Development and Deployment of a Continuous-Flow …...Discussions with Maygan McGuire on aerosol...

UNIVERSITY OF TORONTO DEPARTMENT OF CHEMICAL ENGINEERING AND APPLIED CHEMISTRY

Development and Deployment of a Continuous-Flow Diffusion Chamber for the Field Measurement of Atmospheric

Ice Nuclei

Joel Christopher Corbin MASc. 2009-2010

A thesis submitted in conformity with the requirements for the degree of Master of Applied Science

under the supervision of Professors J.P.D. Abbatt and G.J. Evans.

Department of Chemical Engineering and Applied Chemistry University of Toronto

© Copyright Joel C. Corbin 2011. CC Attribution 3.0 License.

CORBIN, J.C. MEASUREMENTS OF ATMOSPHERIC ICE NUCLEI. MASC 2010

ii

Abstract:

Development and Deployment of a Continuous-Flow

Diffusion Chamber for the Measurement of Atmospheric

Ice Nuclei

Joel Christopher Corbin

Master of Applied Science

Department of Chemical Engineering and Applied Chemistry

University of Toronto

2011

Ice crystals in clouds frequently form upon a subset of aerosol particles called ice nuclei

(IN). IN influence cloud ice crystal concentrations, consequently affecting cloud lifetime

and reflectivity. The present understanding of these effects on climate is hindered by

limited data on the global distribution of IN.

This thesis presents measurements of deposition-mode IN concentrations under conditions

relevant to mid-level clouds, 238 K and 138% RHi. at two Canadian sites: Toronto, a major

city, and Whistler, a pristine coniferous rainforest.

In Toronto, chemically-resolved surface areas were estimated by single-particle mass

spectrometry and regressed against IN concentrations to identify a significant relationship

between IN concentrations and both carbonaceous aerosols (EC and/or OC) and dust. In

Whistler, IN concentrations during a biogenic secondary organic aerosol (SOA) event did

not increase from background levels (0.1 L-1), suggesting that biogenic SOA particles do not

nucleate ice under these conditions.


iii

Acknowledgements

The convention of writing a single name below the title of a Master’s thesis is unfair to a

number of people, some of whom I would like to give due credit here.

First, my supervisor, Professor Jon Abbatt, has been spectacular in his guidance, inspiration

and insight. As I started out, having his hands-on help in the lab allowed me to absorb an

approach and attitude that has probably been the most important thing I’ve learnt from

this degree. As I continued on, the inviting atmosphere created by Jon’s always-open door

was a constant and positive motivation to reach my next research goal and to open up the

next stage of discussion.

My co-supervisor, Professor Greg Evans, has provided guidance perfectly complementary

to Jon’s. Greg’s constant flow of ideas and careful long- and short-term planning

streamlined the completion of this thesis in the most crucial and subtle ways.

A number of coworkers provided many crucial and pivotal ideas. Helpful discussions on

theory and experimental techniques with my “peer advisor” Rachel Chang were frequent

and invaluable. Had Jay Slowik not taught me about diameters, I might not have developed

the conversion performed below. Discussions with Maygan McGuire on aerosol mass

spectrometry measurements and statistics were marvellous and quite fun. Peter Rehbein

and Alexander Keith together brought much insight into mass spectrometry. Peter’s shared

scripts for mass spec data retrieval were much appreciated. Cheol-Hong Jeong’s assistance

with the FMPS correction schemes was much appreciated, not to mention his operation of

the FMPS, APS and ATOFMS during my study.

During the field study at Whistler, a number of Environment Canada employees provided

help and assistance for which I’m very grateful. John Liggio and Jeremy Wentzell operated

the AMS and analyzed the data used below. Discussions with Richard Leaitch and Peter Liu

were excellent and informative. Had Jenny Wong not been operating my instrument on a


iv

few of the crucial days highlighted in this thesis, I would have gathered much less data.

Finally, the logistical efforts of the entire Whistler crew were much appreciated.

Academic thought is useless without soul and inspiration, both of which I may have run out

of had Irina not been there to support me. It’s probably fair to say that she was the only

reason that I remained (mostly) cheerful and optimistic while working on this thesis

around the clock for 3 weeks straight.

Finally, I might have followed an entirely different and much less scientifically-satisfying

life path without the encouragement and support of my parents David and Patrisya in

studying Chemistry abroad. I was not conscious at the time of the significance and value of

their contribution and sacrifice, and can only hope to appreciate it in retrospect.


v

Table of Contents Acknowledgements.................................................................................................................................................... iii Glossary .......................................................................................................................................................................... vii 1 Introduction ..........................................................................................................................................................1 2 Literature Review ...............................................................................................................................................3

2.1 Atmospheric Ice Nucleation Processes ............................................................................................4 2.1.1 Homogeneous nucleation ..............................................................................................................5 2.1.2 Heterogeneous nucleation ............................................................................................................6

2.2 Methods and Instrumentation .............................................................................................................9 2.2.1 Continuous Flow Diffusion Chambers (CFDCs) ..................................................................9 2.2.2 Counterflow Virtual Impactors (CVIs) ................................................................................. 10 2.2.3 Ultraviolet Aerodynamic Particle Sizers (UV-APS) ........................................................ 10 2.2.4 Laser Desorption/Ionization Aerosol Mass Spectrometers (LDI-MS).................. 10

2.3 Recent Advances in Understanding Heterogeneous Ice Nucleation ............................... 11 2.3.1 Dust as ice nuclei ............................................................................................................................ 11 2.3.2 Soot as ice nuclei ............................................................................................................................. 13 2.3.3 Biological ice nuclei ....................................................................................................................... 14 2.3.4 Organic ice nuclei ........................................................................................................................... 14 2.3.5 Inorganic ice nuclei ....................................................................................................................... 16 2.3.6 Lead in ice nuclei ............................................................................................................................ 16

2.4 Modelling and Prediction of Ice Nucleation in the Atmosphere ....................................... 19 2.5 Summary and Outlook .......................................................................................................................... 20

3 The Continuous Flow Diffusion Chamber (CFDC)............................................................................ 22 3.1 Theory of Operation ............................................................................................................................... 22 3.2 Chamber Design ....................................................................................................................................... 24 3.3 Design Advantages and Limitations ............................................................................................... 26

4 Development of the CFDC for Field Measurements ........................................................................ 27 4.1 Sources of Background Signal ........................................................................................................... 27 4.2 Modifications to the CFDC Design ................................................................................................... 28

4.2.1 Chamber cooling ............................................................................................................................. 29 4.2.2 Sample and Sheath Flows ........................................................................................................... 30 4.2.3 Sheath Flow Generation .............................................................................................................. 31 4.2.4 Automated Background Measurements .............................................................................. 31 4.2.5 CFDC Control Program................................................................................................................. 32

4.3 Validation of operation conditions ................................................................................................. 32 4.3.1 Distinguishing ice crystals from water droplets ............................................................. 32

5 Relationships Between IN Concentrations and Aerosol Chemical Composition at College St, Toronto .................................................................................................................................................... 36

5.1 Summary ...................................................................................................................................................... 36 5.2 Methodology .............................................................................................................................................. 37

5.2.1 Experimental .................................................................................................................................... 37 5.2.2 Data Analysis .................................................................................................................................... 40

5.3 Results I: Aerosol Size and Mass Spectrometry ........................................................................ 49 5.3.1 ATOFMS Aerosol Types ............................................................................................................... 49


vi

5.3.2 Aerosol Size and Surface Area .................................................................................................. 53 5.4 Results II: Ice Nuclei ............................................................................................................................... 55

5.4.1 Bulk Aerosol Properties as Predictors of IN ...................................................................... 56 5.4.2 ATOFMS Aerosol Types as Predictors of IN....................................................................... 60

5.5 Conclusions................................................................................................................................................. 68 6 IN Concentrations during a Biogenic Aerosol Event at Whistler, BC ...................................... 70

6.1 Summary ...................................................................................................................................................... 70 6.2 Experimental ............................................................................................................................................. 71 6.3 Results ........................................................................................................................................................... 72

6.3.1 IN during the biogenic period .................................................................................................. 72 6.3.2 IN response to dust........................................................................................................................ 75

6.4 Discussion and Conclusions ............................................................................................................... 78 7 Summary and Future ..................................................................................................................................... 81 8 References ........................................................................................................................................................... 84 9 Appendix A: Complete Aerosol Distributions for Toronto (Chapter 5)................................. 91 10 Appendix B: ATOFMS Mass Spectra (Chapter 5) .............................................................................. 94 11 Appendix C: Meteorological data for Whistler (Chapter 6) ...................................................... 100


vii

Glossary

Term Definition

Ice nucleus (a particle that nucleates ice) IN

Cloud condensation nucleus (a particle that nucleates water) CCN

High-altitude cloud, containing only ice crystals Cirrus cloud

Mid-level cloud, containing both ice crystals and water droplets Mixed-phase cloud

𝑅𝑅𝐻𝐻𝑤𝑤 Relative humidity with respect to water (PH2O / Psat,water)

𝑅𝑅𝐻𝐻𝑖𝑖 Relative humidity with respect to ice (PH2O / Psat,ice)

𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 𝑅𝑅𝐻𝐻 The humidity at which a given type of IN first nucleates ice

Continuous flow diffusion chamber CFDC

The University of Toronto Continuous Flow Diffusion Chamber UT-CFDC

Aerosol A suspension of solid or liquid particles in a gas

Internally mixed An aerosol containing similar particles, each a mixture of different substances

Externally mixed An aerosol containing different particles, each made up of one substance; i.e., different substances exist as separate particles

PBAP Primary biological aerosol particles

SOA Secondary organic aerosol

ATOFMS Aerosol Time-of-Flight Mass Spectrometer (TSI Inc.)

APS Aerodynamic Particle Sizer

FMPS Fast Mobility Particle Sizer

AMS Aerosol Mass Spectrometer (Aerodyne Inc.)

