 contains the collision rate coefficient, we further solve equation (10) and derive the named maximum production rate method (MPR):

11 (11) ttimaximax+1,- , == =t ak131[HIO ]´

[!] where ti,max is the time when the i-mer reaches its maximum net production rate ([ ] ), and τ is the expected lifetime of a single i-mer as it grows to become an (i+1)-mer.

The derivation shows that the ti,max can be used as a characteristic time for each charged cluster which in turn can be used to derive collision rate coefficients. However, atmospheric measurements are normally limited by time resolution and instrument sensitivity. Deriving the � = [[!]] might introduce a large uncertainty. Therefore, we derive and introduce , another form of characteristic time, named the maximum concentration difference (MCD) time:

i +1 (12) tti++1, maxDiff== i 1, max a1

Where tt+1,maxDiff represents the time when the concentration difference between [ui] and [ui+1] reaches its maximum. Under the same assumptions defined at the beginning, we find the maximum concentration difference time (tt+1,maxDiff) equals to the maximum production rate time (ti,max). Therefore, both of them can be used to calculate the collision rate coefficient.

To elucidate the difference between the MPR and MCD methods and the 50% appearance time method (APP50), we applied parameterizations based on the same assumptions as the derivations 7 -3 -1 to the PANDA520 model, with initial conditions of 1) [HIO3] = 2 × 10 cm , 2) Q = 2 s , 3) k1 = 2 × 10-9 cm3 molecules-1s-1. The simulation results showing the concentration of charged clusters containing up to 15 monomers are shown in the Figure 6 with colored solid lines. Points labelled with ui are 50% appearance times of the charged clusters while points labelled with ui,M are characteristics times of MPR and MCD methods. Indeed, MPR and MCD methods reach the same characteristic time for the same charged cluster. However, they are intrinsically different because of the difference in definitions. If the assumptions (1) to (6) for the derivation are changed, they can yield different characteristic times which in turn might result in different collision rate coefficients. Additionally, Figure 6 shows clearly that the MPR/MCD characteristic times appear before the 50% appearance time for a specific charged cluster. This indicates that a charged cluster reaches its largest net production rate and largest concentration difference to its prior charged cluster before the 50% appearance time.

37 e- 120 IO- 3 HIO IO- 3 3 100 I O IO-

) 2 5 3 -3 HIO I O IO- 3 2 5 3 80 I O IO- 4 10 3 HIO I O IO- 3 4 10 3 60 I O IO- 6 15 3 u u u u u u u u u u u u u u u 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 HIO I O IO- u u 3 6 15 3 u u u u u 14,M 15,M u u u 9,M 10,M 11,M 12,M 13,M - 40 u u 6,M 7,M 8,M u 4,M 5,M I O IO u 3,M 8 20 3 2,M HIO I O IO-

Cluster concentration (cm u 1,M 3 8 20 3 20 I O IO- 10 25 3 HIO I O IO- 3 10 25 3 0 I O IO- 0 100 200 300 400 500 600 12 30 3 time (s) HIO I O IO- 3 12 30 3 Figure 6. Schematic plot illustrating the difference between the MPR/MCD and the APP50 methods. The chemical composition and number of iodine atoms in the charged clusters are listed in the legend with respect to colors. ui represents the APP50 points while ui,M represents the MPR/MCD points. The figure is adopted from the paper IV.

In order to build a connection between the MPR (note, not the MCD method) and the APP50 method, and to validate the APP50 method, we define Δti = ti,50 – ti,max, which describes the time difference between the APP50 characteristic time and the MPR characteristic time for the charged cluster containing i-mer. An additional assumption has to be made here in order to continue the derivation: the production rate of the charged cluster i-mer is assumed to be constant between ti,50 and ti,max (this assumption will be validated). A parameter Li is further derived which is the multiple of Δti and a1 as shown in the equation (13).

i-1 i ie!(-inii!+ 2 i !+ 2 ( i m ) + 2 i ) å Õ (13) L ==n=1 mn=+1 Dta´ ii2i!ii 1

Thus, if ΔLi+1 = Li+1 – Li is close to zero, Δti+1 should approximate zero, which in turn suggests that ti+1,50 – ti,50 = ti+1,max – ti,max. According to equation (11), Δti+1,50 = ti+1,50 – ti,50 can therefore be used to calculate the collision rate coefficient. We calculate the Li numerically with respect to cluster sizes and show the results in the Figure 7. The results show that despite the ΔLi+1 is larger than zero for the initial charged clusters with i ≤ 5, it is rather close to zero when i > 5 which suggests that the APP50 method can yield close enough results to the MPR/MCD methods.

38 0.7 L Y 0.65

0.6

number 0.55

0.5

0.45 0 10 20 30 40 50 60 70 80 90 number of iodine atom in the charged cluster Figure 7. Validation of the parameter Li and Yi with respect to cluster size. The figure is adopted from the paper IV.

As a critical assumption was defined before the derivation of the Li, we further validate this assumption by deriving another parameter Yi as shown in the equation (14). We show in paper IV that the closer the Yi is to 0.5, the more valid the assumption that the net production rate of charged cluster i-mer is constant between ti,50 to ti,max. Again, numerically calculated Yi is plotted in the

Figure 7 against the cluster size. We find that when i ≤ 5, the Yi is a bit smaller than 0.5, but when i > 5, Yi indeed approximates 0.5.

i-1 i --[(())()eiLmiLiL+ i i!++i! +ni + + ] å iiÕ iL+ a (14) Yt===n=1 mn=+1 [u ]( i)´1 iiiL+ i eaQi! 1

Overall, the derivations and the numerical calculations show a consistent picture that the MPR and MCD methods can be used to calculate the collision rate coefficient analytically while the APP50 method approximates the MPR and MCD methods when i > 5. A certain level of error is expected when applying the APP50 method for the charged clusters with i ≤ 5.

4.1.3.2 Calculation of the apparent collision rate coefficient

Following the analytical derivation of the MPR, MCD and APP50 methods, we tested the performance of these methods against two example cases simulated by the PANDA520 model: 1) the input of HIO3 concentration was constant and the collision rate coefficient was also constant for all studied charged clusters, 2) both the HIO3 concentration and the collision rate coefficients

39 were set to vary. Simulated charged cluster time evolution was further analyzed by the MPR, MCD and APP50 methods, which in turn resulted in three sets of collision rate coefficients. They were further compared with the true values (initial input in the model) to examine the closeness of the calculated collision rate coefficient to true values.

