Volatile Organics on Cloud Droplet Number for Various Environments

AN INVESTIGATION OF THE INFLUENCE OF THE CO-CONDENSATION OF SEMI- VOLATILE ORGANICS ON CLOUD DROPLET NUMBER FOR VARIOUS ENVIRONMENTS

A thesis submitted to The University of Manchester for the degree of Master of Philosophy Atmospheric Sciences

in the Faculty of Science and Engineering

2020

YICHEN JIA

SCHOOL OF NATURAL SCIENCES

DEPARTMENT OF EARTH AND ENVIRONMENTAL SCIENCES 2020

Yichen Jia

School of Natural Sciences Department of Earth and Environmental Sciences

Contents Abstract ...... 3

Declaration ...... 4

Acknowledgement ...... 5

1 Introduction—Background and Theory ...... 6

1.1 Thesis structure and rationale for submitting in a journal format ...... 6

1.2 Aerosol, cloud and climate ...... 7

1.2.1 Atmospheric aerosols ...... 7

1.2.2 Aerosol-cloud interactions and climate ...... 7

1.2.3 Aerosol particle size distribution and chemical composition ...... 10

1.3 Cloud formation and droplet activation ...... 13

1.4 Volatility of atmospheric organic compounds ...... 17

1.5 PyACPIM description ...... 20

1.6 Co-condensation of semi-volatile organic material ...... 24

1.7 Aims and objectives ...... 25

2 Paper ...... 26

2.1 Preface ...... 26

2.2 Paper title: An investigation of the influence of the co-condensation of semi-volatile organics on cloud droplet number for various environments ...... 28

2.3 Supplementary material for the paper ......

3 Summary and Conclusions ...... 28

4 Reference ...... 30

Word count: 20097

Figure 1: Radiative forcing (RF) and Effective Radiative forcing (ERF) by concentration change and related 5 to 95% (90%) uncertainty interval between 1750 and 2011. Solid bars represent ERF and hatched bars represent RF (Stocker et al., 2013, Figure TS.6)...... 10

Figure 2: Typical number (upper) and volume (lower) size distributions of atmospheric aerosol particles (Seinfeld and Pandis, 2017)...... 11

Figure 3: Köhler curve for (NH4)2SO4 aerosol particles with 0.05 µm, 0.1 µm and 0.5 µm dry diamters

(i.e. various masses of dissolved (NH4)2SO4) at 293 K (Andreae and Rosenfeld, 2008, Figure. 1). .... 17

-3 Figure 4: Volatility distributions with different total condensed aerosol mass COA=1.0 µg m (left panel)

-3 and COA=100.0 µg m (right panel). The green portion represents the material in condensed phase.

COA increases with increasing quantities of α-pinene + ozone products. a) a lower concentration (26

µg m-3) of α-pinene (i.e. precursor) is consumed, resulting in 1.0 µg m-3 of products in the aerosol phase. b) 468 µg m-3 precursor giving 100 µg m-3 SOA mass. As the total aerosol mass loading increases, the 50:50 bin shifts to the right i.e. to the bin with higher volatility (Donahue et al., 2009,

Figure 1)...... 20

Figure 5: Co-condensation of semi-volatile organic vapours along with water vapour during the ascent of aerosol particles. RH, relative humidity and S, saturation ratio (Topping et al., 2013)...... 26

Abstract

Clouds play a critical role in the Earth’s climate system by virtue of their ability to regulate the radiative budget. The brightness and albedo of clouds are highly determined by cloud droplet number concentration (Nd). Atmospheric aerosol particles can serve as cloud droplet seeds—cloud condensation nuclei (CCN) and become cloud droplets through hygroscopic growth and activation. In the atmosphere, a substantial proportion of organic fine particle material is semi-volatile (i.e. partitioning between particulate and vapour phases). During the ascent of an air parcel, this fraction will co- condense onto particles along with water vapour according to the prevailing saturation ratio. The addition of soluble organic material to growing particles will enhance the uptake of more water and suppress the critical supersaturation for the activation of a particle, by which it can result in an increase in cloud droplet number concentrations. A Python version of a parcel model with “bin-resolved microphysics” has been developed to examine and quantify this effect for various continental environments including pristine forest, background, rural, near-city, urban and kerbside. For each environment, the representative volatility distribution of organic material and the resulting condensable semi-volatile organic material were repartitioned using equilibrium partitioning theory to match the representative aerosol particle number size distributions and chemical composition. In this way, along with adjustment of other parameters, we systematically initialised the model, and thereby investigated the co-condensational effect under various environmental conditions and explored the potential role of the transformation of semi-volatile organic material in the atmosphere. It is shown that, across all model scenarios, a maximum of around 70% more seed particles become cloud droplets due to the enhancing effect of co-condensation of semi-volatile organics compared to the simulations without co- condensation. We show that, despite relatively straightforward initialisation conditions in pristine forest, the links between the enhancement by co-condensation and other parameters are complex and non- linear and the activated fraction of particles without co-condensation should be taken into consideration. In the other five environments, the general trends of the percentage enhancement by co-condensation can be concluded as: (1) the enhancement increases with updraft speeds; (2) in summer, the increase is insignificant; (3) in winter, the increase is the most significant in clean environments while very limited in the intermediate and polluted environments. However, the trend of the percentage enhancement is too intricate and complex to be accessible to the simple assumption of behaviour as a function of, for example, per surface area. We found that initial temperature, updraughts and initial volatility distribution are the biggest contributory parameters to the large enhancement by co-condensation. On the basis of this work, a future study could be undertaken to estimate the co-condensational effect on the regional radiative budget under the environmental conditions proposed in this study.

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

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, trademarks 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 and/or Reproductions described in it may take place is available in the University IP Policy (see http://documents.manchester.ac.uk/DocuInfo.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

Acknowledgement

First and foremost, I’d like to thank my supervisor Gordon McFiggans for his guidance and help from the beginning to the end. I also want to say thanks to him for leading me to explore the world of clouds, and keeping me sane and moving forward especially during the lockdown due to COVID-19 pandemic.

That was a really tough time for me and I would not have made any progress without the help from him and my friend Hao Lu. I would also like to extend my deepest gratitude to Emma Simpson, not only for the model development work we have done together and her efforts in answering plenty of questions from me, but also her earnest work attitude and optimistic attitude towards life, which impressed me and affected me a lot. I am also grateful to Aristeidis Voliotis for providing me data and his patient explanation. Thanks also go to David Topping for sharing his valuable ideas and thoughts. Specially thanks to my colleagues, particularly Hugo Ricketts, who is the first person I met in the office 3.16, for helping me get familiar with the working environment and settle down, and for all good memories although the office life only lasted for less than six months. It is a pity that I have not seen them again since I started working from home. Last but not least, I am deeply indebted to my parents for unconditionally supporting me in what I want to do. This MPhil is the start of my research career but I guess it would not be the end.

1 Introduction—Background and Theory

Looking down at the Earth from space, 60%~70% of the earth's surface is covered by clouds (Quante,

2004), leaving only a relatively small piece of ocean and land exposed. While looking up to the sky from the Earth’s surface, clouds are in an endless variety of shapes and forms. Clouds provide us with fresh water, clean the atmosphere, regulate the weather and climate and determine the Earth’s environmental condition. To form a cloud, we first need condensable material (mainly water vapour) and seeds with surface on which the available material can condense. A considerable portion of poorly-understood atmospheric organics exists in the vapour phase and will condense under favourable conditions (e.g. cooling). During the formation of a cloud, this kind of condensable material will condense simultaneously with water vapour, which is referred to as co-condensation, and facilitate the growth of the seeds into cloud droplets (Topping and McFiggans, 2012; Topping et al., 2013). This thesis is concerned with a further determination and quantification of the effect of co-condensation on cloud droplet number under a broad range of environmental conditions, concerning the transformation of atmospheric semi-volatile organic compounds (SVOCs).

1.1 Thesis structure and rationale for submitting in a journal format

This thesis is presented in a journal format. The methods used and the results are presented in a research article entitled “an investigation of the influence of the co-condensation of semi-volatile organics on cloud droplet number for various environments” in Chapter 2. The relevant background and theories are introduced in Chapter 1. This introductory chapter consists of the introduction to atmospheric aerosols, aerosol-cloud interactions and their impacts on climate; to the basic theory of the aerosol particle growth and cloud droplet formation; to the volatility of organic compounds including equilibrium partitioning theory and volatility basis set; and finally to the Python version of the Aerosol-

Cloud-Precipitation Interactions Model (PyACPIM) with more details not introduced in the paper.

Subsequently, in Chapter 2, the contribution of this work to relevant subject is presented as a research article in a journal format along with the supporting material prepared for publication after the submission of this thesis. This chapter can be considered as the Method and Result sections of this thesis, showing the primary outcomes of this MPhil programme. The contribution of authors is presented in Sect. 2.1. Finally, in Chapter 3, conclusions are presented to summarize the main findings of this work and recommendations for future work is made in this chapter as well.

1.2 Aerosol, cloud and climate

1.2.1 Atmospheric aerosols

Atmospheric aerosols, also called particulate matter (PM), are particles (range in size from a few nanometres to tens of micrometres) in the liquid or solid phase suspended in the atmosphere. They are closely linked with people’s daily life in terms of their great impacts on human health, environment quality and, the focus of this study, the Earth’s climate (Prather et al., 2008; Kolb and Worsnop, 2012;

Mcneill, 2017). Both organic aerosol (OA) and inorganic aerosols contribute to the aerosol mass and the relative contribution varies spatiotemporally. Aerosol particles originate from both anthropogenic and natural sources having a variety of physical and chemical properties and thus their behaviour in the atmosphere. In addition, atmospheric aerosols can be classified into primary (directly emitted into atmosphere in particulate phase) and secondary. The term secondary refers to aerosols indirectly formed through heterogeneous phase chemical reactions, gas to particle phase conversion and the partitioning of the oxidation products onto pre-existing particulate matter, etc. This allows the researchers make a distinction between the aerosol whose concentration is controlled by the emission and others controlled by more complex mechanisms (Gilardoni and Fuzzi, 2016).

1.2.2 Aerosol-cloud interactions and climate

Climate is the long-term average of the meteorological elements (e.g. temperature, pressure, humidity, winds, precipitation and atmospheric composition) for a certain region. Clouds play a vital role in weather and climate by virtue of their ability to regulate the Earth’s energy budget and water circulation.

The Earth’s climate will be relatively stable if the incoming shortwave solar radiation is equal to the outgoing i.e. the sum of the longwave radiation emitted by the Earth and the reflected solar radiation

(i.e. at radiative equilibrium). Radiative forcing (RF), in W m-2, is a measure of net change in this energy balance due to some external perturbation (e.g. anthropogenic emissions, solar constant variations, volcanic emissions). The concept of RF can be understood as a “common currency”, allowing the comparison among different types of perturbation (Sherwood et al., 2015).

Aerosols affect the radiative budget and hence climate in two ways. The first is by scattering and absorbing both solar and terrestrial radiation. This is known as the aerosol direct effect (also referred to as “Aerosol-Radiation Interacion” as shown in Fig. 1), and has a net negative (cooling) effect (overall

-0.35 W m-2) (Kreidenweis et al., 2019). The second is that aerosols can affect the climate through their

interactions with clouds, “Aerosol-Cloud Interaction” in Fig. 1. The mechanism is that aerosol particles can serve as surface where atmospheric vapours condense on, enabling further growth of particles and thus formation of cloud droplets. This kind of particles are termed as cloud condensation nuclei (CCN).

Similarly, particles acting as nuclei for ice crystal formation is called ice nuclei (IN) (Kolb and Worsnop,

2012; Fan et al., 2016). In this thesis, the focus is on CCN and warm clouds (i.e. containing only liquid water). This second way by which aerosols affect climate leads to large uncertainties in climate science and limits our ability to predict future climate (Solomon et al., 2007; Schwartz et al., 2010; Boucher et al., 2013), which can be seen in Fig. 1.

In the fifth Assessment Report (Stocker et al., 2013) by the Intergovernmental Panel on Climate Change

(IPCC), a new quantification framework is to use Effective Radiative forcing (ERF), which accounts for rapid adjustments of the components of the Earth’s surface and troposphere that are assumed constant for RF (e.g. troposphere temperature and cloud cover). “Adjustment” refers to the changes directly triggered by the forcing agent and independent of the global mean temperature change (Sherwood et al., 2015). Figure 1 illustrates the radiative forcing of climate from 1750 to 2011 (called Industrial Era) for various forcing agents. It can be seen that the RF due to anthropogenic aerosol-radiation interaction is given a best estimate of -0.35 [-0.85 to + 0.15] W m–2 (hatched bar) while the ERF of -0.45 [-0.95 to

+ 0.05] W m–2 (solid bar), meaning that the inclusion of “adjustments” resulting in further negative forcing. In addition, the total ERF due to aerosols (aerosol-radiation interac. + aerosol-cloud interac.) is estimated as -0.9 [-1.9 to -0.1] W m–2. These estimates of radiative forcing indicate that aerosols are responsible for negative radiative imbalance of the climate system yet there are still substantial uncertainties on aerosol forcing.

The semi-direct effect of aerosols refers to the cloud changes in relation to the absorption of radiation by absorbing aerosols (mainly black carbon) (Boucher et al., 2013). The semi-direct effect can be a positive forcing due to the decrease in brightness of clouds caused by evaporation, which contributes to a forcing of 0.5 W m-2 (Allen and Sherwood, 2010). It also can be negative due to the increase in the emissions of outgoing long wave radiation resulting from the loss of high clouds (especially the clouds at an altitude of above 400 hPa) (Fan et al., 2016; Kreidenweis et al., 2019). An

“aerosol→cloud→radiation→climate” process is known as the indirect effect of aerosols. In AR4, the indirect effect of aerosols has been regarded as the largest uncertainties in radiative forcing (Solomon et al., 2007). The characteristic of the radiative effect of clouds is highly dependent on the microphysical

properties of cloud droplets such as size distributions and number concentrations. The seminal work by

Twomey (1974, 1977) suggested a negative forcing resulting from the increase in optical thickness of clouds (i.e. brighter clouds and higher cloud albedo). With the same amount of liquid water content, the clouds formed by droplets in smaller size with a higher number concentrations reflect more solar radiation back to the space than larger size with lower concentrations. Moreover, higher number concentration and resulting smaller droplet sizes also inhibit precipitation and hence extends the lifetime and coverage of clouds (Albrecht, 1989). Consequently, there is also a positive feedback to increase the liquid water content and further increase the thickness and cool the climate (Rosenfeld, 2000).