𝑑𝑑𝑣𝑣𝑂𝑂 Volume-equivalent particle diameter

𝑑𝑑𝑎𝑎 Aerodynamic diameter (a function of pressure and temperature)

𝑑𝑑𝑣𝑣𝑎𝑎 Vacuum aerodynamic diameter

𝑑𝑑𝑚𝑚 Mobility diameter (effective 𝑑𝑑 during drift an electric field)

𝜌𝜌𝑂𝑂𝑒𝑒𝑒𝑒 Effective particle density

Nd Number of aerosol particles of diameter 𝑑𝑑 per unit volume

SAd Surface area of particles of diameter 𝑑𝑑 per unit volume


viii

Residual The difference between a measured (yi) and predicted (𝑦𝑦�𝑖𝑖) value, i.e. (𝑦𝑦𝑖𝑖 − 𝑦𝑦�𝑖𝑖)

RSS Residual Sum of Squares. The sum of squared residuals for a given prediction.

TSS Total Sum of Squares. The RSS when predicting 𝑦𝑦𝑖𝑖 with mean 𝑦𝑦 (𝑦𝑦�) alone. 𝑇𝑇𝑇𝑇𝑇𝑇 = 𝑅𝑅𝑇𝑇𝑇𝑇 + 𝐸𝐸𝑇𝑇𝑇𝑇.

ESS Explained Sum of Squares (sometimes called Regression SS). Unlike RSS, this is the sum of squared differences between 𝑦𝑦�𝑖𝑖 and mean 𝑦𝑦𝑖𝑖 , i.e. (𝑦𝑦�𝑖𝑖 − 𝑦𝑦�).

RMS Root Mean Square Error, the standard deviation of the data about the regression line. 𝑅𝑅𝑅𝑅𝑇𝑇 = 𝑅𝑅𝑇𝑇𝑇𝑇/𝑑𝑑𝑒𝑒.

𝑘𝑘 Number of predictors in a regression model.

𝑑𝑑𝑒𝑒 Degrees of Freedom. In a regression model of n observations, one degree is lost to the mean and k are lost to the predictors, so 𝑑𝑑𝑒𝑒 = 𝑂𝑂 –𝑘𝑘 – 1.

R2 Multiple correlation coefficient. 𝑅𝑅2 = 𝐸𝐸𝑇𝑇𝑇𝑇/𝑇𝑇𝑇𝑇𝑇𝑇 = 1–𝑅𝑅𝑇𝑇𝑇𝑇/𝑇𝑇𝑇𝑇𝑇𝑇. Should not be compared when k changes.

adjusted R2 R2 adjusted to account for artificial increases in R2 when the number of predictors in a multiple regression is changed.

𝑎𝑎𝑑𝑑𝑎𝑎.𝑅𝑅2 = (𝑅𝑅𝑇𝑇𝑇𝑇/(𝑂𝑂 – 𝑘𝑘 – 1) ) / (𝑇𝑇𝑇𝑇𝑇𝑇/(𝑂𝑂 – 1)). p-value The probability of a given result occurring by chance.

F-ratio The statistical significance of a model prediction. Large values show greater significance. 𝐹𝐹 = (𝐸𝐸𝑇𝑇𝑇𝑇/𝑑𝑑𝑒𝑒) / (𝑇𝑇𝑇𝑇𝑇𝑇/(𝑂𝑂 – 1)).

b The regression coefficient of a specified parameter, i.e. the “slope”.

β The regression coefficient obtained if all values are first normalized to a mean of zero and standard deviation of 1.

Underlined

terms define concepts relating to ice nucleation.

Italicized terms relate to aerosol science.

Plain text terms relate to multiple linear regression and are from Dallal [2010].


1

1 Introduction

Atmospheric ice crystals frequently form upon ice-nucleating particles termed ice nuclei

(IN). These IN influence cloud ice-crystal concentrations, consequently affecting

precipitation, cloud lifetime and cloud reflectivity. Quantifying these effects remains one of

the greatest challenges for current cloud and climate models. The concentration and global

distribution of atmospheric IN are poorly understood, and IN characterization is a major

requirement for an improved understanding of global climate.

This thesis presents measurements of atmospheric IN concentrations under conditions

relevant to mid-level clouds, 238 K and 138% RHi. at two contrasting Canadian sites:

Toronto, a major city, and Whistler, a pristine coniferous rainforest. In Toronto, single-

particle composition was simultaneously measured by aerosol mass spectrometry to

identify four general particle types: organic carbon (OC), elemental carbon (EC), dust and

salt. Using separate measurements of overall aerosol surface area, chemically-resolved

surface areas (SA) for each aerosol type were estimated and regressed against variations in

IN concentrations using stepwise elimination regression. The regression model identified a

statistically significant relationship between carbonaceous aerosols (EC and/or OC) and

dust with IN concentrations. It was unclear whether both EC and OC particles acted as IN,

or whether the observed relationship was due to the presence of EC within OC particles.

In Whistler, a strong biogenic secondary organic aerosol (SOA) event allowed an upper

limit of biogenic IN concentrations to be determined as 2.4 L-1 (95% CI). High variability

due to a local dust source contributed a large uncertainty to this estimate. Mean

concentrations were typically 0.1 L-1 during periods of low dust concentrations. The

Whistler dust showed similar ice-nucleating behavior to dust in Toronto: in Toronto, 5.8 ±

2.0 × 10-4 IN were observed per μm2 of dust SA, while in Whistler the value was 9.4 ± 3.1 ×

10-4 IN μm-2 dust SA. Further studies are required to investigate whether this relationship

is representative of a physical similarity between the two types of dust.


2

The thesis is structured as follows. Chapter 2 provides an introduction to and motivation

for the study of atmospheric ice nuclei, as well as a review of the literature on

heterogeneous ice nuclei.

Chapter 3 describes the Continuous Flow Diffusion Chamber used to perform the field

measurements described here in its original form; Chapter 4 describes modifications

performed on the Chamber as part of this Thesis.

Results are presented in Chapters 5 and 6. Chapter 5 describes results from a field study in

the urban environment of Toronto, Ontario. Chapter 6 reports on a similar study in the

forest of Whistler, British Columbia. Results from each study are compared at the end of

Chapter 6.

Chapter 7 provides a summary of both studies and provides suggestions for future research

based on the findings in Chapters 5 and 6. Page numbers for each Chapter and its

subsections can be found in the Table of Contents.


3

2 Literature Review

Ice-containing clouds cover more than a third of the globe [Wylie and Menzel, 1999]. Ice

crystals within these clouds are thought to initiate most terrestrial precipitation [Lohmann

et al., 2007] and therefore play an essential role in determining cloud lifetime. Cloud

lifetime in turn affects the radiative budget of the Earth [Sassen, 2002], since cloud

reflectivity leads to both cooling or warming of the planet, depending on cloud height

[Khvorostyanov and Sassen, 2002; DeMott et al., 2010].

In addition to issues of precipitation and radiation, ice crystals provide a surface for the

catalysis of heterogeneous reactions, as well as a route for scavenging and deposition of

both gas-phase acids and organics [Abbatt, 2003] and aerosol particles [DeMott, 2002].

Industrially, heterogeneous freezing is of interest in the high-temperature freezing of foods,

freeze-concentration, and in the generation of artificial snow [Möhler et al., 2007].

The concentration of ice crystals within a cloud determines crystal size: more crystals

competing for the same amount of water vapour results in smaller, longer-lived crystals.

Liquid water clouds can freeze homogeneously below -38°C, but above this temperature a

heterogeneous substrate is required to nucleate ice. The concentration of such ice nuclei

(IN) determines the resulting concentration of ice crystals, and thus dramatically affects

the processes noted above. However, the properties of atmospheric IN are poorly

understood.

The major challenge to understanding the role of IN lies in the identification and

characterization of these particles in the atmosphere. To begin with, IN are found at very

low number concentrations (0-10 L-1) and their effectiveness is determined by surface-

structural features on the scale of nanometres [Pruppacher and Klett, 1996]. Regional

concentrations of different IN are often highly variable, and measurement is complicated

by the variety of different mechanisms by which particles can nucleate ice (Section 2.1.2).

Finally, while nucleation conditions are well-defined for repeated freezing upon a specific


4

particle [e.g. Durant and Shaw, 2005], they are difficult to predict for an unknown particle,

and vary widely for different particles of the same substance.

This review presents the state of knowledge with respect to heterogeneous ice nucleation

in the atmosphere. Both field and laboratory studies are discussed. Section 2.1 introduces

fundamental concepts in ice nucleation. A brief description of relevant measurement

techniques is given in 2.3. The application of these techniques to the characterization of

various types of IN are discussed in Section 2.3. Finally, Section 2.4 discusses various

attempts at synthesizing these IN data within cloud models.

2.1 Atmospheric Ice Nucleation Processes

This section reviews the present understanding of ice formation processes as relevant to

cloud formation, with an emphasis on heterogeneous ice formation.

Water may freeze either homogeneously, where ice forms directly from pure water, or

heterogeneously, where a solid substrate termed the ice nucleus (IN) triggers freezing.

Homogeneous freezing of supercooled water occurs at -38 °C for 1 µm sized droplets, and

at even lower temperatures for smaller droplets [Pruppacher and Klett, 1996] or

concentrated solutions [Koop et al., 2000]. In the atmosphere, droplets frequently freeze

heterogeneously upon IN at temperatures up to -5 °C [Sassen et al., 2003]. Homogeneous

freezing is still atmospherically important when IN concentrations are very low or where

cloud updraft velocity is too high for IN to compete; however cloud glaciation is frequently

observed at temperatures too high for homogeneous freezing to occur [Pruppacher and

Klett, 1996; Rosenfeld et al., 2001; Sassen et al., 2003].

Heterogeneous freezing may affect cloud properties even if the cloud subsequently reaches

homogeneous temperatures, since water will be transferred from droplets to low-vapour-

pressure crystals in what is termed the Bergeron-Findeison process. The relative

importance of homogeneous and heterogeneous processes in the formation of cirrus (ice)


5

clouds is not well understood, largely due to poor constraints on the role of IN [Kärcher and

Spichtinger, 2009]. The phenomena of homogeneous and heterogeneous freezing will be

discussed separately below.

