acp-20-9183-2020.pdf

Atmos. Chem. Phys., 20, 9183–9207, 2020

https://doi.org/10.5194/acp-20-9183-2020

the Creative Commons Attribution 4.0 License.

Molecular understanding of new-particle formation

from

α

-pinene between

−

50 and

◦

Mario Simon

, Lubna Dada

, Martin Heinritzi

, Wiebke Scholz

3,4

, Dominik Stolzenburg

, Lukas Fischer

Andrea C. Wagner

1,6

, Andreas Kürten

, Birte Rörup

, Xu-Cheng He

, João Almeida

7,8

, Rima Baalbaki

Andrea Baccarini

, Paulus S. Bauer

, Lisa Beck

, Anton Bergen

, Federico Bianchi

, Steffen Bräkling

Sophia Brilke

, Lucia Caudillo

, Dexian Chen

, Biwu Chu

, António Dias

7,8

, Danielle C. Draper

Jonathan Duplissy

2,13

, Imad El-Haddad

, Henning Finkenzeller

, Carla Frege

, Loic Gonzalez-Carracedo

Hamish Gordon

11,13

, Manuel Granzin

, Jani Hakala

, Victoria Hofbauer

, Christopher R. Hoyle

9,15

Changhyuk Kim

16,17

, Weimeng Kong

, Houssni Lamkaddam

, Chuan P. Lee

, Katrianne Lehtipalo

2,18

Markus Leiminger

3,4

, Huajun Mai

, Hanna E. Manninen

, Guillaume Marie

, Ruby Marten

, Bernhard Mentler

Ugo Molteni

, Leonid Nichman

19,a

, Wei Nie

, Andrea Ojdanic

, Antti Onnela

, Eva Partoll

, Tuukka Petäjä

Joschka Pfeifer

1,7

, Maxim Philippov

, Lauriane L. J. Quéléver

, Ananth Ranjithkumar

, Matti P. Rissanen

2,22

Simon Schallhart

2,18

, Siegfried Schobesberger

, Simone Schuchmann

, Jiali Shen

, Mikko Sipilä

Gerhard Steiner

3,b

, Yuri Stozhkov

, Christian Tauber

, Yee J. Tham

, António R. Tomé

, Miguel Vazquez-Pufleau

Alexander L. Vogel

1,7

, Robert Wagner

, Mingyi Wang

, Dongyu S. Wang

, Yonghong Wang

, Stefan K. Weber

Yusheng Wu

, Mao Xiao

, Chao Yan

, Penglin Ye

11,25

, Qing Ye

, Marcel Zauner-Wieczorek

, Xueqin Zhou

1,9

Urs Baltensperger

, Josef Dommen

, Richard C. Flagan

, Armin Hansel

3,4

, Markku Kulmala

2,13,20,26

Rainer Volkamer

, Paul M. Winkler

, Douglas R. Worsnop

2,10,25

, Neil M. Donahue

, Jasper Kirkby

1,7

, and

Joachim Curtius

Institute for Atmospheric and Environmental Sciences, Goethe University Frankfurt, 60438 Frankfurt am Main, Germany

Institute for Atmospheric and Earth System Research, University of Helsinki, Helsinki, 00014, Finland

Institute for Ion Physics and Applied Physics, University of Innsbruck, Innsbruck, 6020, Austria

Ionicon Analytik GmbH, Innsbruck, 6020, Austria

Faculty of Physics, University of Vienna, Vienna, 1090, Austria

Department of Chemistry & CIRES, University of Colorado Boulder, Boulder, CO 80309-0215, USA

CERN, Geneva, 1211, Switzerland

Faculdade de Ciências, Universidade de Lisboa, Lisbon, 1749-016, Portugal

Laboratory of Atmospheric Chemistry, Paul Scherrer Institute, PSI, Villigen, 5232, Switzerland

TOFWERK AG, Thun, 3600, Switzerland

Center for Atmospheric Particle Studies, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA

Department of Chemistry, University of California, Irvine, CA 92697, USA

Helsinki Institute of Physics, University of Helsinki, Helsinki, 00014, Finland

School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, UK

Institute for Atmospheric and Climate Science, Swiss Federal Institute of Technology, Zurich, 8092, Switzerland

School of Civil and Environmental Engineering, Pusan National University, Busan, 46241, Republic of Korea

Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA

Finnish Meteorological Institute, Helsinki, 00560, Finland

Department of Earth and Environmental Sciences, University of Manchester, Manchester, M13 9PL, UK

Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences,

Nanjing University, Nanjing, Jiangsu Province, China

P. N. Lebedev Physical Institute of the Russian Academy of Sciences, Moscow, 119991, Russia

Aerosol Physics Laboratory, Physics Unit, Faculty of Engineering and Natural Sciences, Tampere University,

33101 Tampere, Finland

Department of Applied Physics, University of Eastern Finland, Kuopio, 70211, Finland

Published by Copernicus Publications on behalf of the European Geosciences Union.

9184

M. Simon et al.: New-particle formation from

-pinene over a broad range of tropospheric temperatures

IDL, Universidade da Beira Interior, R. Marquês de Ávila e Bolama, Covilhã, 6201-001, Portugal

Aerodyne Research Inc., Billerica, MA 01821, USA

Aerosol and Haze Laboratory, Beijing Advanced Innovation Center for Soft Matter Science and Engineering,

Beijing University of Chemical Technology, Beijing, China

present address: Aerospace Research Centre, National Research Council of Canada, Ottawa, ON, K1V 9B4, Canada

present address: Grimm Aerosol Technik Ainring GmbH & Co KG, 83404 Ainring, Germany

Correspondence:

Mario Simon (simon@iau.uni-frankfurt.de) and Joachim Curtius (curtius@iau.uni-frankfurt.de)

Received: 15 November 2019 – Discussion started: 21 January 2020

Revised: 27 May 2020 – Accepted: 19 June 2020 – Published: 3 August 2020

Abstract.

