A CHAMBER AND MODELLING INVESTIGATION OF THE POTENTIAL IMPACTS OF SEMI-VOLATILE MATERIAL ON CLOUD DROPLETS

A thesis submitted to The University of Manchester for the degree of

MPhil Atmospheric Science

in the Faculty of Science & Engineering

2016

WILLIAM T. HESSON

SCHOOL OF EARTH AND ENVIRONMENTAL SCIENCES

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Contents

Title Page Pg. 1

Contents Pg. 2

Abstract Pg. 3

Declaration Pg. 4

Copyright Statement Pg 4.

Acknowledgement Pg. 5

1. Introduction Pg. 6–16

1.1 Overview of Thesis Pg. 6

1.2 Motivation Pg. 6–7

1.3 Literature Review Pg. 7–15

1.3.1 Aerosol Overview Pg. 7

1.3.2 Aerosol Effects Pg. 7–8

1.3.3 Humidity and Droplet Activation Pg. 8–11

1.3.4 Number Concentration and Size Distribution Pg. 11

1.3.5 Aerosol Composition Pg. 11–15

1.3.6 Summary Pg. 15

1.4 Attributing Credit Pg. 15-16

2. A chamber and modelling investigation of the potential impacts of semi-volatile material on

cloud droplets Pg. 17

3. Supplementary Material for “A chamber and modelling investigation of the potential impacts of

semi-volatile material on cloud droplets” Pg. 18

4. Conclusion Pg. 19-20

5. References Pg. 21-27

Word Count

Word Count 20,404

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Abstract

Clouds play an important role in the Earth’s radiative budget (i.e. the amount of energy lost to

and gained from space by the Earth). The concentration of droplets present in clouds is a critical

factor in determining their albedo so any factor which influences the formation of droplets in

clouds will affect the Earth’s radiative budget. Cloud droplets are formed by some aerosol

particles known as cloud condensation nuclei (CCN) given appropriate ambient conditions.

Secondary organic aerosols (SOAs) are one such component which is abundant in the

atmosphere. Globally, SOAs have a large semi-volatile component (i.e. material which partitions

between the gas and aerosol phases) and have been found in modelling work to co-condense with

water, enhancing their CCN activity. In this thesis, the first chamber based evidence for CCN

activity enhancement of SOA via co-condensation is presented. Experiments have been conducted

in a controlled chamber environment to generate SOA from 1,3,5-trimethylbenzene, limonene, β-

caryophyllene and α-pinene. These aerosols were then transferred to a cloud chamber where

evacuations were conducted on the samples in order to produce clouds. The activation observed

in these clouds has been compared to modelled data (which does not include co-condensation)

and a discrepancy has been observed with SOA samples generated from β-caryophyllene and α-

pinene which suggests enhancement from co-condensation. This conclusion is further supported

by additional modelling tests which rule out the possibility of uncertainties in the volatility bin

distribution or in the hygroscopicity parameter κ being responsible for the discrepancy between

chamber and model data. Agreement can be reached however, by including plausible

concentrations of co-condensing material. These findings are placed within the broader context of

SOA properties and may explain some of the discrepancies observed concerning the value of the

hygroscopicity parameter κ.

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Declaration

No portion of the work referred to in the thesis has been submitted in support of an

application for another degree or qualification of this or any other university or other

institute of learning.

Copyright Statement

i. The author of this thesis (including any appendices and/or schedules to this thesis) owns

certain copyright or related rights in it (the “Copyright”) and s/he has given The University of

Manchester certain rights to use such Copyright, including for administrative purposes.

ii. Copies of this thesis, either in full or in extracts and whether in hard or electronic copy, may be

made only in accordance with the Copyright, Designs and Patents Act 1988 (as amended) and

regulations issued under it or, where appropriate, in accordance with licensing agreements

which the University has from time to time. This page must form part of any such copies

made.

iii.The ownership of certain Copyright, patents, designs, trade marks and other intellectual

property (the “Intellectual Property”) and any reproductions of copyright works in the

thesis, for example graphs and tables (“Reproductions”), which may be described in this

thesis, may not be owned by the author and may be owned by third parties. Such Intellectual

Property and Reproductions cannot and must not be made available for use without the prior

written permission of the owner(s) of the relevant Intellectual Property and/or

Reproductions.

iv. Further information on the conditions under which disclosure, publication and

commercialisation of this thesis, the Copyright and any Intellectual Property University IP

Policy (see http://documents.manchester.ac.uk/display.aspx?DocID=24420), in any relevant

Thesis restriction declarations deposited in the University Library, The University

Library’s regulations (see http://www.library.manchester.ac.uk/about/regulations/) and in The

University’s policy on Presentation of Theses

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Acknowledgement

I’d like to thank my supervisors Gordon McFiggans and Paul Connolly for the opportunity to

undertake this project and their support to complete it and for NERC for providing funding for this

project. Thanks are also due to Angela Buchholz particularly for her assistance with those long

days in the laboratory. I’d also like to thank all those who assisted me in learning about and fixing

instrumentation and operation of the chamber facilities, in particular, Rami Alfarra, Mike Flynn,

James Dorsey, Chris Emersic and Lee Paul. Finally, I’d like to thank Dawn Hesson and Pam Bennett

for keeping me sane.

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1.Introduction

1.1 Overview of Thesis

Secondary Organic Aerosol (SOA) have been shown to act as cloud condensation nuclei (CCN) but

uncertainties remain with respect to their hygroscopicity and thereby their ability to activate into

droplets (Whitehead et al. 2014). In this thesis, we will first discuss the background surrounding

the activation of SOA particles into droplets (Chapter 1). A paper format piece of work is then

presented (Chapter 2 and 3) in which new data is presented from experiments conducted using

the combined Manchester Aerosol Chamber (MAC) and Manchester Ice Cloud Chamber (MICC)

facility to generate SOA and to conduct cloud evacuations in a controlled chamber environment in

order to probe their CCN activity. This will be compared to results from modelling this process.

Finally, in Chapter 4, the thesis will be concluded looking at the significance of this work with

respect to the wider field.

1.2 Motivation

The radiative budget of the Earth plays a key role in determining its surface temperature and

clouds are known to be a phenomenon which affects Earth’s radiative budget (Fung et al. 1984;

Chen et al. 2000; Corti & Peter 2009) with the influence of aerosol on warm liquid water clouds

being the largest source of attributable uncertainty in global radiative forcing (Boucher et al.

2013). Adjustments in cloud properties attributable to atmospheric aerosols result from their

influences on both warm (McFiggans et al. 2006)and cold (Hoose & Möhler 2012) clouds, meaning

that aerosols affect the Earth’s radiative budget (Bauer & Menon 2012). As such they play an

important role in Earth’s climate system and a good understanding of aerosols is necessary in

order to understand climate change which is anticipated to have an impact on human health

(Markandya & Chiabai 2009), wealth generation (Trærup et al. 2011) and food security (Vrieling et

al. 2011). A variety of weather phenomena are impacted by the presence and formation of clouds,

including rainfall which, for instance, can be suppressed or postponed by the presence of high

concentrations of cloud condensation nuclei (CCN) due to the particles competing for a limited

supply of water vapour (Chen et al. 2011); this, in turn, can reduce the water supply downwind of

substantial anthropogenic emission sites (Rosenfeld et al. 2007; Andreae & D Rosenfeld 2008). An

important source of CCN in the atmosphere are secondary organic aerosols which are abundant

with a total mass loading of 115 Tg yr-1 (Hallquist et al. 2009). However, questions remain

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concerning their hygroscopicity and hence their CCN activity particularly at low supersaturations

due to disagreement between instruments (Whitehead et al. 2014); additionally, SOAs CCN

activity has been predicted to be enhanced by the co-condensation of semi-volatile material and

water vapour during droplet formation (Topping et al. 2013). This thesis will address the question

of whether co-condensation can be observed in a controlled chamber environment and what this

might mean for the observed discrepancies between different measurements of SOA

hygroscopicity.

1.3 Literature Review

1.3.1 Aerosol Overview

An aerosol mass is defined as a suspension of liquid or solid particles in a gas; each particle is an

aerosol particle. In the atmosphere, aerosol particles come from a number of sources, vary in size,

chemical composition and hence hygroscopicity, phase and other properties which affect their

behaviour and radiative forcing.

1.3.2 Aerosol Effects

The impact of aerosol effects on Earth’s energy budget depends both upon the position in the

vertical profile of the atmosphere and the aerosol’s properties (Wang et al. 2009; Haywood &

Boucher 2000). These effects remain the greatest source of uncertainty in the Earth’s radiative

budget with an overall effect of -0.45±0.5 W m-2 (Boucher et al. 2013), the most important being

the direct, semi-direct and indirect effects.

The direct effect is concerned with the scattering and absorption properties of clouds. In general

this has a net cooling effect on the Earth’s surface by scattering incident solar radiation (Haywood

& Boucher 2000), however where strongly absorbing aerosol, such as black carbon (Bond et al.

2013), are present the result can either have a net warming or cooling effect depending upon the

surface albedo beneath (Haywood & Boucher 2000). The overall direct effect has been estimated

at -0.5 W m-2. The semi-direct effect refers to the warming of clouds due to the presence of

absorbing aerosol which causes evaporation of the cloud (Fischer & Grassl 1975; Bauer & Menon

2012); depending on where this occurs this can either have a negative or positive radiative

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forcing. The net semi-direct effect has been estimated as -0.1 W m-2(Bauer & Menon 2012).

There are several indirect effects. The Twomey effect (Twomey 1977) has a negative radiative

forcing, the presence of aerosol particles causes increased cloud droplet number concentration

thus increasing the optical depth of the cloud making it more reflective and increasing the amount

of incident solar radiation reflected back to space. High concentrations of aerosol mean more

competition for water vapour making it more difficult for rain-size drops to form; this increases

the liquid water content of clouds and this, in turn, increases their reflectivity so more solar

radiation is reflected (Rosenfeld 2000). This suppression of rainfall means that clouds persist for a

longer period of time in the atmosphere, so clouds with a greater concentration of aerosol reflect

solar radiation for longer, providing an additional cooling effect (Albrecht 1989). For the same

reasons, cloud thickness is increased resulting in further cooling (Pincus & Baker 1994). The total

indirect effect has been estimated at -0.7±0.5 W m−2 (Quaas et al. 2009) making it a substantial

source of uncertainty in Earth’s radiative budget.

1.3.3 Humidity and Droplet Activation

Köhler theory describes an idealised aerosol particle response to changes in relative humidity

including its activation into a droplet. Two terms determine the saturation vapour pressure over a

droplet: the Raoult (or solute) term which is associated with the presence of soluble material, and

the Kelvin (or curvature) term associated with the curvature of a droplet (Köhler 1936). Both the

Kelvin and Raoult terms have a dependency upon the size of the particle. The Kelvin term

decreases proportionally to the inverse cube of the diameter of the particle (see Equation 1)

meaning that it is most important at small sizes. The saturation vapour pressure over a curved

surface like that of a particle is greater than the saturation vapour pressure over a flat surface. As

the curvature of a particle reduces with increasing size, the surface tension of water over the

curved surface of the particle tends towards the surface tension over a flat surface and so the

Kelvin term reduces in size. The Raoult term is proportional to the inverse of the diameter of the

particle, as such it also falls as the diameter of the particle increases, however, as the dependency

of the Raoult term is lower by a factor of the square of the particle’s diameter relative to the

Kelvin term, the importance of the Raoult term increases as the diameter of the particle

increases. The Köhler equation can be written as shown in Equation 1 (Seinfeld & Pandis 2006),

where pw is the saturation vapour pressure, Dp refers to aerosol particle diameter, p0 is the

vapour pressure of water over pure water, Mw is the molecular mass of water, σw is the surface

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tension, R is the universal gas constant, T is the temperature and ρw is the density of water over

the droplet.

30

64)(ln

pw

ws

pw

wwpw

D

Mm

DRT

M

p

Dp

Equation 1(Seinfeld & Pandis 2006)

From Equation 1, a critical size can be calculated for any given particle for a given supersaturation:

as soon as a particle passes this size it will experience runaway growth, turning into a droplet. The

hygroscopicity parameter, κ (Petters & Kreidenweis 2007), is the main approach used to

parameterise water activity, aw, the ability of any type of aerosol to take up water relating it to

water activity using the relationship shown in Equation 2 where Vs is the volume of solute and Vw

is the volume of water. A smaller value of κ indicates that the aerosol particle is less hygroscopic

and therefore will activate at a larger size and at a greater supersaturation and vice versa.

1

𝑎𝑤= 1 + κ

𝑉𝑠

𝑉𝑤 Equation 2(Petters & Kreidenweis 2007)

There are a number of approaches to making measurements of κ including, via measuring water

uptake using a Hygroscopicity Tandem Differential Mobility Analyser (HTDMA) (Switlicki et al.

2008), using an electrodynamic balance (EDB) method or measuring activation by exposing the

aerosol to controlled supersaturated conditions using a Cloud Condensation Nuclei Counter

(CCNc) . The EDB method is a single particle approach in which a particle is electrically charged

and levitated. In modern studies, a double ring electrode design is typically used (Davis et al.

1990; Pope 2010; Pope et al. 2010; Gallimore et al. 2011). An electrostatic DC field and an

electrodynamic AC field are used to create a trap to hold the particle. The strength of the DC field

used to balance the gravitational attraction of the particle to the Earth can be used to calculate its

mass so by exposing the suspended particle to different relative humidities, the growth factor of

the particles can be probed and thus a value of κ derived. This approach is difficult to use when

studying chamber derived SOA particles as only a single particle can be studied at one time, the

chemical composition changes between particles and throughout the ageing of the aerosol mass.

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This approach will not be further considered in this thesis. HTDMAs measure the growth factor of

aerosol particles by size selecting particles at an initial relative humidity (e.g. a dried sample with

10 % RH) to get a sample of aerosol particles of the same size; these particles are then exposed to

conditions at a different relative humidity (e.g. 90 %) and size-selected again using the second

DMA to determine how many particles from the initially selected size grow by a set amount.

These particles are then counted with a Condensation Particle Counter. By measuring at a range

of sizes and relative humidities, a picture can be developed of the growth factor of these particles.

This can be used to calculate a value of κ. CCNc’s (Roberts & Nenes 2005; Alofs et al. 1995)

instead measure the number of particles activated when particles are exposed to a controlled

supersaturation. In general, continuous flow thermal gradient CCNc’s (Roberts & Nenes 2005) are

used in modern studies. These operate by exposing the aerosol sample to a column of evenly

increasing temperature which draws water vapour and heat from the walls but since water

vapour diffuses faster than thermal energy, a steady supersaturation can be established which

depends upon the thermal gradient and total airflow (Roberts & Nenes 2005). The number of

particles activated for a selected initial size at a given supersaturation can then be used to

determine a value of κ.