2.1.1 Homogeneous nucleation

The freezing of liquid water from a single, homogeneous phase is termed homogeneous

nucleation. On atmospheric timescales, homogeneous freezing occurs only below -38 °C for

1 µm sized droplets [Koop et al., 2000] due to the energy barrier involved in the formation

of an ordered solid from a disordered liquid. For this barrier to be overcome, a metastable

ice embryo must first form onto which additional water molecules can aggregate

[Pruppacher and Klett, 1996]. The successful formation of a stable ice phase from this

embryo is nucleation.

The homogeneous freezing process is relatively well understood. It is a stochastic process

that can be described in terms of water activity and pressure [Koop et al. 2000]

independent of the nature of any solutes. The rate hfJ at which a given solute freezes can

be expressed as [DeMott, 2002]:

𝐽𝐽ℎ𝑒𝑒 = 𝐶𝐶 𝑂𝑂𝑒𝑒𝑒𝑒 �−Δ𝐹𝐹𝑎𝑎𝑎𝑎𝑂𝑂 − Δ𝐹𝐹𝑔𝑔

𝑘𝑘𝑇𝑇 � (1)

where actF∆ is the activation energy for movement of water from solvent to ice phase, gF∆

is the formation energy of the critical ice embryo, and k is the Boltzmann constant. The pre-

exponential factor C is a function of germ radius and the interfacial energy of the ice-

solution interface. Equation 1 can be extended to calculate hfJ as a function of temperature,

humidity and aerosol size distribution [DeMott in Lynch et al., 2002].

In liquid water, the homogeneous nucleation rate begins to increase rapidly below -35 °C

[Möhler et al., 2007] and depends on solution concentration. For example, 10 μm NH4SO4


6

droplets freeze at -38°C for 0 wt%, but at -78°C for 40 wt% [Bertram et al., 1999].

Homogeneous nucleation is particularly important in the upper troposphere.

2.1.2 Heterogeneous nucleation

If the formation of the initial ice embryo is facilitated by the presence of a solid surface (ice

nucleus, or “IN”) the process is termed heterogeneous nucleation. Because this surface

lowers the activation energy Δ𝐹𝐹𝑔𝑔 , heterogeneous nucleation can occur at much warmer

temperatures, up to -5°C [e.g. Möhler et al., 2007; Pitter and Pruppacher, 1973].

Concentrations of IN in the atmosphere are low (on the order of 1-100 particles L-1), and

typically increase as temperature decreases [Möhler et al., 2007] because each IN will have

a threshold temperature for nucleation.

In general, the temperature at which a given heterogeneous IN will activate is highly

variable for different particles of the same material. Activation conditions are further

affected by the water activity, which depresses the nucleation point of a given IN in analogy

to homogeneous freezing [Zobrist et al., 2008b]. However, this effect is dominated by the

surface features that make a material nucleate ice in the first place. The present

understanding of these surface features and of their activation conditions is poor. This

section describes ice nucleation upon heterogeneous surfaces in general. Specific

atmospherically relevant surfaces are considered afterwards in Section 2.3.

Ice activation at the IN surface is complicated by the numerous pathways by which it may

occur, as illustrated in Figure 1. Most fundamentally, freezing may occur

- directly from the gas phase (deposition freezing)

- upon immersion of an IN within a water droplet (immersion freezing)

- when the surface of a droplet comes into contact with an IN (contact freezing)


7

In the atmosphere, these routes are complicated by consideration of the processes

occurring within a moving air mass. For example, consider a rising air mass. As it cools, it

becomes more humid, gradually becoming more saturated with respect to water vapour. If

the air cools below 0°C, deposition freezing becomes possible. If no deposition occurs and

water saturation is reached, condensation on insoluble IN may lead to immersion freezing.

Whether deposition freezing is possible may be limited by efflorescence (Section 2.3.5).

Figure 1. Ice formation pathways. Contact, immersion and deposition freezing are all modes of

heterogeneous freezing. Contact freezing may occur from within or without. Efflorescence and

deliquescence may form or destroy a solid IN. Photo: rockymtncme.com.

The energy of formation of an ice embryo on a solid substrate, gF∆ , can be modelled by

analogy to the case of a water embryo nucleating on the same surface [Pruppacher and

Klett, 1996]. The embryo is treated as a “cap” on the substrate surface, which comes into

contact with the surface with a contact angle θ. (θ is zero for a completely wet planar

surface.)


8

An effective IN must possess a number of physical features in order to provide a suitable

surface for ice nucleation. First, the IN must be larger than the embryo itself. Pruppacher

and Klett [1996] estimate the deposition mode ice embryo radius gr as 35 nm for pure

water at 268 K, with gr decreasing rapidly to ~10 nm for T<253 K at all concentrations.

Within a water droplet (immersion freezing), gr is roughly four times smaller [Pruppacher

and Klett, 1996].*

The importance of microscopic active sites in IN raises experimental challenges for the

determination of IN behaviour. In particular, there is often a tradeoff between accurate

determination of the activation point and good statistical representation of an IN type.

An ice-nucleating particle need only contain a single active site, comparable in size to the

ice embryo, in order to act as an IN. These active sites may be present as surface inclusions

or physical defects (i.e. edges or vertices in the surface lattice) [Pruppacher and Klett,

1996]. It follows that some active sites will be more effective than others, and the IN

activity of a given particle type is a probability function of particle surface area and

chemistry.

At an active site or across its surface, an IN must provide a geometric arrangement of bonds

at the surface that will align adsorbed water molecules in an ice-like structure [Pruppacher

and Klett, 1996]. This feature lowers the energy barrier to freezing, even if deformation of

the ice or substrate lattice is necessary for the fit. The importance of this feature was

shown by Evans [1965; in Pruppacher and Klett 1996], who demonstrated that ice

nucleated upon an AgI surface assumes a hexagonal structure even under conditions where

other forms are thermodynamically favoured. If aligned correctly, the functional groups –

OH, –NH2 and =O encourage nucleation [Head in Pruppacher and Klett 1996].

* These numbers are based on the application of equilibria conditions and macroscopic properties on a

molecular level and should be taken as rough estimates only.


9

While an accurate determination of the activation conditions for a given particle is

informative from a mechanistic standpoint, these conditions vary significantly between

different particles from the same source. Therefore, bulk measurements for each

mechanism are essential for cloud modelling.

2.2 Methods and Instrumentation

Here, a brief description of the instrumentation discussed within this review is provided.

Many of the recent advances discussed below were largely made possible by instrumental

development. The Continuous Flow Diffusion Chamber (CFDC) is discussed in greater

detail in Chapters 3 and 4. Single-particle mass spectrometry is described in greater detail

in Section 5.2.1.3.

2.2.1 Continuous Flow Diffusion Chambers (CFDCs)

The CFDC exists in multiple versions. A thorough description of the operating principle is

given in [Kanji and Abbatt, 2009]. Briefly, a known temperature difference is created

between two ice-coated walls. The resulting linear vapour pressure gradient translates to a

non-linear saturation profile across the chamber. Samples are introduced and confined to

the centre of this profile by a movable injector and a fixed sheath flow. Thus a continuous

flow of aerosol can be subjected to a controlled temperature and humidity for a controlled

amount of time.

The nucleation of ice upon aerosol particles is detected by an optical particle counter (OPC)

at the exit of the CFDC. Although water droplets as well as ice crystals may grow within the

CFDC, the higher growth rate of ice is taken advantage of by choosing a residence time and

measurement size that resolve the two. Since ice has a lower vapour pressure than water,

ice crystals grow much faster than water droplets below about –5°C [Möhler et al., 2007],

allowing a simple distinction. Above water saturation, both ice and water may grow

substantially, and an evaporation region is needed. In the evaporation region, the humidity

is reduced below water saturation but remains above ice saturation, driving moisture from

droplets to the ice crystals.


10

For studies of IN composition, ice crystals may be recovered using the CVI described in

Section 2.2.2.

2.2.2 Counterflow Virtual Impactors (CVIs)

The counterflow virtual impactor is described fully in Mertes et al. [2007]. Whereas a

normal impactor removes large particles from an aerosol by inertial deposition, the CVI

allows large particles to be recovered while discarding smaller particles.

The impactor can be visualized as a sampling line whose inlet is “shielded” by an outgoing

flow of air. This outgoing flow physically excludes particles below a critical size, as well as

the gas phase, while larger particles possess enough inertia to pass through. This technique

is used to separate ice crystals within clouds or CFDCs from the interstitial aerosol. Ice

crystals are typically evaporated in the sampling process.

2.2.3 Ultraviolet Aerodynamic Particle Sizers (UV-APS)

The ultraviolet aerodynamic particle sizer (UV-APS) is described fully in Hairston et al.

[1997]. The UV-APS measures the terminal velocity and fluorescence of particles

simultaneously. The fluorescence signal is taken to be due to nicotinamide adenine

dinucleotide phosphate (found in all living cells), whence a size distribution of biological

aerosol can be estimated.

2.2.4 Laser Desorption/Ionization Aerosol Mass Spectrometers (LDI-MS)

The laser desorption/ionization aerosol mass spectrometry (LDI-MS) technique is

described in its many incarnations by [Noble and Prather, 2000]. Briefly, particle size is

estimated by terminal velocity before simultaneous ablation and ionization by a high-

energy laser. LDI-MS allows for real-time resolution of particle mixing state. Variations in

elemental ionization efficiencies, laser wavelength and power, and matrix effects mean that


11

LDI-MS spectra are not quantitative. In the discussion below, most LDI-MS measurements

are made by comparing the background aerosol to IN.

2.3 Recent Advances in Understanding Heterogeneous Ice Nucleation

The activity and efficiency of different substances as IN is at present not well understood.

This section gives an overview of the most significant findings from recent experiments and

field work, highlighting the most important concepts in each area.

2.3.1 Dust as ice nuclei

Mineral dusts are excellent IN [e.g. Pruppacher and Klett, 1996] that remain active even

after intercontinental transport [DeMott et al., 2003c; Pratt et al., 2009] and play a major

role in atmospheric ice nucleation.