Highly oxygenated organic molecules (HOMs)

contribute substantially to the formation and growth of at-

mospheric aerosol particles, which affect air quality, human

health and Earth’s climate. HOMs are formed by rapid, gas-

phase autoxidation of volatile organic compounds (VOCs)

such as

-pinene, the most abundant monoterpene in the at-

mosphere. Due to their abundance and low volatility, HOMs

can play an important role in new-particle formation (NPF)

and the early growth of atmospheric aerosols, even with-

out any further assistance of other low-volatility compounds

such as sulfuric acid. Both the autoxidation reaction forming

HOMs and their NPF rates are expected to be strongly de-

pendent on temperature. However, experimental data on both

effects are limited. Dedicated experiments were performed at

the CLOUD (Cosmics Leaving OUtdoor Droplets) chamber

at CERN to address this question. In this study, we show that

a decrease in temperature (from

25 to

−

◦

C) results in a

reduced HOM yield and reduced oxidation state of the prod-

ucts, whereas the NPF rates (

7 nm

) increase substantially.

Measurements with two different chemical ionization mass

spectrometers (using nitrate and protonated water as reagent

ion, respectively) provide the molecular composition of the

gaseous oxidation products, and a two-dimensional volatility

basis set (2D VBS) model provides their volatility distribu-

tion. The HOM yield decreases with temperature from 6.2 %

at 25

◦

C to 0.7 % at

−

◦

C. However, there is a strong re-

duction of the saturation vapor pressure of each oxidation

state as the temperature is reduced. Overall, the reduction in

volatility with temperature leads to an increase in the nucle-

ation rates by up to 3 orders of magnitude at

−

◦

C com-

pared with 25

◦

C. In addition, the enhancement of the nu-

cleation rates by ions decreases with decreasing temperature,

since the neutral molecular clusters have increased stability

against evaporation. The resulting data quantify how the in-

terplay between the temperature-dependent oxidation path-

ways and the associated vapor pressures affect biogenic NPF

at the molecular level. Our measurements, therefore, improve

our understanding of pure biogenic NPF for a wide range of

tropospheric temperatures and precursor concentrations.

1 Introduction

Atmospheric aerosol particles play a key role in the regula-

tion of climate by influencing Earth’s radiative energy bal-

ance (IPCC, 2013). In order to affect the solar radiation bud-

get by acting as cloud condensation nuclei (CCN), newly

formed particles have to reach a size of 50 to 100 nm (Dusek

et al., 2006); i.e., they need to grow fast enough to avoid co-

agulation scavenging by preexisting particles. Furthermore,

fine airborne particles affect the air quality, are responsible

for most air-pollution-related diseases and cause millions of

premature deaths worldwide (WHO, 2016).

Around half of the global CCN originate from nucleation

of organic or inorganic atmospheric vapors (Spracklen et al.,

2008; Merikanto et al., 2009; Kulmala et al., 2013; Gordon

et al., 2017). New-particle formation (NPF) is observed in

many environments and under various conditions around the

globe, from remote locations such as forested areas or ma-

rine and coastal regions to polluted urban areas; from warm

environments, such as the tropics, to cold polar and alpine re-

gions; and from Earth’s surface to the free troposphere (Kul-

mala et al., 2004; Kerminen et al., 2018). Gaseous sulfuric

acid (Ball et al., 1999; Kuang et al., 2008), ammonia (Kirkby

et al., 2011; Kürten et al., 2016), amines (Kurtén et al., 2008;

Almeida et al., 2013; Kürten et al., 2014), iodine (O’Dowd

et al., 2002; Sipilä et al., 2016) and biogenic volatile organic

compounds (BVOCs; Donahue et al., 2013; Riccobono et al.,

2014; Kirkby et al., 2016; Bianchi et al., 2016) have been

identified as key vapors involved in atmospheric NPF. The

relative importance of each these precursors, however, de-

pends on the particular ambient conditions. The chemical

composition of the newly formed particles is also widely

influenced by volatile organic compounds (VOCs), which

undergo atmospheric reactions to form secondary organic

aerosols (SOAs; Jimenez et al., 2009; Hallquist et al., 2009;

Riipinen et al., 2012).

BVOCs emitted by vegetation comprise the dominant frac-

tion of all VOCs, with an estimated global emission rate of

760 TgC per year. Monoterpenes contribute approximately

11 % of all BVOC emissions (Sindelarova et al., 2014).

The dominant monoterpene from vegetation (e.g., conifer-

Atmos. Chem. Phys., 20, 9183–9207, 2020

https://doi.org/10.5194/acp-20-9183-2020

M. Simon et al.: New-particle formation from

-pinene over a broad range of tropospheric temperatures

9185

ous trees) is

-pinene, accounting for

∼

34 % of the total

global monoterpene emissions. Most of its oxidation prod-

ucts lead to oxidized volatile organic compounds (OVOCs)

with a low degree of oxygenation; they are characterized

as intermediate-volatility or semivolatile organic compounds

(IVOCs, 300

< C

∗

(

)

μg m

−

, and SVOCs, 0

∗

(

)

300 μg m

−

, respectively, where

∗

(

)

is the ef-

fective saturation concentration). However,

-pinene has an

endocyclic carbon double bond; oxidation of this functional-

ity by ozone can initiate a rapid oxidation process, known

as autoxidation (Crounse et al., 2013). Autoxidation pro-

ceeds by repeated intramolecular hydrogen shifts (H shifts)

of weakly bound hydrogen atoms within peroxy radicals

(RO

q

). Each H shift is followed by the rapid addition of

molecular oxygen (O

) to form multifunctional peroxy rad-

icals with a high degree of oxygenation, while preserving

the radical functionality. Under low-NO conditions (Berndt

et al., 2018a), these radicals terminate into organic prod-

ucts with a high degree of oxygenation and therefore low

volatility. Although multifunctional RO

q

radicals formed in

the autoxidation process represent an important intermediate

class of compounds in atmospheric chemistry (Rissanen et

al., 2015), knowledge about their complex formation mech-

anisms and kinetics remains far from complete (Ehn et al.,

2017).

The autoxidation pathway leads to highly oxygenated or-

ganic molecules (HOMs) with molar yields of several per-

cent (7 % at 20

◦

C, Ehn et al., 2014; 3.2 % at 5

◦

C, Kirkby

et al., 2016). This class of oxidation products spans a

wide range of volatilities from low-volatility and extremely

low volatility towards ultralow-volatility organic compounds

(LVOCs, 3

−

< C

∗

(

)

3 μg m

−

; ELVOCs, 3

−

< C

∗

(

)

−

μg m

−

; and ULVOCs,

∗

(

)

−

μg m

−

, respectively). While the LVOC and ELVOC

classes mainly contribute to the growth of embryonic clusters

in the atmosphere, the new class ULVOC refers to molecules

with sufficiently low saturation vapor pressure to enable them

to reach supersaturation and drive pure biogenic nucleation

(Donahue et al., 2012; Bianchi et al., 2019; Schervish and

Donahue, 2020).