Since human beings stepped into the industrial age, the additional soluble aerosol particles of anthropogenic origin has been given rise to increases in CCN, which, to a certain extent, increases the cloud droplet number concentration (Kreidenweis et al., 2019). Note that the cloud droplet number concentration does not necessarily increase with CCN concentration given the suppression owing to the dynamic competition of water supply (McFiggans et al., 2006). Such an increase in the cloud droplet number concentration and decrease in the size arising from the perturbations from man-made aerosols leads to the increase in the cloud reflectivity and lifetime. However, the quantification of aerosol-cloud interactions is challenging because it requires to quantify the variations both in meteorological conditions and aerosol properties. For the latter, the determination of the CCN spectrum and thus the aerosol number size distribution and chemical composition are needed, which is heterogeneous spatiotemporally and dependent on local emissions (McFiggans et al., 2006; Wang et al., 2008).

1.2.3 Aerosol particle size distribution and chemical composition

The most common approach to describe atmospheric aerosol particle size distributions is to categorize them by size into multiple continuous log-normal modes. The morphology, and chemical properties of aerosols in each mode are distinctive, reflecting different sources and formation processes of aerosols

(Ueda et al., 2016). Aerosol particles range in size from a few nanometres to over ten micrometres. It should be noted that the definition of different modes varies across literature. As Fig. 2 shows, size distributions of aerosol particles are classified by Seinfeld and Pandis (2017) into (1) Nucleation mode

( < 0.01 µm) which are particles newly formed by nucleation with an extremely short lifetime; (2) Aitken mode (0.01–0.1 µm) from gas-to-particle conversion and combustion activities; (3) Accumulation mode

(0.1–2.5 µm) from coagulation and condensation on existing smaller particles; and finally (4) coarse mode ( > 2.5 µm) formed by mechanical processes (typically comprises sea spray, soil dust).

Furthermore, the particles distributed in the nucleation mode and Aitken mode collectively refer to ultrafine particles. The particles in accumulation mode in addition to the ultrafine particles are identified as fine particles. Kreidenweis et al. (2019) defined the nucleation mode particles (between 3 and 20 nm) as ultrafine particles; fine particles is referred to as particles in the size below 1 µm and coarse mode above 1 µm ( Willeke and Whitby, 1975; Van Dingenen et al., 2004); Jacobson (2005) defined aerosol particles smaller than 0.1 µm as nucleation mode; 0.1–2 µm accumulation mode and particles in these two modes are collectively referred to as fine particles. Therefore, in relevant studies, the

terminology of different modes or the classification by size should be clearly stated. In this thesis, particles with sizes smaller than 1 µm are defined as fine particles and fine particles if there are not further clarifications.

Figure 2: Typical number (upper) and volume (lower) size distributions of atmospheric aerosol particles (Seinfeld and Pandis, 2017).

The number size distribution is one of the most basic properties of aerosol particles and it is more directly associated with the cloud droplet number than surface or mass size distributions. Broadly, particle size distributions are roughly dominated by the Aitken mode; surface area size distributions are dominated by the Accumulation mode and volume by coarse mode (Van Dingenen et al., 2004;

Steinfeld, 1998). Most CCN are composed of submicrometre organic and inorganic matter. The activation of aerosol particles is determined by their dry size. Under typical ambient conditions, with typical chemical composition and updraughts (i.e. supersaturations), the critical size for cloud droplet activation ranges between approximately 50 to 150 nm. For example, under atmospheric circumstances with a range of updraught velocities from 0.1 ms-1 (corresponds to stratiform clouds) to 15 m s-1 (deep convective), aerosol particles whose diameter are greater than approximately 40 nm are likely to activate and become cloud droplets. Furthermore, aerosol particles larger than 200 nm in diameter may activate at any reasonable updraft speeds (McFiggans et al., 2006). Therefore, the submicrometre size range is critical to explore the cloud activation behaviour and will be the focus of this study (also

discussed in Chapter 2 Method Section). The growth and activation of aerosol particles will be introduced in the following section.

The full characterization of atmospheric aerosol chemical composition is challenging because of its diversity, complexity and variability in ambient conditions (McFiggans et al., 2006). Atmospheric aerosols are generally composed of organic and inorganic constituents from both natural and anthropogenic sources. Inorganic particles primarily comprise ammonium, sulphate, nitrate, sodium, chloride, metals and mineral dust. In the fine size range, aerosol composition is normally dominated by ammonium nitrate and ammonium sulphate (Gilardoni and Fuzzi, 2016). Atmospheric nitrate aerosols are mainly generated from the oxidation reaction (e.g. by OH radicals) of nitrogen oxides (NOx) which initially form nitric acids (HNO3) and subsequently neutralized by atmospheric ammonium to form ammonium nitrate. Anthropogenic emissions (e.g. NOX from automobiles, power plants and NH3 from agriculture) are predominately responsible for the production of atmospheric nitrate aerosols (Kim,

2019). The anthropogenic source of sulphate aerosols is mainly fossil fuel burning and the natural sources are from dimethyl sulphide (DMS) which is emitted by phytoplankton and SO2 from volcanoes

(Forster et al., 2007). Similarly, sulphuric acids (H2SO4) are initially formed and then neutralized to form ammonium sulphate. Despite the tiny mass fraction of metals, they can be used as tracers to identify the sources of aerosols and hence also called trace metals (Putaud et al., 2008). Black carbon (BC) is primary aerosol exclusively generated from incomplete combustion processes including both fossil fuel and biomass burning (Putaud et al., 2004; Gilardoni and Fuzzi, 2016). BC strongly absorbs solar radiation especially the visible spectrum and hence is responsible for a net positive radiative forcing

(Forster et al., 2007; Mcneill, 2017). Sea spray aerosol consisting of inorganic salts and organic matter is a main source of chlorides in the atmosphere and power stations are another source. Mineral dust aerosols predominately originate by mechanical processes from areas with sparse vegetation coverage, dry soil and strong wind (Mahowald et al., 2014). They are also produced via anthropogenic sources e.g. industrial and agricultural activities (Solomon et al., 2007). Sea spray and mineral dust aerosols contribute more to coarse size range than fine fraction and they can be found hundreds of kilometres away due to the long-range transport in the atmosphere.

The inorganic components are relatively well characterized compared to the organic aerosol which, although, accounts for 10% to 70% mass fraction of dry particles in fine size range (Alves, 2008) and a considerable fraction of coarse particles as well. Organic compounds can be emitted as in the

particulate phase into the atmosphere and this type is primary organic aerosol (POA). Whereas it is also generated by the oxidation of volatile organic compounds (VOCs) to form semi-volatile or extremely low-volatile organics which condense on pre-existing particles i.e. secondary organic aerosol (SOA)

(Forster et al., 2007). Both biogenic sources and anthropogenic sources contribute to POA and SOA in the atmosphere (Boucher et al., 2013). It is noteworthy that, as demonstrated by Gilardoni and Fuzzi

(2016), POA particles can serve as precursors for SOA particles, which occurs for inorganic aerosols as well but mainly for OA. A portion of POA can partition into gas phase due to the dilution under ambient conditions, referring to intermediate volatile organic compounds (IVOCs) (see Section 1.4).

While in the gas phase, these species may experience oxidation processes and thus the volatility is reduced. As a result, the products partition back into particle phase forming SOA. Another mechanism that POA particles play a role as precursors of SOA particles is via the oxidation and oligomerization in aqueous phase. The less-volatile products stay in particle phase after water evaporation and can be considered as SOA. This occurs for hydrophilic species under ambient conditions favourable to cloud or fog formation. The difficulty of the characterization of organic aerosols is attributed to the complexity that they comprise thousands of individual species covering a variety of physical and chemical properties, from primary and secondary or natural and anthropogenic sources. Therefore, it is challenging to measure and analyse organic components of aerosols individually by a single direct technique. There are also limitations on the approach to directly separate SOA and POA and thus a more common way is to employ indirect methods such as using organic tracers. Furthermore, the gas- particle phase partitioning of semi-volatile organics increases the difficulty of the collection of organic matter (Turpin et al., 2000). The volatility of atmospheric organic aerosols will be introduced in the

Section 1.4.

1.3 Cloud formation and droplet activation

All types of clouds form when the humidity of the atmosphere becomes supersaturated relative to the water (or ice) surface. Cooling is a more important way for air to reach saturation (relative humidity

(RH) > 100%) and form clouds compared to the increase in the absolute humidity. There are usually two mechanisms through which an air parcel cools: isobaric cooling and adiabatic cooling. In some atmospheric processes, the pressure of the system keeps constant and these processes are isobaric.

This mechanism usually corresponds to the formation of dew, frost and radiative fogs due to radiative losses of energy (especially at night) or advection fogs when a warmer airmass moves over cooler

underlying surface. While in this study we focus on the latter i.e. adiabatic cooling: as an air parcel ascends in the atmosphere, it expands and does work to the external environments due to the decreasing pressure and hence cools (Seinfeld and Pandis, 2017). During adiabatic ascent, it is assumed that there is no energy transferred between the rising air parcel and the ambient environments i.e. the absolute value of the energy required to increase the temperature is equal to the absolute value of the work done by the expansion according to the first law of thermodynamics.

The Köhler equation (Köhler, 1936) is the most commonly used formula also the basic equation in cloud physics. It relates the equilibrium size and chemical composition of an aerosol particle to saturation ratio of water over an aqueous salt solution droplet. The equation can be given in the form of a generalized Kelvin equation including the solute effect:

푃푤 푅퐻 푒 4휎푠/푎푀푤 푆 = 표 = = = 푎푤푒푥푝⁡( ) (1) 푃푤 100 푒푠 푅푇휌푤퐷 where S is the saturation ratio of water vapour, RH relative humidity, Pw is the partial pressure of water

표 vapour over the solution droplet in the size of diameter D, 푃푤 is the partial pressure of water over a flat surface composed of pure water at the same temperature. e is the water vapour pressure and es is the saturation vapour pressure of water. 푎푤 represents the activity of water in the solution. σs/a is the solution/air surface tension; Mw and ρw are the molecular weight and density of water, respectively. R is the universal gas constant. The first term (i.e. 푎푤) in Eq. (1) accounts for the solute term or Raoult effect: the presence of salt dissolved in the water leading to a lowering of vapour pressure of water over the solution. The second term (i.e. the exponential term) represents the curvature effect or Kelvin effect: over a spherical (or say convex) surface, the equilibrium saturation ratio of water vapour is higher than over a flat surface and hence supersaturation is needed. The curvature of a particle decreases with its size. Therefore, for larger particles, a smaller supersaturation is needed for equilibrium and a larger supersaturation is needed for smaller particles.

To obtain the general analytical solution, the Köhler equation can be simplified (Jacobson, 2005;

McFiggans et al., 2006), which is a widely used form of the Köhler equation:

퐴 퐵 퐴 퐵 2푀푤휎푤/푣 3푖푚푠푀푤 푆 ≈ 1 + − 3 푆푆 ≈ − 3 퐴 = 퐵 = (2) 푟 푟 푟 푟 푅푇휌푤 4휋휌푤푀푠 where SS refers to supersaturation (SS = S-1), r is the radius of the droplet, the subscripts w and s denote water and solute, respectively. Ms and ms are therefore the molecular weight and mass of the solute. i

is the Van’t Hoff factor representing the degree of dissociation of solute into ions. 퐴 represents the Kelvin 푟

퐵 effect while − represents the Raoult effect. Equation (2) clearly indicates that the Kelvin effect tends 푟3 to increase the supersaturation, while the Raoult effect tends to decrease the supersaturation. By plotting SS as a function of r, we can obtain what is known as Köhler curve. Figure 3 shows Köhler curves for (NH4)2SO4 aerosols with various dry diamters. When SS < 0, the Raoult effect dominates.

Whereas as r increases the solution becomes so dilute that it is close to pure water, in which case the

Kelvin effect is more important. This can also be indicated by Eq. (2) that both the solute and curvature effects decrease with increasing size yet the solute effect decreases faster (by a factor of the square of the droplet radius).

The maximum of Eq. (2) can be worked out by taking the derivative of SS, setting it to zero and solving the radius which is known as the critical radius rc and the corresponding critical supersaturation SSc (%):

3퐵 4퐴3 푟 = √ 푆푆 = √ (3) 푐 퐴 푐 27퐵

It can be seen clearly from Fig. 3 that SSc is lower for hygroscopic aerosol particles ((NH4)2SO4 as an example here) with larger dry diameters. If the ambient water supersaturation (which is determiend by the updraught velocity of the air parcel) is lower than SSc, the hygroscopic growth will be limited. For example, for a certain ambient supersaturation SSB (see point B on the Köhler curve in Fig. 3), a particle will be limited by its corresponding size on the Köhler curve rB. If the particle grows larger, it will shrink back to rB by evaporation because the ambient saturation ratio is lower than the equilibrium saturation ratio corresponding to this larger size. Likewise, if a particle shrinks smaller, it will grow back to rB.

These particles are known as haze particles whose sizes are stable and limited by the ambient saturation ratio. When the ambient water supersaturation is larger than SSc, particles can grow beyond rc, in which case it will subsequently activate and experience runway growth until the available water vapour is not enough i.e. the ambient saturation ratio is lower than the equlibrium saturation ratio at instantaneous radius (McFiggans et al., 2006). For example, an ambient water vapour supersaturation of 0.15% shown by the dashed line in Fig. 3 exceeds the SSc of (NH4)2SO4 aerosols with dry diameters larger than 0.1 µm and hance these aerosols will activate into cloud droplets. In the the Aerosol-Cloud-

Precipitation-Interaction-Model (ACPIM), particles grow beyond their rc are defined as activated droplets or cloud droplets (also see Section 1.6). In addition, larger aerosol particles compete for condensable

water vapour more efficiently and hence the prescence of large particles suppresses the supersaturation an environment can achieve (Simpson et al., 2014). The competition of particles for water vapour hampers the activation of all available CCN into cloud droplets. In summary, the activation of a particle is determined by its chemical composition, dry diameter and the ambient saturation ratio.