Dusts are ubiquitous in the atmosphere, and plumes of dust frequently cover broad swaths

of the Earth [Prospero, 1999]. Lohmann and Diehl [2006] estimated that dust decreases the

radiative forcing of stratiform mixed-phase clouds *

“Dust” refers to those particles in the atmosphere composed of disintegrated clays (which

may be internally mixed with other materials). Clays are layer silicates composed primarily

of SiO2, Al2O3, Fe2O3, and MgO2, which often contain metal inclusions and regular surface

by 1 to 2.1 W m-2 depending on its

composition. Given that atmospheric dust levels have increased two- to fourfold since the

1960s [Mahowald and Kiehl, 2003], the effects of dust on this single type of cloud can be

compared to the forcing of 1.7 W m-2 due to anthropogenic CO2 [Murray et al., 2010a]. The

significance of dust IN has motivated a large number of studies on various dusts, and the

discussion here is not exhaustive.

* Stratified (non-convective) clouds containing both liquid droplets and ice crystals.


12

functional groups.*

* For example, the ice nucleating dust kaolinate has a surface of pseudohexagonally arranged –OH bonds

[Pruppacher and Klett, 1996]. However, no direct evidence exists to relate this surface structure to IN activity.

The majority of submicron ice-nucleating dusts in the atmosphere are

considered to be kaolinite and montmorillonite [Lohmann and Diehl, 2006]. Either

kaolinite, montmorillonite, field samples, or milled surrogate dusts (typically Arizona Test

Dust, or ATD) are typically used in ice nucleation studies.

Dusts can act as IN in the immersion [e.g. Field et al., 2006; Marcolli et al., 2007], deposition

[e.g. Chernoff and Bertram, 2010; Kanji et al., 2008] and contact freezing modes [e.g. Pitter

and Pruppacher, 1973]. In the atmosphere, observed conditions for dust-triggered freezing

vary considerably; nucleation has been observed in the immersion mode at temperatures

as high as -8°C [Rosenfeld et al., 2001; Sassen et al., 2003]. In the deposition mode, mineral

dusts nucleate ice across a wide range of RH depending on the mineral itself, the presence

of impurities, the freezing temperature, and the freezing mechanism.

Chemical processing can inhibit the IN ability of dusts. Exposure to H2SO4 vapour

irreversibly deactivates the IN ability of Arizona Test Dust (ATD) by digesting surface sites

essential to nucleation [Niedermeier et al., 2010; Sullivan et al., 2010b]. However, the same

is not true for HNO3 vapour, which prevents deposition freezing but allows subsequent

condensation freezing [Sullivan et al., 2010a]. These extreme treatments demonstrate the

sensitivity of active sites to chemical processing, but exceed the degree of processing found

in the atmosphere.

More atmospherically relevant experiments using kaolinite and montmorillonite coated

with (NH4)2SO4 showed inhibition of their IN activity [Zuberi et al., 2002]. Similar studies

[Chernoff and Bertram, 2010; Eastwood et al., 2009] have shown that H2SO4 or (NH4)2SO4

coatings increased the RHi required for nucleation by about 30%. Zobrist et al. [2008b]

used H2SO4, (NH4)2SO4 and ten other solutions with four different ice nuclei, including ATD,

to show that this inhibition of freezing is best described as the effect of reduced water

activity.


13

2.3.2 Soot as ice nuclei

The role of soot (“black carbon” or “elemental carbon” aerosol generated by combustion*

The IN activity of biomass burning particles has been observed in both boreal forests

[Sassen and Khvorostyanov, 2008] and the Amazon [Lin et al., 2006]. In the laboratory,

Petters et al. [2009] found 9 of 21 soot types

)

as IN remains an issue of current research [DeMott, 2008]. Anthropogenic activities in

general have a significant impact on the soot loading of the atmosphere, and, perhaps more

significantly, aviation directly injects soot into the cold upper atmosphere [Cziczo et al.,

2009]. The soot particles emitted by aviation may form liquid contrails if the air becomes

water-saturated [Schumann in Lynch et al., 2002], potentially implicating both deposition

and condensation mode freezing upon soot.

†

* The terms soot, elemental carbon (EC) and black carbon (BC) all refer to light-absorbing, carbonaceous aerosol generated by combustion. Which term is used typically reflects the measurement technique used. † It is worth noting that these materials were burnt at low temperatures (unreported) and are considerably different in chemistry to the high-temperature analogues generated within combustion engines.

(mostly plant material) to be IN active at -

30°C. However, soot properties vary widely with combustion temperature and oxygen

level, and the relevance of these laboratory samples remains unknown.

The difficulty of generating representative soot in the laboratory is compounded by the

physico-chemical processes to which soot is subjected in the atmosphere. These processes

are not well understood, and difficult to mimic [DeMott, 2008]. Experiments have shown

that the presence of sulphate [Popovicheva et al., 2008] or organic carbon [Möhler et al.,

2005b] suppresses soot IN activity, and such coatings are rapidly acquired in the

troposphere if not at the emission source itself. More measurements are needed before a

global estimate of the impact of soot, separate from wildfire episodes, can be established.


14

2.3.3 Biological ice nuclei

Biological particles are unique as IN. They may initiate ice formation at temperatures just a

few degrees below 0°C, higher than most other IN [Möhler et al., 2007]. Their sources are

ubiquitous, and they may act as cloud condensation nuclei (CCN) or giant CCN as well as IN.

Bacteria, fungal spores and pollen have all been demonstrated as effective IN [e.g. Möhler et

al., 2007].

A number of recent studies have provided evidence for the importance of biological

particles as IN. Christner et al. [2008] collected fresh (< 8 h) snow samples from 3

continents (North America, Europe, Antarctica) and found biological particles (<1% of

total) active as IN at temperatures between -7°C and -4°C. They were able to inactivate 69-

100% of particles by heating to 95°C for 10 min, demonstrating the role of proteins in IN

activity. Although this study does not conclusively demonstrate that these particles were

not scavenged during precipitation, Pratt et al. [2009] did so by measuring biological

particles within ice during flights through an orographic ice cloud. Ice crystal residues were

enriched in both dust and biological material (phosphate-rich organic aerosol) relative to

the background aerosol.

Prenni et al. [2009] have emphasized that biological particles are important IN even in the

presence of mineral dust. They used a CFDC to monitor IN concentrations in the Amazon

basin over a period of one month during the 2008 wet season. Biological aerosol was

measured by UV-APS (Section 2.2.3). Biological IN dominated the population above -27 °C,

with dust playing an increasing role below that temperature.

2.3.4 Organic ice nuclei

In general, organic aerosols do not effectively nucleate ice. At multiple locations, mass

spectrometry of IN or in-cloud ice crystals above -35°C has identified a depletion of organic

aerosols relative to the background [DeMott et al., 2003a; Kamphus et al., 2010; Pratt et al.,

2009; Richardson et al., 2007].


15

Some organics, such as nonadecanol, are effective IN, owing to the formation of a

monolayer whose structure mimics that of ice [Gavish et al. 1990 and Popovitz-Biro et al.

1994 in Zobrist et al., 2008b]. Cantrell and Robinson [2006] observed significant IN activity

with long-chain alcohol monolayers only after a first freezing cycle, which they explained in

terms of a delay (days) in forming an equilibrium structure. Zobrist et al. [2006] studied the

immersion freezing behaviour of five carboxylic acids and found that oxalic acid was

weakly IN active, though it froze only 2-5 K above the homogeneous point.

Atmospheric organic matter is extremely complex, and single-component monolayers are

unlikely to form in the atmosphere. Kanji et al. [2008] showed that two complex natural

organic materials (leonardite and humic salts) do nucleate ice under conditions relevant to

mid-level clouds (110-130% RHi and 233 K). However, the bulk of organic aerosol in the

atmosphere is not humic in nature, but rather condenses from the gas phase to form

secondary organic aerosol, or SOA [Jimenez et al., 2003]. While Prenni et al. [2009]

observed little to no relationship of IN numbers with SOA mass concentrations in the

Amazon basin, no other studies have been conducted.

Recent evidence has suggested that atmospheric organic aerosol particles may form

amorphous solids in the atmosphere [Virtanen et al., 2010; Zobrist et al., 2008a]. Murray et

al. [2010b] showed that citric acid aerosols form ice-nucleating glasses under conditions

such as those found near the tropical tropopause layer (12 – 18 km altitude; 180–200 K).

This layer is rich in neutralized organic particles, and represents the conduit through which

air enters the stratosphere. Glassy organic IN could therefore dramatically affect the

transport of water vapour into the stratosphere [Froyd et al., 2009].


16

2.3.5 Inorganic ice nuclei

The inorganic aerosol relevant to cloud formation is dominated by sulphates. Pure

sulphuric acid freezes homogeneously according to its water activity [Section 2.1.1].

However, some inorganic aerosol may effloresce, and in crystalline form act as IN.

Abbatt et al. [2006] demonstrated the impact of crystalline ammonium sulphate, (NH4)2SO4,

on cloud formation. In the Northern hemisphere, anthropogenic ammonia emissions allow

(NH4)2SO4 aerosol to form, which may then effloresce and act as an IN. The IN efficiency of

(NH4)2SO4 has been confirmed in the laboratory by Baustian et al. [2009] and Eastwood et

al. [2009]. Along with soot, (NH4)2SO4 IN could represent a source of hemispheric

imbalance in the anthropogenic impacts on ice nucleation that could affect global climate.

The impacts of inorganic IN are likely to be less widespread than dust or soot, since

efflorescence is required prior to nucleation, yet may become important in certain air

masses.

2.3.6 Lead in ice nuclei

The IN activity of lead-containing particles has been known since the 1940s, and the

potential of anthropogenic lead aerosol to affect climate through its IN activity was

recognized by Schaefer [1966]. As PbI, lead is a highly effective ice and cloud nucleus, and

has been used in weather modification (despite its toxicity) along with AgI. Reischela

[1975] has shown that lead remains active as PbO, an effect that is amplified by the

presence of NH4I*

Recently, Cziczo et al. [2009] found lead in 42% of dust particles within ice crystals

sampled directly from a mixed-phase cloud (i.e. 28% of all particles). In a separate

. In the modern age, anthropogenic emissions have become the dominant

source of atmospheric lead [Cziczo et al., 2009].