There are a number of limitations to Köhler theory. Firstly, it assumes that there is a solute and

that it will all be dissolved which is not the case for all aerosol: hydrophilic aerosol particles can

also be activated as droplets as described using FHH activation theory (Sorjamaa & Laaksonen

2007). Many aerosols are only partially soluble, especially some/many organic aerosols and semi-

volatile aerosols such as some SOA and ammonium nitrate. These can condense onto a particle as

it takes up water, effectively increasing what would have been considered its dry size which can

significantly enhance droplet activation (Topping et al. 2013; Topping & McFiggans 2012). Finally,

there is the problem of deliquescence and efflorescence which are described by modified Köhler

theory (Chen 1994). Aerosol particles grow and shrink depending on their hygroscopicity and the

temperature and relative humidity of the surrounding environment along with their

deliquescence and efflorescence points and the particle’s history. Under subsaturated conditions,

hygroscopic aerosol particles such as salts take up water as relative humidity increases. However,

at a threshold relative humidity below saturation, a phase transition occurs and the particle loses

its crystallinity and swells with water producing a step change in its diameter as a function of RH.

Further increases in relative humidity then see the particle grow further but no further step

change occurs. This threshold is known as the deliquescence point. When the relative humidity of

the particle is decreased below its deliquesence point, however, the particle does not shrink back

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to its original size. Instead it will remain wetted though it will continue to shrink until a lower

threshold relative humidity, known as the efflorescence point, is reached when a step change in

particle diameter and change of phase state will occur. For instance, ammonium sulphate has an

efflorescence point of approximately 35 % RH and a deliquescence point of approximately 84.2 %

at 298 K (Seinfeld & Pandis 2006).

1.3.4 Number Concentration and Size Distribution

Aerosol particles vary in size between a few nanometres to tens of microns. These sizes are split

between four modes: the nucleation mode which comprises newly formed particles (sizes

typically between 1 and 10 nanometres), the Aitken mode (10 to 100 nanometres) from which

particles are usually lost by coagulation to the accumulation mode (sizes typically between 100

nanometres and 1 micron) which in turn are typically lost to washout, and finally coarse mode

particles (sizes typically greater than 1 micron) which are usually formed by mechanical processes

with sources such as sea spray and wind-blown dust and are generally lost by sedimentation

(Seinfeld & Pandis 2006).

Loss of atmospheric aerosol particles is most efficient for larger and smaller sizes with residence

times longest for accumulation mode aerosol particles (Seinfeld & Pandis 2006). The smallest

particles are generally lost by coagulation which occurs when particles collide and become a

single larger particle. This is dependent upon the mean free path and sticking efficiency of the

particles concerned. The other major form of aerosol growth is condensation whereby material

from the gas phase condenses onto existing particles causing them to grow. This occurs when gas

phase compounds are above their saturation vapour pressure, encouraging this material to enter

the aerosol phase. The distribution of the condensation is proportional to the surface area of the

particles present (Seinfeld & Pandis 2006).

Aitken mode particles tend to dominate the number concentration of aerosol in the atmosphere

except during a nucleation event (Asmi et al. 2011). Accumulation mode particles with their

relative abundance and size tend to provide the largest amount of surface area amongst

atmospheric nuclei meaning that they tend to be the main site for condensation of secondary

aerosol. The mass of aerosol particles in the atmosphere is generally dominated by the coarse

mode due to their large size (Seinfeld & Pandis 2006).

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1.3.5 Aerosol Composition

There are many different aerosol particles found in the atmosphere including primary and

secondary organic aerosol (Sun & Ariya 2006), sea spray (Grythe et al. 2014), black carbon

(Boucher et al. 2013), mineral dust (Boucher et al. 2013), volcanic ash (Rodríguez et al. 2012) and

sulfate (Langmann 2014), cellular material (Jaenicke 2005), and even pollen (Pope 2010).

As a result of their source, sea spray particles are primarily found in marine and marine-influenced

environments where they make up 50–70 % of aerosol mass (Boucher et al. 2013). In marine

environments, where there are generally few sources of aerosol particles, sea spray particles are

extremely important in terms of the CCN concentrations, scattering etc. and are often found to be

mixed with other aerosol components, such as sulphate, from both natural and anthropogenic

sources (Grythe et al. 2014). The total mass of sea salt aerosol emitted annually is not well known,

however, it has been reported to fall in the range 3000 to 70000 Tg (Grythe et al. 2014). Sea spray

particles occur across a wide size spectrum from approximately 20 nm (Mårtensson et al. 2010) to

the micron scale (Seinfeld & Pandis 2006). Three difference mechanisms produce sea spray

particles: the bursting of film droplets, the jet of water which fills in the gap left by the burst

bubble, and the wind directly ripping water off the surface (Grythe et al. 2014). As such, strong

winds are associated with high concentrations of sea spray aerosol (Kaufman 2005).

Anthropogenic activities, particularly biomass burning and controlled combustion of fossil fuels

such as diesel, are the major source of black carbon in the atmosphere (Ni et al. 2014). Black

carbon is produced by incomplete combustion of fuels. These emissions typically consist of

partially oxidised organics including alkanes, aromatics (Zhang et al. 2011) and poly-aromatic

hydrocarbons (Cheruyiot et al. 2015). Radiative forcing due to black carbon is estimated at +0.4 W

m-2 (Boucher et al. 2013). As a result of its colour, black carbon is a significant absorber of

radiation and modelling work has indicated that when precipitated from the atmosphere it has a

substantial effect on surface conditions, for instance being associated with ice melt in the

Himalayas (Menon et al. 2010). It is also an important source of ice nuclei (DeMott et al. 1999;

Levin et al. 2016).

Anthropogenic activity, such as coal burning, and volcanism are the main sources of sulphate

aerosol in the atmosphere. Radiative forcing due to sulphate aerosol has been estimated to be -

0.4 W m-2 (Boucher et al. 2013). This material is formed by the reaction of sulphur dioxide with

water to form sulfuric acid which may then go on to react with any bases present in the

13

environment such as ammonium to form a sulphate salt.

Radiative forcing due to nitrate aerosol has been estimated at -0.11 W m-2 (Boucher et al. 2013).

These are formed in the atmosphere. NOx is emitted from combustion sites and lightning and can

be oxidised by OH● radicals to form nitric acid which in turn can react with any base materials

present to form salts, most commonly ammonium nitrate which is much more volatile than

ammonium sulphate (Seinfeld & Pandis 2006), and the equilibrium between the aerosol and

gaseous phase can be further shifted to the gaseous phase by the presence of sulphate (Kajino et

al. 2008).

Radiative forcing due to mineral dust has been estimated at -0.1 W m-2 (Boucher et al. 2013).

Mineral dusts are generated as primary particles by the wind. Mineral dusts are another

important source of ice nuclei in the atmosphere (Demott et al. 2015; Koehler et al. 2010).

Radiative forcing due to primary and secondary organic aerosol has been estimated at -0.12 W m-2

(Boucher et al. 2013). Organic components are ubiquitous in atmospheric aerosols (Jimenez et al.

2009) and have been variously found to account for 38–70 % of total sub-micron aerosol mass

(Jimenez et al. 2009; McFiggans et al. 2005). Primary organic aerosol (POA) are emitted as

particles from their source whereas secondary organic aerosol (SOA) are produced in situ in the

atmosphere by the condensation of organic compounds from the gas phase and have been

estimated to comprise 70 % of the organic aerosol mass in the atmosphere with a global sources

estimated at around 115 Tg yr-1 (Hallquist et al. 2009). Primary organic aerosols make a smaller

total contribution to aerosol mass which has been estimated as 55 Tg yr-1 (Trivitayanurak &

Adams 2014). Primary organic aerosol particles generally appear in the accumulation mode

(Hildemann et al. 1991) i.e. the mode that tends to dominate the surface area in the atmosphere

making POA particles ideal sites for the condensation of SOA, creating difficulties in separating

POA and SOA in atmospheric conditions. SOA precursors come from both biogenic and

anthropogenic sources with biogenic material dominating (Cahill et al. 2006). These materials

mostly originate from plants, with components including acetone, methanol, cis-3-hexan-1ol and

terpenes (isoprene, monoterpenes such as α-pinene and limonene, and sesquiterpenes such as β-

caryophyllene) (Hewitt et al. 2011) which are known to produce SOA via ozonolysis and OH●

reactions (Salo et al. 2011; Tritscher et al. 2011). Anthropogenic sources of SOA precursor

material are also significant sources of SOA with estimates of around 10 % of total SOA originating

from urban and industrial sources though this figure is highly uncertain (Spracklen et al. 2011). It

has also been suggested that the presence of anthropogenic material may encourage biogenic

14

aerosol formation (Hoyle et al. 2011).

The oxidation of organic material in the atmosphere, which in general reduces the volatility of

organic material and thereby encourages the formation of aerosol particles, is highly complex

with thousands of compounds generated from any given precursor material. Under daylight

conditions, oxidation of organic material in the atmosphere is dominated by OH● radicals which

are the primary oxidiser in the atmosphere (Lelieveld et al. 2016). OH● is produced by the

photodissociation of ozone by light of wavelengths shorter than 324 nm and by recycling via

radical reaction chains (e.g. the formation of peroxy radicals which reproduce OH●). Recent

modelling work has found that recycling of OH● is the dominant process in the free troposphere

(Lelieveld et al. 2016). An important set of reactions, with respect to the ageing of organic

material in the atmosphere, involve sulphur dioxide which reacts with water to form sulfuric acid

which can react with organic material following dissociation. Organosulfates have been found in

organic aerosol both in situ and in laboratory studies (Hallquist et al. 2009) and are thought to be

produced by the reaction of acidic sulphate, ozone, and terpenes. SO3- for instance, can open CO

bonds by nucleophilic attack; these processes are still poorly understood in terms of their kinetics

and the yields produced (Herrmann et al. 2015). Another significant radical in the atmosphere is

NO3● which is important under night time conditions but not in the daytime as its lifetime is too

short under daylight conditions to allow chemical interactions. At night it provides an important

path for the production of organonitrates which account for approximately 10 % of the organic

material in urban environments (Day et al. 2010). The other major path for the production of

organonitrates is via reactions with NO in daylight chemistry. NO and NO2 are found in highest

concentrations in the atmosphere near anthropogenic sources with fossil fuel combustion the

dominant source (Seinfeld & Pandis 2006), so areas downwind from pollution sites tend to have

the highest concentrations of organonitrates. Further oxidation of organic material occurs in the

aerosol phase (Herrmann et al. 2015) and it has been suggested that SOA particle matter may

even form by oxidation of volatile material in the aqueous phase (Ervens et al. 2011). Aqueous

phase formation of SOA is thought to be a significant source of low volatility SOA (which are

important in the formation of SOA particles), which are more water soluble than those formed in

the gas phase and hence are important for the solute effect with acids, alcohols and glyoxal-like

compounds acting as precursors (Ervens et al. 2011).

SOA particles are known to act as cloud condensation nuclei, however measuring their CCN

activity has proved to be challenging. In addition to their highly complex chemical composition,

SOA particles can exist in an amorphous solid state depending upon their composition and

15

relative humidity which can affect their ability to accommodate water (Virtanen et al. 2010; E.

Saukko et al. 2012; E Saukko et al. 2012). Furthermore, the approaches used to measure their

hygroscopicity have shown inconsistency. When SOA samples are measured with HTDMA and

CCNc the resulting values of κ have proved difficult to reconcile and although progress has been

made in this area, further work is required to get agreement between these methods, particularly

at low supersaturations (Whitehead et al. 2014).

1.3.6 Summary

Aerosols have an impact upon the Earth’s radiative budget via the direct, semi-direct and indirect

effects (Bauer & Menon 2012). These depend on the ambient conditions, the position of the

aerosol within the atmosphere and their properties such as CCN activation ability, size, and ability

to absorb and scatter radiation as a function of wavelength. Secondary organic aerosols pose

particular challenges as their compositions are complex, their phase varies depending upon the

ambient conditions and their hygroscopic properties are not well understood with questions

remaining concerning their ability to activate as droplets at low supersaturations (Whitehead et

al. 2014). Modelling work has suggested that co-condensation of secondary organic aerosol will

have a substantial effect on their ability to act as CCN (Topping et al. 2013) however, evidence for

this is still required either from laboratory or in situ conditions. This thesis will attempt to address

these gaps in knowledge by conducting cloud evacuations of secondary organic aerosol under

chamber conditions and comparing them to modelled results in order to look for evidence of

increased CCN activity as expected by co-condensation and in order that these results might be

compared to other work investigating the ability of secondary organic aerosol to act as CCN.

1.4 Attributing Credit

In the paper presented in Chapter 2, the first author was responsible for planning and leading the

experiments, data analysis of MICC data, conducting the modelling runs and analysing the results,

writing the paper and creating the figures. The contribution of Angela Buchholz was to assist in

the experiments by operating the CCN counter, development of software for data analysis of CCN

counter and DMPS data and conducting the analysis. Paul Connolly was responsible for the

development of ACPIM and developed some of the in-house software to extract data from the

16

MICC instrumentation. Gordon McFiggans assisted in editing the paper and acted in a lead

supervisory role for the project.

17

2.

Title: A chamber and modelling investigation of the potential impacts of semi-volatile material

on cloud droplets

Prepared for publication in Atmospheric Chemistry and Physics Discussions but not submitted.

Page 17

1

A chamber and modelling investigation of the potential

impacts of semi-volatile material on cloud droplets William Hesson1, Angela Buchholz1a, Paul Connolly1, Gordon McFiggans1 1School of Earth and Environmental Sciences, University of Manchester, Manchester, M13 9PL, UK acurrently at: Department of Applied Physics, University of Eastern Finland, Kuopio Campus, P.O. Box 5 1627, 70211, Kuopio, Finland

Correspondence to: William Hesson (william.hesson@googlemail.com)

Abstract. Under appropriate conditions, droplets form on a subset of atmospheric particles that act as cloud

condensation nuclei (CCN). From previous measurements in the ambient atmosphere, it is difficult to

unambiguously establish the degree to which the activation of CCN into droplets is quantitatively 10

understood. A cloud chamber environment provides greater control over the aerosol sample used and the

conditions it experiences, providing the opportunity to better constrain CCN activation. For the first time,

liquid cloud activation using chamber-derived secondary organic aerosol (SOA) produced in individual

experiments from 1,3,5-trimethylbenzene, limonene, β-caryophyllene or α-pinene as well as a separate

experiment using nebulised ammonium sulfate, have been compared to results from Monte Carlo cloud 15

parcel model simulations across expected parametric uncertainties. The formation and evolution of cloud

droplets during a pseudo-adiabatic evacuation of a cloud chamber was modeled using the Aerosol Cloud

Precipitation Interaction Model (ACPIM) using each set of generated parameters. Cloud formation in the

model and chamber are consistent within anticipated variability for ammonium sulfate aerosol, which have

no semi-volatile material, and are thought to be well understood and represented in ACPIM. However, the 20

model under-predicts the number concentration of cloud droplets generated in an evacuation for certain SOA,

most notably in the -caryophyllene system. The under-prediction is most marked in the first chamber

evacuation of the experiment when the concentration of semi-volatile organic vapor may be expected to be

highest. Semi-volatile material is thought to aid the formation of droplets by condensing onto CCN alongside

water (co-condensation) and thus making the particle larger and reducing the supersaturation required for a 25

droplet to form. The Monte Carlo simulation did not include treatment of semi-volatile material and it is

suggested that this observation is an indication of co-condensation of water and semi-volatile organic

material. Further simulations using ACPIM demonstrated that plausible concentrations of semi-volatile

material were able to bring the measured and modeled droplet number concentrations into agreement and that

this was not plausible by varying the hygroscopicity parameter κ. In the limonene experiment the model 30

over-predicts droplet concentration. This is thought to result from an overestimation of the hygroscopicity of

the CCN input into the model.