The fate of the

-pinene peroxy radicals (e.g.,

) is mainly influenced by the presence

of nitrogen oxides (NO

), hydroxyl radicals (HO

q

) or per-

oxy radicals (RO

q

). Rapid bimolecular reactions terminate

the autoxidation chain by forming closed-shell products

and consequently affect the chemical composition of the

oxidation products and the molar yield of HOMs (Presto

et al., 2005; Ng et al., 2007; Ehn et al., 2014; Berndt et

al., 2015; Rissanen, 2018). The reactions with NO and

q

mainly form semivolatile and low-volatility organic

compounds, which are important for the growth of particles

with sizes above a few nanometers (Donahue et al., 2013;

Wildt et al., 2014). Since NO concentrations are usually low

in areas where BVOC emissions predominate, the loss of

q

radicals in bimolecular reactions with NO can gener-

ally be neglected. In contrast, the RO

q

cross-reaction can

form higher-molecular-weight accretion products (ROOR;

Donahue et al., 2011; Berndt et al., 2018b; Valiev et al.,

2019). As shown by Tröstl et al. (2016) and Lehtipalo

et al. (2018), these gaseous dimeric compounds have the

ability to condense irreversibly onto atmospheric particles

or even to contribute to the early-stage growth of molecular

clusters, since they cover a wide range of volatility from

low to ultralow vapor pressure. Furthermore, they are also

potentially important for NPF, especially in environments

dominated by biogenic precursors, e.g., boreal forests (Mohr

et al., 2017; Bianchi et al., 2017).

The bimolecular termination reactions have little or no en-

ergy barrier. Their rates are therefore only weakly affected by

temperature. In contrast, quantum chemical calculations sug-

gest that the intramolecular isomerization through H shifts

within the peroxy radicals has a high activation barrier of

84 kJ mol

−

or more (Rissanen et al., 2014; Kurtén et al.,

2015; Schervish and Donahue, 2020). This results in a strong

temperature dependence of the autoxidation, which slows

down the oxygenation (HOM yield) at lower temperatures.

Consequently, the chemical composition of the initial clus-

ters formed from monoterpene oxidation changes at colder

temperatures. This was shown in Frege et al. (2018) for ion-

induced nucleation of pure HOM particles. Further, cham-

ber studies showed that not only does the SOA formation

rate of monoterpene oxidation have a strong temperature de-

pendence, but also the final HOM distribution is affected by

the autoxidation rate (Saathoff et al., 2009; Kristensen et al.,

2017; Quéléver et al., 2019). Additionally, a recent model

study by Schervish and Donahue (2020) showed that the first

H-shift reaction of the peroxy radical isomerization is the

rate-limiting step of total HOM formation. Stolzenburg et

al. (2018) showed that, despite the reduction in HOM yield,

there was no effect on the growth rate of new particles at

lower temperatures. It was shown that the steep exponential

temperature dependence in the saturation vapor pressure, as

described by the Clausius–Clapeyron relation, counters the

reduction in the oxidation state in terms of their volatility dis-

tribution. Recent measurements of particle composition by

Ye et al. (2019) showed that this leads to sufficient condensa-

tion of even the low-oxygenated and moderately oxygenated

organic products at low temperatures. The volatility of the

oxidation products is relevant in order to characterize their

ability to condense and participate in NPF. The volatility ba-

sis set (VBS) model is therefore a suitable tool to track the

volatility change in the oxidation of volatile organic com-

pounds with temperature.

Model simulations suggest that highly oxygenated organic

molecules have a pronounced effect on NPF on a global

scale, especially in pristine environments dominated by bio-

genic precursors such as the tropical rain forests or at high al-

titudes as well as in the preindustrial atmosphere (Gordon et

al., 2017). Furthermore, recent observations support this con-

clusion, suggesting that oxidation products of BVOCs have a

https://doi.org/10.5194/acp-20-9183-2020

Atmos. Chem. Phys., 20, 9183–9207, 2020

9186

M. Simon et al.: New-particle formation from

-pinene over a broad range of tropospheric temperatures

major impact on the formation of CCN, especially at high

altitudes in the tropical convective regions (Williamson et

al., 2019). However, the lack of knowledge about the mech-

anisms and the accurate representation of NPF from BVOCs

for different environmental conditions, especially their tem-

perature dependence, remains a great challenge for atmo-

spheric chemistry and climate models.

In the current study, we present a comprehensive inves-

tigation of the effect of ambient tropospheric temperature

on the molecular composition of

-pinene oxidation prod-

ucts and NPF rates. The experiments were conducted at the

CLOUD (Cosmics Leaving OUtdoor Droplets) chamber at

CERN (Geneva, Switzerland), using atmospherically rele-

vant concentrations of

-pinene and ozone. To study pure

biogenic nucleation, the addition of other trace gases was

avoided in this study. Going beyond the results of Stolzen-

burg et al. (2018), this study focuses on NPF over a wide

range of tropospheric temperatures from ground level (25

◦

to the upper free troposphere (

−

◦

C).

2 Methods

2.1 The CLOUD experiment

The CERN CLOUD chamber is a 26.1 m

electropolished

stainless-steel vessel for the study of NPF under atmospher-

ically relevant conditions. The use of boiled-off nitrogen

and oxygen from ultraclean cryogenic liquids in a ratio of

79 : 21 minimizes the levels of contaminants (e.g., SO

, NH

or volatile organics) inside the chamber. CLOUD is

operated at a slight overpressure (5 hPa) to avoid contam-

ination at any time, especially when instruments are being

connected or disconnected. The relative humidity is adjusted

with a temperature-controlled Nafion humidifier using ultra-

pure Millipore water. Ozone and other trace gases are intro-

duced by individual gas lines; gas dilution stages are applied

when necessary to achieve the targeted mixing ratios.

To add monoterpene, dry nitrogen is passed through

a temperature-controlled evaporator containing liquid

pinene (Sigma-Aldrich, 98 %). Efficient uniform mixing of

the gases and ions in the chamber is ensured by two magneti-

cally coupled fans located at the bottom and top of the vessel.

The characteristic wall loss rates of condensable gases can be

adjusted by variation in the fan speed.