* A significant observation since anthropogenic activity has also significantly affected atmospheric NH3 as well

as lead.


17

experiment, a dust sample with 8% of particles containing lead was enriched to 44% lead-

containing after IN nucleation. Whether the importance of lead is due to structural features

or chemical interactions remains unknown.

Figure 2. Image of Pb inclusions in a dust IN (energy dispersive X-ray microanalysis) overlaid upon an

image of the entire particle (scanning electron microscopy) [Cziczo et al., 2009].

Experiments have shown that lead inclusions should be > 30 nm in radius to act as active

sites [Baklanova et al., 1990]. Dust particles in the atmosphere are typically about an order

of magnitude larger than this, and only a relatively small Pb inclusion is needed to enhance

a dust IN. Furthermore, Cziczo et al. [2009] found that metallic Pb was not required to

enhance ice nucleation. They observed enhanced ice nucleation upon dust after coating

with a 1% PbSO4 solution, though pure PbSO4 has no IN activity.

PbSO4 may have an atmospheric source from the petrol industry. While tetraethyl lead was

added to automobile petrol starting in 1923, the practice was phased out in the 1980s

[Nriagu, 1990]. Automobile petrol accounted for >99% of global lead sources – both

natural and anthropogenic – in the 1980s. Now, coal combustion smelting, and light

aviation fuel are the major anthropogenic sources. Light aviation fuel directly places lead

above the planetary boundary layer within regions of ice formation. Through the


18

mechanisms discussed above, human activity may potentially amplify the IN activity of pre-

existing aerosol [Cziczo et al., 2009].

Is the IN activity of Pb unique to that element? A study by Gallavardin et al. [2008]

observed enrichment of Rb, Sr and Ba in the mass spectra of IN active dust, which the

authors attributed to the solubility of the salts or lower ionization energies of these

metals.*

*The same desorption/ionization laser (193 nm XeF excimer) was used in this study as was used by Cziczo et

al. [2009].

The authors did not investigate the process any further. It remains a possibility

that these heavy metals play a role in IN activity similar to that of Pb.


19

2.4 Modelling and Prediction of Ice Nucleation in the Atmosphere

The ultimate goal of IN studies is to predict the conditions at which a given air mass will

form ice based on the physical properties and history of that air. Such a prediction will

allow an accurate assessment of the role ice plays within the climate system, and

consequently allow the incorporation of IN effects on the rapidly changing climate of the

Earth. A proper understanding of the feedbacks of ice clouds to the climate system, as well

as the impacts of changing land use and anthropogenic emissions on IN concentrations,

rely on understanding heterogeneous ice nucleation in the atmosphere.

At present, climate models typically contain extremely simple parameterizations of

heterogeneous ice nucleation in the atmosphere, lacking even basic information on aerosol

properties and instead predicting IN as a function only of temperature and ice saturation

[e.g., Meyers et al., 1992]. Improving this limitation is a major goal set out in the most recent

report of the Intergovernmental Panel on Climate Change (IPCC, 2007).

Recently, models have accounted for aerosol number concentrations as well as chemical

properties. Phillips et al. [2008] present an empirical parameterization of heterogeneous

ice nucleation based on dust and metallic aerosols, elemental carbon and insoluble organic

carbon. In the global climate model CAM-Oslo, Hoose et al. [2010] implemented the semi-

empirical parameterization of Chen et al. [2008], deriving aerosol-specific parameters for

Classical Nucleation Theory (Section 2.1.2) using experimental data for dust, bacteria,

pollen and soot. The model performed reasonably well on the spatial scale, successfully

predicting an episode of increased IN numbers in Karlsruhe, Germany [Niemand et al.,

2010], but the success of the parameterization was poor for soot and uncertain for the

biological aerosols.

While chemical resolution within ice models is ideal given the range of properties exhibited

by different IN (Section 2.3), global climate models often lack such a detailed treatment of

aerosols. DeMott et al. [2010] recently proposed a simple parameterization of ice nuclei

concentrations for RHw > 100% that required only data on temperature and aerosol


20

number above 0.5 µm (n>0.5µm). The parameterization was accurate to within a factor of 10.

While the parameterization was limited to one freezing mechanism, it is crucial in its

simplicity.

The surface-specific process of ice nucleation will very likely never be computationally

convenient at the single-particle level. IN activity varies too widely across particles from

the same source to be modelled from first principles. The most useful models are therefore

those where detailed atmospheric measurements are distilled into a simple relationship,

such as that of DeMott et al. [2010]. Improved accuracy and representation of mechanisms

other than immersion freezing are desirable. The required precision will depend on the

precision of other processes within climate models, and should evolve accordingly.

2.5 Summary and Outlook

This chapter reviewed recent findings regarding the nucleation behaviour of different

aerosol types. According to current field observations, the most common ice nuclei are dust

and biological particles. Yet other aerosols such as soot and glassy organics are potentially

important, particularly close to sources and at upper-tropospheric temperatures

respectively. The competition of these IN with water droplets in mixed-phase clouds

controls cloud lifetime, as does the ability of IN to trigger freezing prior to homogeneous

freezing in high-level clouds. The global consequences of these competing processes are

poorly understood.

Future studies on the distribution and activation conditions of biological IN, soot IN, and

glassy organic IN are needed. For all IN and especially soot, the degree to which other

species absorb to and deactivate active sites should be investigated. For all IN, more data

on the temperatures and humidities required for freezing are needed. New techniques

must be applied for biological and organic measurements, as present LDI-MS techniques do

not provide information on the sources of bioaerosols or the identity of organic molecules.


21

Some work has already been done towards identifying bioaerosols [e.g., Fergenson et al.,

2003; Harris et al., 2006].

Low IN concentrations and poor characterization of some particle types, especially soot

and organic aerosols, present the greatest challenges. Reproduction of these aerosols in the

laboratory remains a challenge for atmospheric science in general. In light of the

complexity of ice nucleation itself, field measurements will probably be most useful for

characterization of soot and organic aerosol contributions to atmospheric IN. For IN in

general, further lab studies will provide insight into nucleation mechanisms while field data

will continue to be the most reliable source of quantitative IN parameterizations for some

time.

Heterogeneous ice nucleation plays a major role in the Earth’s radiative balance and

hydrological cycle. Effectively modelling these processes remains a challenge due to very

low IN concentrations, a wide range of IN efficiencies and a lack of knowledge about the

relative importance of different nucleation modes. Laboratory studies into atmospherically

relevant nucleation mechanisms, as well as research into the fundamental physics of

crystallization, will allow an understanding of which mechanisms are most important in

the atmosphere. Field data should be used to parameterize the conditions under which

these mechanisms occur for different aerosols in the atmosphere. With these

improvements, models will be able to better quantify the importance of ice in the climate

system.


22

3 The Continuous Flow Diffusion Chamber (CFDC)

The University of Toronto Continuous Flow Diffusion Chamber (UT-CFDC or CFDC) in its

original form was designed and built by Dr. Zamin Kanji as part of a PhD thesis. Its design

and validation are described in detail in Kanji and Abbatt [2009]. A detailed description of

the original chamber is provided here for completeness. All work except the Figures are

from Kanji and Abbatt [2009]. Modifications made to the chamber as part of this thesis are

described separately in Chapter 4.

3.1 Theory of Operation

The CFDC exposes aerosol particles to controlled conditions of relative humidity (RH) and

temperature (T) conditions. A continuous flow of aerosol is passed between two horizontal,

ice-coated copper plates that control RH and T. If ice nucleates upon any aerosol particle, it

quickly grows to form a large ice crystal.

The UT-CFDC is operated under RH, T and residence time conditions that allow all

nucleated ice crystals to easily grow beyond 5 μm in size. An optical counter then

determines the concentration of 5 μm particles exiting the chamber, which is a measure of

ice nuclei concentrations if no 5 μm particles were initially present.

The internal temperature is controlled by two horizontal copper plates. The upper plate is

held warm relative to the lower, generating a linear temperature profile while avoiding

convective instability. Both plates are coated with ice, which generates a linear profile of

vapour pressure, since 𝑃𝑃𝐻𝐻2𝑂𝑂 above the warmer plate will be higher. These linear

temperature and pressure gradients generate a non-linear saturation profile within the

chamber, because the vapour pressure of ice 𝑃𝑃𝑣𝑣𝑎𝑎𝑒𝑒 ,𝑖𝑖𝑎𝑎𝑂𝑂 is a non-linear function of

temperature*

* 𝑙𝑙𝑙𝑙𝑔𝑔(𝑃𝑃𝑣𝑣𝑎𝑎𝑒𝑒 ,𝑖𝑖𝑎𝑎𝑂𝑂 ) decreases exponentially as inverse temperature increases [Murphy and Koop 2005, Fig. 2].

[Murphy and Koop, 2005].


23

The equilibrium vapour pressure and saturation vapour pressure profiles within the

chamber are shown in Figure 3.

Figure 3. Pressure and saturation profiles within the CFDC (upper plate 253 K, lower plate 233 K)

calculated according to Murphy and Koop [2005]. The partial pressure of water (PH2O, thick red line) is

above the equilibrium pressure (Pvap,ice, dotted black line), generating supersaturated conditions with

respect to both water (dashed blue line) and ice (blue line).

The T and RH within the chamber dictate the degree to which ice nucleation upon a sample

IN is favourable. Residence time within the chamber can also be varied to investigate the

kinetics of nucleation upon a given IN. The precise design of the chamber and conditions

for which it has been validated are discussed in the next section.


24

3.2 Chamber Design

Figure 4. Diagram of the UT-CFDC, courtesy Z. A. Kanji.

This section describes the physical design of the CFDC as detailed in Kanji and Abbatt

[2009].

A diagram of the chamber is shown in Figure 4. Two horizontal copper plates, 50.8 cm by

25.4 cm, are separated by a 1.9 cm insulating polytetrafluoroethylene (Teflon®) spacer.