1 Introduction

Cloud and aerosol effects remain the single largest uncertainty in Earth’s radiative budget (Boucher et al.,

2013; McFiggans et al., 2006). The radiative forcing caused by clouds is dependent upon the size distribution 35

and number concentration of droplets as they have been shown to be a determining factor in cloud lifetime,

cloud depth, cloud liquid water content and cloud reflectivity which in turn determine the amount of energy

absorbed and reflected by clouds and thus affect the Earth’s radiative budget (Albrecht, 1989; Lohmann and

2

Feichter, 2005; Stevens and Feingold, 2009; Twomey, 1977). Changing the Earth’s radiative budget warms

or cools the Earth’s surface and hence alters the climate; understanding droplet formation is, therefore, a 40

crucial part of understanding the global climate.

In the atmosphere, water droplets form on cloud condensation nuclei (CCN): particles suspended in the

atmosphere capable of activating as water droplets under suitably supersaturated conditions with respect to

water. At atmospherically relevant temperatures, the saturation ratios required for homogeneous nucleation of

water droplets (i.e. without a CCN) are several hundred percent; these conditions do not occur in the 45

atmosphere (Rogers and Yau, 1989) and therefore can be ignored. It is, therefore, necessary to have a good

understanding of the characteristics of all significant cloud condensation nuclei in order to understand droplet

formation.

An aerosol particle will form a water drop if it passes its critical size in a supersaturated environment with

respect to water. The Köhler equation(Köhler, 1936), represented by Eq. (1), describes the equilibrium size of 50

particles in the atmosphere by combining Raoult’s Law and the Kelvin Equation for a droplet.

Supersaturation, 𝑆, is defined as the vapor pressure of water, 𝑒, divided by the saturation vapor pressure, 𝑒𝑠.

This is equal to the water activity, 𝑎𝑤, which is the term dealing with the effects of a solute according to

Raoult’s law, multiplied by the Kelvin term shown where 𝜎𝑤 is the air/water surface tension, 𝑀𝑤 is the

molecular weight of water, 𝑅 is the universal gas constant, 𝑇 is temperature, 𝜌𝑤 is the density of water and 55

𝐷𝑝 is the particle’s diameter.

𝑆 =𝑒

𝑒𝑠= 𝑎𝑤 exp (

4𝜎𝑤𝑀𝑤

𝑅𝑇𝜌𝑤𝐷𝑝) (1)

There is no general analytical solution to the Köhler equation.

The Raoult’s law term, the first of the two terms of the right-hand side of Eq. (1), i.e. the water activity, 𝑎𝑤,

is associated with the reduction in equilibrium vapor pressure over a droplet resulting from the presence of a 60

solute. This effect is proportional to the mixing ratio of the solute in the drop (Rogers and Yau, 1989;

Seinfeld and Pandis, 2006). The Kelvin term, the second term on the right hand side of Eq. (1), i.e. the

exponential term, is associated with the air/water surface tension of the droplet. This is proportional to the

exponent of the inverse of diameter of the droplet; as a droplet shrinks, the curvature of the droplet’s surface

increases so that each molecule of water on the surface experiences a smaller attractive force from its 65

neighbouring water molecules as they are averagely fewer in number, meaning that the energy barrier for a

water molecule to leave the surface of the droplet becomes smaller with decreasing droplet size (Rogers and

Yau, 1989; Seinfeld and Pandis, 2006).

A widely used parameterization for water activity is the κ parameterization where water activity is defined as

shown in Eq. (2): 70

1

𝑎𝑤= 1 + κ

𝑉𝑠

𝑉𝑤 , (2)

where 𝑉𝑠 is the volume of the dry solute and 𝑉𝑤 is the volume of water in the particle (Petters and

Kreidenweis, 2007). A higher value of κ indicates greater hygroscopicity and therefore activation at a smaller

critical size and lower critical supersaturation.

Experimental studies conducted to investigate the hygroscopicity of aerosol particles with respect to the 75

activation of CCN into droplets have concentrated either on measuring their water uptake onto particles at

subsaturated conditions with respect to water using Hygroscopicity Tandem Differential Mobility Analysers

3

(HTDMAs) (Switlicki et al., 2008) or at supersaturated conditions using CCN counters (Nenes et al., 2001;

Roberts and Nenes, 2005) although single particle electrodynamic balance methods are also employed(Davis

et al., 1990; Pope, 2010; Pope et al., 2010). The HTDMA provides data to measure κ for a given aerosol 80

sample by probing the shape of the Köhler curve at subsaturated conditions while data on the maximum of

the Köhler curve is provided by the CCN counter also providing a value for κ through measurements of

activated fraction of particles of known size. However, studies employing both methods have found

significant disagreement between HTDMA-derived and CCN counter-derived κ values, particularly at low

supersaturations in the CCN counter. Although sometimes agreement is found, questions concerning the 85

processes used in CCN measurements place a limit on our understanding of CCN activity (Whitehead et al.,

2014).

Causes of the discrepancy between CCN counter and HTDMA measurements broadly fall into three

categories: problems with the instrumentation, problems with modeling the activation, and unaccounted-for

properties of aerosol particles. Instrumentation problems include: underestimation of particle concentration 90

with the DMPS (Differential Mobility Particle Sizer) prior to measurement with a CCN counter leading to

underestimates of CCN concentration, size selection limitations of DMPSs, and concerns over whether or

not particles reach equilibrium with respect to water between DMAs in an HTDMA (Fors et al., 2011).

Attempts to create models based on Köhler theory have struggled to find agreement with HTDMA data; an

analysis of 5 different modeling methods found that discrepancies of 15% and 6–10% were typical for critical 95

supersaturation and difference in CCN concentrations respectively (Rissler et al., 2010). Finally, there is the

possibility that the properties of the aerosol particles themselves could be the cause of the discrepancy. It has

been suggested that humic-like substances may have surface effects, reducing the surface tension term of the

Köhler equation and thus increasing droplet activation; alternatively, they may inhibit droplet activation by

acting like a surface film, slowing water diffusion and therefore particle growth (Graber and Rudich, 2005). 100

However, studies exploring the possibility of this effect have either found no improvement in agreement

(Jurányi et al., 2010) or that implausible changes to surface tension would be required (Good et al., 2010a).

The degree to which aerosol are internally mixed and the consistency of their size and variability with time

may also be important in making atmospheric CCN behavior predictable (Jurányi et al., 2010). It has been

suggested that another cause of the discrepancy between CCN counter and HTDMA measurements may be 105

semi-volatile material, such as those associated with secondary organic aerosols (SOAs), facilitating

increased apparent hygroscopicity and droplet production via co-condensation (Topping et al., 2013). It is

clear that more work is required in this area to improve our understanding of the cause of the discrepancies

between CCN counter and HTDMA data in order to accurately quantify CCN behavior in clouds (Whitehead

et al., 2014). 110

In various studies, a gap has been found to exist between the CCN counter measurements and the predicted

number of CCN to be activated based upon the conditions to which the aerosol sample was exposed in the

CCN counter. Calculating supersaturation based on the temperature profiles measured, CCN data is 50–

80% of the anticipated value (McFiggans et al., 2006). Usually, this difference between the measured and

predicted concentrations is within the margin of error, but that margin of error is very large when considering 115

applying the values derived to a global model where a difference of 10% in CCN data is very significant

(McFiggans et al., 2006). Studies conducted in locations with a significant organic component to the aerosol

4

have shown particularly poor agreement (Dusek et al., 2003; McFiggans et al., 2006; Roberts et al., 2002)

suggesting that organics may play a role in this discrepancy. It has been suggested that humic-like organic

substances may delay the formation of droplets (Bigg, 1986; Graber and Rudich, 2005). Kinetic effects may 120

also play a role in this discrepancy. The time profile of the supersaturation produced in a CCN counter differs

from clouds as in clouds there is competition for water vapor due to the growth of particles which is not

necessarily matched by increased water availability from quasi-adiabatic evacuation whereas in a CCN

counter supersaturation is held constant throughout a measurement. The residence time inside an instrument

can bias the results; ammonium nitrate has been shown to evaporate with a long residence time in a HTDMA 125

(Gysel et al., 2007) reducing the Raoult term and therefore leading to lower than anticipated growth factors,

while delays in activation potentially caused by slower water uptake as a result of less hydrophilic surface

layers may cause a bias if the residence time is too short for the particle to equilibrate (Bigg, 1986; Graber

and Rudich, 2005). Another issue is that atmospheric measurements with a CCN counter require there to be

no meaningful mixing with other air masses or loss of water through precipitative scavenging, making 130

unbiased measurements of cloud droplet concentration difficult to obtain (McFiggans et al., 2006).

One approach to studying the activation of aerosol particles into droplets has been inverse modeling to find

parameter values which minimise the discrepancy between modeled and simulated atmospheric data from a

range of environments (Partridge et al., 2011, 2012) however this falls short of using real atmospheric data

(Partridge et al., 2012) and hence it is difficult to suggest that it can be truly representative of in situ droplet 135

activation in the presence of semi-volatile material. Several studies have attempted to compare experimental

results to modeling data in order to predict behavior with varying degrees of detailed microphysical

calculations (Fountoukis and Nenes, 2005; Fountoukis et al., 2007; Hsieh et al., 2009; Lance et al., 2009;

Nenes and Seinfeld, 2003). A significant approach is based upon the work of Nenes & Seinfeld (Fountoukis

and Nenes, 2005; Fountoukis et al., 2007; Nenes and Seinfeld, 2003). Nenes & Seinfeld’s approach (Nenes 140

and Seinfeld, 2003) involves using the aerosol size distribution for each aerosol chemical composition

considered to calculate a “CCN spectrum” i.e. the number of aerosol particles which will be activated based

on the maximum supersaturation achieved for a given updraft velocity or cooling rate. The aerosol

population is then split between particles which experience more or less growth between the time they reach

critical saturation and the maximum supersaturation the droplet experiences than their diameter at their point 145

of critical saturation. This allows treatment of the particles appropriate to their growth around activation and

identifies particles which have critical diameters closest to the critical size for which kinetic effects may be

important in determining activation. This approach was later extended in order to use lognormal aerosol size

distributions (Fountoukis and Nenes, 2005), inclusion of a size dependent mass transfer coefficient

(Fountoukis and Nenes, 2005) and modifications of this to correct for the presence of large CCN (Barahona 150

et al., 2010; Morales Betancourt and Nenes, 2014) as well as constraining the parameterization by comparing

the output to data from in situ cloud studies (Fountoukis et al., 2007; Hsieh et al., 2009). Accurately

describing the effect of organic material on CCN activation and sensitivity issues relating to particles from

different size modes remain challenging for such parameterizations and a lack of comparison to cloud data

from a controlled laboratory environment may have introduced confounding factors into comparisons with 155

atmospheric data.

5

Discrepancies remain between CCN counter measurements and theoretical predictions and between HTDMA

and CCN counter data. Neither the data from CCN counter and HTDMA studies nor the theoretical work

conducted on CCN activation provides a comprehensive understanding of droplet activation, particularly in

the presence of semi-volatile material therefore there is a real gap in our understanding of the CCN activity. 160

In situ studies of droplet activation are difficult to conduct as activation is difficult to monitor: in cloud CCN

have activated into droplets, outside cloud they are aerosol. One possibility to study the formation of droplets

is to artificially generate aerosol and in particular aerosol with semi-volatile material like SOA and conduct

experiments that simulate cloud formation. There is little information from controlled cloud evacuation

experiments looking at the CCN activity of SOAs. Such conditions provide the opportunity to test whether 165

co-condensation could be observed on CCN. In this study we present the first cloud chamber evacuations on

chamber-generated SOA and compare the experimental data to model results, not including a co-

condensation treatment, but varied across all parametric and experimental uncertainties, to test the degree of

agreement between experimental and modeled data and to look for evidence of co-condensation in a chamber

environment. The parameters varied in this investigation were the number concentration of aerosol particles, 170

Np, the size distribution width parameter, σ, the modal size, Dm, initial temperature, Ti, and the hygroscopicity

parameter, κ. Under-prediction of CCN activation by the model could be indicative of co-condensation as

this process is not being modeled. We then use a representation of semi-volatile co-condensation within the

model to demonstrate that it is able to explain the observed droplet formation in the chamber.

This study aims to test our understanding of CCN activation of SOA and whether co-condensation is required 175

for chamber evacuations on SOA samples and model data to be consistent or whether agreement can be

found by only considering the variability of parameters from experimental data. Experimental data from SOA

evacuations i.e. activation data from the MICC on various SOA samples and additionally on a well

understood aerosol system (ammonium sulfate) will be compared to the results of Monte Carlo simulations of

each evacuation using the chamber version of ACPIM (Aerosol Cloud Precipitation Interaction Model) 180

without the inclusion of semi-volatile material. It is then anticipated that any discrepancy between the results

of the Monte Carlo simulation and the number concentration data from the chamber evacuations will be due

to the presence of semi-volatile materials. The inclusion of the ammonium sulfate system allows comparison

between SOA systems and a system where semi-volatiles are not relevant. Additionally, a series of tests

using the model and including semi-volatiles will be conducted varying the volatility distribution and total 185

concentration of the semi-volatile organic material to determine their effect upon droplet activation. A further

series of tests using the model will be shown in which the value of the hygroscopicity parameter κ (Petters

and Kreidenweis, 2007) will be varied outside the range of the Monte Carlo simulation in order to determine

whether changing the hygroscopicity of the particles can explain any discrepancy between the Monte Carlo

simulation results and the number concentration data from the chamber evacuations. 190

2 Methods

Material from the photo-oxidation of precursors of secondary organic aerosol were created in the Manchester

Aerosol Chamber (MAC) facility or the MAC was used as a holding container for nebulised ammonium

sulfate aerosol. Their properties including size, number concentration, hygroscopicity and chemical

composition were measured, the instrumentation employed is shown in Table 1. These particles were 195

6

transferred from the MAC to the Manchester Ice Cloud Chamber (MICC). Cloud formation was induced in a

series of quasi-adiabatic evacuations at an initial temperature of ~286 K from approximately atmospheric

pressure to approximately 700 mbars. Following each evacuation, the MICC was refilled to approximately

atmospheric pressure using clean air meaning that each evacuation took place at successively lower

concentrations of aerosol particles and semi-volatile material. A schematic diagram of the combined MAC 200

and MICC facility is shown in Fig. 1.