The performance of the three methods is accurate and comparable in the first example case, which is expected. The first example case is similar to the simulation shown in the Figure 6, except that additional loss and production processes were added to better describe the cluster dynamics in the chamber. However, in the second example case, the APP50 method produces the most precise results among the three methods, while the MCD method yields the worst results. Considering that the HIO3 concentration is rarely constant in atmospheric conditions and in laboratory experiments and that the collision rate coefficient is not constant for different charged clusters, the APP50 method is thus the preferred method. Therefore, the APP50 method is applied to calculate the collision rate coefficient for subsequent analysis in this thesis.

To calculate the collision rate coefficient, we repeated additional seven experiments, similar to the one shown in the Figure 5 and calculated the apparent collision rate coefficients which are shown in the Figure 8 as gray triangles. Additionally, collision rate coefficients expected from three theoretical methods were calculated. The first one is the widely used average dipole orientation (ADO) method which considers the thermal rotational energy of polar molecules, developed by Su and Bowers 1973. The ADO theory considers the charged cluster as a sizeless point, and the polar molecule is captured by the charged cluster if they get close enough. The maximum distance that a polar molecule can be captured is defined as the capture radius. However, the ADO theory does not consider the physical sizes of the colliding counterparts. Therefore, if the sum of the radii of the colliding counterparts is larger than the capture radius, the charged cluster will collide with the polar molecule even though the polar molecule is not “captured”. In order to account for the size effect, Kummerlöwe and Beyer (2005) extended the ADO theory by including a size effect, which resulted in the hard-sphere average dipole orientation theory (HSA theory) and the surface charge capture theory (SCC). The HSA theory treats the charge at the center of the charged cluster while in the SCC theory, the charge is drawn to the surface of the charged cluster. We calculated the expected collision rate coefficients using these three methods for charged iodic acid clusters and show the results in Figure 8. The results show that the expected values from the HSA theory do not differ significantly from the values expected by the ADO theory. This is mainly because the studied charged clusters are below 1.35 nm in mobility diameter (equals to around 1.05 nm in mass diameter), and the capture radius of the charged clusters is much larger than the physical sizes of the colliding counterparts. On the other hand, the values from the SCC theory are significantly higher than the values from the other two theoretical methods owning to the extended capture radius by drawing the charge to the surface.

40 Additionally, our results show that the measured apparent collision rate coefficients are significantly higher than the theoretical expectations from the ADO and HSA theories and a bit higher than the values from the SCC theory.

6 A 5 ) 4 -1 s

3 3

-9 10

6 5 4 3

2 CLOUD (before correction) CLOUD (after correction) -10 Weighted mean (after correction)

Collisionratecoefﬁcient (cm 10 ADO theory (charged clusters) 6 HSA theory (charged clusters) 5 SCC theory (charged clusters) 4 Kinetic theory (neutral clusters, no dipole)

15 B Enhancement (CLOUD/kinetic theory) Weighted mean Enhancement (SCC/kinetic theory) 10

5 Enhancementfactor 0 0 2 4 6 8 10 12 No. iodine atoms in cluster

0.78 0.91 0.99 1.06 1.11 1.17 1.21 1.25 1.29 1.32 1.35 Mobility diameter (nm)

Figure 8. Collision rate coefficients for ion-induced iodic acid nucleation. A) Collision rate (reaction rate) coefficients measured between neutral HIO3 monomers and charged clusters containing up to 11 iodine atoms. The 5 -3 -1 experimental conditions are 20-41 ppbv O3, 34-44% RH, +10 °C, 0.4-3.5 pptv I2 and (0.44-3.2)×10 I atom cm s . The grey triangles are calculated from the 50% appearance times of a total of 8 experiments with (0.76-2.0)×107 cm- 3 HIO3. The red circles are the final experimental values after applying corrections from a kinetic model. The experimental points are horizontally shifted from integers to avoid overlaps. The solid curves show theoretical expectations for the charged collision rate coefficients from average dipole orientation theory (ADO, red curve, Su and Bowers 1973), hard-sphere average dipole orientation theory (HSA, green curve, Kummerlöwe and Beyer 2005) and surface charged capture theory (SCC, blue curve, Kummerlöwe and Beyer 2005). The expected collision rate coefficients between neutral monomers and neutral clusters, ignoring dipole-dipole interactions, are shown by the dashed black curve. Panel B) shows the measured enhancement factors for charged versus neutral collision rate coefficients (ratios of the corrected CLOUD measurements divided by the neutral collision rate coefficients). The black dotted line is the ratio of the SCC value to the neutral kinetic theory value. For both panels, the hollow markers show the weighted mean values from the trimer to 11-mer, with ±1σ errors indicating statistical without (inner caps) and with systematic errors (outer). The figure is adopted from the paper V.

4.1.3.3 Correction of the apparent collision rate coefficient

However, as discussed earlier, the APP50 method is not an analytical method, and has a certain deficit in predicting collision rate coefficients for charged clusters containing 5 monomers or less. Additionally, the derivation of the three methods contains a set of assumptions which may result in inaccuracy in the estimation of the collision rate coefficients. In fact, it is well known that the calculated growth rates from the APP50 method can differ from the real growth rates, especially when the sink processes, e.g., particle coagulation, are high (Olenius et al. 2014; Cai et al. 2021). Therefore, in this thesis, a correction procedure is developed to account for these uncertainties.

The PANDA520 model was used to derive the correction factor for the calculated apparent collision rate coefficient by the APP50 method. To run the model, a set of collision rate coefficient predicted by the SCC theory was used as initial input, together with a full set of parameterizations describing the cluster dynamics in the CLOUD chamber (detailed in the paper IV). The selection of the SCC method was because its prediction produced the closest values to the calculated apparent collision rate coefficient. Iodic acid time series, initial charged cluster distribution and initial total ion concentrations were applied to the model to simulate the charged cluster distribution in the chamber. Charged cluster distributions produced from such simulations were used to calculate the apparent collision rate coefficient by the APP50 method. Finally, the calculated apparent collision rate coefficient was compared with the true values from the SCC theory, thus obtaining a set of correction factors which describe the difference between the calculated apparent collision rate coefficient and the true values.