Rubber o-rings sit within grooves in the plates and Teflon spacer. The plates are cooled by

copper coils (0.95 cm o.d., 172 cm long) on either plate, filled with a polysiloxane coolant

(Syltherm XLT, Dow Corning Corporation) that is cooled by an external chiller (Neslab,

ULT-80). Four holes (0.32 cm) within each plate allow thermocouples to be inserted

halfway to monitor the internal temperature.

The inner walls of each copper plate are coated with ice by wetting a layer of quartz-fibre

filter paper on each wall prior to cooling. The filter paper is wet through five sealable ports

(16 mm) in the upper plate. Upon freezing, ice rises above the filter paper to form a smooth

layer above it.

outlines of Teflon® spacer

Teflon® spacer sandwiched between copper plates

O-ring groove

to OPC

sample inlet through movable injector

sheath inlet

25.4 cm

50.8cm

20.3 cm 45.7 cm

d, injector position (cm)


25

A variable position stainless steel sample injector introduces aerosols to the centre of the

chamber. A sheath flow normally retains the sample aerosol at this position, and is

introduced through four holes (64 mm) in the Teflon spacer. The injector similarly contains

six ports (1.6 mm) that distribute sample aerosol evenly within the chamber. The

outermost sample injection port is 6.2 cm from the wall to avoid inhomogeneities in T and

RH. At the chamber exit, a triangular corridor directs the sample aerosol through a 64 mm

port and towards the measurement region.

Total flow through the chamber is dictated by the pump of an Optical Particle Counter

(OPC, Climet, CI-20). The OPC includes a differential pressure gauge and feedback system

that maintains the flow at 2.83 L min-1 when sampling at atmospheric pressure. The sample

flow is normally set at 10% of the total flow. An initially-dry and particle-free sheath flow

makes up the remainder. The Reynolds number for this chamber at the specified flow is 20

at 223 K, well below the critical value for a transition into turbulent flow. Thus, the

triangular outlet should not cause mixing of the sample and sheath flows.

At room temperature, the sample and sheath gases are expected to equilibrate to chamber

T and RH over 0.3–2 s. The sample injector is normally midway into the chamber, and the

sheath flow is given about 10 s to equilibrate prior to sample exposure.

Aerosols exiting the chamber are sized by the OPC into two bins, > 0.5 μm and > 5 μm. Data

from the smaller size bin is normally discarded but can be useful for diagnosing droplet

growth at high RH. The 5 μm channel is used to determine IN concentrations.

Kanji and Abbatt [2009] validated that the UT-CFDC reproduces the well-characterized

homogeneous freezing behaviour of sulphuric acid aerosol and performed a number of

experiments to validate the operation of the chamber. These experiments will not be

detailed here, since separate validation experiments are described in Section 4.3 below.


26

3.3 Design Advantages and Limitations

The UT-CFDC is a simple and lightweight ice nucleation chamber. The variable position

sample injector allows residence time within the chamber to be varied to assess nucleation

rates and losses due to gravitational settling. The continuous flow design allows for

continous monitoring of an aerosol sample as well as the measurement of large numbers of

particles [Kanji and Abbatt, 2009].

The chamber is limited to a single flow rate (2.78 L min-1) and a single critical size for ice

crystal growth (5 μm). Long sample times are required for sufficient sampling statistics

when ice crystal numbers are low. The chamber cannot be used below about 220 K, as ice

crystal growth becomes too slow to be observed as 5 μm crystals [Kanji and Abbatt, 2009].

With the current detector the chamber is unable to differentiate between water droplets

and ice at RHw above 100%. Future implementation of a phase-sensitive detector (e.g.

depolarization measurements) would allow this limitation to be overcome. Finally, the

present pump is not powerful enough to operate at reduced pressure such as would be

experienced during high-altitude airplane measurements [Kanji and Abbatt, 2009].


27

4 Development of the CFDC for Field Measurements

The University of Toronto Continuous Flow Diffusion Chamber (UT-CFDC) in its original

form is described in detail in Kanji and Abbatt [2009] and in Chapter 3. This chapter

describes modifications made to the CFDC as part of this Thesis. A number of modifications

were made to the chamber for its deployment in the field. These modifications improved

instrument portability and reduced the amount of supervision required during continuous

operation.

The UT-CFDC in its original form was unsuitable for field measurements for a variety of

reasons: (i) the chamber required two separate chillers (over 150 kg each) to cool the

upper and lower plates, (ii) background signals were typically of a similar magnitude to

expected IN concentrations in the field (0-10 /L), and (iii) a constant supply of nitrogen

was required to provide a sheath flow. Each of these issues is addressed below.

4.1 Sources of Background Signal

The major sources of background in the CFDC were identified as

1. leaks where the Teflon insulation and rubber o-rings met the copper walls,

2. contamination from the nitrogen/room air sheath flow,

3. frost dislodged from the chamber walls and the sample inlet.

The first two points were trivial and were solved by replacing the rubber o-ring and

filtering the incoming sheath air respectively. The last point remained the largest source of

background for the CFDC, though leaks became significant in certain environments. Frost

and leaked aerosol combined typically generated a background signal of 0 – 0.3 L-1,

depending on the ice formed on that day as well as the ambient aerosol loading.


28

It was found that forcing a high flow of nitrogen through the sample inlet dramatically

reduced the frost background. A similar flow through the sheath inlets did not have much

effect. This suggested that the background is largely due to moisture deposited

upon/within the sample inlet during normal operation. Frost may also have formed on the

sample inlet when water drips from the upper plate during the wetting procedure

described in Chapter 3. For this reason, the wetting port directly above the sample inlet

was not used. The forced flow typically comprised a pressure of 20-30 psi for two or three

10 minute periods during initial cooling of the chamber. If the OPC measured a significant

background through the filtered inlet afterwards, the treatment was repeated.

During field operation, 2 hours of measurements were regularly followed by a 30 minute

background measurement (filtered inlet). The background typically decayed slowly during

measurement. On extremely humid days (e.g. during rainfall), the diffusion dryer used

upstream of the chamber was unable to completely dry the air, and after roughly six

measurement hours the sample inlet became clogged. Such clogging was observable

through a gradual decay of the sample flow rate, but did not result in an increased

background.

4.2 Modifications to the CFDC Design

A number of features were added to the CFDC to increase its portability and ability to

operate unsupervised. The changes are listed below and detailed in the following

subsections.

1. For portability, the chamber was converted to operate using one chiller rather than

two.

2. To avoid the need for nitrogen cylinders, a simple zero air generator was

constructed.

3. To monitor sample dilution accurately, the sample and sheath flows were computer

monitored and recorded.


29

4. To provide feedback on instrument status, a computer program was designed and

written to control the instrument, record IN measurements, and graph data in real-

time.

4.2.1 Chamber cooling

A simple normally-closed solenoid valve was introduced in order to split the flow of

coolant from the chiller to either plate of the chamber. The valve was used to periodically

cool the warmer plate, while the lower plate was cooled continuously. The period at which

the warmer plate was cooled was determined by a Differential Temperature Meter (DTM),

which also powered the valve.

The temperature of each plate was monitored using two Ni/Cu thermocouple wires. When

the temperature difference between the plates exceeded a preset value, the DTM allowed

the solenoid valve to open, cooling the upper plate. The valve was afterwards closed when

the temperature difference fell to 0.1 K below the setpoint. After closing the valve, the

upper plate is warmed by heat from the ambient air. The heating rate was controlled by an

insulating layer of foam rubber and expanded polystyrene, engineered to allow a heating

rate of ~0.02 K/min. The entire setup restricted the variation of the temperature difference

to <0.1 K about the mean temperature.


30

Figure 5. Diagram of the solenoid valve and differential temperature meter.

While the chiller used in our setup (Neslab, ULT-80) was capable of accepting commands

via an RS-232 computer interface, no computer control was used, and the average

temperature was set manually. Since the chamber temperature at steady state depends on

the air temperature outside the instrument (indirect sunlight entering an air-conditioned

room was observed to change the chamber temperature by up to 2 K), computer control of

the chiller temperature is recommended for future studies.

4.2.2 Sample and Sheath Flows

The original CFDC required the calculation of sample flows from the total flow measured by

the OPC at the outlet and the sheath flow measured at the inlet. This was acceptable for

short periods, but the buildup of frost at the chamber inlet created a constantly changing

sample flow that had to be manually recorded at least every two hours.

To allow for longer unsupervised operation, and to improve our estimate of sample

dilution, a capacitance manometer (MKS, 220CD) and mass flow meter (MKS, 179A) were

used to monitor the sample and sheath flows, respectively. The manometer was installed

across an aerosol impactor (TSI Inc.) at the inlet and calibrated with a bubble flow meter

Chiller

Solenoid Valve

Differential ΔT Meter

Coolant(Syltherm (siloxane mixture))

thermocouples

Cold Plate

Warm Plate

Chiller

Solenoid Valve

Differential ΔT Meter

Coolant(Syltherm (siloxane mixture))

thermocouples

Cold Plate

Warm Plate


31

(Gilibrator, SIS). Both flows were monitored at 2 Hz*

4.2.3 Sheath Flow Generation

using a data acquisition board from

National Instruments (NI, Model USB-6221) and a program written in LabView 8.6.

The sheath flow within the chamber was originally generated by a pressurized nitrogen

cylinder [Kanji and Abbatt, 2009]. Because one such cylinder lasted only 20 hours under

normal conditions, a renewable source of clean, dry air was designed to replace nitrogen in

the field. The requirements for this sheath flow generator were simple: it had to be particle

free and free of moisture.†

4.2.4 Automated Background Measurements

A simple setup was designed as a renewable source of sheath flow. A brushless DC blower

(Ametek, SE12RE21SA) pushed ambient air through custom-built aluminum diffusion

dryers (0.5” core, 3” diameter), which were filled with moisture-indicating silica gel (Sigma

Aldrich). The silica gel was dried at 373 K for ~9 hours every 3 days. The sheath flow was

passed through a HEPA filter before entering the chamber. The sheath flow was monitored

at 2 Hz using LabView.