2.1 Aerosol chamber and instrumentation

2.1.1 The aerosol chamber and its operation

The Manchester Aerosol Chamber has a maximum volume of 18m3, consisting of two Teflon sheets at the

top and bottom and two Teflon tubes, these are sealed between three aluminum sections effectively creating a 205

Teflon bag. The central section of the bag’s frame remains stationary and is the location for ports into and out

of the chamber for the purposes of cleaning, introduction of gases to set experimental conditions (e.g. salt

aerosol, Volatile Organic Compounds, ozone, water vapor and NOx), sampling aerosol from the chamber by

instruments and transferring aerosol to the Manchester Ice Cloud Chamber (MICC).

The Teflon bag can be inflated and deflated using a 3-phase blower (See Sect. S1.1.1 for further 210

information). Volatile Organic Compounds (VOCs) are added to the Teflon bag as precursors to the

formation of SOA; in these experiments -pinene, -caryophyllene, 1,3,5 trimethylbenzene and limonene

were used, each in a separate experiment (for more details see Sect. S1.1.2). NO2 is introduced into the

chamber from a compressed gas cylinder (see Sect. S1.1.3). Ozone was also required for the photo-oxidation

experiments and was introduced to the chamber from the ozonizer (see Sect. S1.1.4). For the ammonium 215

sulfate experiment, seed aerosol particles were nebulised into the chamber (see Sect. S1.1.5).

The aerosol chamber has seven rows of 16 halogen bulbs and a 6kW Xenon arc lamp. Photochemical

nucleation and growth experiments generally use three rows of 16 halogen lamps and the arc lamp to

approximate to tropospheric illumination. As it has been found to be infeasible to produce SOA in the MAC

using 1,3,5-trimethylbenzene as a precursor under the usual UV conditions used, the UV filter for the arc 220

lamp was removed for this experiment. This substantially increases the intensity of UV radiation of shorter

wavelengths than ~350 nm above the tropospheric approximation used in the other experiments. In all the

photochemical oxidation experiments, as the aging period was relatively brief (between 1.5 and 2.5 hours –

see Table 2) and the method of production was nucleation with high mixing ratios of precursor material, the

concentration of particles produced was often much higher than required for cloud chamber evacuations, 225

necessitating dilution of the aerosol prior to transferring it so that the number concentration of aerosol

particles in the MICC for the first cloud chamber evacuation was less than 12000/cc. After production and

aging of aerosol in MAC and preparation of the MICC (see Sect. 2.2.1) the aerosol sample was transferred to

the MICC (see Sect. 2.4).

Ongoing sampling with the instrumentation employed in the SOA experiments provides size distribution and 230

concentration information (see Table 1).

A series of inflation and deflation cycles of the chamber bag following experiments were used to remove

aerosol material followed by leaving the bag with a mixing ratio of 1-2 ppm of ozone overnight. At least five

cycles of inflating and deflating the MAC’s Teflon bag were used to remove impurities. 1,3,5-

7

trimethylbenzene is known to have low reactivity to ozone and so extra cleaning was completed by exposing 235

the bag to the unfiltered arc lamp source with mixing ratios of 150-400 ppm of ozone for 30-60 minutes

following backgrounds and experiments involving 1,3,5-trimethylbenzene.

Further details of general MAC operation and the facility can be found in the supplementary material,

another publication (Alfarra et al., 2012) and references therein.

2.1.2 Aerosol chamber instrumentation 240

A variety of instrumentation is employed during the generation of aerosol particles in the chamber to monitor

the conditions in the chamber bag and the aerosol mass being produced (see Table 1). Properties monitored

include humidity, temperature, ozone mixing ratio, NOx mixing ratio, aerosol particle concentration, size,

composition, and ability to act as CCN.

Humidity and temperature in the chamber bag were monitored by an EdgeTech DewMaster dew point 245

hygrometer with the sensor measuring near the chamber wall and an in-house Sensirion sensor measuring

conditions in the center of the chamber. Ozone mixing ratio was monitored by sampling with an ozone

analyzer while NOx, NO, and NO2 concentrations were monitored by an NOx analyzer, these two instruments

shared a sample line, the inlet of this line was positioned in the center of the chamber.

A TSI 3776 butanol CPC, located in the laboratory directly above the chamber, was also used to monitor 250

aerosol particle concentration in the aerosol chamber. CPCs work by exposing the aerosol sample they are

measuring to supersaturated conditions with respect to either butanol or water in order to grow any particles

present, these particles are then counted using an optical scattering method.

A DMPS (sizing particles between approximately 30–450 nm) was used to measure the number and size

distribution of the particles during the growth and aging of the SOA particles in MAC and to monitor the 255

number concentration and size distribution of ammonium sulfate particles in MAC. The DMPS sizes

particles using a DMA (Differential Mobility Analyser) which sizes according to electrical mobility size by

ionizing the particles being sampled and exposing them to a potential difference between two cylindrical

plates to accelerate the charged particles towards the central cylinder. A small exit slit in the central cylinder

allows size-selected particles to be extracted. The size of these particles depends upon the potential difference 260

applied across the two cylinders. The particles can then be counted using a CPC, in this case, a 3786 TSI

Water CPC was employed. In the Differential Mobility Particle Size, the potential difference is changed in

discrete steps providing sizing information across a range of sizes at a time resolution of ~10 minutes.

A Vienna style DMA (Williams et al., 2007; Winklmayr et al., 1991) was used to size particles which were

then measured by Condensation Particle Counter (CPC) which creates a large supersaturation meant to 265

activate all particles into drops to be counted – combining with the DMA to act as a DMPS and a continuous

flow Cloud Condensation Nuclei Counter (CCNc) (Good et al., 2010b; Lance et al., 2006; Roberts and

Nenes, 2005) which exposes aerosol to a controlled supersaturation to measure their activity as cloud

condensation nuclei (CCN). This experimental setup and calibration procedure have been described in the

literature (Good et al., 2010a) and further information on the calibration can be found in the supplementary 270

material (see Sect. S1.2.1) The DMA operated in the size range of approximately 20–450 nm with a scan

time of approximately 7.5 minutes run at 10 minute intervals. After the differential mobility analyzer (similar

to the DMA used in the DMPS described above), the flow was diluted and split between a butanol CPC and

8

either one or two CCN counters depending upon the experiment. The flow was diluted after the DMA and the

experimental set up was such that dilution was held constant in all experiments. After the DMPS scan was 275

completed, the supersaturation in the CCN counter was set to a new value. The 2.5 minute interval between

DMPS scans enabled the CCN counter to stabilize at its new supersaturation. By comparing the number of

particles above 2 microns after being exposed to supersaturated conditions detected by the CCN counter to

the total count provided by the CPC running parallel to it, the activated fraction of aerosol for that size and

supersaturation is obtained. 280

The raw data from both counters was inverted to correct for charging efficiency and multiple charged

particles. This yielded size spectra of all particles (from the CPC) and those of particles activated under a

controlled supersaturation (from the CCNc). The ratio of these spectra is the activated fraction as a function

of the dry particle size which was fitted to a sigmoidal function. The turning point (i.e. the point at which

there is 50% activation) provides the activation size (D50) and the set supersaturation is the critical 285

supersaturation (SScrit). The fitting error for the turning point of the sigmoidal function was used as the

uncertainty of the D50 value. A look-up table was created to determine the hygroscopicity parameter κ using

Eq. 6 from Petters and Kreidenweis work (Petters and Kreidenweis, 2007) for each SScrit and D50 pair. In the

same way, the minimum and maximum values for κ, κmin, and κmax were derived from the corresponding

SScrit/D50max and SScrit/D50min pairs respectively. This was used as uncertainty in κ as a direct propagation of 290

uncertainties is not possible.

An Aerodyne High Resolution Aerosol Mass Spectrometer (AMS) (Canagaratna et al., 2007; Decarlo et al.,

2006) was employed in these experiments in order to gain information regarding the chemical composition of

the SOA material. The fraction of material with m/z = 44 (indicative of the CO2+ ion) is known to be a proxy

for the degree of oxygenation of material in organic aerosol particles thus AMS data can be used as a proxy 295

to measure the degree of oxygenation of the secondary organic material which is thought to increase their

hygroscopicity and therefore their CCN activity (Jimenez et al., 2009). Data was analyzed using the in-house

software as found in previous studies (Alfarra et al., 2012).

2.2 Cloud chamber and instrumentation

2.2.1 Cloud chamber and basic instrumentation 300

The Manchester Ice Cloud Chamber (MICC) is a 10m tall, approximately cylindrical stainless steel chamber

with a diameter of 1m. Ports are positioned throughout the chamber allowing access for measurements to be

made. The ports at the base of the chamber were used for the pressure, cloud and aerosol probes in these

experiments (see Fig. 1). Two Varian rotary vacuum pumps positioned at the top of the MICC allow the

pressure in the chamber to be reduced by removing material from the chamber; this can be refilled with clean 305

air using the MAC blower system (see Sect. 2.2.2).

Pressure is measured using an LEX-1 piezoresistive manometer located at one of the lower ports of the

chamber. Temperature is monitored using eight K-type thermocouples located at different points along the

height of the chamber, a temperature gradient across the chamber of ~1.5 K (coldest at the bottom, warmest

at the top) is generally observed making it necessary to measure temperature throughout the chamber’s height 310

in order to establish conditions throughout the chamber. Both pressure and temperature measurements were

recorded throughout each experimental evacuation and refill (see Sect. 2.2.2–2.2.4).

9

A CR4 Dew Point Hygrometer was used to measure the relative humidity of the chamber. However, as this

instrument is sensitive to pressure changes it was only employed between evacuations when the chamber was

at approximately atmospheric pressure. Further details can be found in the supplementary material (see Sect. 315

S1.2.2).

2.2.2 Cloud chamber preparation

The MICC was prepared for cloud evacuations by sealing all the ports and reducing the pressure of the

chamber to 200 mbars using Varian rotary vacuum pumps before refilling it to atmospheric pressure with

clean air from the MAC’s blower system via the connecting stainless steel tube approximately 20m in length 320

which leads from the inflation/deflation line in the MAC system to a port near the top of the cloud chamber.

This process was repeated three times, each time diluting the initial concentration of particles in the chamber

and thus reducing the background particle count which was recorded after the MICC was refilled using a

CPC (see Sect. 2.2.5 and Table 3).

2.2.3 Transfer of aerosol from the aerosol chamber to the cloud chamber 325

Once the aerosol sample had been generated in the MAC and the MICC had been prepared, the MICC

underwent a further evacuation to 200 mbars with measurements taken by the WELAS (see Sect. 2.2.6 and

Table 3) between atmospheric pressure and 700 mbars at which point the WELAS was isolated from the

chamber (this was taken as a background measurement). Once the pressure reached 200 mbars, the pumps

were isolated from the chamber then the valves in the MAC chamber were configured so as to expose the 330

MAC to the MICC and a part of the aerosol mass inside the MAC was drawn through the connecting

stainless steel tube into the MICC until the MAC had shrunk sufficiently for the MICC to have returned to

atmospheric pressure.

2.2.4 Cloud generation in MICC

In these experiments, partial activation of the aerosol particles into cloud droplets was required in order to 335

gain information regarding their CCN activation properties; as such it was necessary to have some control

over the rate of reduction in pressure during evacuations. This was achieved using either one or two Varian

rotary vacuum pumps and through the use of critical orifices of varying diameter (4 mm, 5.7 mm and 7 mm)

installed in the line between the chamber and the vacuum pump. During fitting and removal of these critical

orifices, the blower system was used to create a small overpressure inside the MICC to prevent 340

contamination of the aerosol sample with lab air.

Upon reaching atmospheric pressure, measurements were taken with the chilled mirror hygrometer, CPC, and

SMPS (see Sect. 2.2.5 and Table 3) to measure the MICC’s dew point and the number and size of the aerosol

particles. Once the pump orifices had been set, the MAC and MICC were isolated from one another and the

aerosol sample in MICC underwent a quasi-adiabatic pressure and the growth of droplets into a cloud was 345

observed with the WELAS (see Sect. 2.2.6 and Table 3) to approximately 700 mbars and then refilled to

atmospheric pressure with clean air from the MAC lab blower system. In the case of the ammonium sulfate

experiment, air was drawn from the MAC instead of the blower system, allowing control of the total

10

concentration of ammonium sulfate aerosol. MICC’s temperature and pressure were measured throughout

these experiments using the probes mentioned in Sect. 2.2.1. 350

2.2.5 Aerosol particles size and number

The number concentration and size distribution of particles in the MICC were measured at atmospheric

pressure using a TSI 3010 condensation particle counter (CPC), similar in mode of operation to the butanol

CPC used in the MAC (see Sect. 2.1.2). The size distribution was measured with a TSI Scanning Mobility

Particle Sizer (SMPS). This employs a DMA in a similar way to that used for a DMPS (see Sect. 2.1.2), 355

however, in an SMPS the voltage is not held constant to measure a single size before undergoing a step

change to measure the next size, instead the voltage is continuously varied during a scan from the smallest to

the largest size. This improves the time resolution of the data, requiring approximately 3 minutes to complete

a scan. In general, two scans were completed between each cloud evacuation. The CPC and SMPS used can

only operate near atmospheric pressure so measurements of aerosol size and number were only made after 360

the MICC had been refilled and thus was at approximately atmospheric pressure. To further facilitate this, the

blower system’s clean air supply remained on during these measurements to ensure that sampling from the

MICC did not reduce the pressure.

Concentration data measured using the CPC was taken to be representative of the concentration of aerosol

particles throughout the chamber. SMPS measurements underwent diffusion and multi-charge corrections 365

using the Aerosol Instrument Management, TSI software and were used to measure total concentration and

size distribution.

2.2.6 Cloud droplets size and number

The WELAS (WhitE Light Aerosol Spectrometer) measures the size distribution and number concentration

of droplets present. Details on the WELAS can be found in the literature (see Table 3). The WELAS uses a 370

forward scattering white light technique to measure particles with diameters between 0.8 and 84 µm; its

precision is determined by Poisson counting statistics that depend upon the concentration being measured.