The final corrected collision rate coefficients are shown in the Figure 8, with an average correction factor of 0.79. The final values agree surprisingly well with the expected values from the SCC theory, which are much higher than the expected values from the ADO and HSA theories. This suggests that the SCC theory can be potentially used to predict the collision rates between the charged iodic acid clusters and iodic acid molecules. More importantly, the good agreement between theoretical predictions and the final measured values suggests that ion-induced iodic acid nucleation proceeds at the kinetic limit – every collision between charged iodic acid clusters and iodic acid molecules leads to cluster growth with negligible evaporation. Thus, ion-induced iodic acid nucleation is strictly a barrierless process, and thus technically not “nucleation”, as the latter by definition must involve an energy barrier. However, we still use the name “ion-induced iodic acid nucleation” since this “ion-induced nucleation” is a commonly used term in aerosol science.

4.2 Neutral nucleation of iodine oxoacids

4.2.1 Iodine oxoacid nucleation revealed at CLOUD

While our observation at CLOUD confirms the ion-induced iodic acid nucleation observed at the Mace Head observatory (Sipilä et al. 2016), we find that the neutral nucleation proceeds via a novel mechanism. The measured charged and neutral mass defect plots during nucleation events are shown in Figure 9, and the nucleation mechanisms are summarized in Figure 10. In Figure 9A, the charged cluster composition is similar to the charged clusters measured at the Mace Head observatory (Sipilä et al. 2016), except for some small sulfur containing charged clusters were observed in Sipilä et al. (2016). As for the neutral nucleation, Sipilä et al. (2016) suggested that the neutral iodine clusters were primarily in the form of (HIO3)0-1·(I2O5)n, while in this thesis, I find that these clusters are relatively weakly bonded at -10 and +10 ºC (no clusters of the same type are observed beyond HIO3·I2O5). Instead, we observe alternating addition of HIO2 and HIO3, onto the initial HIO3 molecule, as shown by the orange-colored clusters in Figure 9B. Despite the soft tuning and the shortened ion molecule reaction time of the nitrate-CIMS deployed, collision induced cluster fragmentation and charging induced reactions could still occur to a certain degree.

Therefore, a certain fraction of HIO3·HIO2 pairs in the clusters may dehydrate to form I2O4, as shown by the pink-colored clusters in Figure 9B. However, for completeness, the direct condensation of I2O4 is not excluded in this thesis, despite its measured concentration being around

1% of the measured HIO3. Therefore, our observation suggests that HIO2 plays an important role in stabilizing HIO3, similar to the role of ammonia and amines in the sulfuric acid nucleation.

43 0.0 A - H2O·IO3 -0.1 - - IO2 HNO3·IO3 - IO3 -0.2 - - HIO3·IO3 HIO2·IO3 - HIO3·HIO2·IO3

-0.3 - I2O4·IO3 - I2O5·IO3

-0.4 - HIO3·I2O4·IO3 - HIO3·I2O5·IO3 Massdefect(Th)

-0.5 - I2O5·I2O4·IO3 - 2I2O5·IO3 0.0 B HIO2 -0.1 IO HIO3

OIO HIO3·HIO2

-0.2 I2O2 HIO3·HIO3

I2O3 2HIO ·HIO I O I2O5 3 2 -0.3 2 4 HIO3·I2O4

2HIO2·2HIO3 HIO3·I2O5 -0.4 HIO3·HIO2·I2O4 Massdefect(Th) HIO / I O clusters 3 2 5 3HIO3·2HIO2 2I2O4 -0.5 Pure HIO3 / HIO2 clusters 2HIO3·HIO2·I2O4 Mixed HIO3 / HIO2 clusters

Other iodine clusters HIO3·2I2O4 -0.6 0 200 400 600 800 1000 Mass/charge, m/z (Th) Figure 9. Charged and neutral mass defect plots during nucleation events. Cluster mass defect (difference from integer mass) versus m/z of A) negatively charged and B) neutral clusters containing up to five iodine atoms during 7 nucleation events. The experimental conditions are A) 36 ppbv O3, 40% RH, +10 °C, 168 pptv I2 and 1.5×10 I atom -3 -1 5 -3 -1 cm s and B) 46 ppbv O3, 43% RH, +10 °C, 49 pptv I2 and 2.4×10 I atom cm s . The event shown in panel A) is continued in Figure S3 of the paper V up to clusters containing twelve iodine atoms. In order to simplify panel B), water molecules and nitrate charger ions are ignored (Figure S4 of the paper V shows the same event where they are included). Charged clusters are measured with the APiTOF(-) and neutral clusters with the nitrate-CIMS (preceded by an ion filter). We find that no nucleation takes place for positively charged clusters (Figures S5 and S6 of the paper V). Blue circles indicate clusters containing only HIO3 and I2O5. Orange circles indicate clusters containing only HIO3 and HIO2. Pink circles indicate clusters containing HIO3, HIO2, I2O5 and I2O4. Red circles indicate other iodine- containing neutral clusters. The area of the circle indicates signal strength on a logarithmic scale. Further details of the clusters and their signal strengths are provided in Table S2 of the paper V. The figure is adopted from the paper V.

To confirm the measurement, we used quantum chemical calculations to compute the formation free energy of several molecular dimers involving HIO3 (table S3 and Figure S7, paper V). We -1 find that the most stable is the HIO3·HIO2 dimer (-12.9 kcal mol ). On the other hand, the -1 -1 HIO3·HIO3 (-7.7 kcal mol ) and HIO3·HOI (-1.6 kcal mol ) dimers are much less stable. These calculations are in favor of the dominant HIO3·HIO2 dimer shown in the 1 to 2 steps in Figure 10 and not HIO3·HIO3 in our experimental conditions.

44 A Ion-induced nucleation: sequential addition of HIO3 onto IO3- core ion no. iodine atoms 1 2 3 4 5 6 7 8 9 10 11 12

B Neutral nucleation: sequential addition of HIO2 then HIO3 C Iodine oxide formation in cluster 1 2 3 4 5 ion-induced: + H2O (evap.)

neutral: + H2O (evap.)