The ion concentration in the chamber can be regulated

to values that are representative of the full range of tropo-

spheric and stratospheric conditions by the controlled irradia-

tion with a 3.5 GeV c

−

secondary

beam from the CERN

Proton Synchrotron. This simulates the ionizing muon irradi-

ation in the upper troposphere and stratosphere. Furthermore,

as the chamber is continuously exposed to galactic cosmic

rays (GCRs), a 20 kV m

−

electrical high-voltage clearing

field (HVCF) can be imposed by energizing two electrode

grids located at the top and bottom of the chamber, removing

all ions within seconds. Thus, the CLOUD chamber enables

investigation of NPF under ion-free conditions as well as of

ion concentrations that are found throughout the troposphere.

Photochemical processes, such as the photodissociation of

ozone to produce OH

q

radicals, can be controlled by homo-

geneous illumination with UV light of adjustable intensity.

The light from four 200 W Hg–Xe UV lamps (UVH LC8,

Hamamatsu Photonics K.K., Japan) is guided by a fiber-optic

system into the chamber to avoid any heat load from the

light sources and to establish near-homogenous illumination

(Kupc et al., 2011).

A thermal housing surrounds the chamber to maintain a

high temperature uniformity and to control the chamber tem-

perature in a range from

−

70 to 100

◦

C with a precision of

1 K. This stability is mandatory as many of the NPF and

oxidation processes are highly sensitive to temperature. The

temperature inside the chamber is measured with several ar-

rays of thermocouples, while the chamber wall temperature

is monitored by a set of calibrated Pt100 sensors (Dias et al.,

2017).

Similar to previous CLOUD experiments, state-of-the-art

instruments are used to determine the chamber conditions,

the concentration of important gas species, and aerosol prop-

erties during nucleation and early-growth studies (Kirkby et

al., 2016; Lehtipalo et al., 2018; Stolzenburg et al., 2018).

All key instruments are placed in the midplane of the cham-

ber to ensure sampling from well-mixed conditions inside the

chamber. The sampling lines protrude 40 cm into the cham-

ber to avoid sampling close to the walls and to reduce mem-

ory effects. Prior to changing to a new chemical system, the

chamber and the sampling lines are rinsed with ultrapure wa-

ter and subsequently heated up to 100

◦

C to clean the cham-

ber from residual chemicals of previous experiments. Apply-

ing high ozone concentrations for several hours during the

cleaning helps achieve contamination levels that are below

parts per trillion by volume of inorganic and

150 ppt

total organic compounds (Schnitzhofer et al., 2014). More

details about the CLOUD experiment can be found in Kirkby

et al. (2011) and Duplissy et al. (2016).

The experiments reported here were performed during

the CLOUD10 (fall 2015), CLOUD12 (fall 2017) and

CLOUD13 (fall 2018) campaigns. Within these three cam-

paigns, sets of experiments at five different temperatures

were performed to study the HOM production and NPF from

-pinene oxidation.

-Pinene was added to the chamber at

volume mixing ratios ranging from 100 to 2000 ppt

, while

ozone levels were kept between 30 and 40 ppb

. OH

q

rad-

icals were mainly formed by the ozonolysis of

-pinene

with an 80 % yield (Chew and Atkinson, 1996) and also by

UV photolysis of ozone. The relative humidity was com-

monly held at 40 % in CLOUD10 and CLOUD12 and 80 %

in CLOUD13.

Before starting a NPF sequence (run), the CLOUD cham-

ber was cleaned from residual particles and organic com-

pounds by flushing the chamber with clean synthetic air for

Atmos. Chem. Phys., 20, 9183–9207, 2020

https://doi.org/10.5194/acp-20-9183-2020

M. Simon et al.: New-particle formation from

-pinene over a broad range of tropospheric temperatures

9187

several hours while operating the mixing fans at a high speed

and periodically activating the HVCF to remove all charged

aerosol particles efficiently. The results reported here were

obtained without any addition of SO

, NO

or other trace

gases in order to achieve a pure biogenic system, to isolate

the chemistry of biogenic precursors and to avoid the inter-

ference with other potentially nucleating compounds. Fur-

thermore, no OH

q

radical scavenger was used during the ex-

periments to ensure a faithful simulation of atmospheric con-

ditions. The instruments and methods relevant for the present

study are described in the following sections.

2.2 Nitrate CI-APi-TOF

The

nitrate

chemical

ionization–atmospheric-pressure

interface–time-of-flight mass spectrometer (CI-APi-TOF)

uses nitrate anions (

(

HNO

)

(

−

)

, with

0–2) as

reagent ions which are produced by exposing a sheath gas

enriched by nitric acid (HNO

) flow to a corona discharge

(Kürten et al., 2011). Based on the free-jet-flow design

of Eisele and Tanner (1993) the nitrate reagent ions are

electrostatically pushed into the sample flow in the center

of the ion–molecule-reaction drift region without mixing of

both gas streams. After a reaction time of

∼

50 ms within

the sample flow, the ions and charged clusters enter the

atmospheric pressure interface of the mass spectrometer

(APi-TOF, Tofwerk AG, Switzerland) where they are

focused by two segmented quadrupole units and an ion

lens assembly, while the pressure is gradually reduced to

around 10

−

mbar. In the time-of-flight region, the ions

are separated according to their mass-to-charge ratio and

counted by a microchannel plate detector. The data are

processed and analyzed in IGOR Pro (WaveMetrics, Inc.,

USA) using the software package Tofware (Version 3.1,

Aerodyne Inc., USA).

The chemical ionization with nitrate anions is selective not

only towards strong Lewis acids, like sulfuric acid (H

;

Jokinen et al., 2012) or iodic acid (HIO

; Sipilä et al., 2016),

but also for bases, like dimethylamine (

(

)

NH) when ion

clusters are being formed, including the nitrate reagent ions

(Simon et al., 2016). Highly polar functional groups, like car-

boxylic acids (COOH), hydroperoxides (R-O-OH) and per-

oxy acids (R(O)-O-OH), which are the most abundant func-

tional groups in HOMs, can also be detected (Hyttinen et

al., 2015). While strong acids are mostly detected as de-

protonated anions (e.g., HSO

−

), HOMs are charged mainly

through adduct-ion formation

(

HOM

−

)

. Here, the in-

dex

denotes a specific HOM (with a specific exact mass).