2.2.7 Sampling and measurement strategy and techniques

The WELAS sampled for 1 second followed by a 5 second gap in measurements. This was due to the

limitations of the software being used to record the WELAS data. The WELAS recorded throughout each 375

experimental evacuation as did the pressure and temperature probes. As SMPS, CPC and the hygrometer

were pressure sensitive, they were only used at approximately atmospheric pressure, i.e. when the chamber

had been refilled following an evacuation either from the MAC’s Teflon bag (in the case of the initial

transfer) or its blower system (in the case of subsequent evacuations).

2.3 Model 380

The Aerosol Cloud Precipitation Interaction Model, ACPIM, (Connolly, 2009; Connolly et al., 2012;

Dearden, 2009; Dearden et al., 2011; Topping et al., 2013) was employed to create simulations of the

conditions in the cloud chamber. These can then be compared to the results from the cloud chamber to test

11

whether or not the model and therefore our theoretical understanding of cloud formation on SOA agrees with

chamber data. 385

2.3.1 Model structure

ACPIM can model the formation, growth and dissipation of cloud from aerosol by solving a series of coupled

ordinary differential equations solving for conservation of water vapor mass, conservation of mass of semi-

volatile organic material (when the presence of semi-volatile organic material was being modeled), the

hydrostatic equation, conservation of energy in the air parcel, change of mass in each size bin, and rate of 390

change of parcel “height” (Topping et al., 2013) – in the case of a chamber study this physically refers to the

effective updraft velocity caused by the reduction in pressure during an evacuation. These equations are

solved for each time step with outputs every 10 seconds enabling a description of the cloud’s life to be

created.

In cases where semi-volatile organic material was modeled, as the size of the particles is of a similar 395

magnitude to the mean free path of the organics, diffusivity was treated in accordance with transition regime

condensation theory.

2.3.2 Model methods, configuration, and inputs

ACPIM was run using a full moving bin method with 60 size sections. This is the least numerically diffusive

but is unable to simulate collision-coalescence effects, though these are negligible in the MICC. In model 400

simulations considering organic material, 10 volatility bins were used. SMPS data was fitted to a lognormal

distribution. This enabled calculations of parameters pertaining to the distribution i.e. the distribution width

parameter, total concentration and modal size for use in ACPIM to be obtained. These were assigned random

values within the variability limits of the measurement. κ was likewise varied within the variability limits

(see Table 1) of the value obtained from CCNc data except for experiments where the aerosol particles were 405

ammonium sulfate which has a well-known value for κ. Initial temperature was varied between a maximum

of the warmest temperature recorded by any of the temperature probes at the time of the beginning of the

evacuation plus 1σ of the temperature probe measurement to a minimum of the coldest temperature recorded

by any of the temperature probes at the time of the beginning of the evacuation minus 1σ of the temperature

probe measurement of the range of temperatures measured at the start of the evacuation by the MICC’s eight 410

temperature sensors plus one error bar in the temperature probe measurement in both the cooler and warmer

end of the variability range. The pressure profile used in the model was fitted to the temperature profile of the

chamber experiment fitted to an exponential decay curve. The measured value was used for the initial

pressure; as the range is very small, this was not thought to have any impact upon the cloud. Initial RH was

calculated based on the assumption that it passes 100 % during the time interval at which the WELAS begins 415

to observe droplets and that water vapor is conserved until this point. In this case, RH is a function of

temperature and hence the initial RH can be calculated from the initial temperature and the temperature at the

point of first droplet activation. The range of variables used for the Monte Carlo simulation is shown in Table

4.

12

2.3.3 Model simulations 420

Base case simulations were generated by conducting model runs using the midpoints of the range of values

for all the parameters being varied (i.e. simulations using the expectation value for each parameter) for each

experimental evacuation. A Monte Carlo simulation of each experimental evacuation was conducted. For

each experimental evacuation 5000 model runs were conducted across the parameter space (see Table 4).

Further tests were conducted based on the results from the Monte Carlo simulation: a series of tests were 425

conducted varying the value of the κ parameter outside the expected variability limits and a series of model

simulations with the inclusion of semi-volatile components were conducted. In order to explore the potential

impact of the co-condensation of semi-volatile material on activating droplets, it is necessary to define the

volatility distribution of components. The distribution of oxidation products of the precursors injected into

MAC at the point of transfer to MICC is highly uncertain. A coupled model of photochemical oxidation and 430

microphysics of the SOA formation including treatment of the partitioning to the Teflon walls would be

necessary to determine the mixing ratios of all partitioning species. The uncertainties in such processes are

too large to make a prediction of the volatility distribution transferred to MICC. Instead, an uncertainty

analysis has been conducted, initially using a volatility distribution shape following that determined in the

field measurements in Mexico City of Cappa & Jimenez (Cappa and Jimenez, 2010) but including four other 435

distributions. For each distribution shape the total concentration of semi-volatile material was varied between

1.885×10-10 and 1.885×10-7 g m-3; altering the total concentration found in Cappa & Jimenez’s work by

orders of magnitude. The molecular weight used as an estimate in all model simulations for SOA was 132.14

g mol-1; leading to a concentration of between 0.03424 pptv and 34.24 pptv. These simulations were then

repeated using different volatility bin distributions to test the sensitivity of droplet activation to volatility. 440

2.4 Data analysis

Particles measured by the WELAS were assumed to have been activated as CCN. Over the period used as a

measurement of activation, the WELAS distributions are closed distributions, measuring 0 counts in the

lowest size bins. Droplet number concentration data obtained using the WELAS was corrected for

temperature and pressure to conditions at standard ambient temperature and pressure. Activation measured 445

was determined by finding the initial peak in the data set, this is taken as the point before the first reduction

in the concentration after the onset of cloud during an evacuation, and the nine subsequent points; meaning

that the average is based on sampling 10 times for 1 second over a period of 1 minute. Simulated number

concentrations from ACPIM were very stable after cloud activation until dissipation, suggesting that it should

be possible to average droplet number concentration without having a substantial impact on the ratio of 450

chamber number concentration to model number concentration obtained. The modeled maximum droplet

number concentration was taken as the peak concentration from the model (based on data with a 10 second

time resolution). As number concentration was very stable in the model this reflects the droplet number

concentration through much of the lifetime of the cloud.

Ratios of number concentration obtained from the WELAS, corrected to standard ambient temperature and 455

pressure, to model outputs for the base case simulations (see Table 4) and the range of model outputs from

Monte Carlo simulation were calculated. Ratios were also calculated between corrected WELAS data and the

model simulations conducted using the base case but with additional variation in the κ parameter in order to

13

consider the sensitivity of the number concentration to κ and the change in κ required to get agreement

between the corrected WELAS number concentration data and the total droplet concentration from the model 460

simulation. Likewise, model simulations were conducted using the base case values with a semi-volatile

component included to investigate the quantity of semi-volatile material necessary to get agreement between

the corrected WELAS number concentration data and number concentration from modeled data. This was

calculated as a ratio.

3 Results 465

3.1 MAC data

Secondary organic aerosol, including particles, were produced by photo-oxidation in the MAC from α-

pinene, β-caryophyllene, limonene and 1,3,5-trimethylbenzene and ammonium sulfate particles were

nebulised into the MAC. Hygroscopicity of the CCN is treated in the model using the single parameter κ

(Petters and Kreidenweis, 2007). Data on the hygroscopicity of the SOA particles was collected using the 470

CCN counter. Figure 2 shows the size diameter and the κ value calculated from this, at which half of the

particles activated. The values used in the ACPIM simulations for the hygroscopicity variable κ were taken

as an interpolation between the data point before and the data point after the start of the transfer of aerosol

between the chambers.

3.2 MICC data 475

The material generated was successfully transferred to the MICC. After transfer, evacuations were conducted

on the aerosol sample, an example is shown in Fig. 3. Data was collected regarding the temperature, pressure

and droplet number concentration throughout the evacuation. The total number concentration shown in Fig.

3(c) is from measurements made by the WELAS corrected for temperature and pressure to standard ambient

temperature and pressure. This data has been compared to model outputs in order to test our understanding of 480

cloud activation on SOA (see Fig. 7–10). Two evacuations were conducted on the 07/11/2013 α-pinene

precursor SOA after which the experiment was curtailed by contamination of the sample. Three evacuations

were conducted on the limonene precursor SOA and 14/11/2013 α-pinene precursor SOA. Four evacuations

were conducted on the 1,3,5-trimethylbenzene precursor SOA and β-caryophyllene precursor SOA. Six

evacuations were conducted on the ammonium sulfate sample, however, in three of these evacuations, the 485

first, third and fourth, the WELAS recording failed and so these results are not presented here.

3.3 Model inputs

In addition to requiring information regarding the hygroscopicity of aerosol particles (as described in Sect.

3.1), it is necessary to describe the temperature, pressure and aerosol population’s size distribution and

number concentration inside the chamber. An SMPS was employed in the MICC in providing data on the 490

size distribution of the aerosol particles as shown in Fig. 4. Temperature and pressure data were recorded

during each cloud evacuation (e.g. see Fig. 3), fits to the temperature are shown in Fig. 5 and were used to fit

the pressure data. A full table of the model inputs is shown in the methods section (see Table 4) and a

comparison of κ value ranges (the single parameter used as a measure of hygroscopicity and therefore the key

14

activation parameter) used in this study with values found in the literature can be found in the supplementary 495

material (see Table S1).

The SMPS distributions shown (Fig. 4) differ between cloud evacuations using the same SOA sample; a

reduction in the concentration of aerosol particles is observed as the initial sample is diluted by the air from

the MAC filtration system (e.g. the 1,3,5-trimethylbenzene sample – see Fig. 4 (i)–(l)). No significant

differences are observed in the size distribution between cloud evacuations on the same sample, implying that 500

material which partitions to the aerosol phase during expansions, both water and organic matter, evaporates.

Differences in size distribution can also be observed between the different samples. The ammonium sulfate

sample (Fig. 4 (f)–(h)) was prepared by nebulising aerosol particles into the MAC, this resulted in a broader,

less smooth aerosol size distribution than for the SOA particles which were generated by photochemical

nucleation. In the example of a photonucleation experiment, in addition to loss of particles by coagulation 505

and wall loss, the particles generated grow continuously as precursor compounds are oxidised into less

volatile ones with the amount of material condensing related to the volume of the existing particles, in the

case of nebulised material however the smoothing effect of condensing material is absent which leads to a

more uneven distribution as shown in Fig. 4(f) which shows the distribution after 1 cloud expansion has been

performed on the ammonium sulfate sample. Fig. (g)-(h) show the ammonium sulfate sample after 4 and 5 510

evacuations have been performed on the sample including one to 200 mbar in order to reduce the

concentration of the sample. These measurements show a less well-defined distribution of material. One

contributing factor to this is likely to be the reduction in concentration (approximately 1 order of magnitude)

between 4(f) and 4(g)-(h). At lower concentrations, the total number of aerosol particles sampled is reduced

and so sampling errors are increased. Other differences in the sample may be due to cloud processing and 515

further coagulation of particles (4(g) and 4(h) were recorded more than 2 hours after 4(f)). The total

concentration of aerosol particles present and their size varied considerably between samples. Preliminary

attempts to photonucleate particles using 1,3,5-trimethylbenzene precursor were unsuccessful under the usual

conditions of illumination employed in the MAC so the UV filter for the arc lamp was removed in order to

expose the precursor material to greater amounts of short wavelength UV in order to generate aerosol 520

particles. Nevertheless, this sample produced less material in the aerosol phase. The modal size of particles

generated was smaller in the 1,3,5-trimethylbenzene experiment (~75 nm) than in other photonucleation

experiments except for the β-caryophyllene experiment (which produced a higher concentration of particles)

and the number concentration generated in the MAC was also lower than that observed in other experiments.

The α-pinene experiment on 14/11/13 (Fig. 4 (i)–(l)) has a much larger modal size than observed in the other 525

photonucleation experiments. In this experiment, the number of particles produced in the nucleation event

was much lower than observed in the α-pinene experiment on 07/11/13 (Fig. 4 (a)–(b)). The reason for this

remains unclear.

Ensuring good agreement between the temperature profile observed during cloud expansions and those

modeled is necessary in order to accurately calculate the RH and hence the activation in the MICC. In all 530

cases, a good match was achieved between the measured average temperature in the MICC and the

temperature in the model (see Fig. 5). It should be noted that this fit is to the average temperature in the

MICC. A temperature gradient was present across the MICC with the top of the chamber approximately 1.5

K warmer than the bottom. In all cases, experimental data is only shown for the period over which the

15

evacuation took place. The pressure fits shown in Fig. 5 are based on a quasi-adiabatic expansion as 535

calculated in the model. The good quality fit achieved indicates that temperature and pressure inputs into the

model are a good match for the chamber experiment.

3.4 Comparing model and experimental data

Data from the chamber experiments was compared to a variety of model simulations. The base case model

simulation (i.e. model simulation using the expectation value for all variables) for each evacuation, shown 540

alongside the total concentration from the WELAS (Fig. 6), was compared to an averaged period of 60

seconds of number concentration data from the WELAS starting from the peak concentration (Fig. 7) as

discussed in Sect. 2.6. Results from a Monte Carlo simulation using random values in the intervals indicated

for the variables being altered (see Table 4) were also compared to the average WELAS number

concentration (Fig. 8). In an effort to explore the changes required to get agreement between model and 545

experimental data, the effect of altering the value of κ (Fig. 9) and of the inclusion of semi-volatiles (Fig. 10)

to the base case scenario were investigated.

The droplet number concentrations calculated by the model were steady after the onset of cloud up until the

cloud started to evaporate whereas the WELAS measurements from the MICC indicate that after an initial

peak, the droplet concentration decreases throughout the cloud’s lifetime suggesting that loss of water vapor 550

to the MICC’s walls is not well captured by ACPIM. Due to the large amount of variability between data

points obtained by the WELAS (see Fig. 6), an average was taken of 10 measurements starting from the

initial peak concentration (i.e. the measurement directly before the first reduction in droplet number

concentration was observed in the WELAS). These averages were taken as the peak concentration observed

by the WELAS and were used in the ratios of WELAS and model data (see Fig. 7–10). 555

The ratios between the WELAS peak number concentration and the peak number concentrations from the

base case ACPIM results (see Fig. 7) are always in agreement with and broadly near the center of the range

of ratios from the Monte Carlo simulation (see Fig. 8). The results from the Monte Carlo simulation show

agreement between the model and the WELAS (assuming ±10 % variability in the peak WELAS

concentration) for all experiments except in the cases of the first evacuation with the α-pinene (7/11/13) and 560

all three β-caryophyllene evacuations where the model under-predicts the droplet concentration and the

second limonene evacuation where the model over-predicts the droplet concentration. The four expansions

where the model under-predicts the droplet number concentration also show the greatest underestimation by

the model when using the base case simulations but varying κ (see Fig. 9) up to 250 % of κ’s value calculated

from measurements using the CCN counter and not reaching agreement between model and WELAS results 565

at any value of κ in some cases. The limonene experiment shows the greatest overestimation by the model in

the case of the κ varying simulations with a κ of approximately 55 % the original value required for

agreement in the most extreme case.