IO3- HIO3 I2O5

HIO2 I2O4

Figure 10. Nucleation mechanisms for iodine oxoacid clusters. Schematic representations of the nucleation mechanisms for A) ion-induced (charged) and B) neutral (uncharged) iodine oxoacid clusters, interpreted from the - mass defect plots. Ion-induced nucleation involves condensation of iodic acid (HIO3) alone onto an IO3 ion, whereas neutral nucleation involves repeated stepwise condensation of iodous acid (HIO2) followed by iodic acid. Iodine oxide formation takes place in the clusters, as shown in panel C). Pairs of HIO3 molecules always dehydrate to form I2O5 in charged clusters (panel A). However, HIO3 molecules do not form I2O5 in neutral clusters but some may combine with HIO2 and dehydrate to form I2O4 (panel B). The relative intensities of the final neutral clusters in panel B) are (HIO3)3·(HIO2)2 : (HIO3)2·HIO2 ·I2O4 : HIO3·(I2O4)2 = 0.38 : 0.46 : 0.16, indicating that the formation rate of I2O4 in the neutral clusters is comparable to the monomer collision rate. The figure is adopted from paper V.

4.2.2 Revisiting neutral iodine nucleation at Mace Head

While the iodine oxoacid nucleation mechanism was revealed at laboratory conditions at the CLOUD chamber, it is essential to compare this with the observations under ambient conditions at Mace Head (Sipilä et al. 2016). We observed a series of iodine neutral clusters containing multiples of HIO2 and HIO3 at CLOUD, which slightly contrasts with Sipilä et al. (2016), who concluded that the dominant composition of neutral iodine clusters was I2O5 with at most one HIO3 present. This is likely because of the stronger fragmentation level in the instrument deployed in Sipilä et al. (2016). Additionally, the longer residence time (200-300 ms) in the ion molecule reaction chamber in the chemical ionization inlet used in Sipilä et al. (2016) could also accelerate the conversion of HIO3 molecules to I2O5 molecules in charged clusters.

Importantly, a reanalysis of the nitrate-CIMS mass spectrum observed at Mace Head revealed additional information that supports the iodine oxoacid nucleation mechanism. First, our reanalysis

45 of the data from Sipilä et al. (2016) showed a clear presence of HIO3·HIO2 dimers (measured as - HIO3·HIO2·NO3 ), with a concentration three times higher than that of the HIO3·HIO3 dimer - 8 -3 (measured as HIO3·IO3 ) at a gaseous HIO3 concentration of 10 cm , supporting HIO2 as the key stabilizer. Second, pure HIO3 nucleation could only explain a small fraction of the observed neutral clusters in Sipilä et al. (2016). As noted also by Sipilä et al. (2016), in the Figure 2 of their paper there were much more neutral clusters with lower oxygen numbers than clusters that could be - explained by pure HIO3 addition (clusters with a common composition of (HIO3)0-1(I2O5)n·IO3 ). Based on our reanalysis and experimental data from CLOUD, we can conclude that these clusters were likely originating from iodine oxoacid clusters ((HIO3)n·(HIO2)m), but were converted by dehydration processes in the ion molecule reaction chamber or in the low pressure chambers of the nitrate-CIMS.

It is well known that the Mace Head observatory is an iodine hotspot. In other locations of the 8 -3 world, the HIO3 concentrations are commonly much less than 10 cm (see chapter 5). To predict the importance of neutral HIO3 - HIO2 nucleation against neutral pure HIO3 nucleation, we carried out experiments with a fixed light intensity but variable I2 concentrations. In these experiments, iodine atom production rates were linearly proportional to the I2 concentration and the results are shown in Figure 11. We find that with a decreasing iodine atom production rate, the ratio between

HIO3 and HIO2 is decreasing. Thus, our results indicate that the neutral HIO3 - HIO2 nucleation will be more important if the HIO3 concentration is much lower than that measured at Mace Head (around 108 cm-3).

Figure 11. Iodine oxoacid and oxide production versus I2. (A) measured concentrations of iodine oxoacids and B) normalized signals of iodine oxides when iodine vapor is adjusted between equilibrium mixing ratios of 0.4 and 4 pptv at fixed experimental conditions of 38 to 42 ppbv O3, 34 to 44% RH and +10°C. The I atom production rate is 4 -3 -1 5 -3 -1 4.4×10 cm s to 3.8×10 cm s . HIOx is measured with a nitrate-CIMS, and IO, OIO and I2 are measured with a n bromide-CIMS. The lines are power-law fits to the HIO3 and HIO2 concentrations of the form HIOx = k × I2 , with fitted values for n of (1.07±0.04) and (0.51±0.04), respectively, and the power-law fits to the IO and OIO normalized

46 n signals of the form IOx = k × I2 , with fitted values for n of (0.59±0.03) and (0.81±0.02), respectively. The figure is adopted from paper V.

4.3 Growth of nucleation mode particles by iodic acid

The primary composition of iodine particles was conventionally thought to be iodine oxides, especially I2O4 and I2O5 (Saunders and Plane 2005; Saunders et al. 2010; Sipilä et al. 2016; Gómez Martín et al. 2020) either contributed by direct vapor condensation, or formed from other iodine oxides or iodic acid. However, the growth mechanism of nucleation mode iodine particles has never been studied at atmospherically relevant conditions.

The Filter Inlet for Gases and AEROsol (FIGAERO) is a recently developed method which has been widely used to detect aerosol composition (Lopez-Hilfiker et al. 2014). It collects particles on a polytetrafluoroethylene (PTFE, Teflon) filter for a fixed time (30 mins in this study) and evaporates the collected materials with a controlled temperature ramp (15 mins in this study). The connected mass spectrometer then measures the molecular composition of the evaporated constituents and produces individual thermograms which contain volatility information of the measured constituents.

The FIGAERO is an ideal device for this thesis to study the composition of nucleation mode - - particles. However, it commonly uses iodide anion (I ) and its water adduct (H2O·I ) as the reagent ions, which interfere with its application in studying the iodine particle composition since a certain level of impurities containing iodine, from the ion source, can be expected (paper II). Therefore, we changed the reagent gas from iodomethane (CH3I) to dibromomethane (CH2Br2) and used - - bromide (Br ) and its water adduct (H2O·Br ) as the reagent ions. The details of the modifications and characteristics of the Br--FIGAERO instrument are provided in the Methods part of this thesis and in the paper II.