Frequent background measurements were necessary to account for the varying frost

background in the CFDC. An automated 3-way ball valve (Swagelok, 42ACX-3600) was used

to alternate between unfiltered and filtered air during atmospheric measurement. Custom

LabView software determined and recorded the duration of each measurement.

* This high sampling rate allowed the effects of the 3-way valve described in 4.2.4 to be accounted for. † Since ice nucleation depends only on temperature and the partial pressure of water, there is no difference

between nitrogen and compressed air for our chamber.


32

While switching between states, the 3-way valve temporarily (~8 s) cut off the flow of

sample aerosol to the chamber. The resulting spike in sample flow typically returned to

normal values after ~25 s.

4.2.5 CFDC Control Program

A program was written in Igor Pro (WaveMetrics, v. 6.1) to monitor and record IN

measurements during operation. The program was capable of starting and stopping the IN

counter, and recording and graphing IN concentrations in real-time.

4.3 Validation of operation conditions

For field measurements, the CFDC had to distinguish ice crystal from water droplet

formation for a range of particle sizes. These growth mechanisms depend on the

temperature, relative humidity (RH) and residence time (tR) in the chamber. Experiments

were performed to select the ideal residence time and maximum feasible humidity at a

temperature close to the minimum for heterogeneous nucleation (235 K). This low

temperature allowed the maximum number of potential heterogeneous ice nuclei to be

measured.

4.3.1 Distinguishing ice crystals from water droplets

Ice crystals grow faster than water droplets because of the lower vapour pressure of ice.

This fact is exploited in the CFDC to allow 5 μm particles to be identified as ice. However, at

a high enough saturation ratio, water droplets can also grow to 5 μm at high enough

residence time (tR).

To investigate the effects of residence time on droplet growth, 500nm H2SO4 particles were

selected with an Electrostatic Classifier (EC, TSI 3080) after nebulization of a 70 wt %

solution. This monodisperse aerosol was passed through the chamber at humidities


33

ranging from 103 – 108% for residence times of 11.5 to 14.8 s. As shown in Figure 6,

500nm droplets were only capable of growth to 5 μm above 102% RHw. Relative to RHw,

the growth rate was only weakly dependent on tR.

Figure 6. Number of 500nm H2SO4 droplets (initially 106 L-1) reaching 5 µm as CFDC humidity was

increased from 100 to 108% RHw. No 5 μm droplets are expected below 102% RHw.

Based on Figure 6, a residence time of 12.2 s was selected for field measurements. Above

12.2 s, the fraction of activated particles plateaus due to vapour depletion within the

chamber [Kanji and Abbatt, 2009]*

A question remains as to whether aerosols larger than 500 nm are capable of reaching 5

μm within 12.2 s at RHw below 102%. Because the nebulizer used above could not generate

supermicron aerosols in sufficient number, an ultrasonicator (Crane, EE-865) was used to

generate aerosol from an NH4NO3 solution (25% w/w). The resulting aerosol reached a

and any further increase would only increase the

likelihood of droplet detection above water saturation.

* The residence times in Figure 6 of Kanji and Abbatt [2009] are incorrect. The 14 s point should be labeled

12.2 s after correction of an estimate in the chamber volume.


34

maximum of 4 μm in dry aerodynamic diameter. The aerosol was dried and an

Aerodynamic Particle Sizer (APS, TSI 3320) monitored particle concentrations with a 1

minute resolution. Because the electrostatic classifier could not select particles > 1 μm in

diameter, different aerosol impactors were used to provide <1 μm and <2 μm polydisperse

test aerosols.

Figure 7. Droplet activation of H2SO4 and NH4NO3 aerosols over 12.2 s. As RH was increased, initially-

larger droplets reached 5 μm earlier, however droplet growth always began at 100% RHw as

expected. Note that absolute number concentrations and not activated fractions are shown.*

Figure 7

shows that larger aerosols do reach 5 μm at lower saturations than 102% RH, but

that the minimum humidity for droplet activation is above 100%. To confirm that < 5 μm

particles did not influence the IN background below 100% RHw, conditions were fixed at

95% RHw, 12.2s tR and the ultrasonicator was rapidly switched on and off to produce short

(5 minute) bursts of <4 μm particles. These had no effect on the background.

* The APS cannot measure particles below 523 nm, and it was shown in Figure 6 that those particles can reach

5 μm under these conditions.


35

The experiments performed above show that 5 μm counts in the chamber are due only to

(i) ice nuclei or (ii) >5 μm particles. Either an aerosol impactor was used to prevent >5 μm

aerosol from entering the chamber, or an aerodynamic particle sizer (APS 3321) was used

to account for 5 μm aerosol. Background subtractions accounted for 5 μm frost dislodged

from the walls.

Based on these experiments, concentrations of deposition-mode ice nuclei in the

atmosphere were measured by operating the chamber at 238 K and 95% RHw (134% RHice)

with a 12.2 s residence time.


36

5 Relationships Between IN Concentrations and Aerosol Chemical Composition at College St, Toronto

5.1 Summary

Few studies have measured ice nuclei levels in an urban setting. The majority of field

measurements have been made in the free troposphere [DeMott et al., 2003a; DeMott et al.,

2003c; Pratt et al., 2009; Richardson et al., 2007] or in relatively pristine environments

[Prenni et al., 2009]. While laboratory studies have shown that the IN activity of soot and

dust decreases with sulphuric acid coatings [Chernoff and Bertram, 2010; DeMott et al.,

1999; Eastwood et al., 2009; Möhler et al., 2005a], no field studies on this effect have been

published. Knopf et al. [2010] have recently reported the only study of urban ice nuclei

using Mexico City aerosol particles. They observed active IN at 120–135% RHi for 210–240

K, showing that anthropogenic aerosols may indeed be a source of IN to the upper

troposphere.

Here, in-situ measurements of atmospheric ice nuclei made using the University of Toronto

Continuous-Flow Diffusion Chamber (UT-CFDC) [Kanji and Abbatt, 2009] are presented.

The chamber was operated at a temperature relevant to ice formation in mixed-phase

clouds (239 K) and at high humidity (134% RHice) in order to estimate an upper limit on

deposition-mode IN at this temperature. The mean observed IN concentration was

moderate, at 6.0 ± 0.7 𝐿𝐿−1.

The CFDC sampled aerosol from the roadside inlet of the Southern Ontario Centre for

Atmospheric Aerosol Research at 200 College St, Toronto, alongside an Aerosol Time-Of-

Flight Mass Spectrometer (ATOFMS). Single particle mass spectra from the ATOFMS were

used to estimate the relative concentrations of five general aerosol types: dust, salt, organic

(OC), elemental carbon (EC) and an unclassified category. These types were combined with

size data from a co-located Fast Mobility Particle Sizer (FMPS) and Aerodynamic Particle

Sizer (APS) to estimate chemically resolved number concentration and surface areas for

particles 0.24 nm to 3.0 µm in diameter.


37

Hourly size-resolved aerosol surface areas were estimated for these types and regressed

against IN numbers. A stepwise elimination procedure identified three types as most

important: dust and carbonaceous (EC or OC) particles, which showed a positive

relationship to IN, and salt aerosol, which showed a negative relationship probably

indicative of dust-inhibition by snowfall. Although our approach was unable to clearly

isolate the relationship between EC and OC, the results support the hypothesis that dust

originating from a polluted urban environment is able to effectively nucleate ice and

suggest that urban EC/OC may also act as ice nuclei.

Experimental and analysis methodologies are described in Section 5.2.1 and 5.2.2.

Characterization of the aerosol is described in Section 5.3, and the regression model in

Section 5.4.

5.2 Methodology

5.2.1 Experimental

Ice nuclei were measured semi-continuously from January 19th to February 9th as part of

the Seasonal Particulate Observation in Regional Toronto (SPORT) campaign. The sampling

site was a roadside building in downtown Toronto (43.66°N, 79.40°W), where a major

nearby intersection sees ~33 000 vehicles/weekday [Jeong et al., 2010]. Air was sampled at

50 L/min into an insulated inlet of diameter 10cm, approximately 15 m from the nearest

road, and 6 m above ground.

5.2.1.1 Aerosol Size Measurements

An Aerodynamic Particle Sizer (APS, TSI Model 3321) and a Fast Mobility Particle Sizer

(FMPS, TSI Model 3091) sampled continuously through the measurement period, with

resolutions of 60 s and 1 s, respectively. The APS measures the aerodynamic diameter of

particles accelerated to terminal settling velocity by light scattering, with a resolution of 51


38

size bins from 0.52 to 20 µm. The smallest size bin (< 0.52 µm) was discarded since these

counts simply represent any particle whose scattering signal did not cross a threshold

value [TSI, 2008].

The FMPS measures the mobility diameter of aerosol particles in the range 5.6 to 520 nm.

Incoming aerosol is electrically charged before being passed at 10 L/min into a sizing

region within a sheath flow of 40 L/min. An electric field within the sizing region attracts

particles through the sheath flow towards a series of 32 electrometers. Each electrometer

independently records the current produced by arriving particles, which is then related to

the drift time and subsequently particle size.

A detailed comparison of two FMPSs to a Scanning Mobility Particle Sizer (SMPS) by Jeong

and Evans [2009] showed that the FMPS significantly underestimates aerosol numbers in

the smaller size ranges (~10-40 nm). Furthermore, Hayden and Jeong [2009, pers. comm.]

found that the instrument underestimates sizes for the larger size bins (100-500 nm) and

developed corrections for the reported sizes using monodisperse NH4NO3 particles and

polystyrene latex spheres. Both of these corrections have been applied here.

5.2.1.2 Ice Nuclei Measurements

The UT-CFDC is described in detail in Chapters 3 and 4. The chamber was operated at 239

K, with a humidity setpoint of 134% RHi (95% RHw). Scans of RHw from 80% to 110% were

performed 1-2 times per day. These scans were used to identify the humidity at which large

numbers of ice crystals began to nucleate, which ranged from 96.6% to 100% RHw. Hence,

the measurements reported here were all made below the point at which large numbers of

IN activated.