Results from simulations using varying quantities of semi-volatile organic material proportioned into

volatility bins according to the findings of Cappa and Jimenez (Cappa and Jimenez, 2010) show a large range 570

of WELAS/Model droplet number concentration ratios encompassing agreement between model and

WELAS for the evacuations where the model under-predicts the number concentration in the base case and

Monte Carlo simulations (see Fig. 10). But simulations using the range of volatility bin distributions (see Fig.

16

11) produce very similar results (see supplementary material – Fig. S1–S4). The evacuations using the two

different α-pinene experiments, show very different responses to the addition of semi-volatile material: both 575

evacuations conducted with the α-pinene sample from 07/11/13 show substantial changes in number

concentration with the addition of semi-volatile material, however, the difference is much smaller for the

evacuations conducted with the α-pinene from 14/11/13 (see Fig. 10).

4 Discussion

As the WELAS measures aerosol particles, this raises the concern of whether or not the particles measured 580

by the WELAS are in fact activated droplets or merely swollen aerosol. However, for aerosol particles of the

sizes being considered here, the lower bound of the size detection limit for the WELAS is thought to be

above the critical size for the particles and hence they can be considered droplets. The distribution of material

in the WELAS is a closed distribution indicating that we are well capturing the activated droplet mode.

No substantial differences were observed between the volatility bin distributions used in this study (see Fig. 585

10 and Fig. S1–S4) with the number concentrations obtained from the model almost solely dependent upon

the total semi-volatile concentration rather than the volatility bin denoted by differing values of log10C* from

-6 to 3 (i.e. a ratio of vapor: aerosol under dry conditions of between 1:100000 and 1000:1). This suggests

that as relative humidity is increased, the amount of semi-volatile material in the condensed phase also

increases such that by the time the aerosol particle enters a supersaturated regime with respect to water, the 590

semi-volatile material, whatever its initial volatility, is dominated by the condensed phase meaning that the

increase in size of the distribution of aerosol particles is dependent upon the amount of semi-volatile material

and not its degree of semi-volatility. From this it can be surmised that the exact volatility distribution is not

critical to determining activation and therefore the use of the Cappa and Jimenez (Cappa and Jimenez, 2010)

distribution is a sufficient approximation for the purposes of this study. In itself insensitivity to volatility 595

distribution is an important finding supporting the work of Topping et al. (Topping et al., 2013) which

modeled the growth of aerosol particles as material condensed onto them, the proportion of semi-volatile

material in the condensed aerosol phase increases with increasing RH and decreases as volatility, expressed

as logC*, increases. Even for material modeled as logC*=3 (i.e. the most volatile material included), the

condensed phase dominates the partition between the aerosol and gas phases at RH=99.999 %. As in these 600

experiments droplets are activated, the RH must exceed 100 % inside the chamber. The droplets are therefore

subjected to higher relative humidity than in this previous work where the condensed phase dominated, it is

reasonable to expect therefore that the partition between the condensed aerosol and vapor phase would be

dominated by the condensed aerosol phase in all evacuations.

In most cases, the range of values for the ratio between number concentration in MICC measured by the 605

WELAS and the modeled data is in agreement (see Fig. 8). The ammonium sulfate system has been

extensively studied, the value of κ for ammonium sulfate is well known and it is not anticipated that there

would be any semi-volatile material present in this sample. The evacuations completed with the ammonium

sulfate aerosol sample show consistency in number concentration between WELAS data, with an estimated

uncertainty of ±10 %, and model data, indicating that the model is capturing non-semi-volatile behavior well. 610

It should be noted that ammonium sulfate particles, unlike the other aerosol particles used in these

experiments were not photo-nucleated but nebulised. One effect of this is that the size distribution was much

17

less similar to a lognormal distribution than the photo-nucleated populations. This means that the fits to the

ammonium sulfate experiment were not as close as in other experiments and therefore were less well treated

by the model. This may have introduced an extra degree of error in the result from the model for each 615

evacuation with this sample. However, this is not required to explain the observations.

The largest discrepancy between the model and chamber data is the first evacuation of the β-caryophyllene

experiment where the model under-predicts the droplet number concentration observed by the WELAS in the

chamber. In the subsequent evacuations with this aerosol sample, the range of the chamber/ model number

concentrations draws closer to agreement (Fig. 10) but in no evacuation are the results from the β-620

caryophyllene MICC experiment consistent with the model results. This discrepancy between experimental

evacuations and model results could be explained by the presence of semi-volatile material. As the

concentration of semi-volatile material will be highest in the first evacuation, because it has not been diluted

by evacuations and refills and the least amount of semi-volatile material has been lost to the chamber walls,

the impact of the semi-volatile material will be greatest in the first evacuation and reduced in each 625

subsequent evacuation. Semi-volatile material is not included in the Monte Carlo simulation so the

discrepancy between the Monte Carlo simulation and the WELAS data in the β-caryophyllene experiment is

explicable as an effect of the presence of semi-volatiles. Figure 10 shows base case simulations with the

inclusion of varying amounts of semi-volatile material for various volatility distributions (as shown in Fig.

11). In the β-caryophyllene experiment, consistency between the model and WELAS number concentration 630

data is achieved with between 1.885×10-8 g m-3 and 1.885×10-9 g m-3 for the first evacuation and 1.885×10-9 g

m-3 and 1.885×10-10 g m-3 for the subsequent two evacuations.

Another possible explanation for the discrepancy observed in the β-caryophyllene experiment was that the

values assigned to κ for the Monte Carlo simulation were incorrect as measurements of κ have been shown to

differ between instruments and our understanding of the formation of droplets in clouds is incomplete 635

(Whitehead et al., 2014). A set of base case simulations but using different values for κ were compared to

the concentrations measured by the WELAS (Fig. 9). However, these show that a substantial difference in the

value of κ would be required (560%, 400% and 270% of the measurement value for the first, second and

third β-caryophyllene evacuation respectively) in order to achieve the change in activation required for the

model to match the WELAS data, assuming an error of 10 % in WELAS number concentration. 640

The model and WELAS droplet concentration ratios from evacuations conducted on the sample from the 7 th

November on the α-pinene sample appear to show a similar pattern to the β-caryophyllene experiment

although only for the first cloud evacuation is the range of ratios between the WELAS measured chamber

number concentration and the model number concentrations from the Monte Carlo simulation is inconsistent.

This effect again could be explained by the presence of semi-volatile material. The α-pinene experiment on 645

the 14th November exhibits different behavior with the range of chamber data/model suggesting a consistency

(Fig. 7 and 8). The difference between these two values is thought to be due to the difference in modal

diameter of the aerosol particles (~90 nm on the 7th November and ~140 nm on the 13th of November). At

larger sizes, it is thought that co-condensation would have a smaller impact on droplet activation as fewer

particles will cross the critical size threshold for activation when the semi-volatile organic material 650

condenses: both because more particles will already be past the critical size threshold and because the size

difference induced by the condensation of a similar quantity of semi-volatile material will have a smaller

18

effect on the radius of the aerosol particles as volume is proportional the cube of the radius. The idea of a

reduced semi-volatile effect at larger sizes is supported by the base case simulations with the addition of

varying quantities of semi-volatile material (Fig. 10) where it is clear that the increase in the number of 655

particles activated due to the presence of semi-volatile material is much smaller for the α-pinene experiment

on the 14th than on the 7th. This size dependency can also be observed in the 1,3,5-trimethylbenzene

experiment where semi-volatile material has little impact on activation and the range of values from the

Monte Carlo simulation are broadly consistent with the WELAS data from the MICC.

In the limonene experiment, activation in the chamber evacuations is lower than in the model (Fig. 7 and 8). 660

An explanation for this finding could be that there is a systematic overestimation of κ from the CCN counter

measurements as a result of the reduced particle concentration following the DMA sizing stage meaning that

more semi-volatile material and fewer particles are present in the CCN counter leading to an instrument

artefact whereby the particles being measured have artificially increased sizes when entering the CCN

counter and hence are nearer their critical size and therefore more easily activated into droplets. In the case of 665

the limonene experiment, this explanation would require a significant change to the value of κ (to between 55

% and 90 % of the original value assuming a WELAS number concentration uncertainty of 10 %, see Fig. 9)

but a much smaller difference than that required to explain the observations made in the β-caryophyllene

experiment (560 % in the case of the first β-caryophyllene evacuation). This systematic over-estimation of κ

could be occurring in all SOA experiments in this study in which case co-condensation may be balancing this 670

effect in some experiments (e.g. 1,3,5-trimethylbenzene). Another possibility is that it may be the case that

limonene-derived semi-volatiles are less hydrophilic than expected because this material is volatilized when

the chamber is refilled.

In the 1,3,5-trimethylbenzene experiment, we see that there is agreement in all cases between the range of

number concentrations Monte Carlo simulation which does not include semi-volatile material. The 675

competing factors which can be used to explain the number concentration discrepancies observed in the

limonene and β-caryophyllene may also be occurring in evacuations conducted with the 1,3,5-

trimethylbenzene sample in such a way that the results show agreement.

The strength of the conclusions from this study could have been enhanced by the use of additional cloud

droplet measuring instrumentation, the WELAS primarily being intended for the measurement of aerosol. It 680

had been intended that a Cloud Droplet Probe, DMT (Crosier et al., 2011; Rosenfeld et al., 2008) should have

been included in this study, however, as a result of a mass flow control configuration problem during these

experiments, CDP concentrations are thought to be potentially quantitatively erroneous for all experiments so

only WELAS data has been used. The inclusion of this or other droplet measuring instrumentation should be

pursued in future chamber activation on SOA. Additionally, ACPIM should have been run in a manner which 685

produces size data rather than showing the bin sizes from these experiments and providing an activated

droplet number as this would further confirm that the WELAS and model were measuring the activated

droplets.

5. Conclusion

Transfers of SOA (derived from α-pinene, β-caryophyllene, limonene and 1,3,5-trimethylbenzene) and 690

ammonium sulfate photo-oxidised and nebulised into the MAC respectively, to the MICC were conducted

19

and a series of evacuations to generate clouds were conducted on each sample. These cloud evacuations were

modeled using ACPIM. A Monte Carlo simulation of each evacuation was conducted and additional

simulations using the expectation values for each of the variables, simulations using the expectation values of

the variables but varying the hygroscopicity parameter κ outside the expected range and simulations using the 695

expectation values for the variables but introducing semi-volatile material with various distributions and

varying the total concentration of semi-volatile material between 1.885×10-7 g m-3 and 1.885×10-10 g m-3

(equating to between 34.24 pptv and 0.0342 pptv). The ammonium sulfate evacuation chamber number

concentration data and number concentrations from the Monte Carlo simulation were consistent. In the

evacuations using β-caryophyllene derived SOA showed the Monte Carlo simulation under-predicted the 700

droplet number concentration measured in the chamber. The first evacuation on the sample had the greatest

under-prediction which reduced with each subsequent evacuation. Varying the value of κ was only able to

render the chamber droplet concentration measurements and model droplet concentrations consistent by

changing the value of κ by a factor of 2.7 to 5.6. This is thought to be unrealistic. However, concentrations of

semi-volatile material between 1.885×10-8 g m-3 and 1.885×10-10 g m-3 were able to produce agreement, 705

suggesting that co-condensation of semi-volatile material is being observed. A similar pattern to the β-

caryophyllene experiment, but less pronounced, was observed in the α-pinene experiment conducted on

07/11/13. This is not observed in the α-pinene experiment conducted on 14/11/13. The difference between

these is thought to be due to the insensitivity of the droplet activation of large aerosol particles to the

presence of semi-volatile material. The Monte Carlo simulation overestimates the total number concentration 710

for the limonene experiment, this is thought to be due to problems in measuring κ and requires smaller

changes to the value of κ in order for the model and chamber measured number concentrations to achieve

agreement. This work indicates that co-condensation is occurring under controlled chamber conditions, the

first time such an observation has been made and demonstrating the potential importance of co-condensation

to understanding and modeling cloud activation. 715

Author contribution

William Hesson was responsible for planning and leading the experiments completed, data analysis of MICC

data, conducting the modeling runs and analyzing the results, completing the manuscript and creating the

figures. The contribution of Angela Buchholz was to assist in the experiments by operating the CCN counter,

development of software for data analysis of CCN counter and DMPS data and conducting the analysis. Paul 720

Connolly was responsible for the development of ACPIM and developed some of the in-house software to

extract data from the MICC instrumentation. Gordon McFiggans assisted in editing the paper and acted in a

lead supervisory role for the project.

Competing interests 725

The authors declare that they have no conflict of interest.

20

Acknowledgements

This research was funded by NERC as part of the ACID-PRUF project.

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900

25

Table 1: Instrumentation used for measurements in the aerosol chamber. The DMPS used in these experiments

(Alfarra et al., 2012; Williams, McFiggans, & Gallagher, 2007) follows general principles for a differential

mobility particle sizer (Kready, Quant, & Sem, 1983), employing a differential mobility analyser (Brechtel

Manufacturing) and a 3786 water CPC (TSI) and uses sheath air at relative humidities (RHs) similar to that of the

MAC. 905

Target

measurement

Instrument Technique Uncertainty Reference

Aerosol mass and

composition

High Resolution

Aerosol Mass

Spectrometer

(AMS), Aerodyne

Mass Spectrometry ±20–25% (Mass) (Canagaratna et al.,

2007; Decarlo et

al., 2006)

Particle number

concentration and

size distribution

(mass inferred)

Differential

Mobility Particle

Sizer (DMPS),

University of

Manchester

(further description

in the caption)

Electrical Mobility

Sizing, optical

counting

±10% (Number

concentration) –

CPC error

5–10% (diameter

sizing)

(see caption)

Particle number

concentration

3776 Butanol

Condensation

Particle Counter

(BCPC), TSI

Optical counting ±10% (Shi et al., 2005)

CCN Droplet

Activity

Aerosol particle

sizing using an in-

house DMPS,

droplet activity

measured with

Cloud

Condensation

Nuclei Counter

(CCNc), Droplet

Measurement

Technologies

Initial sizing using

electrical mobility

sizing,

supersaturation

induced by

continuous flow

thermal gradient,

optical counting of

particles

Calculated based

on uncertainty in

the sigmoidal fit to

the values of the

diameter at which

50% of particles

activate (D50). See

section 2.1.2 for

further details.