We show an example case of the evolution of particle size and chemical composition during iodine oxoacid particle formation experiments in Figure 12. The experiment started from particle free conditions, and iodine particle formation processes were triggered by turning on the green light source. A combination of a Particle Size Magnifier (PSM), a Condensation Particle Counter (CPC) and a Scanning Mobility Particle Sizer (SMPS) followed the particle distribution. The Br-- FIGAERO measured the particle composition along the particle growth from around 1 nm to above

10 nm. Surprisingly, we find that the mass spectrum is dominated by HIO3; 78% of the total mass is contributed from HIO3, excluding water. This is in distinct contrast to earlier studies which suggested that iodine particles were composed primarily of iodine oxides. Again, we emphasize that earlier experiments were carried out at highly elevated vapor concentrations, e.g., the I2 concentration was around 1013 molec cm-3 (Saunders and Plane 2005) compared to our experiment

47 of around 2 × 108 molec cm-3. Additionally, the transmission electron microscopes (TEMs) which was used to analyze iodine particle composition required putting the iodine particle sample under a vacuum condition which might alter the particle composition (e.g., HIO3 + HIO3 à I2O5 + H2O), thus making this method difficult to interpret.

2 A 5 10 ) -3 4 10 (cm

(nm)

3 p p

10 d d 10 9 2 8 10 1 7 10 /d log 6 0 10 dN 5 4

3 Particle diameter, Particlediameter,

-6

) 10

-3 B

m Particle iodic acid (FIGAERO) 3 -7 Particle size distribution 10

-8 10

-9 10

-10 10

Particlevolume concentration (cm -11 10 20:30 21:00 21:30 22:00 22:30 23:00 23:30 00:00 8 Nov 2019 Time (UTC) Figure 12. Evolution of particle size and chemical composition during iodine oxoacid nucleation. A) Evolution of the particle size measured by the PSM (below 2.5 nm) and nano-SMPS (above around 4 nm). The experimental 5 -3 -1 7 -3 conditions are 40 ppbv O3, 40% RH, +10 °C, 8 pptv I2, (2.9-5.3)×10 I atom cm s and (3.1-7.1)×10 cm HIO3. The event is started by switching on green illumination (528 nm), and HIO3 is increased towards the end. B) Evolution of the particle volume concentration derived from i) the particle size distribution (blue circles) and ii) the HIO3 volume for particles collected and analyzed with the FIGAERO (hollow red squares). Particle concentrations in the size range between 2.5 nm and 4 nm are obtained by interpolation between the PSM and nano-SMPS distributions and are verified by measurements of the total number concentrations above 2.5 nm threshold with the PSM. The FIGAERO collects particles on a Teflon filter for 30 min and then evaporates the sample with a controlled temperature ramp over the following 15 min at the inlet of the mass spectrometer. The FIGAERO data points are centered on the 30 min collection interval. The bars indicate ±1σ total errors. The FIGAERO mass spectrum shows that HIO3 dominates the particle composition (80% mass fraction). This is independently confirmed by the close agreement between the volume concentrations measured by the particle sizers and by the FIGAERO (panel B). The figure is adopted from paper V.

In order to exclude the possibility that some of the evaporated constituents were not measurable by the Br--FIGAERO which could therefore bias our understanding of the condensing vapors, we further carried out calibration experiments to quantify the measured particle phase HIO3 signal.

48 Iodic acid particles were prepared by nebulizing iodic acid (≥ 99.5%, Sigma-Aldrich) water solution with an atomizer (TOPAS ATM221), dried with a home-made diffusion dryer. The particle size distribution was characterized by a nano-SMPS and had a geometric mean diameter of 14 nm and a total number concentration of 6000 cm-3, comparable to those recorded during the iodine particle formation experiments in the CLOUD. The particle phase HIO3 from the FIGAERO measurements was therefore calibrated by the total volume calculated from the nano-SMPS measurements. We applied the calibration factor to the Br--FIGAERO and calculated the volume contribution of iodic acid as shown in Figure 12B. We found that the calculated iodic acid volume was close to the measured total particle volume within measurement uncertainty, suggesting that iodic acid is the major component in the freshly formed particles, consistent with the particle phase mass spectrum measured by the Br--FIGAERO.

Finally, in Figure 13B we show the dependence of particle growth rates on the HIO3 concentration and compared them with the growth rates of H2SO4 condensation reported by Stolzenburg et al. (2020). At the same temperature (+10 ºC), the growth rates of iodine oxoacid particles are identical to the growth rates of H2SO4·NH3 particles. Since H2SO4 (0.55 nm in mass diameter) and HIO3 (0.5 nm in mass diameter) molecules have comparable sizes, the close agreement indicates that the iodine oxoacid particles grow at the dipole-dipole enhanced kinetic limit for HIO3 – every collision between iodine oxoacid particles and HIO3 molecules leads to particle growth, with negligible evaporation. Furthermore, it also implies that the main condensing vapor is indeed the HIO3, consistent with the evidence from particle phase mass spectrum and particle volume. Since if a second species is contributing to the particle growth significantly, the growth rates would appear to be much higher than the expected kinetic limit for HIO3 in Figure 13B.