The concentration of the sample is achieved by normaliza-

tion of the product ion count rates per second (cps) with the

intensity of the reagent ions (cps) expressed by the following

Eq. (1):

[

HOM

]

HOM







[

HOM

−

]

∑

[

(

HNO

)

−

]







(1)

Three different correction factors are considered to obtain

a concentration from the raw count rate. First, a general

calibration coefficient,

, of the mass spectrometer is ap-

plied, which is determined from a calibration using sulfu-

ric acid as described in Kürten et al. (2012). Here, we as-

sume that all HOMs with an oxygen-to-carbon (O

C) ratio

≥

6 have a collision-limited charging efficiency when

reacting with the nitrate ions similarly to sulfuric acid. In

addition, we assume that the charging efficiency of the ni-

trate CI-APi-TOF technique does not change significantly

with temperature or humidity (Viggiano et al., 1997). The es-

timated detection limit of the instrument for sulfuric acid is

about 5

molec. cm

−

; however, due to a better signal-to-

noise ratio at higher mass-to-charge ratios, some HOM

can

even be quantified at lower concentrations. Second, the mass-

dependent transmission efficiency TE

of the instrument is

considered by depleting the reagent ions by various perfluo-

rinated acids according to the method described by Heinritzi

et al. (2016) in a separate characterization experiment at the

beginning and end of the campaign. Third, a temperature-

dependent sampling-line loss correction factor, SL

HOM

, is

considered. It depends on the sample flow rate, the diffusion

coefficient of the target molecule and the length of the sam-

pling line. We assume laminar flow diffusional loss in the

120 cm sampling line. To reduce wall losses we applied a

core-sampling technique as described by Knopf et al. (2015)

and Fu et al. (2019). A fraction of 8.5 standard liters per

minute (slm) of the total flow in the inlet line (40 slm) is sam-

pled from its center. This setup minimizes the section length

that transports the sample to the instrument at the smaller

flow rate to 30 cm, reducing the sampling loss rate of HOMs

to less than 30 %.

As the molecules detected by the nitrate CI-APi-TOF have

typically very low saturation vapor pressures, we assume that

they are irreversibly lost upon contact with a surface. The

diffusion coefficients

for each HOM

are approximated

with the expression

(cm

−

)

−

, where

(g mol

−

) is the mass of the molecule. The wall loss rate in-

side the chamber at each temperature is determined from the

following expression:

wall

(

)

wall

(

)

√

(2)

where

wall

is an empirical parameter.

wall

is derived from

dedicated sulfuric acid decay experiments at all relevant tem-

peratures and ranges between 0.0071 and 0.0077 cm

−

for

−

50 to

◦

C. For these experiments the measured wall

loss rate and the diffusivity of sulfuric acid (0.078 cm

−

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M. Simon et al.: New-particle formation from

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298 K and a relative humidity of 40 %) is scaled to the cham-

ber temperature by the parameterization (

298 K)

(Han-

son and Eisele, 2000). The sampling line and the sheath flow

of the ion source are thermally insulated and are operated

at the same temperature as the CLOUD chamber to avoid

evaporation or condensation due to changes in the thermal

conditions during the measurements. Note that ions formed

in the CLOUD chamber, e.g., from GCRs or from the CERN

pion beam, are removed by an electrostatic filter in the ni-

trate CI-APi-TOF inlet. Therefore, ions from the chamber do

not interfere with the CI detection scheme. Finally, the sam-

ple ion signals are background corrected for a pure nitrogen

sample without any VOC addition to the chamber.

2.3 PTR3-TOF

The PTR3-TOF-MS, or PTR3 for short, described in Breiten-

lechner et al. (2017) uses proton transfer or ligand-switching

reactions from hydronium water clusters to ionize the ma-

jority of organic compounds, specifically those of which

have proton affinities larger than that of the water clusters.

(

)

primary ions, produced in a corona discharge

from humidified nitrogen, are transferred through a source

drift region into the tripole, where the ion–molecule reac-

tions take place. A core flow of typically 2 slm drawn from

the laminar sample gas (10 slm) enters the tripole reaction

region through a critical orifice. A pressure controller main-

tains a constant pressure of typically 70–80 hPa in the reac-

tion region.

By applying a tunable radio frequency signal on the tripole

rods, it is possible to adjust the collision energy between

ions and sample gas molecules. Elevated collision energies

suppress cluster ion formation of both primary and product

ions but could also lead to unwanted fragmentation of certain

product ions. Low collision energies on the other hand in-

crease unwanted clustering of ions with water molecules and

decrease the ionization efficiency for molecules with a proton

affinity close to that of water. During CLOUD experiments

we adjusted the collision energy to

E/N

values (

being

the electric field strength and

the sample gas number den-

sity) of 62–72 Td (1 Townsend equals 10

−

V cm

) by using

a radio frequency (RF) of 10 MHz and an RF amplitude of

800–900

at a pressure of 75–77 hPa. With these settings

even volatile organic compounds are detected and humidity

effects are minor. Primary and product ions were analyzed

with a long-TOF (LTOF, Tofwerk AG, Switzerland). All data

were acquired using the TofDaq recorder by Tofwerk and an-

alyzed with the TOF-Tracer software written by Lukas Fis-

cher running on Julia 0.6 (https://github.com/lukasfischer83/

TOF-Tracer, last access: 15 November 2019).

Precursor molecules are calibrated using a gas standard.

More-oxidized molecules have typically higher proton affini-

ties; their concentrations are estimated by using the sensi-

tivity of 3-hexanone. Oxidized organic compounds might

undergo fragmentation in reactions with H

(

)

pri-

mary ions, especially when containing hydroperoxide groups

(Bernhammer et al., 2017). Therefore, concentrations are

lower-limit estimates.

Furthermore, data are corrected for the duty cycle trans-

mission effects of the TOF and sampling-line losses. In Bre-

itenlechner et al. (2017) a correction factor of 5 for the inlet

line losses led to good agreement with the nitrate CI-APi-

TOF for most highly oxygenated molecules containing more

than five oxygen atoms in the

-pinene system (Fig. S1 in

the Supplement). The compounds measured by the PTR3

span several orders of magnitude of volatility, from volatile

organic compounds (VOCs) to extremely low volatility or-

ganic compounds (ELVOCs). Therefore, the correction for

sampling-line losses of less oxidized molecules can only be

done by changing the inlet flow rate or the fan speed inside

the CLOUD chamber for each inlet temperature and testing

the instrument’s response for different compounds due to en-

hanced wall collisions. We then applied a scaled sampling-

line loss correction factor ranging from 1 (no correction for

VOCs, unaffected by changing the number of wall collisions)

to 5 (maximum inlet correction for ELVOCs), which de-

creased during the tests to 20 % (or less) of their value be-

fore changed inlet flow or fan speed conditions. Molecules

that contain more than five oxygen atoms are considered

ELVOCs and are automatically corrected by a factor of 5

since these compounds are often too close to the detection

limit of the PTR3 to get a reasonable response during the

tests. Further details about the method are given in Stolzen-

burg et al. (2018).