For CCN counter:

(Alofs et al., 1995;

Lance et al., 2006;

Roberts and Nenes,

2005)

For DMPS:(Alfarra

et al., 2012;

Williams et al.,

2007)

26

Table 2: Experimental conditions in MAC for photochemical secondary organic aerosol experiments. Precursor,

ozone and NOx mixing ratios shown are for the initial conditions when starting exposure to light. The

NOx/precursor ratio was held approximately constant. The ozone mixing ratio for the β-caryophyllene experiment 910 is difficult to measure as β-caryophyllene reacts more quickly with ozone than the ozone concentration can

equilibrate throughout the MAC, the same procedure was used as in the α-pinene and 1,3,5-trimethylbenzene

experiments so approximately the same mixing ratio will have been introduced.

SOA precursor Precursor

mixing ratio

Ozone mixing

ratio

NOx mixing

ratio

Light

exposure time

UV filter in

use

1,3,5-

trimethylbenzene

1000 ppb 55.4 ppb 20.9 ppb 2 h 26 min No

α-pinene

(07/11/2013)

250 ppb 44.2 ppb 84.9 ppb 1 h 55 min Yes

α-pinene

(14/11/2013)

250 ppb 32.4 ppb 78.8 ppb 1 h 31 min Yes

β-caryophyllene 39 ppb See caption 81.2 ppb 1 h 26 min Yes

Limonene 80 ppb 14.6 ppb 75.3 ppb 1 h 40 min Yes

915

27

Table 3: Instrumentation used at the Manchester Ice Cloud Chamber.

Target

measurement

Instrument Technique Uncertainty Reference

Particle number

and size

distribution (mass

inferred)

SMPS – 3080/1 TSI

Electrical Classifier

with (low flow

mode) 3776 Butanol

CPC, TSI

Electrical mobility

sizing, optical

counting

±20 % (number

conc.)

±3.5 % (diameter)

(Liu and Deshler,

2003; Quant et

al., 1993)

Particle number

in MICC

3010 Butanol

Condensation

Particle Counter

(BCPC), TSI

Optical counting ±12 %

Water droplets

(number and size

White Light Aerosol

Spectrometer

(WELAS), Palas

Xenon lamp

scattered 90° ± 12°

flux measurement

±10 % (Benz et al., 2005;

Möhler et al.,

2008)

Dew point CR-4 Hygrometer,

Buck Research

Instruments

Chilled mirror

hygrometer

±0.1K

Temperature Type K

Thermocouples and

TC-08 control box,

PicoTechnologies

Thermocouples ±0.5K

Pressure LEX-1 Manometer,

Keller

Piezoresistive strain

gauge

±0.05 %

28

Table 4: Variability limits for the Monte Carlo simulation of evacuations in the MICC: Dm shows the modal

diameter in nm, lnσ refers to the natural logarithm of the distribution width parameter σ, N refers to the number 920 concentration of aerosol particles per cm3, Ti to the initial temperature in K, and initial values for relative

humidity (Ti).

Experimental

Evacuation

Dm Lnσ N κ Ti RHi

α-pinene 07/11/13

evacuation 1

87.561784-

93.913416

0.211476745-

0.233503255

9094.4-

13641.6

0.07825-

0.84063

286.81-

288.31

0.9928

α-pinene 07/11/13

evacuation 2

87.6863655-

94.0470345

0.21129615-

0.23330385

5912.69872-

8869.04808

0.07825-

0.84063

286.63-

288.03

1.0000

Limonene

evacuation 1

84.453326-

90.579474

0.19660142-

0.21707858

8607.80384-

12911.70576

0.077417-

0.087833

286.39-

287.9

0.9569

Limonene

evacuation 2

83.1356185-

89.1661815

0.196705975-

0.217194025

6129.1856-

9193.7784

0.077417-

0.087833

286.31-

287.72

0.9485

Limonene

evacuation 3

81.8853645-

87.8252355

0.19135466-

0.21128534

3853.67256-

5780.50884

0.077417-

0.087833

286.49-

287.68

0.9616

Ammonium

sulfate seed

evacuation 2

38.4545745-

41.2440255

0.48148528-

0.53163472

9510.97696-

14266.46544

0.61 286.4-

287.98

0.9756

Ammonium

sulfate seed

evacuation 3

39.2524365-

42.0997635

0.55469279-

0.61246721

1360.61944-

2040.92916

0.61 286.42-

288

0.9809

Ammonium

sulfate seed

evacuation 5

37.364993-

40.075407

0.625077315-

0.690182685

1434.3928-

2151.5892

0.61 286.55-

288

0.9922

1,3,5 –

trimethylbenzene

evacuation 1

72.746332-

78.023268

0.14909543-

0.16462457

4308.04184-

6462.06276

0.08788-

0.095507

286.02-

287.61

0.9836

1,3,5 –

trimethylbenzene

evacuation 2

72.808092-

78.089508

0.15095841-

0.16668159

3130.19936-

4695.29904

0.08788-

0.095507

286.13-

287.6

0.9684

1,3,5 –

trimethylbenzene

evacuation 3

73.3817845-

78.7048155

0.142850645-

0.157729355

1926.27048-

2889.40572

0.08788-

0.095507

286.31-

287.98

0.9656

1,3,5 –

trimethylbenzene

evacuation 4

74.23552-

79.62048

0.147298985-

0.162641015

1453.61088-

2180.41632

0.08788-

0.095507

286.24-

287.78

0.9726

α-pinene 14/11/13

evacuation 1

139.2248925-

149.3241075

0.20295076-

0.22408924

3838.36616-

5757.54924

0.074308-

0.091018

286.61-

288.14

0.9970

α-pinene 14/11/13

evacuation 2

137.002208-

146.940192

0.19051822-

0.21036178

2468.25256-

3702.37884

0.074308-

0.091018

286.68-

288.14

0.9949

α-pinene 14/11/13

evacuation 3

133.6307875-

143.3242125

0.197105185-

0.217634815

1308.23016-

1962.34524

0.074308-

0.091018

286.64-

287.94

0.9926

α-pinene 14/11/13

evacuation 4

129.447802-

138.837798

0.21401458-

0.23630542

766.94824-

1150.42236

0.074308-

0.091018

286.18-

288.11

0.9782

β-caryophyllene

evacuation 1

74.688684-

80.106516

0.25659698-

0.28332302

4994.6292-

7491.9438

0.041553-

0.045073

286.82-

288.15

0.9944

β-caryophyllene

evacuation 2

74.0526525-

79.4243475

0.261282945-

0.288497055

3354.65928-

5031.98892

0.041553-

0.045073

286.62-

288.13

0.9759

29

β-caryophyllene

evacuation 3

73.402339-

78.726861

0.25703421-

0.28380579

2064.48072-

3096.72108

0.041553-

0.045073

286.26-

288.05

0.9836

30

925

Figure 1: Schematic diagram of the combined MAC/MICC facility. The blower and air filtration system can be

used to inflate and deflate the MAC and to transfer filtered air to the MICC. A seed “drum” can be used to

introduce nebulised aerosol into the MAC but in these experiments, ammonium sulfate was nebulized directly into

the chamber. Ports for sampling from the chambers, a line for introducing NO2, and the ozonizer and humidifier 930 for introducing ozone and water vapor are shown.

31

Figure 2: D50 (the diameter for which 50% of the particles activate) and κ values are shown for all experiments.

Variability bars are shown only for the α-pinene experiment conducted on the 7th Nov. 2013 in order to retain 935 clarity. The labeled vertical black lines indicate the time at which photochemical nucleation was stopped for

transfer.

32

Figure 3: Example MICC data relating to the first evacuation on the α-pinene experiment on 07/11/13. (a) 940 Formation and growth of SOA particles in the MAC: total particle number concentration (solid black line), total

mass concentration (open black circles) overlaid on a color plot of particle number distribution with diameter

shown as dN/dlogDp (color scale x103). A dilution was carried out prior to transfer resulting in the reduction in

concentration observed at approximately 1.4 hours. (b) WELAS size distribution data at standard ambient

temperature and pressure. (c) Total WELAS concentration at standard ambient temperature and pressure and 945 (d) average temperature (blue) and pressure (green). The vertical pink line indicates the end time for the

evacuation.

33

Figure 4: Fitting of SMPS data to a lognormal function. Aerosol size distributions measured by the SMPS in the 950 MICC (black) fitted to a lognormal function (red) as inputted into ACPIM for each experimental evacuation are

shown. Data is shown for α-pinene (07/11/13) evacuation 1–2 (a)–(b), limonene evacuation 1–3 (c)–(e), ammonium

sulfate evacuation 2, 4 and 6 (f)–(h), 1,3,5-trimethylbenzene evacuation 1–4 (i)–(l), α-pinene (14/11/13) evacuation

1 (m)–(p), β-caryophyllene evacuation 1–3 (q)–(s). The other evacuations from the ammonium sulfate experiments

have been excluded from this study because of data collection problems. The ammonium sulfate seed distributions 955 are much broader (i.e. have a much larger value for σ) than the SOA samples.

34

Figure 5: Temperature and pressure fits for input into the model (temperature is shown in orange and pressure in

green) to MICC data (temperature shown in red and pressure in blue). An exponential decay function was fitted

to the temperature data from the MICC, this was then used to calculate the initial RH in the model using the 960 initial temperature and the temperature at the time of initial cloud formation and the cloud onset time. The

pressure was fitted based on the initial pressure and the reduction in temperature during the cloud evacuation.

Good agreement was achieved in all cases between modeled and actual temperature and pressure. The difference

is most pronounced in evacuations (m), (n), (o) and (p). Data is shown for α-pinene (07/11/13) evacuation 1–2 (a)–

(b), limonene evacuation 1–3 (c)–(e), ammonium sulfate evacuation 2,4 and 6 (f)–(h), 1,3,5-trimethylbenzene 965 evacuation 1–4 (i)–(l), α-pinene (14/11/13) evacuation 1–4 (m)–(p), β-caryophyllene evacuation 1–3 (q)–(s).

35

Figure 6: Comparison of base case simulations (i.e. simulations using the expectation values for all variables)

results from ACPIM (blue) with WELAS data corrected to standard ambient temperature and pressure (red). 970 The RH used for the ACPIM simulations was set such that for the base case in all the fields varied, the onset of

droplets is found to be at the same for the chamber experiment and ACPIM simulation to the nearest output time

from ACPIM (i.e. 10 second interval). The model output gives an extended maximum of number concentration,

this is taken as the activation from the model. WELAS data is much more variable and therefore is averaged over

10 data points (see Sect. 2). Data is shown for α-pinene (07/11/13) evacuation 1–2 (a)–(b), limonene evacuation 1–3 975 (c)–(e), ammonium sulfate evacuation 2,4 and 6 (f)–(h), 1,3,5-trimethylbenzene evacuation 1–4 (i)–(l), α-pinene

(14/11/13) evacuation 1–4 (m)–(p), β-caryophyllene evacuation 1–3 (q)–(s).

36

Figure 7: Ratio of WELAS concentration at standard ambient temperature and pressure to model concentration 980 for base case simulations (i.e. simulations using the expectation value for all variable inputs). The base cases from

the α-pinene experiment on 07/11/13 and β-caryophyllene show substantial underestimation of the number

concentration of aerosol by the model in comparison to the WELAS data while the second limonene evacuation

shows the number concentration predicted by the model to be much higher than that from the WELAS data. The

variability bars shown relate to an assumed ±10 % uncertainty in the WELAS total number concentration. 985

37

Figure 8: Colour plot of the number of Monte Carlo simulation tests as a function of WELAS (at standard

ambient temperature and pressure) /model number concentration. The first evacuation on the α-pinene 07/11/13

and all three β-caryophyllene evacuations show the model underestimating the total number concentration 990 compared to the WELAS data. The second limonene evacuation shows the model overestimating the total number

concentration compared to the WELAS data. The dark gray lines indicate the maximum and minimum value of

the WELAS/model number concentration ratio from the Monte Carlo distribution with an additional variability

of ±10 % to account for uncertainty in the WELAS measurements.

995

38

Figure 9: Model/WELAS (at standard ambient temperature and pressure) number concentration ratio is shown

for evacuations using the base case variables except for the hygroscopicity variable κ. The red line indicates a ratio

of 1. For some evacuations, no value of κ is sufficient to get a ratio less than 1. Each line is labeled with the

experiment and the evacuation number, P. refers to α-pinene with the two experiments with α-pinene 1000 differentiated by the date (either 7th or 14th), Lim refers to limonene, A.S. refers to ammonium sulfate, TMB to

1,3,5-trimethylbenzene and Caryo. to β-caryophyllene.

39

Figure 10: Ratio of maximum concentration in the model using the base case simulation values for all variables to 1005 the WELAS concentration observed for all evacuations with varying amounts of semi-volatile material. For each

chamber evacuation five bars are shown, from left to right these are with a total semi-volatile concentration of

1.885×10-7 g m-3 (purple), 1.885×10-8 g m-3 (blue) 1.885×10-9 g m-3 (green), 1.885×10-10 g m-3 (orange) and no semi-

volatiles (red). Each set of five bars is labeled with the experiment and the evacuation number. A range of

volatility distributions (see Fig. 11) were used to investigate the effect on activation. Here we show the results for 1010 the distribution employed by Cappa and Jimenez (Cappa and Jimenez, 2010). The results from other distributions

can be found in the supplementary material (see Fig. S1–S4). The error bars shown assume a ±10 % error in the

WELAS concentration.

40

1015

Figure 11: A range of volatility bin distributions were employed when investigating the effect of the shape of the

semi-volatiles distributions. Here we show as an example used in experiments with 1.885×10-8 g m-3, the same

shapes of distributions were used in experiments with semi-volatile mass concentrations but with varying total

concentrations of 1.885×10-7 g m-3, 1.885×10-9 g m-3 1.885×10-10 g m-3.

18

3.

Title: Supplementary Material for “A chamber and modelling investigation of the potential

impacts of semi-volatile material on cloud droplets”

Prepared for publication in Atmospheric Chemistry and Physics Discussions but not submitted.

Page 18

1

Supplementary material

S1 Supplementary methods

In this section, detailed descriptions of procedures will be supplied as referred to in the main text.

S1.1 MAC 5

S1.1.1 Chamber bag

When inflating the Teflon bag in the MAC, air is extracted from the laboratory through a drier and a series of

three filters (purafil/charcoal, pure charcoal and then a HEPA filter) in order to remove particles and

chemically significant trace gases. This air may then be passed through a humidifier and/or ozone generator

or blown into the chamber bag directly. By changing the position of the valves, the chamber can be deflated 10

as the blower draws gas from the chamber to an exhaust line, this can also be done via the ozonizer and

humidifier if required. A series of inflations and deflations (at least five) are completed as part of the pre-

experimental procedure. During this period, the humidity can be controlled by drying the laboratory air using

the drier at the inlet for lab air to reduce the humidity of the air entering the Teflon bag and by warming the

double deionised water inside the humidifier and diverting the gas flow into the chamber to go via the 15

humidifier in order to increase the humidity inside the Teflon bag. This allowed the humidity to be regulated

such that it was in the 45–60 % RH range for all photo-oxidation experiments.