49 1000 100 A B 6

100 ) 5 ) -1 -1 4 s

-3 10 3 (nmh

(cm 2

1.7 1 1.8-3.2 10 0.1 6 5 4 0.01 -10ºC +10ºC HIO3 (beam) 3

HIO3 (gcr) NucleationJ rate, Growth rate, GR Growthrate, 2 HIO −10 ºC 0.001 HIO3 (neutral) 3 HIO3 model (iin) HIO3 +10 ºC H SO +NH (gcr) 2 4 3 H2SO4+NH3 +10 ºC 0.0001 1 2 3 4 5 6 2 3 4 5 6 2 3 4 5 6 7 8 9 2 3 6 7 8 7 8 10 10 10 10 10 -3 -3 Iodic acid, HIO3 (cm ) Iodic acid, HIO3 (cm ) -3 -3 Sulfuric acid, H SO (cm ) Sulfuric acid, H SO (cm ) 2 4 2 4 Figure 13. Nucleation and growth rates versus iodic acid concentration. A) Nucleation rates at 1.7 nm diameter versus iodic acid concentration at +10 °C (red symbols and curves) and -10 °C (blue symbols and curves). Hollow circles show the nucleation rates for neutral conditions, Jn, solid triangles for gcr conditions, Jgcr, and hollow squares for beam conditions, Jbeam. To guide the eye, the measurements are connected by approximate curves. The red band shows a kinetic model prediction for HIO3 ion-induced nucleation, Jiin (= Jgcr – Jn), at +10 °C (see Supplementary Materials in the paper V for further details). The lower and upper limits correspond, respectively, to zero and two H2O molecules per iodine atom in the cluster. For comparison, the gcr nucleation rates measured for sulfuric acid with 100 pptv ammonia are shown at +10 °C (light grey curve) and -10 °C (dark grey curve) (Dunne et al. 2016). B) Mean growth rates of particles (neutral, gcr and beam) between 1.8 nm and 3.2 nm diameter versus HIO3 concentration at +10 °C (filled red circles) and -10 °C (filled blue circles). For comparison, the dashed grey line shows the growth rates of H2SO4·NH3 particles measured at +10 °C (Stolzenburg et al. 2020). The bars in both panels represent ±1σ measurement errors. The experimental conditions are 36-44 ppbv O3, 34-73% relative humidity (RH), 0.4-168 pptv I2 4 7 -3 -1 and an I atom production rate of 4.4×10 -1.5×10 cm s . An overall systematic scale error on the HIO3 concentration of -33%/+50% is not shown on the data points. The figure is adopted from paper V.

4.4 Nucleation and growth rates

Although iodine particle formation processes have been studied for more than 30 years, quantitative measurements of the nucleation and growth rates have been lacking. In this thesis, dedicated experiments were carried to obtain these values.

In Figure 13A we show the measured nucleation rates at 1.7 nm (mobility diameter), J1.7, versus the HIO3 concentration at +10 ºC and -10 ºC and under three ionization conditions: neutral condition, Jn (circles, ions eliminated from the chamber); galactic cosmic ray condition, Jgcr (triangles, boundary layer ion concentrations of around 700 cm-3) and beam enhanced condition, -3 Jbeam (squares, ion concentrations of around 2500 cm , comparable to the upper free troposphere conditions). The measured nucleation rates depend largely on the HIO3 concentration, charging state and temperature. The charge enhancement on the nucleation rates at +10 ºC is remarkable,

50 7 -3 e.g., the Jgcr is 33 times larger than Jn at HIO3 of 10 cm . However, no significant difference between Jgcr and Jn is observed at -10 ºC, indicating that neutral nucleation at this HIO3 concentration is the dominant process at -10 ºC. This is mainly because of the significant 7 -3 nucleation rate enhancement of around 100 times at a HIO3 concentration of 10 cm , when lowering the temperature from +10 ºC to -10 ºC. Additionally, ion-induced iodic acid nucleation has been shown in this thesis to proceed at the kinetic limit already at +10 ºC. Lowering the temperature does not further enhance this process.

Sulfuric acid – ammonia new particle formation has been shown to be a fast and relevant process in polar regions (Jokinen et al. 2018; Beck et al. 2021). Recently, several studies additionally outlined the importance of HIO3 in the particle and cloud condensation nuclei (CCN) formations in the Arctic (Baccarini et al. 2020; Beck et al. 2021). It is therefore important to quantitatively compare the iodine oxoacid particle formation rates with the sulfuric acid – ammonia particle formation rates. The nucleation rates of H2SO4·NH3 are plotted in the Figure 13A by a dark grey (-10 ºC) and a light grey line (+10 ºC) (Dunne et al. 2016). It is evident from the comparison that the iodine oxoacid particle formation proceeds at a much faster rate compared to the sulfuric acid system (with 100 pptv ammonia) at equal acid concentrations ([H2SO4] = [HIO3]). In view of the co-existence and comparable concentrations of both acids in Arctic environments (Baccarini et al. 2020; Beck et al. 2021), the importance of iodine oxoacid particle formation needs to be considered when estimating CCN formation and its climate effects in Arctic environments.

In Figure 13B, the growth rates of iodine oxoacid particles are shown together with the growth rates from the sulfuric acid – ammonia system. In addition to the growth rates proceeding at the dipole-dipole kinetic limits as discussed in the last section, we can also observe a clear temperature enhancement in the growth rates measurements. The growth rates measured at -10 ºC are around two times faster than the growth rates measured at +10 ºC. This additional factor can be attributed to the coagulation process of iodine oxoacid clusters. The fast iodine oxoacid particle formation at -10 ºC leads to a large population of clusters which contribute to growth of particles, in addition to the HIO3 monomer condensation, similar to the sulfuric acid dimethyl amine system (Lehtipalo et al. 2016).

51 5 Global observation of HIO3

5.1 HIO3 in pristine environments

Sulfuric acid – ammonia nucleation is a known process that plays an important role in pristine environments such as the free troposphere (Dunne et al. 2016), the Arctic (Beck et al. 2021) and the Antarctic coastal region (Jokinen et al. 2018). We have further shown in this thesis that the iodine oxoacid nucleation is a rapid process which is even faster than the sulfuric acid – ammonia

nucleation at the same acid concentrations. A question therefore arises: is the HIO3 concentration

comparable to H2SO4 in these pristine regions? In such pristine and cooler regions of the 6 -3 atmosphere, ~10 cm level of HIO3 can lead to a significant new particle formation and sustained growth at a few times 0.1 nanometers per hour and thus has a potential to affect the regional CCN formation.