2.4 Particle measurements and formation rate

determination

The particle number size distributions in the size ranges be-

tween 1.2 nm and 1 μm in the chamber were measured by a

series of aerosol-particle-counting instruments. The concen-

tration of the smallest particles was measured with a particle

size magnifier (PSM; Airmodus Ltd.; Vanhanen et al., 2011).

The PSM was operated in scanning mode for the determina-

tion of the particle concentration at different cutoff diameters

and for the particle number size distributions between 1 and

3 nm (Wimmer et al., 2013; Lehtipalo et al., 2014; Kürten

et al., 2015). Additionally, a butanol condensation particle

counter (CPC 3776, TSI Inc.) with a fixed cutoff diameter

of 2.5 nm was used. A DMA-train (differential mobility ana-

lyzer train) measured the size distribution of particles in the

1.8 to 8 nm size range with a 10 s time resolution; it consists

of six differential mobility analyzers (DMAs) with PSM or

CPC detectors that are operated in parallel, each measuring

a fixed size (Stolzenburg et al., 2017). A commercial nano-

scanning mobility particle sizer (nSMPS 3982, TSI Inc.) re-

solved the particle size distribution between 8 and 63 nm.

For larger particles (

50 nm) two additional SMPS systems

were used.

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The ion concentration and the size distribution of charged

clusters and small particles of both polarities were measured

using a neutral cluster and air ion spectrometer (NAIS; Airel

Ltd.; Manninen et al., 2009). This instrument determines the

ion mobility distribution in the range from 0.82 to 45 nm in

mobility-equivalent diameter, as well as the total particle size

distribution in the size range of 2.5 to 45 nm by charging all

sampled aerosols with a periodically activated corona dis-

charge source.

The particle formation rates used in this study were deter-

mined using the full particle size distribution following the

method presented in Wagner et al. (2017) and Lehtipalo et

al. (2018). In practice, the particle formation rates at the cut-

off diameter, 1.7 nm, were determined from the time deriva-

tives of the total particle concentrations with diameters larger

or equal to 1.7 nm. The formation rates are corrected for the

particle losses in the chamber such as dilution, coagulation

and wall losses.

2.5 Trace gas and water vapor measurements

Trace gas monitors were used to measure the concentration

of ozone (O

, Thermo Environmental Instruments TEI 49C),

sulfur dioxide (SO

; Thermo Fisher Scientific, Inc. 42i-TLE)

and nitrogen oxides (NO, ECO PHYSICS CLD 780 TR;

, CE-DOAS, University of Colorado Boulder; and CAPS

, Aerodyne Research Inc.). The water vapor concentra-

tion in the chamber was monitored with a chilled dew-point

mirror (Edgetech Instruments) and a direct tunable diode

laser absorption spectrometer (TDL hygrometer; Werle et al.,

2002).

2.6 Experimental errors

The overall scale uncertainty for the HOM and oxidation

product (OVOC) measurements is

78 %/

−

68 %. The un-

certainty in the formation rates was determined by using

the error propagation method of both systematic and statis-

tical uncertainties including those associated with the parti-

cle concentration measurement (10 %), as well as their dilu-

tion (10 %) and diffusional (20 %) losses. The statistical er-

rors include uncertainty in d

and the coagulation sink,

which varied from run to run, depending on the stability of

the measurement conditions. The reproducibility (run-to-run

uncertainty) under identical conditions is about 30 % as de-

scribed in more detail by Kirkby et al. (2016) and Lehtipalo

et al. (2018).

2.7 Volatility basis set model

The ambient temperature and the concentration of the oxida-

tion products significantly determine their saturation vapor

pressure. HOM are mainly assigned to the volatility class of

LVOC and ELVOC (Bianchi et al., 2019). However, this as-

signment depends strongly on the temperature. Since the def-

inition of HOM has no direct relation to the physical proper-

ties of HOMs, the volatility classification introduced by Don-

ahue et al. (2011) is used in the present study to discuss the

contribution of different HOMs and less oxidized products

to NPF. In principle, the saturation vapor pressure of an or-

ganic molecule is determined by its mass and its functional

groups, which affect the strength of the interaction with its

neighboring molecules, and by the temperature.

The determination of the exact volatility of the oxidation

products is challenging because the individual compounds

cannot be isolated, as they are highly reactive and frag-

ile species with extremely low saturation vapor pressures.

However, experimentally derived volatilities from desorption

thermograms measured with a FIGAERO (Filter Inlet for

Gases and AEROsols) show a good agreement with the com-

bination of semiempirical methods and theoretical model cal-

culations (Lopez-Hilfiker et al., 2014; Schobesberger et al.,

2018). This was recently verified in a complementary study

of the

-pinene ozonolysis products examined here (Ye et al.,

2019), in which the volatility distribution of molecules in the

nucleated particles, measured with a FIGAERO inlet over a

wide range of temperatures, is in good agreement with those

estimated by Stolzenburg et al. (2018).

Here we follow the same approach as described in Stolzen-

burg et al. (2018). We combine the semiempirical group-

contribution methods (SIMPOL; Pankow and Asher, 2008)

with the two-dimensional volatility basis set (2D VBS) intro-

duced by Donahue et al. (2011). It is based on the relation-

ship between a typical molecular composition and its known

volatility by parameterizing the saturation vapor pressure of

an unknown molecule according to its mass and oxidation

state (Donahue et al., 2012, 2013):

−

(3)

Therefore, the volatility can be expressed as the logarithm of

the saturation mass concentration, log

∗

, from the num-

ber of carbon atoms,

, and oxygen atoms,

, within the

specific molecule,

log

∗

(

300 K

)

[

μg m

−

(

−

)

−

(

−

add

)

−

(4)

Based on Donahue et al. (2011) and a revised version given in

Stolzenburg et al. (2018), the parameter

25 represents

the baseline carbon backbone for a volatility of 1 μg m

−

without the addition of any functional groups. The parameter

475 accounts for roughly a half-order-of-magnitude

decrease in saturation vapor pressure per carbon atom ac-

cording to the mass of the molecule, while

3 consid-

ers a more than 2-orders-of-magnitude decrease in volatil-

ity per oxygen atom assuming an equal proportion of car-

bonyl (

O) and hydroxyl (

−

OH) groups in the molecule.