S1.1.2 VOCs

VOCs were introduced to the MAC bag as follows. A glass bulb with a gas line attached at either end to a

nitrogen gas supply and to the inflation/deflation gas line for the Teflon bag is attached to the MAC. During 20

the pre-experimental procedure for all photochemical oxidation experiments, the bulb is heated with electric

tape for ~5 minutes, then as the Teflon bag changes from inflating to deflating during the pre-experimental

procedure, nitrogen gas is run through the bulb into the inflation/deflation gas line. As it is deflating, the

nitrogen and any impurities from the glass bulb are flushed to the exhaust (i.e. away from the chamber).

During the deflation prior to the final inflation of the bag before the beginning of photochemical oxidation 25

experiments, the bulb is reheated. Once the final inflation of the bag has begun, the VOC material for the

experiment is injected through a cork into the heated glass bulb where it is vaporised and carried by nitrogen

gas flowed through the bulb to the inflation/deflation gas line whereupon, as the bag is being inflated, it is

carried into the Teflon bag.

S1.1.3 NO2 30

NO2 is introduced to the MAC as follows. The volume inside a gas line between the gas cylinder and a mass

flow controller is exposed to the compressed gas, the mass flow controller is then set to allow the gas to flow

to an exhaust line at a rate of 10 cm3 min-1 for 30 seconds in order to fill the volume between the mass flow

controller and the cylinder with NO2. Nitrogen gas is then flowed through the volume after the mass flow

2

controller to ensure that any NO2 present has gone into the exhaust. During the final inflation of the bag

before a photochemical experiment, the gas line between the mass flow controller and the exhaust is switched

using a three-way valve to a gas line leading into the Teflon bag. A controlled quantity of NO2 gas is then

flowed through the mass flow controller and flushed into the chamber using nitrogen gas. This may be

repeated if further NO2 is required. 5

S1.1.4 Ozone

Ozone was introduced into the MAC for photo-oxidation experiments. At the beginning of one of the pre-

experimental inflations of the bag, the ozonizer was switched on to start producing ozone, during the

subsequent deflation of the bag, the ozone produced was vented to exhaust. This was done in order to remove

contaminants from the ozonizer. Just prior to the completion of the final inflation of the bag, the inflation is 10

paused in order to turn on the ozonizer to generate ozone which is then flushed into the chamber. Having the

ozonizer on for 2 minutes before flushing the ozone generated into the chamber for 20 seconds generates

~30ppb ozone and this was the ozone level used at the start of the photochemistry experiments. After the

ozone has entered the chamber it is crucial to illuminate the chamber quickly in order to avoid ozonolysis

under dark conditions having too much impact on the formation and composition of the SOA generated 15

particularly when using VOCs which react quickly with ozone such as -caryophyllene.

S1.1.5 Ammonium sulfate experiment

For the ammonium sulfate experiment, ammonium sulfate aerosol particles were introduced into the chamber

by flowing compressed air filtered with HEPA and charcoal filters through a nebulizer filled with ammonium

sulfate solution as the bag was inflated to approximately half of its maximum volume. The inflation of the 20

bag was then paused to encourage coagulation of the aerosol particles to increase their size and decrease the

number concentration. Further injections of ammonium sulfate aerosol using the method described above

were used to further increase the number concentration. The number concentration can be changed by

partially deflating and re-inflating the chamber bag to reduce concentration and via nebulising further

ammonium sulfate aerosol. 25

S1.2 Instrumentation

S1.2.1 DMA calibration

Polystyrene latex spheres of known size were used to calibrate the particle sizing of the DMA. Ammonium

sulfate particles were used to calibrate the supersaturation used in the CCN counter. The measured D50 and

SScrit pairs for ammonium sulfate were compared to the theoretical values calculated with Aerosol Diameter 30

Dependent Equilibrium Model (Topping et al., 2005) yielding a correction factor for each set supersaturation.

This correction showed that the CCN counter was measuring droplet activation with supersaturations

between 0.04% and 1.3% (i.e. RH between 100.04% to 101.3%) during these experiments. Data shown in

this study is from the CCN counter which operated in all experiments.

3

S1.2.2 Hygrometer

The Dew Point Hygrometer uses the chilled mirror method to find the dew point; changing the temperature

of a mirror and detecting the presence of droplets on the mirror by alterations in its optical properties when

droplets are present. It warms the mirror when drops are present and cools it when they are not. As droplet

formation/evaporation and detection is not instantaneous, the hygrometer tends to take some time (generally 5

4–8 minutes) to reach balance and only provides data when the balance has settled.

S2 Supplementary results

Additional results as pointed to in the main text are shown here.

S2.1 Hygroscopicity parameter κ

This work rests upon accurate calculation of the value of the hygroscopicity parameter κ from the 10

experiments conducted in order to model the concentration of droplets that would be anticipated with and

without the possibility of co-condensation. As such a comparison between the values used in this study and

those found in other work is included here (see Table S1) as this is the key parameter in determining

activation. It should be noted that ammonium sulfate is used as the standard for calibration of the CCN

counter employed in this study. 15

S2.2 Semi-volatile distributions

A variety of semi-volatile distributions were used to test the effect this distribution had on the number

concentrations activated (see Fig. 11). In all cases, the results were very similar indicating that the volatility

distribution does not play an important role in determining activation in these experiments. This is thought to

be due to virtually all semi-volatile material entering the condensed aerosol phase when exposed to 20

supersaturated conditions. Here we show the WELAS/Model concentration ratios for the reverse of the

Cappa & Jimenez distribution (Fig. S1), the narrow Gaussian distribution (Fig. S2), the broad Gaussian

distribution (Fig. S3), and a flat distribution (Fig. S4). The results from the Cappa & Jimenez distribution is

shown in the main text (see Fig. 10).

25

4

Table S1: Values for the hygroscopicity parameter κ. It should be noted that the aerosol generated in

different experiments are generated under different conditions: varying concentrations of precursor

VOCs, NOx and ozone, different experimental time and different illumination conditions. The work of

Alfarra et al. employs the same chamber facility and therefore illumination conditions were very

similar. 5

SOA precursor κ range used in

Monte Carlo

simulation

Literature κ

values

Sources for

literature κ

values

1,3,5-

trimethylbenzene

0.08788-

0.095507

0.035-0.065 (Massoli et al.,

2010)

α-pinene

(07/11/2013)

0.07825-

0.84063

0.042-0.121 (Alfarra et al.,

2013)

α-pinene

(14/11/2013)

0.074308-

0.091018

0.042-0.121 (Alfarra et al.,

2013)

β-caryophyllene 0.041553-

0.045073

0.009-0.011 (Alfarra et al.,

2013)

Limonene 0.077417-

0.087833

0.077 (Alfarra et al.,

2013)

Ammonium

Sulfate

0.61 0.6-0.67 (Andreae and

Rosenfeld,

2008; Petters

and

Kreidenweis,

2007)

5

Figure S1: Ratio of maximum concentration in the model chamber activation for all expansions with varying

amounts of semi-volatile material with the reverse of Cappa & Jimenez distribution (see Figure 11). For each

experiment five bars are shown, from left to right these are with semi-volatile concentration 1.885×10-7 g m-3

(purple), 1.885×10-8 g m-3 (blue), 1.885×10-9 g m-3 (green), 1.885×10-10 gm-3 (orange), and no semi-volatiles (red). 5 Each set of five bars is labeled with the experiment and the evacuation number.

6

Figure S2: Ratio of maximum concentration in the model chamber activation for all expansions with varying

amounts of semi-volatile material with the narrow Gaussian distribution (see Figure 11). For each experiment five

bars are shown, from left to right these are with semi-volatile concentration 1.885×10-7 g m-3 (purple), 1.885×10-8 g

m-3 (blue), 1.885×10-9 g m-3 (green), 1.885×10-10 gm-3 (orange), and no semi-volatiles (red). Each set of five bars is 5 labeled with the experiment and the evacuation number.

7

Figure S3: Ratio of maximum concentration in the model chamber activation for all expansions with varying

amounts of semi-volatile material with the broad Gaussian distribution (see Figure 11). For each experiment five

bars are shown, from left to right these are with semi-volatile concentration 1.885×10-7 g m-3 (purple), 1.885×10-8 g

m-3 (blue), 1.885×10-9 g m-3 (green), 1.885×10-10 gm-3 (orange), and no semi-volatiles (red). Each set of five bars is 5 labeled with the experiment and the evacuation number.

8

Figure S4: Ratio of maximum concentration in the model chamber activation for all expansions with varying

amounts of semi-volatile material with the flat distribution (see Figure 11). For each experiment five bars are

shown, from left to right these are with semi-volatile concentration 1.885×10-7 g m-3 (purple), 1.885×10-8 g m-3

(blue), 1.885×10-9 g m-3 (green), 1.885×10-10 gm-3 (orange), and no semi-volatiles (red). Each set of five bars is 5 labeled with the experiment and the evacuation number.

References

Alfarra, M. R., Good, N., Wyche, K. P., Hamilton, J. F., Monks, P. S., Lewis, a. C. and McFiggans, G.:

Water uptake is independent of the inferred composition of secondary aerosols derived from multiple 10

biogenic VOCs, Atmos. Chem. Phys., 13(23), 11769–11789, doi:10.5194/acp-13-11769-2013, 2013.

Andreae, M. O. and Rosenfeld, D.: Earth-Science Reviews Aerosol – cloud – precipitation interactions . Part

1 . The nature and sources of cloud-active aerosols, Earth-Science Rev., 89, 13–41,

doi:10.1016/j.earscirev.2008.03.001, 2008.

Massoli, P., Lambe, A. T., Ahern, A. T., Williams, L. R., Ehn, M., Mikkilä, J., Canagaratna, M. R., Brune, 15

W. H., Onasch, T. B., Jayne, J. T., Petäjä, T., Kulmala, M., Laaksonen, A., Kolb, C. E., Davidovits, P. and

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Petters, M. D. and Kreidenweis, S. M.: A single parameter representation of hygroscopic growth and cloud 20

condensation nucleus activity, Atmos. Chem. Phys., 7(8), 1961–1971, doi:10.5194/acp-7-1961-2007, 2007.

Topping, D. O., McFiggans, G. B. and Coe, H.: A curved multi-component aerosol hygroscopicity model

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2004, 2005.

25

19

4. Conclusion

In this study we probed the properties of secondary organic aerosol (SOA) with respect to the

aerosol particles’ ability to act as cloud condensation nuclei (CCN); in particular, evidence was

sought regarding whether or not co-condensation increases the concentration of droplets formed

in a cloud with SOA particles acting as CCN. The experiments were conducted under controlled

chamber conditions and an ammonium sulphate experiment was carried out as a reference case.

SOA were generated from α-pinene, β-caryophyllene, 1,3,5-trimethylbenzene and limonene in the

Manchester Aerosol Chamber (MAC) and transferred to the Manchester Ice Cloud Chamber

(MICC) where a series of evacuations were conducted to generate clouds.

Co-condensation of water and semi-volatile material has been demonstrated to occur in a

chamber environment using SOA. In particular, co-condensation is required to explain the droplet

activation observed during chamber evacuations for SOA derived from β-caryophyllene and α-

pinene. The impact of co-condensation upon droplet activation during an expansion appears to

depend upon the size of the aerosol particles. The same quantity of organic material condensing

onto smaller particles results in a greater increase in their diameter proportionally compared to

larger particles as particle volume increases proportionally to the cube of the diameter, however,

the ability to nucleate is related to particle diameter rather than volume (recall the Köhler

equation — Eq 1. Of Chapter 2). It should also be noted that co-condensation may be occurring

in all the SOA experiments because of the potential for overestimation of the hygroscopicity

parameter κ, as is required to explain the observation in the limonene experiment that the

number of droplets activated is significantly lower than predicted by ACPIM. Additionally, the

presence of co-condensation may not be observable in experiments with larger aerosol particle

size distributions as the increase in particle diameters will not be significant in determining the

activated fraction. This can be most clearly be demonstrated by considering the two α-pinene

experiments where a significant increase in activation occurs in the experiment with a modal

particle size of 90 nm but not in the experiment with a modal particle size 140 nm.

In the wider context of our efforts to understand cloud droplet activation, these results support

the work of Topping et al. (Topping et al. 2013), providing experimental data from a controlled

environment which demonstrates the occurrence of co-condensation with SOA. This is anticipated

to be responsible for negative radiative forcing in the atmosphere by increasing the number

concentration of droplets present in clouds. Forcing due to this effect will depend upon the

amount of condensable organic material present, the size distribution of the aerosol particles, the

20

surface albedo and the intensity of solar radiation (which is dependent upon latitude and season).

However, this work points to a requirement to include co-condensation of semi-volatile material

into cloud models on all scales up to and including global circulation models wherever

condensable organic vapour is present. This provides a reason to expand upon the work of

Connolly et al. (Connolly et al. 2014) by creating a multi-mode scheme for parameterising co-

condensation of organic vapours for use in models beyond the parcel scale used in this thesis.

Such a scheme could use the results presented here as a controlled data set by which to judge a

model’s ability to replicate co-condensation of organic vapour. The results from this work also

suggest that at relative humidities relevant to cloud formation, the volatility of the organic vapour

present does not affect the activated fraction of SOA particles as material of all relevant

volatilities partitions heavily towards the condensed phase, which may make including a co-

condensation scheme less computationally expensive.1

This work may also have some explicative power concerning the discrepancies between HTDMA

and CCN counter measurements of the hygroscopicity parameter κ of organic aerosol samples. In

this work measurements from a CCN counter were used to calculate the value of κ. However,

these values did not correspond to the effective κ observed in some of the experiments due to

the co-condensation of material from the gas phase. This introduces a potential bias into

measurements made with a CCN counter and HTDMA as there is substantial potential to lose

condensable vapour from the sample. The exact nature of these discrepancies would likely

depend upon instrument set-up and the aerosol sample. It has been noted in a meta-analysis of

closure studies (i.e. studies attempting to explain the difference between HTDMA and CCN

counter data) that the discrepancies between CCN and HTDMA data are particularly noticeable at

low supersaturations (Whitehead et al. 2014) and that this is thought to be due to a relatively

large uncertainty in the size of the particles. Co-condensation causes particles to grow and is

shown in this study to have the largest impact on smaller particles by introducing the greatest

change in size. While co-condensation may not be able to explain all discrepancies between CCN

counter and HTDMA measurements, it may go some way to explaining the discrepancies

observed.

21

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