The CCN activity in terms of the condensation of water vapour on particles solely comprising NaCl,

(NH4)2SO4 can be well represented by the conventional Köhler theory. However, unknown organic compounds, which account for a large fraction of ambient aerosol composition in the atmosphere, cannot be fully predicted by this well-established theory because of our inability to accuratly predict the activity of all molecules (Seinfeld and Pandis, 2017). The extended Köhler theory, which includes the impact of organic compounds on surface tension and solute effect, has been successfully applied in laboratory to describe the CCN activities of both single and multi-component aerosols. Nevertheless, ambient aerosols consist of thousands of different species of organic compounds. The lack of property data (e.g. densities, molecular weights, water activity) of most of the organics limits the application of this approach for ambient aerosols (Petters and Kreidenweis, 2007; Wang et al., 2008 and references therein). A single hygroscopicity parameter κ proposed by Petters and Kreidenweis (2007) is widely used to parameterize the water activity aw of the solution. This relates the water activity to the ability of a particle to uptake water:

1 푉 = 1 + 휅 푠 (4) 푎푤 푉푤 where Vs is the volume of dry particle (i.e. solute) and Vw is the volume of water. The value of multi- component κ follows a simple mixing rule: 휅 = ∑푖 휀푖휅푖 where i refers to the component i and εi is the volume fraction. For aerosol particles with unknown chemical composition, values of κ can be obtained by fitting experimental hygroscopicity growth factor and CCN-activation data. Values of κ fall within the range of 0 to 1.4 (0.5 to 1.4 corresponding to highly-CCN-active salts and 0.01 to 0.5 corresponding to from slightly to very hygroscopic organics). In the atmosphere, it typically ranges from 0.1 to 0.9.

The equation defining the “κ- Köhler theory” can be derived as (Petters and Kreidenweis, 2007a):

3 3 퐷 −퐷푑 4휎푠/푎푀푤 푆(퐷) = 3 3 푒푥푝⁡( ) (5) 퐷 −퐷푑(1−휅) 푅푇휌푤퐷 where Dd is the dry diameter. Equation (5) describes the relationship between particle dry diameter and

CCN activity. In the PyACPIM model, the equilibrium saturation ratio and the activation of particles are

calculated from the “κ- Köhler theory”. Equation (5) also indicates that a more hygroscopic particle requires a lower supersaturation to activate into cloud droplets.

Figure 3: Köhler curve for (NH4)2SO4 aerosol particles with 0.05 µm, 0.1 µm and 0.5 µm dry diamters (i.e. various masses of dissolved (NH4)2SO4) at 293 K (Andreae and Rosenfeld, 2008, Figure. 1).

1.4 Volatility of atmospheric organic compounds

Organic components in the atmosphere are the most poorly understood fraction of aerosols (Prather et al., 2008) due to the complexity and uncertainty in their species. The proportion of organic components in aerosols is determined by the volatility of organics that governs the exchange of species between gas and particulate phase (Donahue et al., 2011; Hidy, 2019). Volatility of organic compounds is often measured by its pure vapour pressure and is highly dependent on the polarity of the molecule (Seinfeld and Pandis, 2017). When VOCs in the atmosphere act as precursors i.e. are oxidized to form SOAs, more functional groups are added (oxygen-containing substituents) and the polarity of the products is increased relative to the precursor and thereby the vapour pressure is lowered (i.e. decrease in volatility). In this case, a part of the products generated from such oxidation reactions partition into particulate phase (Donahue et al., 2012 and references therein). Besides oxidation, other chemical transformation primarily oligomerization and fragmentation also determine the volatility of organic compounds. Similar to oxidation, oligomerization will result in a decrease in volatility and hence the organics tend to partition into the particle phase. Whereas fragmentation will lead to an increase in volatility (Gilardoni and Fuzzi, 2016).

Normally, organic compounds in the atmosphere mostly exist in the particulate phase with vapour pressures < 10-11 atm; both in the vapour and particulate phase with vapour pressures between 10-5 and 10-11 atm and in the particulate phase for > 10-5 atm (Seinfeld and Pandis, 2017). Using the ideal gas law, saturation mass concentration of a pure organic compound (Cο), in µg m-3, can be obtained from its vapour pressure above pure liquid. While for a mixture, the effective saturation mass concentration C* =⁡γCο where γ is the activity coefficient. Volatility is usually expressed by Co and C* due to the fact that mass concentration measurements are pervasive in aerosol research (Donahue et al.,

2011; Seinfeld and Pandis, 2017). Hereafter, the term “saturation concentration” refers to C*.

For an organic compound i at equilibrium between two phases, the saturation concentration of the species i can be expressed as (Donahue et al., 2006; Cappa and Jimenez, 2010):

푣푎푝 6 ο ∗ 퐶푖 퐶푂퐴 10 푀푖γ푖푝푖 퐶푖 = 푎푒푟 = (6) 퐶푖 푅푇

푣푎푝 푎푒푟 where 퐶푖 is the mass concentration of species i in the gas phase whilst 퐶푖 in the condensed phase,

-3 퐶푂퐴 is the total mass concentration of organic aerosols, all in µg m . 푀푖 is the molecular mass, χi is the

ο mole fraction,⁡푝푖 is the vapour pressure of pure liquid. Note that the unit of pressure is in Pa instead of torr in the studies where this theory was proposed (Pankow, 1994a, 1994b; Odum et al., 1996).

푝 푀 Equation (6) is obtained from the ideal gas law 퐶푣푎푝 = 푖 푖 and a version of Raoult’s law 푝 = 휒 γ 푝ο, 푖 푅푇 푖 푖 푖 푖 where 푝푖 is the partial pressure, assuming ideal organic solution. Or it can be simplified that the γi is

∗ approximately constant across targeted ambient conditions (pseudo-ideal solution) and thus the 퐶푖 will also keep constant (Donahue et al., 2009, 2012b). Another assumption is that organic compounds in the system have similar molecular weight i.e. the average Mave ≈ Mi (Donahue et al., 2009; Seinfeld and

Pandis, 2017).

The ratio of the concentration of i in aerosol phase to the total amount (vapour + aerosol phase) is

푎푒푟 푎푒푟 퐶푖 퐶푖 defined as a partitioning coefficient 휉푖 and 휉푖 = = 푎푒푟 푣푎푝 . Therefore, we obtain the equation for 퐶푖,푡표푡푎푙 퐶푖 +퐶푖 the basic gas-particle equilibrium partitioning:

∗ −1 퐶푂퐴 = ∑푖 퐶푖휉푖 ; 휉푖 = (1 + 퐶푖 ⁄퐶푂퐴) (7) An essential feature of the equilibrium partitioning can be seen from Eq. (7): for an organic constituent with C* = COA, it is expected to be half in condensed phase and half in vapour phase (i.e. ξi = 0.5).

Volatility Basis Set (VBS) framework (Donahue et al., 2006) is an empirical approach to treat absorptive partitioning of atmospheric organic aerosols. Organic compounds are categorized (logarithmically) into uniform basis set referred to as “bins” by powers of 10 along an axis of the saturation concentration values at 300 K i.e. there is a group of organic compounds reside in a “bin” with fixed representative volatility. When temperature changes, a basis set shifts according to the Clausius-Clapeyron equation.

Hereafter the volatility basis set is also called volatility distribution.

Figure 4 illustrates volatility distributions spanning a range of typical ambient saturation concentrations from 0.01 µg m-3 to 1000 mg m-3. The height of each bar represents the total mass loading (gas + aerosol phase) of this species and the coloured area is the total mass loading in the aerosol phase. It is clearly shown the feature that there is 50% material in each phase when C* = COA . Furthermore, Fig.4

(b) shows that almost all material is in the aerosol phase for C* < 10 µg m-3 whereas in the vapour phase

* -3 ∗ when C > 1000 µg m . This can be explained by Eq. (7) that the organic species in the bin 퐶푖 = 0.1퐶푂퐴

∗ * will be ~90% in the aerosol phase and 퐶푖 = 10퐶푂퐴 will be ~10%. Organic constituents with C beyond the range from 0.1COA to 10COA will almost be in only one phase. Therefore, the range of volatility in which organic compounds can be considered as semi-volatile normally spans about a factor of 100 in

C*, centred around COA (Donahue et al., 2012b). In addition, the range of semi-volatiles shifts as COA

-3 -3 changes. As shown in Fig. 4, this range is C* = {0.1, 1, 10} µg m for COA = 1.0 µg m , say remote

-3 -3 regions, whereas {10, 100, 1000} µg m for COA = 100 µg m , say extremely polluted areas. Actually,

-3 1.0 to 100 µg m is a typical range of COA levels in the atmosphere. On the basis of the VBS, organic compounds can be classified according to their effective saturation concentrations (Donahue et al.,

2009; Seinfeld and Pandis, 2017):

Extremely Low Volatility Organic Compounds (ELVOCs): C* < 10-3 µg m-3, always in particle phase and play a crucial part in the nucleation (Seinfeld and Pandis, 2017). Low Volatility Organic Compounds

(LVOCs): C* = {10-3, 10-2, 10-1} µg m-3, mostly reside in the particle phase. Semi-Volatile Organic

Compounds (SVOCs): with volatility bins 1, 10 and 100 µg m-3, compounds in this class can be found significantly in both gas and particle phase under typical ambient conditions, the main focus of this study.

Intermediate Volatility Organic Compounds (IVOCs): with C* = {103, 104, 105, 106} µg m-3, entirely in gas phase when they are immediately emitted into the atmosphere, but can be converted to condensable

species due to gas-phase oxidation reactions. And Volatile Organic Compounds (VOCs): C* < 106 µg m-3, a traditional category for the majority of emissions of gas-phase organics.

-3 Figure 4: Volatility distributions with different total condensed aerosol mass COA=1.0 µg m (left panel) and -3 COA=100.0 µg m (right panel). The green portion represents the material in condensed phase. COA increases with increasing quantities of α-pinene + ozone products. a) a lower concentration (26 µg m-3) of α-pinene (i.e. precursor) is consumed, resulting in 1.0 µg m-3 of products in the aerosol phase. b) 468 µg m-3 precursor giving 100 µg m-3 SOA mass. As the total aerosol mass loading increases, the 50:50 bin shifts to the right i.e. to the bin with higher volatility (Donahue et al., 2009, Figure 1).

1.5 PyACPIM description

The Aerosol-Cloud-Precipitation-Interaction-Model (ACPIM) is a bin-resolving air parcel model originally developed to explore the influence of aerosol on mixed-phase cloud for the Aerosol Properties

Processes And InfluenceS on the Earth’s climate (APPRAISE) project. It has been extended to include organic components with varying volatilities in Topping et al., (2013). A Python version of the ACPIM

(PyACPIM) has been used in this study which accounts for multi-component and multi-mode aerosol microphysics and semi-volatile organic material. It is publicly available and can be accessed at https://github.com/emmasimp/py-cloud-parcel-model. This model can be run either for an ascending air or the conditions, assuming adiabatic expansion. In this study, the use of PyACPIM is restricted into simulated atmospheric adiabatic ascend and liquid-phase clouds.

Herein the governing equations are presented. The rate of change of water vapour mixing ratio (ωv) is described by conserving water mass:

푑휔 푑푚 푣 = −∑푁 푙,푖 (8) 푑푡 푖 푑푡 where i subscript refers the bin i, Ni is the number mixing ratio of aerosol particles and 푚푙,푖 the liquid water mass in this bin. One of the input parameters for the PyACPIM to describe the aerosol number size distributions is the number concentration of particles (# m-3). Under low troposphere atmospheric conditions, the density of air is approximately 1kg m-3. Therefore, number mixing ratio (# kg-1) is approximately numerically equal to the number concentration (# m-3). Analogously, the conversion between gas and particle phase organic components in each volatility bin follows mass conservation as well:

푑휔표푟𝑔,푗 푑푚표푟𝑔,푖,푗 = −∑푁 (9) 푑푡 푖 푑푡 where j denotes the volatility (i.e. the type of the organic component) and 휔표푟푔,푗 is the mixing ratio of organic vapour in the j th volatility bin (see Section 1.4 for VBS); 풎풐풓품,풊,풋 is the mass of the j th organic component in the i th size bin.

The variation rate in temperature and pressure are driven by the updraught velocity (w) which is predetermined by the input value. The vertical change in atmospheric pressure with altitude follows the

푑푃 hydrostatic equation, ⁡ = −휌 푔, where P is pressure, z is height, ρa is the density of air and g is the 푑푧 푎 gravitational field strength. The updraught velocity is assumed to be constant throughout the simulation

푑푧 and thus = 푤. Combining it with the equation of state for moist air 푃 = 휌 푅 푇 (Jacobson, 2005), 푑푡 푎 푚 where Rm is the specific gas constant for moist air and T is temperature, we yields the change rate of pressure with regards to time (i.e. time derivative):

ⅆ푃 푃 = − 푔푤 (10) ⅆ푡 푅푚푇

The time derivative of temperature is expressed via the first law of thermodynamics for adiabatic expansion (Seinfeld and Pandis, 2017):

푑푇 푅 푇 푑푃 퐿 푑휔 = 푚 − 푣 푣 (11) 푑푡 푃푐푝,푚 푑푡 푐푝,푚 푑푡 where Lv is the latent heat of vaporisation and cp,m is the specific capacity of moist air at constant pressure.

The rate of change of water mass in the size bin i can be described by the diffusional particle growth equation (Jacobson, 2005):

푑푚 4휋푟푖퐷푣(푆−푆푒푞,푖)푒푠푎푡휌푎푒푟,푖 푖 = (12) 퐷푣퐿푣푆푒푞,푖푒푠푎푡휌푤 퐿 푀 휌 푅푇 푑푡 ( 푣 푤−1)+ 푊 휅푎푇 푅푇 푀푤 where ri is the radius of the aerosol particles in bin i, esat is the saturation vapour pressure of water vapour calculated via the equation from the Buck equation (Buck, 1981; Buck 1996); S is the saturation ratio and Seq is the equilibrium vapour pressure (calculated through κ – Köhler, see Section 1.3); ρw is the density of water and ρaer the density of aerosol (including liquid water); Mw is the molecular weight of water; R is the Universal gas constant. Dv and κa are the diffusivity and thermal conductivity of air, respectively. These two terms are modified according to the transition regime. The Knudsen number is the ratio of the mean free path of a molecule and the representative scale of the size of a particle

(Jacobson, 2005). The Knudsen number for air is between 1 and 10). It is between the continuum regime (the particle size is large compared to the mean free path of an air molecule) and the free- molecular regime (the mean free path is large compared to the particle size) (Jacobson, 2005; Seinfeld and Pandis, 2017). Dv and κa can be adjusted as (Pruppacher and Klett, 1996):

∗ 퐷푣퐹푣 ∗ 휅푎퐹ℎ 퐷 = 푟 , 휅 = 푟 (13) 퐷 퐹 휅 퐹 ⁄(푟+5.6×10−7)+ 푣 푣 ⁄(푟+2.16×10−7)+ 푎 ℎ ⁄ 2휋푀 ⁄ 2휋푀 푟훼 √ 푤 푟훼 √ 푎 푐 푅푇 푡 푅푇 respectively, where Fv, Fh, αc and αt are the ventilation coefficient of water vapour, thermal ventilation factor, the mass accommodation (or condensation) coefficient and the thermal accommodation coefficient, respectively. The mass accommodation coefficient is the fraction of gas molecules sticking to the particle and stay in the condensed phase. The thermal accommodation coefficient is the fraction of the molecules reflecting off the particle (Jacobson, 2005). In this study, they are all taken as unity throughout the simulations. Furthermore, the change rate of mass of organics can be described by analogous growth equation by substituting corresponding molecular weights of organics, saturation vapour pressures and equilibrium relation (Topping and McFiggans, 2012; Topping et al., 2013). The temperature variation of saturation vapour pressure of organic vapours was calculated via the Clausius-

Clapeyron equation and the equilibrium vapour pressure is determined by Köhler theory (see Section

1.3).