The carbon–oxygen nonideal interaction

=−

3 is a

nonlinearity term that adjusts the volatility estimation from

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M. Simon et al.: New-particle formation from

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organics dominated by carbonyl (

O) and hydroxyl (

−

OH)

groups at low O

C ratios towards HOMs, which mainly con-

sist of hydroperoxyl (

−

OOH) and peroxy acid (

−

C(O)OOH)

groups at high O

C ratios. While the additional oxygen in

the

−

OOH group (log

∗

=−

4) has an almost negligible

effect in reducing the saturation vapor pressure compared to

the

−

OH group (log

∗

=−

2), neither covalently bound

dimers nor the ability of hydroperoxide and peroxy acid

functionalities to form intramolecular hydrogen bonds are

included in the nonlinear terms

and

(Donahue et al.,

2012; Kurtén et al., 2016). Therefore, a free parameter

add

included to adjust the effect of oxygen atoms in the molecule

and to account for the different functionalities. To ob-

tain this parameter, measured monomer and dimer prod-

ucts with known chemical composition are fitted separately

with the group-contribution method SIMPOL (Stolzenburg

et al., 2018). A fit to the data yields

mono

add

904 for HOM

monomers and

add

139 for HOM dimers. Consequently,

the saturation vapor pressure of any oxidation product mea-

sured in the CLOUD chamber can be estimated based on its

elemental composition.

In addition, the gas-phase saturation ratio,

∗

, for each ox-

idation product can be determined based on the quantitative

vapor-phase measurement of the oxidized molecule concen-

tration,

[

OVOC

]

; the molecular mass,

; and the associated

saturation concentration,

∗

(Donahue et al., 2013):

∗

[

OVOC

]·

∗

(5)

It should be noted that we can only estimate the volatility

from the elemental composition, while two molecules with

an identical detected mass may have different volatilities de-

pending on their exact chemical structures and functional

groups.

To account for the dependence of the volatility on temper-

ature,

, the saturation concentration,

, can be described

according to the Clausius–Clapeyron equation:

log

∗

(

)

log

(

300 K

)

vap

(

)

(

300 K

−

)

(6)

According to Donahue et al. (2011) and Epstein et

al. (2009), we can approximate the evaporation enthalpy

vap

[

kJ mol

−

]=−

log

∗

(

300 K

)

129

(7)

Thus, a change in temperature of 15 to 20 K will result in

a shift of the volatility bin by 1 order of magnitude. This

study focuses mainly on the oxidation products classified as

ELVOCs and ULVOCs, which will initiate cluster growth and

form new particles. However, ELVOCs will condense on any

particle of any size with negligible re-evaporation but may

not contribute significantly to nucleation itself, while UL-

VOCs in contrast may efficiently nucleate. To account for

our incomplete knowledge of the exact chemical structures

and functional groups of the oxidation products, we assume

an overall uncertainty of

1 bin in the volatility distribution

(corresponding to 1 order of magnitude in

∗

at 300 K).

2.8 HOM formation and its dependence on

temperature

Two parameters, AP

oxrate

and

HOM

, are used to describe

and characterize the overall HOM formation. To account for

the different oxidant concentration of [O

] and [OH

q

] among

the experiments and the temperature dependence of the ini-

tial reaction rate coefficient of

-pinene by these oxidants

(Fig. S2a), the

-pinene oxidation rate is used as follows:

oxrate

[

molec

−

(

)

[

]

[

]

(

q

)

[

]

[

q

]

(8)

Here, [AP] and [O

] are the measured gas-phase concentra-

tions of

-pinene by the PTR3 instrument and ozone by a

trace gas monitor, respectively. The IUPAC-recommended

rate coefficients of the

-pinene ozonolysis reaction

(

)

−

640 K

molec

−

) and

the reaction of

-pinene with OH

q

(

q

)

−

440 K

molec

−

) are used. The temperature

dependence of these rate coefficients is shown in Fig. S2a

for typical oxidant concentrations used in our experiment.

The main sources of OH

q

radicals are the ozonolysis of

pinene and the UV photolysis of ozone. In dark conditions

(UV off), the temperature-dependent ozonolysis rate is a

major source of OH

q

radicals with a yield of 80 % (Chew

and Atkinson, 1996), with a resulting steady-state OH

q

con-

centration of 0.5–1

molec. cm

−

. The formation of

q

radicals depends mainly on the absolute humidity in

the chamber since singlet

oxygen, which is formed dur-

ing the ozone photolysis, is subsequently recombined with

O. The OH

q

radical concentration by UV was estimated

from dedicated actinometry experiments, forming sulfuric

acid, during the same campaign. The OH

q

production by

UV yields 1–3

molec. cm

−

◦

C, while at low

temperatures the OH

q

production is comparatively small (

≤

molec. cm

−

◦

C), due to the lower humidity

in the chamber.

The total oxygenated organic fraction [OxOrg] can be es-

timated as follows:

[

OxOrg

]

oxrate

−

(

dil

wall

)

·[

OxOrg

]

(9)

The dilution loss rate

dil

is determined by dividing the total

flow into the chamber by its volume (

278 K

dil

∼

−

)

equaling total outflow at constant chamber pressure. Since

the focus of this study is on compounds that are relevant

for nucleation and early growth, we assume that oxygenated

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M. Simon et al.: New-particle formation from

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9191

organics are irreversibly lost due to condensation on a wall

or particles. The chamber wall loss rate was determined to

278 K

wall

−

, which is the major loss. An addi-

tional loss is due to the condensation sink (

∼

001 to

−

) to particles and the dilution loss (

278K

dil

∼

−

). The total loss rates for oxygenated organics

is then

278 K

loss

∼

−

. Note that the condensation

sink in the CLOUD chamber is lower than in other cham-

ber experiments, where similar experiments have been con-

ducted. Based on the production terms, the cumulative sinks

and the total measured

[

HOM

]

by the nitrate CI-APi-TOF,

the HOM yield (

HOM

) can be expressed as

HOM

[

]

(

[

OxOrg

]

[

HOM

]

(

loss

)

oxrate

)

100

(10)

3 Results and discussion

3.1 Evolution of gases and particles during an

experimental CLOUD run

A typical CLOUD experiment (“run”) is performed after es-

tablishing a constant level of ozone. Starting from ion-free

conditions,

-pinene is added to the chamber at a constant

rate, as shown in Fig. 1 at 12:30 UTC, 20 October 2018. Due

to chemical reactivity, the ozone concentration varied be-

tween 34 and 40 ppb

. As soon as

-pinene was added to the

chamber, peroxy radicals (RO

q

) and HOMs started to form.