Global measurements of HIO3 were carried out at ten boundary layer sites in order to answer this

question. The frequency of daily maxima of HIO3 is shown in the Figure 14 and the original time series for the statistics is shown in the Figure S9 in the paper V. The conditions for rapid iodine particle formation are regularly satisfied in mid-latitude sites such as Mace Head which is known as an iodine hotspot (O’Dowd et al. 2002). Additionally, it shows that iodine oxoacid particle

formation is also expected in Helsinki, a coastal city, as the HIO3 concentration frequently reached 107 cm-3. In relatively pristine coastal polar sites, such as Villum and Ny Ålesund in the Arctic and the Neumayer in the Antarctic, our data indicate that iodine oxoacid particle formation will occur

because of the low temperatures and suitable HIO3 concentrations. Indeed, iodine oxoacid nucleation has been observed in the Arctic (Sipilä et al. 2016; Beck et al. 2021) and its subsequent growth to CCN sizes was also recently confirmed in central Arctic (Baccarini et al. 2020). The

measured HIO3 concentrations were comparable or higher than H2SO4 in the Arctic (Baccarini et al. 2020; Beck et al. 2021), highlighting the importance of iodine oxoacid particle formation. In

Réunion, a high-altitude marine site, and Aboa, an Antarctic site, the HIO3 concentrations are

relatively low which indicate that HIO3 has a minor role in NPF and growth in these locations at the measurement periods.

5.2 HIO3 in polluted environments

Iodine particle formation is commonly considered irrelevant for polluted urban settings.

Interestingly, we have measured noticeable amount of HIO3 in all of the three urban sites (Helsinki, Finland; Beijing and Nanjing, China). Aside from Helsinki being a coastal site, Beijing and Nanjing are far from the coast and are commonly considered inland sites. The daily occurrence of

HIO3 is unlikely to be explained by remote transportation from nearby ocean sources, thus

52 5 - indicating a potential local source. In Nanjing, the HIO3 concentration was on the order of 10 cm 3 for most of the time, but occasionally approaching 106 cm-3 (representing a growth rate of roughly 0.2 nm h-1, at +10°C). Therefore, iodine oxoacid particle formation is expected to be a negligible 6 -3 channel in Nanjing. However, the daily maxima of HIO3 were frequently exceeding 10 cm , and 6 -3 they were very often higher than 2×10 cm in Beijing. This indicates that HIO3 could contribute at maximum around 0.4 nm h-1 in the initial growth of nucleation mode particles. This could account for around 10-20% of the total growth rates of 1-3 nm particles in summer Beijing (on average 0.5-2 nm h-1 in summer 2018, Beijing) (Deng et al. 2020). Beside from our observation of

HIO3 in the summer season, a recent study suggested that organic iodine compounds could be observed in fine particulate particles in winter seasons, indicating coal combustion as a potential source for these species.

Figure 14. Frequency of daily maxima of iodic acid at diverse sites. Pie charts showing the percentage of days where the daily HIO3 maxima fall into the indicated range (evaluated for one-hour-averaged data). Each pie chart represents 2-4 weeks’ data at the location, date and mean temperature indicated. Additional information on the sites is provided in the paper V. Sectors outlined by light blue and dark blue lines indicate that iodine oxoacid particle formation is expected to be dominated by ion-induced or neutral nucleation, respectively. Sectors outlined by a black line indicate comparable ion-induced and neutral nucleation rates. Sectors without any outline indicate that the -3 -1 expected nucleation rates are below 0.01 cm s . The systematic uncertainty between HIO3 measurements at different sites is estimated to be a factor three. All measurements are above the HIO3 detection limit of the instruments. The figure is adopted from paper V.

53 6 Conclusions and outlook

Comprehensive measurement of inorganic iodine species at ambient relevant levels (parts per trillion levels) has long been challenging. This complicates studies of iodine chemistry and particle formation processes. To address the aim I, we describe a bromide chemical ionization method that is capable of measuring iodine oxides and oxoacids simultaneously, both in gaseous and particulate phases (paper II). While earlier optical spectroscopic and mass spectrometric methods were commonly restricted by their very specificity (i.e., only capable of measuring a limited number of iodine species) or had high detection limits, the Br-MION-CIMS achieves an unprecedented performance both in its sensitivity and its coverage of measurable inorganic iodine species. It has

been confirmed to be able to measure I2, I, IO, OIO, I2O4, HOI, HIO2, HIO3, ICl and IBr, in total ten different iodine containing species, with the potential of measuring other iodine species if

present at significant amounts. The estimated detection limit for I2 at a two-mins data resolution is 6.3 × 105 cm-3 (0.03 pptv) which is comparable or better than the performance of previous state of

the art techniques, e.g., the I2 detection limit was around 0.2 pptv in Raso et al. (2017) and Finley and Saltzman (2008). Such a low detection limit allows the deployment of the Br-MION-CIMS in most ground-based sites where active iodine chemistry is present. Additionally, in order to measure the composition of iodine particles, the chemical ionization method of the FIGAERO inlet was replaced from iodide (I-) to bromide (Br-) anion to be able to distinguish iodine species evaporated from heating the particles and produced from the ion source. Combined with the APi-TOF, which

measures atmospheric charged clusters, and the nitrate-CIMS, which measures neutral HIO3 and iodine oxoacid clusters, we were able to measure atmospheric iodine chemistry comprehensively from the molecular iodine precursor to its oxidation products, both in gaseous (charged / uncharged) and particulate phases.

The advancement in measurement techniques enabled us to carry out ambient observations at the Mace Head observatory where we found the first field evidence of heterogeneous uptake of HOI on aerosols (paper III; aim II). This reaction further results in the release of IBr and ICl from the particles to the atmosphere. Both these compounds are photochemically active, and their photolysis rapidly produces reactive halogen atoms, which in turn contribute to catalytic ozone destruction and iodine oxoacid particle formation processes. For example, simulation results suggest that the inclusion of this mechanism leads to a 32% increase in iodine atom production

rate and 12% more O3 loss at Mace Head. This mechanism is also expected to be important over the open ocean where emissions of HOI are expected.