The ordinary differential equations (ODEs), Eq. (8) to Eq. (12), together describe the variation with time of water vapour mixing ratio, organic vapour mixing ratio, temperature, pressure and height during the lifting of air parcel. In the PyACPIM, the Python package Assimulo is used to solve these ODEs which provides a high-level interface for a wide variety of solvers (Andersson et al., 2015).

Whether a particle behaving as cloud condensation nucleus is activated into cloud droplet is determined by using the optimization algorithm in an open-source Python library SciPy (Virtanen et al., 2020). The

SciPy “minimize_scalar” method allows us to calculate the minimization of a scalar function of one variable. Using this method and adding a negative sign to κ- Köhler equation i.e. Eq. (5), the maximum of κ- Köhler curve can be computed, in other words, the critical supersaturation and the corresponding critical radius for can be computed. The particles having sufficient soluble condensed mass for their growth to reach corresponding critical diameters will be recorded as activated droplets. The height at which the air parcel is lifted is determined by the specified ascend time (“runtime”) and at the given updraught velocity. The PyACPIM model is time dependent and the outputs are as a function of time

(Simpson et al., 2014).

In this study, the number of size bins, n, is 70 for each log-normal mode. This number has been considered as the most efficient to capture the increase in cloud droplet number due to the additional organic vapour condensing on the initial aerosol population (Topping et al., 2013). Thus, there are 140 size bins for bimodal distributions and 210 for trimodal distributions. The size (upper bound diameter) of aerosol in the smallest size bin, Ds, can be specified by the user. In this study, it was set as within the range of 1 nm to 5 nm considering very small aerosol sizes were simulated at the order of magnitude of 10 nm.

Initially, before the ascent, the cumulative number concentration of aerosol bounded by Di (the upper- bound diameter of the bin i) can be expressed in the terms of the error function (Zender, 2002):

푁0 푁0 1푛(퐷푖⁄퐷푚) 푁(퐷 < 퐷푖) = ⁡ + 푒푟푓( ) (14) 2 2 √2푙푛휎𝑔

where Dm is the median diameter for a certain mode and σg is the geometric standard deviation, erf is the Gauss error function (Kissell and Poserina, 2017):

2 푡 2 푒푟푓(푡) = ∫ ⅇ−푥 ⅆ푥 (15) √휋 0

When Di = Ds, the number of aerosol particles in the smallest bin is obtained. In this model, the number of aerosol particles Ni is set to be the same for each following larger-size bin, 푁푖 = ⁡ 푁0/70, where N0 is the total number concentration of a certain mode and i subscript denotes every bin. Therefore,

푁(퐷 < 퐷푖+1) = 푁(퐷 < 퐷푖) + 푁0/70 , by rearranging the Eq. (14) in terms of Di, the upper-bound

(maximum) diameter for each bin can be numerically solved and calculated. Moreover, the maximum diameter for the bin i is actually the lower-bound (minimum) diameter for the adjacent bin i+1. Therefore, by subtracting the cumulative concentration N(D < Di) from N(D < Di+1) (Zender, 2002), the truncated concentration for particles within the size range between Di and Di+1, N(Di, Di+1) is given by:

푁0 푙푛⁡(퐷푖+1/퐷푚 푙푛⁡(퐷푖/퐷푚 푁(퐷푖,⁡⁡⁡퐷푖+1) = [푒푟푓 ( ) − 푒푟푓 ( )] (16) 2 √2푙푛휎𝑔 √2푙푛휎𝑔

The initial dry densities of aerosol population which comprises multicomponent condensed organic

1 푥 material along with the inorganic “core” is calculated by adopting = ∑ 푗 (applying ideal mixing 휌푡표푡푎푙 휌푗 rule), where ρtotal is the density of the mixture, xj, ρj are the mass fraction and the pure component density of component j, respectively (Topping et al., 2011). Therefore, the mass of each aerosol particle in each bin can be obtained assuming they are spherical (Seinfeld and Pandis, 2017). In summary, every size bin except the smallest has the same number concentration, whilst the diameter widths of bins (i.e. the differences between maximum and minimum diameter for a certain bin) are different.

Throughout the ascent of air parcel and growth of particles, number of particles in a size bin remains the same as the initial whereas the size of particles in this bin grows.

1.6 Co-condensation of semi-volatile organic material

As demonstrated previously, the formation of cloud droplets by the condensation of water vapour on inert aerosol particles comprising single involatile solute and its impacts are relatively well understood.

However, taking into account the organic compounds which exist in both particle and gas phases, the description of the growth and activation of lifting particles becomes more complex and it is relatively unexplored. Nevertheless, this semi-volatile material accounts for a substantial proportion of fine particulate matter in the atmosphere.

Figure. 5 illustrates the adiabatic ascent of an air parcel including the presence of condensable semi- volatile organic material. During the lifting, both water (in blue) and organic vapours (in green) will condense simultaneously on the aerosol particle according to their prevailing saturation ratio (or RH for

water). Topping and McFiggans (2012) calculated the co-equilibration of semi-volatile organic vapours through the adaption of Köhler equation and found that the addition condensed semi-volatile organic material substantially increases the soluble mass of particles and thereby increases their effective dry size, and in this way the Köhler curves are shifted towards larger sizes and flattened with reduced critical supersaturation (can see Fig. 3). Organic compounds in the condensed phase in the growing aqueous solution droplet become more and more diluted rapidly due to the abundance of condensing water vapour in the typical atmosphere. As a consequence, the vapour pressure of each organic component over the droplet decreases according to the solute effect, increasing its tendency to continue to condense into the particle phase. Growing particles will take up more water due to the additional soluble mass owing to co-condensation. This is confirmed by Hu et al. (2018) by observing that κ reached up to 8.48 with the condensation of butylene glycol and tri-ethylene under controlled laboratory conditions. Beyond the critical water supersaturation, particles activate into cloud droplets and continues to grow unimpededly until available water vapour is depleted. In cloud, organic vapours will be completely captured by droplets due to the further dilution by condensed liquid water. This effect of co-condensation on cloud drop formation was confirmed by Topping et al. (2013) and quantified under a range of atmospheric reasonable conditions using the ACPIM. Connolly et al. (2014) modified previously developed parameterisations to include this effect and the proposed new parameterisation was further extended by Crooks et al. (2018) to the case of multiple aerosol modes. These parameterisations are meaningful since the parcel model is too computationally expensive to be applied into large-scale climate models. The bin-solving cloud parcel model with detailed microphysics such as

PyACPIM can be used to evaluate these parameterisations given that relatively few simplifications are made in such models (Simpson et al., 2014).

1.7 Aims and objectives

Cloud influence weather and climate profoundly. The effect of the co-condensation of semi-volatile organics on cloud droplet formation is not normally considered in cloud parcel models and is completely untreated in large-scale models. Further modelling work (Topping et al., 2013) suggested that the role of co-condensation on cloud droplet formation is large enough to be globally significant.

This project aims to develop the Python version of the cloud parcel modelling framework to investigate the effect of co-condensation. The first objective of this work was to develop the PyACPIM that was originally not available for the simulations for multiple species of semi-volatiles and to make the

PyACPIM publicly available. The second objective was to investigate the effect of co-condensation under a wider range of environmental conditions which have not been previously explored (e.g. much higher aerosol particle number concentrations, different initial temperatures). The third was to quantify the potential role of the atmospheric transformation of semi-volatile organic material on cloud droplet formation. To achieve this, we systematically initialised the model with parameters (e.g. multi-modal aerosol number size distributions, chemical composition and organic components volatility distributions) which are representative of the conditions in typical continental environments categorized by pollution levels.

Figure 5: Co-condensation of semi-volatile organic vapours along with water vapour during the ascent of aerosol particles. RH, relative humidity and S, saturation ratio (Topping et al., 2013).

2 Paper

2.1 Preface

Chapter 2 consists of a paper entitled: An investigation of the influence of the co-condensation of semi- volatile organics on cloud droplet number for various environments. This is in journal format in preparation to submit to Atmospheric Chemistry and Physics. To improve readability, figures and tables and corresponding captions in this paper are placed in appropriate positions in the text instead of at the end of the text as would be typical for a manuscript submitted for publication. The pagination sequence of the supplementary material follows the pagination sequence of the main text of the paper.

The first author Y.J., who is the candidate submitting this thesis, was responsible for developing the existing numerical model, designing the model initialisation framework and model scenarios, performing the modelling runs, processing and analysing data, creating the plots and writing the paper. E.S. contributed to the design and development of the PyACPIM. A.V. performed the thermodenuder measurement. D.T. designed the computational framework to extend the ACPIM. G.M. conceived and directed the research, contributed to the design of simulations and analysis of the results, and commented on the manuscript.

2.2 Paper title: An investigation of the influence of the co-condensation of semi- volatile organics on cloud droplet number for various environments

Page 28

An investigation of the influence of the co-condensation of semi- volatile organics on cloud droplet number for various environments

Yichen Jia, Emma Simpson, Aristeidis Voliotis, David Topping, Gordon McFiggans 5 Department of Earth and Environmental Sciences, University of Manchester, Manchester, M13 9PL, UK Correspondence to: Gordon McFiggans ([email protected])

Abstract. A substantial proportion of atmospheric fine particle material is semi-volatile. The co-condensation of semi-volatile organic material along with water vapour, will result in an increase in cloud droplet number concentration and hence affect the 10 Earth’s climate. By systematically initialising a cloud parcel model with representative parameters for pristine forest, background, rural, near-city, urban and kerbside environments, we confirmed and examined this effect under various environmental conditions and investigated the potential role of the atmospheric transformation of semi-volatile organic material on cloud droplet number. Across all model scenarios, the co-condensation of semi-volatiles results in the formation of a maximum of around 70% more cloud droplets compared to the simulations without co-condensation. We show that, 15 despite a relatively straightforward initialisation in the pristine forest environment, the relationship between the enhancement by co-condensation and the initial parameters are complex and non-linear. In the other five environments, the general trends of the percentage enhancement by co-condensation are: (1) the enhancement increases with updraft speeds; (2) in summer, the increase is insignificant; (3) in winter, the increase is the most significant in clean environments while very limited in the intermediate and polluted environments. However, the trend of the percentage enhancement is too intricate and complex to be 20 accessible to the simple assumption of behaviour as a function of, for example, condensable vapour per particle or per surface area. We found that initial temperature, updraughts and initial volatility distribution are the biggest contributory parameters to large predicted enhancement.

1 Introduction

Cloud radiative forcing has a profound influence on the earth’s energy balance and hence weather and climate (Forster et al., 25 2007). The indirect effect of aerosols has been considered as the highest uncertainties on current global radiative forcing research in previous Intergovernmental Panel on Climate Change (IPCC) assessment reports (Stocker et al., 2013; Wang et al.,

2008). A change in number concentrations of aerosol particles (Na) that are able to act as cloud condensation nuclei (CCN)

will lead to changing cloud droplet number (Nd) and hence modify the key properties of clouds such as brightness and lifetime (Twomey, 1977; Albrecht, 1989; Boucher et al., 2013). The formation of cloud droplets by the condensation of water vapour 30 on inert involatile particles and its impacts are relatively well understood (McFiggans et al., 2006). Nevertheless, semi-volatile organic material accounts for a non-negligible proportion of fine particulate matter in the atmosphere (Eatough et al., 2003;

Jimenez et al., 2009). However, the effects of the co-condensation of this fraction along with water vapour on particle activation remain relatively unexplored by cloud parcel models and have not been completely treated in general circulation models (GCMs), which are the main tools to evaluate the effects of aerosols on climate (Lohmann and Feichter, 2005). 35 As an air parcel ascends and cools, its ability to hold water and semi-volatile organic material diminishes and therefore the vapours (both water and organic vapours) condense towards the particle phase. The organic compounds with higher volatility

than water such as CH4 and C2H6 will stay in the gas phase, whereas the least volatile will completely condense before reaching water supersaturation. The semi-volatile organic vapours will condense rapidly on lifting particles under water subsaturation because they are much sparser than water vapour in the atmosphere. In the cases in Topping et al. (2013), most of the semi- 40 volatile organic vapours condense into the aerosol phase at cloud base. Dilution due to the substantial increase in liquid water promotes the subsequent capture of soluble and miscible semi-volatile organics until completely depletion after the runaway growth of particles in cloud (McFiggans et al., 2006; Topping et al., 2013). Yu (2011) suggested that the inclusion of atmospheric semi-volatile organics significantly contributes to the particle growth and hence the CCN number concentrations

(NCCN). Topping and McFiggans (2012) demonstrated that the inclusion of co-condensation of semi-volatile organic material 45 significantly suppresses the critical saturation ratio of a single involatile particle by calculating the equilibrium of semi-volatile material (including water) on the particle. This provided an upper bound of the facilitating effects of the co-condensation on the activation of cloud droplets. Besides the modification by the co-condensation on the particle mass, size distribution and chemical composition, particle hygroscopicity will also be altered (Petters and Kreidenweis, 2007; Gao et al., 2018). Recent laboratory work (Hu et al., 2018) confirmed that co-condensation significantly facilitates water uptake of particles by observing

50 the co-condensation towards (NH4)2SO4 seed particles. The addition of semi-volatile organic material has therefore been theoretically and experimentally shown to result in the uptake of more water, demonstrating the mechanism by which it can result in an increase in cloud droplet number concentrations. Kulmala et al., (2004) interpreted newly found feedbacks between forests and climate may be a manifestation of the potential role of the co-condensation of semi-volatile material due to an increase in the biogenic organic aerosol emission. 55 Gao et al., (2018) coupled a microphysical box model with the volatility basis set (VBS) framework along with the cloud droplet activation scheme by Abdul-Razzak and Ghan, (2000) and demonstrated that semi-volatile organic aerosols result in fewer cloud droplets in both clean and polluted environments, other than under special conditions. However, this work on the basis of a box model does not take into account that addition organic vapours condense simultaneously along with water in an ascending air parcel, which has been demonstrated to be critical in the process. Conversely, Topping et al., (2013) explained 60 theoretically the enhancement by the co-condensation of SVOCs and systematically quantified this effect for a range of aerosols and updraught velocity (w) regimes. Nonetheless, the investigated parametric range of their study was limited. For example, initial aerosol number concentrations of 300, 600 and 900 cm-3 can be greatly exceeded in densely populated cities, where concentrations might be several orders of magnitude higher (De Almeida Albuquerque et al., 2012; Hussein et al., 2019; Quality and Group, 2012; Wu and Boor, 2020).