In contrast to previous CLOUD campaigns, in CLOUD12

and CLOUD13 the fan was switched to 100 % speed during

the addition of

-pinene. The high fan speed increases turbu-

lent mixing in the chamber and leads to a faster deposition of

oxidation products and particles onto the wall (

wall

). Conse-

quently, the steady-state concentration of condensable mate-

rial (ELVOCs and ULVOCs) was shifted well below the nu-

cleation threshold by increasing the fan speed from its stan-

dard value (12 %) to 100 %. The concentration of the peroxy

radicals measured by the CI-APi-TOF, however, is not much

affected by the strong fan mixing. Reaction rate constants

for highly functionalized RO

q

from

-pinene self- and cross-

reactions are in a range of 1 to 10

−

molec.

−

at 300 K (Berndt et al., 2018a). Due to their high reactiv-

ity, the lifetime of the RO

q

radicals is mainly determined by

chemical loss rates and relative weakly by the wall loss rate.

After the precursors reached a steady-state concentration

(13:23 UTC in Fig. 1), the mixing fans were switched from

100 % to 12 % speed, reducing HOM and cluster wall loss

rates by a factor of 2 to 3. Consequently, a new steady-

state concentration of

-pinene oxidation product monomers

) and dimers (C

) was established on the wall loss

timescale. Due to the increased gas-phase concentration of

condensable material, a NPF event was initiated. Molecu-

lar clusters started to form and grew into aerosol particles.

After the particle formation rate had reached a steady state

under neutral conditions (

), the HVCF inside the chamber

was turned off (15:38–17:40 UTC in Fig. 1). Due to natural

ionization at intensities of ground-level GCRs, the ion con-

centration increased to

1000 cm

−

. Maintaining all other

chamber parameters as constant, we observed an enhance-

ment of up to 2 orders of magnitude or more in the nucleation

rate of new particles due to ion-induced cluster stabilization

(

gcr

; Kirkby et al., 2016).

During some stages, the UV light was also turned on to

study its effect on the oxidation chemistry by comparing

the results with (06:00–08:20 UTC in Fig. 1) and without

(03:09–05:41 UTC in Fig. 1) photochemical reactions un-

der otherwise identical conditions. The particle formation

sequence was then repeated at various concentrations of

pinene and different temperatures over the range of atmo-

spheric interest. In the data analysis, we assume that the parti-

cles observed at a 1.7 nm mobility diameter are stable against

evaporation and serve as a valid proxy for NPF in the cham-

ber.

3.2 Effect of temperature on

α

-pinene oxidation and

HOM formation

Temperature has a strong effect on peroxy radical isomer-

ization and, consequently, on the production rate of closed-

shell oxygenated products. HOM formation is, in princi-

ple, controlled by the production rate and lifetime of the

precursor peroxy radicals, while the lifetime of the radi-

cals is determined by the competing reaction of the uni-

molecular autoxidation and the bimolecular terminations.

The unimolecular H-shift reaction has a much higher pre-

exponential term for the rate constant given by the molecular

vibration frequencies compared with that for the bimolecu-

lar process, which mainly depends on the bimolecular colli-

sion frequency (Praske et al., 2018). However, the higher ac-

tivation energy barrier of the H-shift reaction partly or fully

compensated this. Quantum chemical calculations for differ-

ent RO

q

radicals from

-pinene oxidation suggest activation

energies between 92 and 121 kJ mol

−

for the autoxidation

process (Rissanen et al., 2015). Because of this high acti-

vation energy barrier, temperature has a substantial effect on

the intramolecular H shift and will strongly reduce the autox-

idation at lower temperature. In contrast, the temperature de-

pendence of the bimolecular reaction (like molecular dimer

formation) is much weaker or, in some cases, even exhibits

a slightly negative dependence. Consequently, the competi-

tion at lower temperatures between the termination reaction

and the slower unimolecular autoxidation rate influences the

oxidation state of the products and their distribution. This

temperature dependence of the

-pinene oxidation was pre-

viously observed in the composition of naturally HOM ions,

charged by cosmic rays in the CLOUD chamber (Frege et

al., 2018), and is confirmed here for neutral HOMs and their

gas-phase clusters, as shown in Fig. 2. A strong decrease in

the mean O

C ratio of the detected oxidation products can

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M. Simon et al.: New-particle formation from

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Figure 1.

Typical CLOUD experiment sequence of a NPF experiment by

-pinene oxidation for three different precursor concentrations. The

figure shows an example

-pinene NPF run during the CLOUD13 campaign. The experiment is conducted at a temperature of

◦

C and at

a relative humidity of 80 %. The vertical lines indicate a change in the experimental conditions in the chamber (e.g. change in settings for fan

speed, UV illumination, clearing field) marking a new stage within the run.

(a)

Change in fan speed and UV light intensity during the run. N,

GCR and CLEAN indicate neutral (high-voltage clearing field on), galactic cosmic ray (high-voltage clearing field off) and cleaning (neutral

periods including high fan speed to clean the chamber of particles) conditions, respectively.

(b)

Time series of ozone,

-pinene and negative

ions.

(c)

Combined size distribution of aerosol particles measured by the DMA-train (1.8–8 nm) and nanoSMPS (8–63 nm).

(d)

Evolution of

the nucleation rate at 1.7 nm (

) and the total particle concentration above 2.5 nm, measured with a scanning PSM (1.7 nm) and a butanol-

based CPC (2.5 nm). Furthermore, the loss rates to the chamber walls (

wall

, dashed black line) and the determined particle condensation

sink (CS, gray line) are shown.

(e)

Evolution of total HOM concentration and partitioning into HOM monomers (C

), HOM dimers (C

)

and peroxy radicals (RO

q

) as well the fraction of ultralow-volatility organic compounds (ULVOCs) measured by the nitrate CI-APi-TOF.

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