While almost all earlier laboratory studies have focused on neutral iodine particle formation processes, we carried out the first laboratory experiment investigating the ion-induced iodine particle formation processes (aim III). Our results confirm the study by Sipilä et al. (2016) which

suggested that the ion-induced iodine particle formation proceeds by sequential addition of HIO3

54 onto existing ions (papers IV and V). A dehydration process occurs whenever there are two HIO3 molecules in the charged cluster, which forms I2O5 as the final product. Additionally, we followed the time evolution of the charged cluster growth processes, and found that all of the neighbouring charged cluster pairs had a difference of one iodine atom. This is direct evidence that ion-induced iodine cluster formation is primarily contributed by HIO3, instead of I2Oy. We further developed a method combining an appearance time method and a kinetic model to calculate the ion-polar molecule collision rate coefficients between charged iodic acid clusters and iodic acid molecules. We found that the calculated collision rate coefficients were in close agreement with predictions from surface charge capture theory, indicating that the ion-induced iodic acid nucleation proceeds at the kinetic limit (papers IV and V). Therefore, it is strictly a barrierless aggregation process rather than an actual nucleation process (although we still use the term “ion-induced nucleation” to be consistent with conventions in atmospheric science).

One of the key findings of this thesis is the iodine oxoacid particle formation mechanism (paper V; aim III). While Sipilä et al. (2016) concluded that neutral iodine nucleation proceeds via the same mechanism as ion-induced iodic acid nucleation, I find in this thesis that neutral HIO3·I2O5 clusters are relatively weakly bound at the studied temperatures (-10 ºC and 10 ºC). On the other hand, we observe repeated sequential addition of HIO2 followed by HIO3, onto an initial HIO3 molecule. Therefore, our observation highlights the key stabilizing role of HIO2 in neutral iodine oxoacid nucleation. Quantum chemical calculations further support this conclusion by revealing

HIO3·HIO2 as the most stable dimer, compared to e.g., HIO3·HIO3 and HIO3·HOI. Once the neutral clusters exceed a critical size, their growth is sustained by HIO3 alone. Thus, the growth of iodine particles is not limited by the lower concentration of HIO2. Indeed, particle phase mass - spectrum measured by the Br -FIGAERO-CIMS suggests HIO3 as the dominant species that evaporates from freshly formed iodine particles and it alone is able to explain the initial growth of particles between 1.8 and 3 nm (paper V).

Although iodine particle formation is commonly thought to be important only in coastal regions, our global boundary layer observations of HIO3 clearly suggest a much wider role (paper V; aim IV). The conditions for abundant iodine oxoacid particle formation are frequently reached at coastal sites (Mace Head and Helsinki) and polar regions (Villum and Ny Ålesund, Arctic;

Neumayer, Antarctic). The estimated average growth contribution from HIO3 in June-2019, Beijing is around 0.2 nm h-1 which could account for around 10-20% of the growth rate of 1-3 nm particles. This seemingly small role is surprising as iodine species are generally not expected to contribute to the particle growth under polluted environments.

Despite advances in iodine measurement techniques and a deepened understanding on iodine particle formation, there are still major challenges to overcome. 1) Although iodine oxoacid particle formation has been shown to be important in coastal and polar regions, its role over remote

55 oceans remains unclear. Comprehensive measurements of iodine species over the open oceans are sorely needed to understand quantitatively global iodine sources and chemistry. 2) The iodine oxoacid particle formation mechanism has never been included in global simulations to investigate its global impact (Cuevas et al. 2018). In view of the declining anthropogenic sulfur and the increasing atmospheric iodine, the relative role of iodine oxoacid particle formation is likely climbing in the future. 3) The formation mechanisms of HIO3 and HIO2 remain unclear, although some hints are revealed in paper V.

56 7 Review of papers and the Authors contribution

Paper I presents the rapid growth of atmospheric particles by nitric acid and ammonia condensation. Extremely high growth rates, above 100 nm h-1, are observed when there are sufficient amounts of nitric acid and ammonia to sustain this acid-base system to be out of the equilibrium, so that free condensation of both is feasible. This mechanism may contribute to urban atmospheric particles under inhomogeneous settings, especially in wintertime. I participated in the experiments and discussions, carried out nitric acid calibration using the same technique as described in the Paper II, provided nitric acid time trace and wrote the instrumental and calibration sections (Br-MION-CIMS).

Paper II reports the application of two bromide chemical ionization mass spectrometers in the

detection of iodine containing species and sulfuric acid. Calibrations of H2SO4, Cl2, HOI and I2 were carried out either by offline methods or online inter-comparisons. Formation enthalpies of selected ion clusters were calculated to estimate instrument sensitivities for a few iodine species which we could not quantify directly. The presented method can measure iodine oxides, oxoacids and sulfuric acid simultaneously with detection limit down to a few 105 molec cm-3. I designed the experiments, performed initial laboratory characterization of the Br-MION-CIMS, handled its deployment at the CLOUD chamber, carried out part of the calibration experiments and quantum chemical calculations, and wrote part of the manuscript.

Paper III describes the direct field evidence of autocatalytic iodine release from aerosols. HOI, ICl and IBr were measured for the first time with the same technique as described in the Paper II. HOI is proposed to react heterogeneously with aerosols containing halogens to release ICl and IBr, with an accommodation coefficient much higher than earlier expected. Mean daily maxima of ICl and IBr were measured to be 4.3 and 3.0 pptv, respectively, which could contribute to 32% increase in the iodine atom production rate. The enhanced production of iodine atoms further increased the ozone loss rate by 10-20%, revealing an underestimated ozone loss in the marine boundary layer. I prepared and carried out half of the field observations, participated in the data analysis and interpretation, and wrote a small part of the manuscript.

Paper IV presents the determination of the collision rate coefficient between charged iodic acid clusters and iodic acid using an appearance time method. The widely-used appearance time method was analytically derived in this study and was applied to calculate collision rate coefficient. I analytically derived the appearance time method together with two other novel methods, performed the majority of data analysis, developed the kinetic model (PANDA520), calculated part of the cluster formation energies and theoretical rate coefficients, and wrote the manuscript with comments from others.

57 Paper V shows the ion-induced and neutral nucleation mechanisms of iodine oxoacids. It shows that after the photolysis of I2, rapid oxidation leads to the formation of iodine oxoacids (HIO3 and

HIO2) which in turn form particles effectively. Ion-induced nucleation involves mainly HIO3, which proceeds at the kinetic limit, while the neutral nucleation is triggered by repeated sequential addition of HIO2 and then HIO3. I designed and coordinated the experiments, analyzed most of the data from mass spectrometers, interpreted major part of the results, and wrote the initial versions of the manuscript with comments from others.

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