65 In this study, through the development of an existing cloud parcel model (Sect. 2.1), we investigated the co-condensation of SVOCs in a wider range of continental conditions including pristine forest, background, rural, urban and kerbside. In addition, the potential influence of the evolution of the transformation of SVOCs in the atmosphere on cloud droplet formation was explored by systematically initialising the model using typical and coupled aerosol number concentrations, size distributions, chemical composition and volatility distributions of SVOCs for each environment.

70 2 Methods

2.1 PyACPIM and initialisation method

A bin-resolving cloud parcel model Aerosol-Cloud-Precipitation-Interaction-Model (ACPIM) was originally created to explore the aerosol-cloud interactions for the Aerosol Properties Processes And InfluenceS on the Earth’s climate (APPRAISE) project (Dearden et al., 2011). The python version of this model, PyACPIM, with binned microphysics, was developed and 75 used in this study. It can be run for simulated atmospheric adiabatic ascent or cloud chamber simulations. Additionally, this bin scheme solves liquid-phase and ice-phase cloud processes numerically. For the purpose of this work, we focus on warm cloud droplet activation by simulating adiabatic ascent of an air parcel in the atmosphere. The detailed microphysics of ACPIM was described in the supplementary material of Topping et al., (2013). To include the co-condensation of organics with varying volatility, the volatility basic set (VBS) framework (Donahue et al., 2006) is adopted in the PyACPIM. The activation of 80 growing aerosol particles is determined using the single hygroscopicity parameter κ, i.e. the critical supersaturation and diameter are calculated by the Eq. (6) in Petters and Kreidenweis (2007) which defines the “κ-Köhler theory”. The calculation of the co-condensation of semi-volatile organic vapours follows traditional Köhler theory (Köhler, 1936). Connolly et al. (2014) proposed two distinct ways to initialise the aerosol size distribution and chemical composition. In our work, we performed the second method to initialise the model given that it is more appropriate for a model like the 85 PyACPIM that explicitly solves growth equations for the condensation of semi-volatile organics. This method refers to that, at the beginning of the model runs, the input aerosol population with explicitly specified mass-weighted composition has already been equilibrated with the semi-volatile organic components. In other words, the input log-normal size distribution parameters represent the aerosol population having a desired mass fraction (Sect. 2.2.1) of semi-volatile organic material in the particulate phase. It should be noted that the simulations in the absence of co-condensation have the same initial chemical 90 composition as the simulations in the presence of co-condensation i.e. the same dry mass fraction of particle-phase organic material and hence the same dry diameters at the start of model runs. In this case, we can literally quantify the effect of the simultaneous condensation of water and semi-volatile organic material, i.e. the effect of co-condensation, rather than taking into account the potential role of different dry chemical composition distribution. The way to determine the input volatility distribution of semi-volatile organics and the repartitioning, and hence the amount of potential condensable organic vapours 95 is described in Sect. 2.2.2.

2.2 Model Simulations

2.2.1 Initial size distributions and chemical composition

Most particles potentially serving as seeds to form cloud droplets are composed of submicrometre mixtures of aerosols (Topping et al., 2013; Ueda et al., 2016; Wu and Boor, 2020). Under atmospherically reasonable supersaturations encountered 100 under typical ambient conditions and updraughts, most moderately-soluble particles with diameters larger than 200 nm will activate. The critical dry diameter range determining the number of activated droplets is normally between 50 and 150 nm (McFiggans et al., 2006). A study on the CCN formation using a cloud parcel model (Reutter et al., 2009) also presented that the initial cloud droplet number concentration in convective clouds is mostly dominated by the variability of number concentrations in the Accumulation and Aitken modes (roughly in line with the critical dry size range for activation 105 demonstrated previously) and encountered updraft velocities (i.e. supersaturation). Pöschl et al. (2010) also demonstrated that fine mode or submicrometre size aerosols contribute most to CCN, whereas supermicrometre coarse particles are more relevant to ice nuclei. Therefore, the number concentration size distribution in the sub-micron fraction is particularly indispensable for the prediction of cloud droplet number concentrations. The measurement of number size distributions covering both sub-micron and super-micron size ranges are typically a 110 combination of different techniques, which might cause potential inconsistency. The log-normal fittings of particle size distributions usually ignore the coarse mode because it is often presented as the tail of accumulation mode (Wu and Boor, 2020). The representative size distributions for different environments used to initialise the model are therefore log-normal fittings for mainly sub-micron particles (Table 1). Large CCN or giant CCN with dry diameters greater than 5 µm (Medina and Nenes, 2004) are thus not included. Particles whose range of median diameters spans up to the size of giant CCN (Barahona 115 et al., 2010; Simpson et al., 2014) may result in an atmospherically unrealistic number of large particles and hence confounding problems on the formation of cloud droplets (Topping et al., 2013). Soluble giant CCN may compete for available water vapour and hence suppress the maximum supersaturation and thereby prevent smaller particles from activating. However, Segal et al., (2007) suggested that this “competition effect” is not as efficient as shown in previous studies and it may be diminished or eliminated by the presence of hydrophobic coatings with film-forming compounds (Andreae and Rosenfeld, 2008). Therefore, 120 the ability of large CCN to inhibit the activation of an aerosol population with smaller size is not taken into consideration in this study; however, it should be further examined in a separate study. Aerosol number concentration size distribution parameters under the “Pristine Forest” condition in Tab.1 was determined in the Amazonian Aerosol Characterization Experiment (AMAZE-08) campaign performed in the Amazonia forest during the wet season (Pöschl et al., 2010). The Amazonia Rainforest during the wet season wet can be considered as the continental 125 environment where the anthropogenic activities have the minimum impact, while in the dry season it is influenced by biomass burning. Whitehead et al. (2016) reported that aerosol number concentrations are very similar during the transition from wet to dry season and the main difference is like to be attributable to biomass burning. Therefore, the distribution parameters from

Pöschl et al. (2010), which has been used to investigate CCN activation and cloud droplet formation using a numerical parcel model, were chosen to represent the extremely clean continental environment in this study. It was suggested that bimodal log- 130 normal distributions are present with a ratio of 0.81 of Aitken mode (centred at 67 nm) and Accumulation mode (centred at

-3 150 nm) number concentrations. The total number of aerosol particles (Na) varied from 10 to 1000 cm and we selected discrete values of 10, 100, 300, 500, 800, 1000 cm-3 to span the range for our investigation. The chemical composition was retrieved from the same literature as shown in Tab. 2. Notably, secondary organic aerosols (SOAs) substantially contribute more to submicrometre fraction in the pristine forest in contrast to the other five continental environments. Under such conditions, 135 most primary organic material directly emitted from the Amazonian rainforest is assumed to be associated with supermicrometre particles and hence we assumed that all organic matter consists of SOA (84.5% mass fraction in Tab.2). To investigate the effects of the co-condensation under various other continental environmental conditions, starting

submicrometre particle number size distribution log-normal parameters, particulate matter (PM) number concentration Na,

geometric standard deviation σg and geometric mean count diameter Dg have been extracted from the fittings reported in an 140 aerosol phenomenology study ( Putaud et al., 2002; Van Dingenen et al., 2004) as shown in Tab.1. This work has characterized particle distributions through synthetically gathering aerosol field measurement data during 10 years across 31 European sites categorized under five ambient conditions: kerbside, urban, near-city, rural and natural, and hence comprehensive data sets were obtained. The criteria for the category of environments were proposed by Larssen et al., (1999) among those kerbside sites are defined as within the street canyons and the urban are sites fewer than 2500 vehicles per day within a radius of 50 m. 145 For near-city, rural and natural sites, the distances from main anthropogenic sources become farther: 3–10, 10–50 and >50km, respectively. Note that in our study, background in Tab.1 refers to the natural environment in Van Dingenen et al., (2004) for the purpose of avoiding an ambiguity between natural and pristine forest. Additionally, clean environments refer to the pristine forest, background and rural; intermediate refer to the near-city; polluted environments refer to the urban and kerbside hereafter. 150 Data was selected for sites where continuous measurements have been sufficient to provide summer and winter average values. We initialised the model with 303.15 K for summer simulations and 278.15K for winter simulations. Although diurnal meteorological variations have not been accounted for in the PyACPIM, the figures in Tab.1 for different periods of the day provide a range of realistic and typical combinations of log-normal parameters that would be experienced in each environment in summer/winter. Thus, typical log-normal parameters for morning, afternoon and night were retained to explore the influence 155 of them on co-condensation instead of extracting size distributions for a specific season and period of the day to represent the situation in an environmental condition e.g. summertime afternoon (Crooks et al., 2018). As Tab.1 shows, aerosol particles are log-normally distributed into two modes in near-city winter and urban cases, whereas three modes for the other cases. From

background to kerbside conditions, all Na, surface areas and volumes generally increase as pollution level rises. An exception

is the intermediate condition (near-city) where the volume is larger than the urban, though Na is smaller. This results from 160 more particles with larger median diameters being present under the near-city condition than under the urban classification. Furthermore, this can be regarded as a manifestation of the role of the near-city as a transition between clean and polluted

areas. Tab.1 clearly shows that median diameters of mode 2 and mode 3 (i.e. Dg2, Dg3) generally decrease with pollution levels,

especially Dg3. This indicates the dominance of anthropogenic emissions in polluted environments (e.g. combustion emissions by vehicles) which produces much more small particles through the nucleation (Harris and Maricq, 2001; Myung and Park, 165 2012). Putaud et al., (2004) presented the accompanying chemical characterisation for the identical aerosol phenomenology study. Environments were further categorized into 3 types as shown in Tab. 2 according to the characteristics of chemical contribution (background and rural sites have similar aerosol chemical composition and so do near-city and urban). This study provided mass-weighted compositions (%) in the fine fraction. Inorganic compounds are primarily nitrate, non-sea-salt-sulphate and 170 ammonia. Mineral dust and sea salt have already been excluded given their small contributions (~5%) (Putaud et al., 2004) in

+ - 2- fine particles. The chemical composition of inorganic components is presented in the form of NH4 , NO3 , SO4 , while the properties of each neutral salt are required for the input of the PyACPIM. A simplified ion-pairing scheme proposed by Gysel et al. (2007) is adopted to convert ion mass concentrations to salt mass concentrations and hence the relative concentration (%)

+ as shown in Tab. 2, with a further assumption that aerosol is fully neutralized by NH4 . In other words, it can be reasonably

175 assumed that nitrate in the fine aerosol preferentially combines with ammonia to form particulate NH4NO3 and the residual

ammonia combines with sulphate and subsequently the residual sulphate forms particulate H2SO4. In this way we can determine the input values of densities, κ, and enthalpy of vapourisation etc., for each component of aerosols. We gave an educated assumption that primary organic aerosols consist of hydrocarbon-like OA (HOA) that is a surrogate of anthropogenic combustion primary OA (Cappa and Jimenez, 2010). Therefore, the percentage contribution of HOA was reasonably changed 180 from higher to lower from more polluted environments to less polluted environments by constraining that HOA accounts for 40% organic mass in polluted environments and 20% in clean environments. The values of the percentage contributions are in line with the order of magnitudes of data reported by Jimenez et al., (2009) for environmental types which were similarly classified, respectively.

185 Table 1: Log-normal mode parameters of initial number size distributions under 6 different continental environmental conditions retrieved from the literature. Corresponding values of total number (i.e. the sum of the number concentrations of each mode), surface areas, volume, the available amount of condensable organic vapours, the ratio of potential condensable organic mass and total number (i.e. condensable organic mass per particle) are calculated. The equations used to obtain total number, surface area and volume were reported in Putaud et al., (2002) and the approach to calculate the mass of available organic vapours are introduced in Sect. 2.2.2; The parameters of 190 Mode 1 are blank if the distribution is fitted into two modes. The parameters of pristine forest aerosol size distributions had been measured and determined during the AMAZE-08 by Pöschl et al., (2010). Whilst data for kerbside, urban, near-city, rural and background environments are from a synthetic review of aerosol physical characteristics across Europe by Putaud et al., (2002) and Van Dingenen et al., (2004) .

Mode 1 Mode 2 Mode 3 Condensable Condensable Different initial Total Surface organic Volume organic Environments number concentrations N1 N2 N3 Number area material per Dg1 Dg3 (µm3/cm3) material / size distributions 3） σg1 3） Dg2 (µm) σg2 3） σg3 (#/cm3） (µm2/cm3) particle (µgm- (#/cm (µm) (#/cm (#/cm (µm) (µg/m3) 3/#)

Na = 10 4.48 0.067 1.32 5.52 0.150 1.43 10 1 0.02 0.18 1.79E-02

Na = 100 44.75 0.067 1.32 55.25 0.150 1.43 100 6 0.18 0.93 9.28E-03

Na = 300 134.25 0.067 1.32 165.75 0.150 1.43 300 17 0.55 1.95 6.51E-03 Pristine forest Na = 500 223.76 0.067 1.32 276.24 0.150 1.43 500 29 0.92 2.74 5.47E-03

Na = 800 358.01 0.067 1.32 441.99 0.150 1.43 800 46 1.47 3.73 4.66E-03

Na = 1000 447.51 0.067 1.32 552.49 0.150 1.43 1000 58 1.84 4.31 4.31E-03 Morning 24092 0.021 1.77 23586 0.020 2.00 6872 0.111 1.74 57883 633 26.28 7.69 1.33E-04 Summer Afternoon 23586 0.020 2.00 13734 0.030 1.57 18452 0.074 1.97 55771 932 32.19 8.11 1.45E-04 Night 16333 0.017 1.84 8192 0.029 1.79 9670 0.072 2.00 34195 486 16.89 5.07 1.48E-04 Kerbside Morning 11442 0.018 2.00 40878 0.025 1.60 24044 0.066 2.00 76364 1015 33.39 8.05 1.05E-04 Winter Afternoon 11478 0.023 2.00 37570 0.026 1.61 31623 0.066 2.00 80671 1307 43.00 9.70 1.20E-04 Night 12069 0.021 1.56 13212 0.031 2.00 4531 0.087 2.00 29812 411 15.32 4.70 1.58E-04 Morning 3461 0.025 1.49 7412 0.059 2.00 10873 221 7.04 2.31 2.13E-04 Summer Afternoon 4933 0.027 1.56 5572 0.067 2.00 10505 222 7.64 2.50 2.38E-04 Night 3047 0.033 1.85 3373 0.073 2.00 6421 170 6.19 2.15 3.35E-04 Urban Morning 15390 0.027 1.67 9736 0.068 2.00 25127 429 14.21 3.71 1.48E-04 Winter Afternoon 14511 0.027 1.69 11314 0.066 2.00 25824 462 15.08 4.10 1.59E-04 Night 5633 0.026 1.69 5477 0.070 2.00 11110 241 8.57 2.72 2.45E-04 Morning 632 0.010 1.93 2575 0.030 1.87 3609 0.095 1.89 6817 247 10.25 4.35 6.38E-04 Summer Afternoon 638 0.010 1.69 2508 0.027 1.61 4863 0.093 1.90 8010 311 13.03 5.18 6.46E-04 Night 256 0.018 1.73 2084 0.047 1.71 2859 0.120 1.81 5199 288 12.80 5.41 1.04E-03 Near-city Morning 6889 0.041 1.84 6067 0.125 1.62 12956 551 19.06 7.66 5.91E-04 Winter Afternoon 4731 0.035 1.91 7416 0.110 1.71 12147 543 19.39 7.55 6.22E-04 Night 2908 0.052 1.74 9541 0.118 1.62 12449 710 24.06 9.11 7.32E-04 Morning 993 0.027 1.54 639 0.059 1.38 1415 0.104 1.86 3047 116 4.83 3.00 9.85E-04 Summer Afternoon 2089 0.028 1.53 553 0.060 1.36 1459 0.114 1.69 4101 118 4.10 2.88 7.02E-04 Night 1790 0.047 1.76 553 0.070 1.42 773 0.153 1.61 3116 124 4.57 3.21 1.03E-03 Rural Morning 758 0.033 1.42 338 0.061 1.34 871 0.116 1.75 1967 77 3.03 2.29 1.16E-03 Winter Afternoon 670 0.032 1.39 426 0.058 1.36 854 0.113 1.71 1950 69 2.41 2.03 1.04E-03 Night 520 0.036 1.37 400 0.063 1.34 746 0.130 1.63 1666 72 2.60 2.19 1.32E-03 Morning 71 0.024 1.53 1534 0.073 1.63 324 0.232 1.39 1929 110 4.40 3.27 1.70E-03 Summer Afternoon 185 0.026 1.56 1364 0.085 1.61 276 0.246 1.38 1825 114 4.63 3.41 1.87E-03 Night 160 0.040 1.56 1718 0.082 1.70 224 0.245 1.38 2101 117 4.48 3.28 1.56E-03 Background Morning 287 0.024 1.51 716 0.058 1.55 425 0.198 1.52 1428 86 3.99 3.01 2.11E-03 Winter Afternoon 374 0.022 1.72 711 0.061 1.66 354 0.209 1.44 1439 78 3.38 2.73 1.90E-03 Night 20 0.011 1.42 1170 0.049 1.75 410 0.199 1.49 1600 87 3.79 2.90 1.82E-03

Table 2: Relative contribution (%) of initial fine mode aerosol chemical composition in different environments (Putaud et al., 2002, 195 2004; Pöschl et al., 2010). BC refers to black carbon; HOA refers to hydrocarbon-like organic aerosol; SOA refers to secondary organic aerosol.

Pristine Background Near-city Composition Kerbside Forest & Rural & Urban BC - 11.0 10.8 22.1 HOA - 6.3 11.9 15.1 SOA 84.5 25.2 17.8 22.6

NH4NO3 - 8.8 29.5 18.2

(NH4)2SO4 6.2 33.0 25.0 18.2

H2SO4 9.3 15.7 4.9 3.9

2.2.2 Initial volatility distributions

The VBS proposed a method to logarithmically categorise organic components into bins by a factor of 10 according to the 200 volatility (characterized by C*, effective saturation concentration) of each bin (Donahue et al., 2006) i.e. each bin has a representative volatility for a surrogate (C* = 0.001, 0.01, 0.1, 1, 10, 100 µgm-3 in our case). This was retrieved from chamber experiments of SOA formed from anthropogenic and biogenic VOC precursor and their mixtures using thermodenuder (TD) measurements. The volatility distribution is dependent on the mass fraction remaining (Karnezi et al., 2014) as the SOA particles pass through tube heated to different temperatures in steps from ambient to 90 °C (i.e. components with higher 205 volatility evaporate whereas lower are kept in particulate phase). An evaporation kinetics model was used to estimate the volatility distribution as shown in Fig. 1 (Voliotis et al., 2020 in prep). Figure 1 illustrates the VBS retrieved from TD measurements for the SOA from the precursor ο-cresol, α-pinene + isoprene and α-pinene + isoprene + ο-cresol (mixture) and the sum of the absolute mass loadings of the condensed-phase SOAs in each bin are 48.29, 73.07 and 43.04 µg m-3 respectively. From the left to the right, the volatility of the surrogates of the SOA components in each bin increase. It should be noted that 210 the TD-retrieved SOA volatility distribution used in this study was not corrected for the particle losses in TD. The evaluation of the potential effect is presented in Sec. 5 in the Supplementary. In clean environments especially where there is an abundance of plant species, a large amount of non-methane biogenic organic compounds comprising isoprene and α-pinene is emitted to the atmosphere (Claeys et al., 2004; Plewka et al., 2006). The formation of aerosol particles in these areas is associated with the secondary oxidation products of the isoprene and α- 215 pinene (Hämeri et al., 2001). SOA from ο-cresol may be considered more representative of that formed from aromatic VOC in anthropogenic emissions e.g. diesel exhaust, gas stations and combustion of coal (Badanthadka and Mehendale, 2014). Therefore, we used volatility distributions of oxidation products from ο-cresol, the mixtures, and α-pinene + isoprene to represent the volatility distributions of SOAs for the polluted, intermediate and clean condition, respectively. Additionally, HOA as stated in Sect. 2.2.1, is a surrogate of primary OA associated with anthropogenic activities and the volatility profile 220 of HOA was taken from Cappa and Jimenez (2010). As demonstrated in Sect. 2.2.1, we have specified the initial chemical composition (Tab. 2) for different environments. For each model run there is a corresponding initial number size distribution. Therefore, we can derive a mass loading of SOA in condensed phase to match the specified mass-weighted aerosol composition. The volatility distributions from TD measurements were subsequently repartitioned iteratively to reach new equilibrium by the absorptive equilibrium partitioning 225 theory along with VBS ( Donahue et al., 2006; Cappa and Jimenez, 2010). It is noteworthy that, by assuming that the total (gas + particle) mass in each volatility bin has the same fractional contribution in the atmosphere as in the chamber experiments where the volatility was determined, the calculation was further constrained instead of using a Newton-Raphson scheme in the previous study (Topping et al., 2013). In this way the total mass loading in each volatility bin was obtained with a desired SOA contribution to the specified chemical composition. Analogously, the repartitioning of HOA produces the total mass of HOA 230 in each volatility bin. Thus, for each model run, the corresponding input volatility distribution under dry conditions and the

amount of condensable organic vapours was obtained, along with the achievement of the specified chemical composition as shown in Tab. 2. By systematically initialising the number concentrations, size distributions, chemical composition and volatility distributions of SVOCs, we conducted 252 model runs taking into account co-condensation, and 252 analogous model runs 235 without co-condensation that we will refer to as base-case simulations hereafter. In this way, we are able to investigate the impact of the co-condensation of SVOCs under various environmental conditions and also the potential influence of the transformation of SVOCs in the atmosphere on cloud droplet formation.

240 Figure 1: TD-retrieved volatility distributions of chamber SOA, uncorrected for particle losses in TD (Voliotis et al., 2020 in prep). SOAs from the photo-oxidation of anthropogenic (ο-cresol), biogenic (α-pinene + isoprene), and mixed precursor (α-pinene + isoprene + ο- cresol) are coloured in red, green and yellow respectively.

2.2.3 The setup of other parameters

As the SVOC component in each volatility bin is actually a representative component, assuming values of parameters that 245 describe the properties of the component in each bin were assigned. The density of condensed SVOCs was assumed to be 1.5 g cm-3. This is a moderate value for the SOA material (Slowik et al., 2010). An enthalpy of vaporisation of 150 kJ mol-1 was assumed (Cappa and Jimenez, 2010; Topping et al., 2013). Van’t Hoff factor is 1 and we assumed that condensing semi- volatile material is soluble. For SVOCs with C* = 0.001 to 100 µg m-3 i.e. high volatility to low volatility, molecular weights

were assumed to decrease progressively from 200 g mol-1 to 150 g mol-1 with a step of 10 g mol-1; κ = 0.12, 0.12, 0.10, 0.10, 250 0.08, 0.08 respectively, which is a reasonable assumption by comparing κ values for different types of SVOCs in previous studies ( Topping et al., 2011; Kawana et al., 2017; Wittbom et al., 2018). Each model run starts from 101325 hPa and 70% relative humidity, 278.15 K for winter and 303.15 K for summer predictions. Updraught velocities were set 0.1, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0 m s-1 to represent stratiform clouds to trade wind cumulus clouds.

3 Results and discussion

255 3.1 Pristine forest simulations

Owing to the assumption of all sub-micron OA being secondary and the constant assumed modal diameter and breadth, the pristine forest simulations are initialised in such a manner as to be able to most straightforwardly investigate the sensitivity to particle number concentration and updraught speed. It should also be noted that the fraction of OA is the largest of all environments and potentially can exhibit substantial impact on cloud droplet number concentration through co-condensation. 260 In these simulations, the percentage increase in cloud droplet number owing to co-condensation ranges from 0% to 50%. The

only changing parameters are the aerosol particle number concentration (Na) and the updraft speed for each model run (there

are also variations in initial volatility distributions to match different Na). Therefore, we use the results for pristine forest

environment to explore and interpret the dependence of the enhancement by co-condensation on the variations in Na and -3 -1 updraughts. Fig. S1 (a) shows an example output with Na = 300 cm and w = 1.5 m s . The condensed semi-volatile material 265 leads to around 25% more particles grow into cloud droplets. Fig. S1 (b) shows the dynamic evolution of the bimodal size

distribution with the same model scenario. The additional condensed organic mass leads to a shift towards larger sizes (Dg1

from 200 nm to 400 nm, Dg2 from 700 nm to 1000 nm, approximately) compared to the simulation in the absence of co- condensation at 100% RH. Since water supersaturation is dependent on updraught, then at the same updraught, the number of activated droplets 270 becomes more limited as the aerosol number increases even without co-condensation, and it is only at the highest updraughts that there’s enough potential to keep increasing the enhancement with aerosol number by co-condensation. If almost all aerosol particles are already activated without co-condensation, co-condensation will have very limited potential to increase cloud droplet number.

In Fig. 2, simulation is represented as a single point. Points are connected according to corresponding Na in (a) and to 275 corresponding updraughts in (b). X-axes show the fraction of particles that are activated into cloud droplets in base-case

simulations without the co-condensation of SVOCs. In the absence of co-condensation, at the same Na, a larger fraction of particles is activated as updraught increases. This is illustrated by Fig. 2 (a) that the colours of points become darker along the x-axis and also by (b) that the lines shift towards the right. At the same updraught, although the absolute activated droplet

number increase with Na, as might be expected the fraction of particles activated into cloud droplets reduces with Na (the

280 transparency of points increasing towards the right in (b), most easily seen in the grey 0.1 m s-1 line). This results from the increase in the competition for available water vapour as the number of potential seed particles increases, resulting in an

increased likelihood of activating a larger fraction of particles into cloud droplets with reducing particle concentration, Na, and high updraught speeds without co-condensation. This results in co-condensation having modest impacts on cloud droplet number under low particle concentrations, since nearly all particles have already been activated (the overlapped regions in the 285 lower right in Fig. 2). At the other extreme, co-condensation would also be unable to substantially increase the cloud droplet

number under low updraughts and high Na conditions since, even without co-condensation, there is an insufficient potential of supersaturation to activate such a large number of sub-cloud particles. Such conditions will limit the capacity for additional organic material to carry an increased fraction of particles across the minimum dry size for activation during lifting. It can be seen that enhancement by co-condensation is relatively low for both high updraught-low particle number and high particle 290 number-low updraught cases. That is, co-condensation is most effective under conditions supporting an intermediate activated fraction in the absent of co-condensation. Fig. 2 (a) illustrates that the enhancement by co-condensation turns from increasing with updraughts to decreasing with

-1 -1 updraughts as Na reduces. Although from 2.5 m s to 3.0 m s there is a decline in the percentage enhancement at Na = 1000 -3 -3 cm (red line), we would expect an increase were a simulation with a higher Na run (e.g. 1100 cm ). This indicates that at the

295 highest Na the enhancement by co-condensation has a potential to keep increasing as updraughts become higher. There is an -1 -3 optimum updraught at which the enhancement by co-condensation is maximum for each Na e.g. 1.5 m s for 800 cm and 2.5 -1 -3 m s for 1000 cm . It is clearly shown that the optimum updraughts increase with Na, which is in agreement with Topping et al. (2013). From another perspective, Fig .2 (b) shows that the enhancement by co-condensation turns from decreasing with

Na to increasing with Na as updraughts rise. This analogically implies that only at highest updraughts is there enough potential

300 for the enhancement by co-condensation to remain increasing as Na increases. Similarly, an optimum Na for the enhancement exists which increases with updraughts (e.g. 500 cm-3 for 0.5 m s-1 and 1000 cm-3 for 3 m s-1). Furthermore, we would expect

the optimum Na for each updraught and the optimum updraught for each Na to be coupled, if we have smaller discrete space

for Na and updraughts among different model runs. Clearly for a given initialisation, the amount of available organic vapours per aerosol particle (i.e. the ratio of the potentially

305 condensable organic mass and Na), which is referred to as Cp hereafter, reduces with Na (see Tab. 1). Thus, the points in Fig.

2 (b) can be reversely coloured according to Cp as displayed in the colour bar at the bottom (there is more condensable material for each particle as the colour becomes lighter). Note that almost all organic vapours condense onto the ascending aerosol population before reaching the cloud base. The vast majority of the remainder will be scavenged by growing particles as well due to dilution in cloud (see Fig. S2), such that the amount of available organic vapours converges on the actual amount 310 scavenged by growing particles. Previous studies (Topping and McFiggans, 2012; Topping et al., 2013) demonstrated that the

Nd enhancement by co-condensation is highly dependent on Cp, Nonetheless, it can be seen in Fig .2 (b) that the enhancement

does not necessarily increase with Cp: it turns from increasing with Cp to decreasing with Cp as the updraught increases.

It also can be seen from Fig. 2 that a critical base-case (without co-condensation) activated fraction of around 0.7 exists.

This critical fraction reasonably corresponds to the optimum Na and updraft paring mentioned above. With an activated fraction

315 is smaller than this optimum, increasing updraughts, reducing Na and increasing Cp will all result in an increase in the enhancement by co-condensation. Hence, it is plausible that the increase in the availability of condensable material (both organic material and water supply) per particle will increase co-condensation impacts when the activated fraction is smaller than 0.7. Whereas when larger, the contrary is the case, which is clearly because high percentage of particles have been already activated into cloud droplets even without co-condensation and thereby semi-volatile material does not play a significant role 320 on the droplet number. Under the relatively simple conditions in the pristine forest environment, we have demonstrated that the dependence of the enhancement by co-condensation on aerosol particle number concentrations, updraughts and the amount of potentially condensable organic vapours per particle is not straightforward. It is clear that the activated fraction of particles without co- condensation plays a role and, despite a relatively straightforward initialisation, the links between the enhancement of cloud 325 droplet number concentrations by co-condensation and other parameters are complex and non-linear.

330

Figure 2: Enhancement in cloud droplet number vs fraction of particles that are activated into cloud droplets without co- condensation. (a), each point represents a simulation with a specific initial particle number concentration (Na) and updraft velocity. The points for the same Na are connected by lines in different colours as denoted by the legend. The points are also coloured by the values of -1 -1 -3 335 updrafts (from light to dark—0.1m s to 3 m s ) and the colour bar at the bottom shows an example in grey corresponding to Na = 10 cm . (b), analogous to (a), but the points for the same updraught speed (w) are connected by lines in different colours as denoted by the legend; -1 the points are coloured by Na and the upper colour bar shows an example in grey corresponding to w = 0.1 m s ; furthermore, points can also be coloured reversely by the availability of condensable organic vapours, as shown by the lower given that the amount of potential condensable vapours declines with Na (see Tab.1).

340

3.2 The other environments

In kerbside, urban, near-city, rural and background (i.e. polluted, intermediate and clean) environmental conditions all parameters for initial number concentrations, size distributions, chemical composition, volatility distributions, updraughts and even starting temperature were varied systematically and comprehensively, such that the complexity was dramatically 345 increased compared to the pristine forest environment and previous studies. As shown in Tab. 1, each category of environments

has six initital number size distributions in three modes (or two modes) and each mode has a specific combination of Na, σg

and Dg. The initial chemical composition was also explicitly specified with variations in the relative contribution of each component (inorganic or organic) in different environments. As demonstrated in Sect. 2.2.2, SOA and HOA volatility distributions respectively from chamber TD measurements (Voliotis et al., 2020 in prep) and literature (Cappa and Jimenez, 350 2010) were iteratively repartitioned to obtain the model-input volatility distribution. This distribution meets the desired condensed-phase organic mass that was calculated according to the corresponding initial number size distribution and aerosol composition. Therefore, the initial volatility distributions vary according to the number size distributions and chemical composition of aerosol population. The amount of organic vapours available to co-condense is thus dependent on the initial size distribution, mass-weighted aerosol composition and volatility distribution. This can be summarized that the volatility 355 distribution and the amount of organic vapours for co-condenstaion are tightly coupled with the initial particle number size distribution and composition for every model scenario. In addition, having a linear decrease in hygroscopicity (as defined by κ) and molecular weights for organic components with increasing volatility further makes the situation more complex than the sensitivities that have been assessed in the previous study (Topping et al., 2013). All these collectively make the situation highly complex with consequent challenges to interpretation. 360 Fig. S3 comprehensively summarizes all model predicted results for these five environments. The red refers to the summer (ascent starts from 303.15 K) and blue to winter (278.15 K). From (a) to (g), updraught velocities increase from 0.1 m s-1 to 3.0 m s-1. The left panel in each subplot illustrates the variation trends of the activated fraction of particles without co- condensation as the aerosol particle concentration rises. Every value of number of particles corresponds to a scenario in Tab. 1. The pollution levels are also coloured according to volume of particles as displayed in the colour bar. For all simulations 365 without co-condensation, fractions of activated droplets range broadly from 0.006 to 0.68 and the absolute activated droplet number roughly from 200 cm-3 to 5000 cm-3. These are comparable with the values reported by Crooks et al., (2018) under similar environmental conditions whereas different volatility distributions and chemical composition. The right panel shows the variation of the percentage increase in cloud droplet number triggered by the co-condensation of SVOCs. The co- condensation of SVOCs results in 0 to nearly 70% more particles activated into cloud droplets across all model runs. Fig. 3 (a) 370 shows the nearly 70% instance that around 600 more particles become cloud droplets in the background environment in winter, -3 Na = 1428 cm . In this case, almost all sub-micron aerosol particles become cloud droplets. Fig. 3 (b) shows the dynamic evolution of particle size distributions under the same initial condition. During the activation, co-condensation leads to

dramatic growth in sizes of small particles before reaching the cloud base. The first two modes shift towards larger sizes (Dg1

from 50 nm to 200 nm, Dg2 from 200 nm to 300 nm) and become much narrower compared to the case in the absence of co- 375 condensation at 99.92% RH (before activation).

-3 Figure 3: An example simulation in the case of background environment in winter (i.e. start from 278.15 K) with Na = 1428 cm (corresponds to morning number size distribution in Tab. 2), at updraft speed of 3 m s-1. (a): Simulated cloud droplet number as the air parcel ascends. Solid blue line: with co-condensation; dashed orange line: without co-condensation. (b): Evolution of the particle size 380 distribution during the lifting up. X-axis is for wet diameters of particles. Dashed grey line refers to the initial dry distribution; solid lines are for aerosol particle distributions close to cloud base and green refers to the case with co-condensation; red without co-condensation. Corresponding height and ambient relative humidity are displayed in the legend.

As updraughts become higher, base-case activated fractions increase. The increase is very much less significant under 385 polluted conditions than under clean conditions, both for the winter and summer (primarily stems from the extremely high particle number concentrations and small particle sizes in the areas with a high level of anthropogenic emissions). In addition,

at the same updraught, the general trend is that activated fractions decline as Na increases. These two features confirm the

expected dependence of the activated fraction without co-condensation on Na and updraughts as demonstrated in Sect. 3.1. The fluctuations and any individual nonconformities are subject to the variability in the parameters which was not exhibited 390 in under pristine forest conditions i.e. the variability in size distributions, chemical composition and initial temperature. In addition to this, having different κ values and molecular weights in each volatility bin, along with the variations in the volatility distributions across clean, intermediate and polluted environments also add complexity to interpret the enhancement in cloud droplet number by co-condensation. In this case, an analogous critical activated fraction (0.7 as discussed in Sect. 3.1) and the

optimal Na and updraughts cannot be readily identified. 395 As Fig. S3 comprehensively summarized, in general, the enhancing effect of semi-volatile organics on droplet number increases with updraught velocities both in summer and winter (i.e. initial temperature of 303.15 K and 278.15 K, respectively). In winter, percentage enhancements by co-condensation increase drastically from single-digit percentage to tens of percent as

updraughts increase in the clean environment in contrast to very limited (even decreases) in the polluted environment (0% to not exceeding 10%). The enhancement in droplet number in clean areas is overall more significant than in intermediate and 400 polluted environments in winter. There is a noteworthy exception at updraft speed equal to 0.5 m s-1 that the enhancement in

-3 the urban and near-city environment (especially Na = 6087 and 10873 cm cases) is larger than the clean environment and also larger than that with updraughts higher than 0.5 m s-1. By contrast in summer, the enhancements stay below 8% (mostly below 5%) for all environments even at the highest updraughts. At low updraughts, co-condensation plays a slightly more important role in the clean condition than the polluted, however the difference between environments reduces as updraughts rises. It 405 should be noted that the discussion here is in terms of different environments rather than individual model scenarios. The existence of apparent discrepancies from the general trend is inevitable in consideration of the variations in initial parameters across all environments. What stands out from the Fig. S3 is the large enhancement occurring at the highest updrafts in clean areas, especially the background environment. Under these conditions, the highest enhancements by co-condensation (up to 70%) are achieved. 410 Even though a large fraction of particles would have already been activated into cloud droplets in the absence of co- condensation, the addition of condensing organic vapours will still exert a great influence upon cloud droplet number. This might be attributable to the critical base-case activated fraction not having been reached (we considered it as 0.7 for the simpler pristine forest environment in Sect. 3.1), such that there is still potential to increase the enhancement by co-condensation with updrafts higher than 3.0 m s-1 (i.e. stronger cumulus clouds) if there were enough available cloud droplet seed particles. 415 Furthermore, as described previously in Sect. 2.2, the background and rural environments have the same specified chemical composition and the amount of condensable organic vapours was both calculated from the volatility distribution of the oxidation product from α-pinene + isoprene as precursor. However, in winter at high updraughts, there are still very substantial differences in the enhancement between these two environments (e.g. at 3.0 ms-1, the percentage enhancement is 60% - 70% for the background and 30% - 40% for the rural). These indicate that the updraught velocity and the initial temperature are the 420 most important parameters contributing significantly to the large percentage enhancement (>10%) in cloud droplet number. The aerosol particle number size distribution also contributes to the large enhancement, yet high updraught and low initial temperature condition is a prerequisite. In the clean environment, the SOA of biogenic origin (α-pinene + isoprene as precursor) was assumed to account for 80% in dry particle-phase organic material while HOA was set to 20% (see Sect. 2.2.2). We performed more simulations using the 425 same conditions as the near 70% enhancement example (Fig. 3) but with different proportions of SOA in organic mass. In this way the initial volatility distributions under dry conditions were varied and thus the amount of available gas-phase organic material. It should be noted that the mass fraction of the total semi-volatile organic (HOA + SOA) in condensed phase remained unchanged. Fig. 4 shows that the percentage increase owing to co-condensation declines from around 70% to 30% as the fraction of SOA in organic material decreases from 80% to 20%. Fig. S4 shows the accompanying volatility distributions of 430 SVOCs under the dry condition and the increases in condensed-phase organic material during the ascent of air parcel encountering 75%, 85% and 99.99% relative humidity. It can be clearly seen that the components having higher volatility

condense more during the particle growth. From the upper-left plot to the lower-right, as the proportion of SOA reduces from 80% to 20%, there are increasingly more organic components located in low volatility bins (small Log C*) i.e. more organic vapours were partitioned into the particle phase and hence the amount of the gas-phase organic material available for co- 435 condensation reduces, since the total amount of these two phases was not changed. This indicates that, for conditions with a potential for significant influence of co-condensation on the cloud droplet number, there is considerable sensitivity of the percentage enhancement to the volatility distribution of organic components. Additionally, given that co-condensation has great impacts on the particles distributed in the first two log-normal modes (see Fig. 3 (b)), the geometric standard deviations of mode 1 and mode 2 were changed to 2.2 to test the sensitivity to the breadth of particle size distributions. The right plot in 440 Fig. 4 shows that wider distribution will lead to a slightly more significant decrease in activated droplet number with co- condensation than without and thus the enhancement effect is slightly weakened. However, the effect of a wider distribution is not pronounced compared to changing the volatility distribution of organic components.

Figure 4: Sensitivities to initial volatility distributions (left) and breadth of distribution (right). The left plot is the simulation of cloud 445 droplet number using the same initial condition as Fig. 3 but with different ratios of the HOA and SOA. The ratio in Fig. 3 is 20% HOA and 80% SOA that is coloured in orange with corresponding no-condensation simulation. SOA is from α-pinene + isoprene as a precursor. The right plot shows the effect of changing σg1, σg2 to 2.2. Dashed lines denote the predicted droplet number in the absence of co-condensation while the solid lines in the presence of co-condensation.

450 Previous studies (Topping and McFiggans, 2012; Topping et al., 2013), demonstrated that the enhancement in the cloud droplet number concentration by co-condensation is determined by the amount of potentially condensable semi-volatile organic

material per aerosol particle (Cp). In Sect. 3.1, it was shown that the enhancement is not solely controlled by Cp but also depends upon the activation behaviour without co-condensation. However, in Sect. 3.1, this was only shown for comparable

size distributions with only the particle number concentration (Na) varying. In these subsequent cases, size distributions were

455 typical values for the specific environment and varied across all scenarios (i.e. besides Na, geometric standard deviations and median diameters vary as well). Therefore, it is more sensible to explore the association between the enhancement by co-

condensation and the amount of available material per surface area of particles (Cs). In analogy to the discussion about the

linkage between the enhancement and Cp in Sect. 3.1, the enhancement effect of co-condensation does not necessarily follow the increase in the amount per surface area of particles considering the base-case activated fractions. Furthermore, the 460 relationship is even more intricate given the variations in much more parameters across different categories of environments

here. It is highlighted from Fig. 5 that, in winter, the dependence of the percentage enhancement by co-condensation on Cs is increasingly evident as updraughts rise. In contrast, there is no clear linkage in summer and low updraughts. This is likely attributable to the sufficient water supply (i.e. updraughts) and lower prevailing temperature under this condition, such that these give the additional vapour mass per surface area a full potential to exert its impacts on cloud droplet formation along 465 with water. In other words, only at high updraft speeds with low starting temperature, does the enhancement have a sufficient

potential to keep increasing as Cs increases. This, again, reveals that the initial temperature and the updraught velocity are the parameters contribute most to the largest enhancements by co-condensation.

470 Figure 5: Percentage enhancement by co-condensation as a function of the amount of potentially condensable organic material per surface area of particles at updraughts of 0.1, 1.0, 1.5, 2.0, 2.5, 3.0 m s-1. Each point represents a result from a model run coloured according to the pollution levels (Volume). Summer is in red and winter in blue. The labels for all subplots are the same.

As stated previously, the amount of SVOCs in the vapour phase for co-condensaion is determined by the aerosol particle 475 size distribution, mass fraction of organics and the volatility distribution. In our study, the range of the volatility of organic compounds characterized by C* is binned into decadal spacing of the basis set from C* = 0.001 to C*=100 µgm-3. This range is based on the chamber photo-oxidation experiment and volatility retrieval from thermodenuder measurement. This retrieval becomes challenging for the highest volatility bins (Karnezi et al., 2014). To evaluate the potential impact of such TD measurements on the predicted cloud droplet number, a C* = 1000 µgm-3 species of semi-volatile organics was added with an 480 assumption that the mass of condensed-phase material in this bin is 10% of the next highest volatility bin (i.e. C* = 100 µg m- 3). Fig. 6 shows the predicted activated droplet number at different updraughts in background, near-city and urban environments (afternoon number size distribution), with and without the C* = 1000 µgm-3 bin. The reason to select these three environmental categories is that the corresponding SOA volatility distributions cover the anthropogenic, biogenic and their mixtures as precursor. It is evident that the addition of more volatile semi-volatile organic vapours and resulting extra 485 condensed organic material have very limited impacts on cloud droplet number. Only in background and near-city environments, there are extremely slight increase in activated droplet number i.e. the sensitivity of the enhancement by co- condensation to a C* = 1000 µgm-3 bin is quite low. Thus, an upper limit of C* = 100 µgm-3 constrains the initial volatility distribution well and the ability to measure a higher volatility bin is not necessary. In this study, the calculation of the input volatility distributions and hence amount of potentially condensable organic 490 vapours is based on the equilibrium absorptive partitioning theory for a bulk aerosol i.e. it is size independent. In each volatility bin, the faction of condensed-phase organic material is determined by the partitioning coefficient (Donahue et al., 2006). In the future work, PyACPIM can be developed to be able to implement the multiple-mode equilibrium partitioning model proposed by Crooks et al., (2016) to obtain size-resolved initial volatility distributions and amount of available organic vapours. This allows a more accurate calculation of the mass of additional condensed organic material and therefore a better estimation 495 of the effect of co-condensation. Similarly, the initial relative contribution of each aerosol component was assumed to be the same for all modes as shown in Tab. 2. More model simulations can be established in further work to evaluate the sensitivity to size-segregated initial chemical composition. For example, assuming the proportion of organics increases from mode 1 to mode 3 under dry conditions (Plewka et al., 2006; Pöschl et al., 2010; Sun et al., 2010). In addition, particularly in the polluted environments, it is a fact that there is considerable amount of insoluble condensed organic material. Therefore, the enhancing 500 effect of co-condensation would be expected lower in polluted environments, which potentially leads to larger differences in percentage enhancement between polluted and clean environments and needs further examination in future work.

Figure 6: Activated droplet number as a function of updraught velocities. The plot on the top is for summer and bottom winter. Initial 505 number size distributions are afternoon distributions (see Tab. 1). Each colour represents an environmental type (green, blue, red for background, near-city and urban, respectively). Dashed lines are for 7 volatility bins (i.e. adding semi-volatile organics with C* = 1000 µgm- 3) and solid line 6 volatility bins (original).

4 Conclusion

Using an extended air parcel model with detailed cloud microphysics, the effect of the co-condensation of SVOCs on cloud 510 droplet number under a wide range of ambient conditions were investigated in this study. Across all scenarios, additional condensed organic material, along with water, results in up to nearly 70% more particle seeds further activating into cloud droplets. In the pristine forest environment under relatively simple initial conditions, it is shown that the enhancement by co-

condensation is not simply dependent on Na, updraught velocities and the amount of organic vapours available to co-condense

on each particle (Cp). It is suggested that the base-case activated fractions (from simulations assuming no co-condensation) 515 are needed to be considered. In the background, rural, near-city, urban and kerbside environments, the general pattern of the percentage enhancement can be concluded as: (1) the enhancement increases with updraughts; (2) in summer (model runs starting from 303.15 K) the increase is insignificant (from 0% to below 10%); (3) in winter (model runs starting from 278.15 K) the increase is striking in the clean environment (single-digit percentage to up to nearly 70%) while very limited in the intermediate and polluted 520 environments (of the similar order of magnitude to summer). The fluctuations and discrepancies are attributable to the fact that the variations in the factors demonstrated above give rise to the complexity. This indicates that atmospheric semi-volatile organics significantly influence the cloud droplet formation in clean areas with low temperature. Special attention should be 21

paid to such environmental conditions to address the effect of semi-volatiles on cloud properties and hence climate in relevant studies. 525 It is plausible that the initial temperature and updraught are the biggest contributory parameters to the large enhancement cases and the volatility distribution of organics may have considerable impacts as well. In contrast, the impact of the breadth of particle size distributions is relatively negligible. Further research could focus on quantifying the relative importance of the factors, including but not limited to the ones evaluated in this work e.g. using an analogous method as proposed by Feingold (2003). Furthermore, it is also indicated that a TD-measured volatility distribution with an upper limit of C* = 100 µg m-3 is 530 sufficient to effectively constrain the volatility of SOAs even for the largest enhancement case and thereby the ability to measure a higher volatility bin is not necessary.

Owing to the variability in Na, size distributions, chemical composition, the volatility of organic components and the resulting availability of condensable organic vapours, the trend of the percentage increase in cloud droplet number by co- condensation is too intricate and complex to be accessible to a simple assumption of behaviour as a function of, for example, 535 condensable vapour per particle or per surface area. Note that this study was not aimed to explain how the variations in each factor or parameter affect the co-condensational facilitation effect by individually comparing every model scenario. Instead, this study set out to investigate the potential influence of the atmospheric transformation of SVOCs on cloud droplet number under various ambient conditions by systematically and comprehensively designing the model initial conditions with moderate assumptions. Separated modelling work should be conducted to analyse the sensitivity of the co-condensational facilitation to 540 every factor for every environmental category proposed in this study, which would be meaningful to understand the intrinsic links between the enhancing effect and each factor. The relatively more accurate estimation of the radiative effect of co-condensation is challenging considering, for example, the spatiotemporal distribution of cloud cover is heterogeneous and the influences of other cloud properties vary as well. To give a full and more realistic picture of the change in radiative budget or in forcing owing to co-condensation impacts, more 545 factors (e.g., the intensity of solar radiation, diurnal variability of temperature profiles, cloud fraction) should be taken into account, which suggests a requirement for a regional or global climate model with a treatment of co-condensation. Future work can focus on the development of parameterisation schemes with respect to co-condensation on the basis of Connolly et al. (2014) and Crooks et al. (2018), using the investigation and quantification in this study as a reference, it can be anticipated to obtain more intuitive insights into the effect of co-condensation on cloud droplet number and the consequent change in cloud 550 albedo and hence radiative budget and climate.

Author contributions. G.M. and Y.J. designed the research. E.S., Y.J. and D.T. developed the numerical model. Y.J. and G.M. designed the model simulations. A.V. performed the thermodenuder measurement. Y.J. carried out the model simulations. Y.J. performed data analysis. Y.J. and G.M. co-wrote the paper. 555 Competing interests. The authors declare that there is no conflict of interest. Data availability. Raw data is available on request.

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2.3 Supplementary material for the paper

S1. Example model output from the pristine forest

715 This is to compare the simulation in the presence of the effect of the co-condensation of semi-volatiles with that in the absence of co-condensation, in the pristine forest with moderate aerosol number concentration and updraught velocity.

Figure S1: An example simulation for the pristine forest environment with initial aerosol particle number concentration of 300 cm- 3 at updraft speed of 1.5 m s-1. (a): Simulated cloud droplet number as the air parcel ascends. Solid blue line: with co-condensation; dashed 720 orange line: without co-condensation. (b): Evolution of the particle log-normal size distribution during the simulated adiabatic ascent. X- axis is wet diameters of growing particles. Dashed grey lines refer to the initial dry aerosol size distribution; solid lines are for particle distributions approaching the cloud base with (green) and without (red) co-condensation. Corresponding height and ambient relative humidity (RH) are displayed in the legend.

725 S2. Variations in the mass loading of semi-volatile organic vapours with altitude

Figure S2: Variations in the mass loading of semi-volatile organic vapours with altitude, example simulations for w = 0.1 m s-1 (left) -1 -3 and 3.0 m s (right) in the pristine forest. Dashed blue lines show the relative humidity (supersaturation) for Na = 1000 cm simulation; 730 solid lines are the changing mass loading of semi-volatile organic vapours (the sum across volatility bins from C* from 0.001 to 100 µg m- 3 ) for simulations with various Na.

S3. Summative plots for the other five continental environments

735

740

Figure S3: Base case predictions (without co-condensation) of the activated fraction of cloud droplets in different environments (left). Percentage enhancement by the co-condensation of SVOCs in different environments (right); (a) to (g) refer to the updraft -1 -1 velocity from 0.1 ms to 3.0 ms . X-axes show the total aerosol number concentration (Na) from low too high for each run, i.e. from clean 745 conditions to polluted conditions. Pollution levels are also illustrated by volume concentrations given that σg and Dg vary for each run as well (see colour bar). Corresponding initial conditions are shown in Tab. 1. Note that the scales of y-axes change with updraughts. The labels for all subplots are the same.

S4. Supporting plots for Fig. 4

750 Figure S4: Volatility distributions and predicted condensed mass in each volatility bin as the air parcel meets higher relative humidity (RH). These are accompanying plots for the left plot in Fig. 4. Volatility distributions vary with different mass fractions of SOA. Green bars represent the amount of particle-phase organic material in each volatility bin under the dry condition and yellow in the vapour phase. Coloured lines represent the additional mass of condensed organics as the air parcel ascends. Note that the absolute mass loadings are 755 different.

S5. The evaluation of the sensitivity to particle losses in TD

Figure S5: TD-retrieved volatility distributions of chamber SOA, corrected for particle losses in TD (Voliotis et al., 2020 in prep).

760 Figure S6: Percentage difference in activated droplet number between the simulations using the initial volatility distributions being corrected for the particle losses in the thermodenuder (TD) and the simulations without such loss corrections. These corrections for the TD losses lead to decreases (in the order of 4%) in activated droplet number compared to the case being uncorrected.

Compared to the SOA volatility distribution retrievals without the corrections for particle losses in TD (Fig. 1 in the main 765 text), Fig. 5 shows that such corrections result in very small differences in the volatility bins where C* < 1 µg m-3, however relatively large differences in SOA with C* ≥ 10 µg m-3, especially for the ο-cresol case. This is because the particle losses in the TD unit are higher at lower temperatures, so the SOA mass fraction remaining (MFR) appears to be lower than it should be and it consequently leads to high contributions of higher volatility material (C* ≥ 100 µg m-3). When these losses are therefore corrected, the MFR raises more at lower temperatures and the SOA becomes less volatile. 770 To evaluate the potential effect of the corrections for TD losses, a number of sensitivity analyses were conducted. Figure S6 illustrates that in the kerbside, urban, near-city, rural and background environments in winter and summer at updrafts of 1 m s-1 and 3 m s-1, the corrections for the particle losses in the TD reduce activated droplet number compared to the case not being corrected. However, the percentage decreases are generally lower than 4%. In other words, across a wide range of initial conditions, such corrections lead to decreases in the enhancement of co-condensation but the decreases are extremely modest 775 (in the order of 4%). This indicates that, despite the differences in retrieved SOA volatility distributions between the cases being corrected and uncorrected for TD losses, there is no significant impact on the results and therefore the conclusions presented in the main text. In future work, the effects of the uncertainties in initial organic volatility distributions on simulated cloud droplet number in extreme cases (e.g. w > 3 m s-1) may require further examination.

3 Summary and Conclusions

In this study, a Python version of an existing “bin-resolved” cloud parcel model PyACPIM has been developed to allow the calculation of the diffusion of water vapour and condensable semi-volatile organic material towards aerosol particles in an air parcel lifted adiabatically. The enhancing effect of the condensation of additional organic material onto a population of aerosol fitted into multimodal log- normal distribution has been examined in this work with a wider range of parameter space for various environments above and over previous studies. I used the oxidation reaction products from ο-cresol and α-pinene + isoprene to represent SOA of anthropogenic and biogenic origin, respectively, and HOA to represent anthropogenic POA. In this case, based on VBS, the experimental volatility distributions of

SOA measured by thermodenuder (TD), along with POA taken from literature, can be repartitioned according to the equilibrium absorptive partitioning theory to reach a new equilibrium with the ambient organic aerosol mass loading. This ambient loading is determined by the specified representative mass- weighted chemical composition and aerosol particle number size distribution for a targeted type of continental environment. In this way, a new modelling framework has been conducted to investigate the impact of the co-condensation of semi-volatile organic on cloud droplet formation.

I highlight the important findings that, across all simulations, up to approximately 70% more particle seeds further become cloud droplets. The general pattern of the enhancement of co-condensation across typical continental environments can be concluded as: (1) the enhancement increases with updraughts; (2) in summer the increase is insignificant (from 0% to below 10%); (3) in winter the increase is the most significant in the clean environment (single-digit percentage to up to nearly 70%) while very limited in the intermediate and polluted environments (of the similar order of magnitude to summer). This indicates that atmospheric semi-volatile organics have the most significant potential to enhance cloud droplet formation in clean areas with low temperature. Special attention should be paid to such environmental conditions to address the effect of co-condensation in future radiative budget and climate research. I also highlight that it is plausible that initial temperature, updraughts and initial volatility distributions contribute most to the most significant enhancement by co-condensation.

Further modelling work could be carried out to explore the sensitivity of the co-condensational effect to the parameters that vary with different categories of environments proposed in this study, which would be helpful to quantify the relative importance of each parameter or factor and thereby

understand the intrinsic relationship between the enhancement by co-condensation and the parameters. Besides, this work also suggests that a TD-measured volatility distribution with an upper limit of C* = 100 µg m-3 is sufficient to effectively constrain the volatility of SOAs even for the largest enhancement case. The ability to measure a higher volatility bin is not necessary.

Nonetheless, either in the pristine forest environment with relatively straightforward initial conditions or the background, rural, near-city, urban and kerbside environments with varying initial parameters, the links between the percentage enhancement by co-condensation and the input parameters are not straightforward. By increasing the cloud droplet number, co-condensation can increase cloud albedo and hence result in a cooling effect. However, relatively more accurate estimations of the co- condensational effect on the regional radiative budget should be a focus of future work. This will enable the determination of the effects of transformations of organic compounds on cloud properties and hence climate.

In addition, the hygroscopicity parameter κ, enthalpy of vaporisation, density and molecular weight of organic components were assigned atmospherically reasonable values and held constant in this study.

The sensitivity to these parameters and their potential effect may be investigated in future research. Of course, the assumption that the volatility distribution of SOA could be represented by the products from

ο-cresol and α-pinene + isoprene and their mixtures is due to our limited ability to measure the volatility of organic material, which requires an improvement in the volatility measurements and thus the estimation of ambient volatility distribution could be better constrained. PyACPIM may be further developed to include typical vertical profiles of temperature and relative humidity to account for the diurnal or seasonal variability. It also can be combined with a regional model to explore the effect of the changes in both anthropogenic and biogenic emission scenarios. However, as demonstrated previously, such frameworks pose a great challenge to computational power. In addition, though I focus on warm clouds in this work, the potential role of co-condensation on ice nuclei behaviour and thus ice cloud formation could also be explored by PyACPIM. A study to quantify the impact of co-condensation on the formation of precipitation could also be conducted and thus the effects of atmospheric semi-volatile organics could be further investigated. Note that for these studies, there is a need to take into account supermicrometre aerosol particles and giant CCN.

4 Reference

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