The CFDC shared its sample line with the ATOFMS, with both instruments sampling at 0.1

L/min. The aerosol was dried before entering the CFDC, but not the ATOFMS, using a silica

gel diffusion drier (internal diameter 0.5”, length 20”). The gel was refreshed once weekly

by an overnight flow of nitrogen.


39

A dry nitrogen sheath flow was used to restrict the CFDC sample to the centre of the

chamber. The sheath flow was monitored at 0.1 Hz using a mass flow meter, while the OPC

monitored the total flow. The sample flow rate was deduced from the difference between

these flows.

No aerosol impactor was used during normal operation. The only commercial impactor

appropriate for the CFDC sample flow of 0.2 ± 0.1 L min-1 (TSI, Inc.) had a range of

maximum particle diameters ranging from 1.2 – 2.2 µm. This 1 µm variation is over a

crucial size range since larger particles are typically good IN, and may have dominated

variations in the measured IN concentration. To avoid this problem, no impactor was used;

instead, the >5 μm aerosol fraction measured by the APS was subtracted afterwards.

To test the impact of >5 μm aerosol on our IN numbers, the 1.2 – 2.2 µm impactor

described above was occasionally inserted directly upstream of the CFDC inlet. The flow

during these periods was carefully controlled to give a 1.5 µm cutoff size. In these trials, IN

numbers were completely unaffected. Regardless, APS >5 μm concentrations were

subtracted from the IN data here. The correction ranges from 0–10 particles L-1, or 0.02% –

43% of the IN concentration.

5.2.1.3 Single Particle Mass Spectrometry

A TSI 3800-100 Aerosol Time-Of-Flight Mass Spectrometer sampled particles directly from

the shared sample line, recording size and bipolar mass spectra for 446,527 particles

during IN measurement periods. The ATOFMS employs an Aerodynamic Focusing Lens

(TSI, AFL-100) to focus and isolate particles of aerodynamic diameter 0.3 to 3 µm in

diameter from the bulk gas. Particles are sized according to their transition time between

two 50 mW Nd:YAG lasers (532 nm) before a pulsed UV laser (Nd:YAG, 266 nm, ~108 W

cm-2) simultaneously desorbs and ionizes the particle within an evacuated time-of-flight

chamber. The instrument attains a nominal resolution of 500 𝑚𝑚/Δ𝑚𝑚; in practice, mass


40

resolution is poorer for 𝑚𝑚/𝑧𝑧 > 100. Depending on the size and composition of a given

particle, complete ablation may or may not be achieved.

The ATOFMS was size-calibrated using nine sizes of polystyrene latex spheres (PSL,

Polysciences Inc., density 1.05 g/cm3) ranging from 0.220 to 2.003 µm. The time-of-flight

region was calibrated using a standard solution of metal nitrates (TSI Inc.). The instrument

operated continuously throughout the measurement period, with occasional interruptions

due to a malfunction of a sizing laser. Periods during which the ATOFMS measured zero

particles were omitted, and the remainder was scaled using APS/FMPS measurements with

one hour time resolution as described in Sections 5.2.2.3 and 5.2.2.4 below.

5.2.2 Data Analysis

5.2.2.1 Derivation of Ice Nuclei Concentrations

The optical particle counter (OPC, Climet CI-20) of the CFDC was operated at a flow rate of

2.78 L/min, reporting the number concentration of particles >5.0µm every 7s (0.33 L)

according to their light-scattering response. Typically, zero, one or two IN were detected

per sample volume, with the majority of measurements resulting in zero counts. Such

samples of low counts from a relatively large population of atmospheric IN are best

represented by Poisson counting statistics. The uncertainty in such a count of 𝐶𝐶 is √C. After

counting the number of IN in a volume 𝑉𝑉, the average concentration is 𝐼𝐼 = 𝐶𝐶/𝑉𝑉. The

uncertainty in 𝐼𝐼 is then:

Δ𝐼𝐼 = 𝐼𝐼 × ��Δ𝐶𝐶𝐶𝐶 �

2

+ �Δ𝑉𝑉𝑉𝑉 �

2

Δ𝐼𝐼 =𝐶𝐶𝑉𝑉 ×

Δ𝐶𝐶𝐶𝐶

Δ𝐼𝐼 =√𝐶𝐶𝑉𝑉 =

√𝐼𝐼𝑉𝑉𝑉𝑉


41

Δ𝐼𝐼 = �𝐼𝐼/𝑉𝑉 (2)

assuming zero uncertainty in the constant sampling volume 𝑉𝑉. In this work, IN counts were

integrated over 85 L (30 minutes) rather than 0.33 L to reduce Δ𝐼𝐼.

Approximately every two hours, a background measurement was taken by filtering the air

entering the CFDC. The background was typically 0.3 𝐿𝐿−1 due to frost dislodging from the

chamber walls and minor leaks. The two-hour backgrounds were interpolated and

subtracted from IN measurements, taking the uncertainty as the standard error of the

measurement.

IN signals were therefore well above the method detection limit of 𝐼𝐼 = 0.01 𝐿𝐿−1 (1 count in

30 minutes), but strongly affected by variations in the frost background. The two-hour

backgrounds were interpolated and subtracted from IN measurements before accounting

for chamber dilution.

5.2.2.2 Reconciliation of Particle Size Measurements

The FMPS, APS and ATOFMS each measure particle size under different physical conditions.

The FMPS measures electrical mobility diameters 𝑑𝑑𝑚𝑚 from 5.6 – 562 nm, while the APS

measures aerodynamic diameters 𝑑𝑑𝑎𝑎 between 0.523 µm and 19.81 µm. The ATOFMS

measures vacuum aerodynamic diameter, which is not equivalent to the aerodynamic

diameter measured by APS.

To compare these discrepant definitions of size, all measurements were converted to

equivalent aerodynamic diameter (𝑑𝑑𝑎𝑎) at STP as derived below.

Derivation of the FMPS to APS Conversion. The FMPS measures electrical mobility

diameter 𝑑𝑑𝑚𝑚 according to the migration velocity of electrically charged aerosol particles

within an electric field. 𝑑𝑑𝑚𝑚 is defined as the diameter of a charged sphere with the same

migration velocity as a given particle [DeCarlo et al., 2004]. A particle with mobility


42

diameter 𝑑𝑑𝑚𝑚 can be described in terms of a sphere of equivalent volume, diameter 𝑑𝑑𝑣𝑣𝑂𝑂 .

During migration, the particle experiences opposing drag and electrical forces. At steady

state, these forces can be equated to give

𝑑𝑑𝑚𝑚 = 𝑑𝑑𝑣𝑣𝑂𝑂 ∙ 𝜒𝜒 ∙𝐶𝐶𝑎𝑎(𝑑𝑑𝑚𝑚 )𝐶𝐶𝑎𝑎(𝑑𝑑𝑣𝑣𝑂𝑂) (3)

where 𝐶𝐶𝑎𝑎(𝑑𝑑) is the Cunningham slip correction (defined below) and 𝜒𝜒 is the dynamic shape

factor. Here we assume spherical particles, so 𝑑𝑑𝑚𝑚 = 𝑑𝑑𝑣𝑣𝑂𝑂 . To compare the FMPS with the

APS and ATOFMS, we must now convert 𝑑𝑑𝑚𝑚 to aerodynamic diameter 𝑑𝑑𝑎𝑎 .

In general, 𝑑𝑑𝑎𝑎 is defined as the diameter of a sphere with standard density (1.0 g/cm3) with

the same terminal settling velocity (where 𝐹𝐹𝑔𝑔𝑔𝑔𝑎𝑎𝑣𝑣𝑖𝑖𝑂𝑂𝑦𝑦 = 𝐹𝐹𝑑𝑑𝑔𝑔𝑎𝑎𝑔𝑔 ) as a given particle. 𝑑𝑑𝑎𝑎 is given

by

𝑑𝑑𝑎𝑎 = 𝑑𝑑𝑣𝑣𝑂𝑂�

1𝜒𝜒𝜌𝜌𝑒𝑒𝜌𝜌0

𝐶𝐶𝑎𝑎(𝑑𝑑𝑣𝑣𝑂𝑂)𝐶𝐶𝑎𝑎(𝑑𝑑𝑎𝑎)

(4)

where 𝜒𝜒 is the shape factor, 𝐶𝐶𝑎𝑎(𝑑𝑑) the Cunningham slip correction (defined below), 𝜌𝜌0 is

standard density 1.0 𝑔𝑔/𝑎𝑎𝑚𝑚3, and 𝜌𝜌𝑒𝑒 is the particle density. Here, 𝜌𝜌𝑒𝑒 was taken as 1.5 𝑔𝑔/𝑎𝑎𝑚𝑚3

after the bulk aerosol measurements of Hand and Kreidenweis [2002] and Khlystov et al.

[2004]. Although the average shape factor for atmospheric aerosols has been estimated as

1.2 for particles 0.05-20µm in size [Hand and Kreidenweis, 2002], we approximate 𝜒𝜒 = 1.

Therefore, assuming spherical particles, we can convert the FMPS mobility diameter into

APS aerodynamic diameter similarly to Jeong et al. [2010]:

𝑑𝑑𝑎𝑎 = 𝑑𝑑𝑚𝑚�1.5 ×

𝐶𝐶𝑎𝑎(𝑑𝑑𝑚𝑚 )𝐶𝐶𝑎𝑎(𝑑𝑑𝑎𝑎) (5)

where the Cunningham slip (defined below) for 𝑑𝑑𝑎𝑎 was found by setting 𝐶𝐶𝑎𝑎(𝑑𝑑𝑎𝑎) = 𝐶𝐶𝑎𝑎(𝑑𝑑𝑚𝑚 )

initially and solving iteratively until 𝐶𝐶𝑎𝑎(𝑑𝑑𝑎𝑎) converged to a stable value (within 0.001%).


43

Cunningham Slip. The Cunningham slip 𝐶𝐶𝑎𝑎 accounts for the reduction in drag due to non-

zero gas flow at the particle surface [DeCarlo et al., 2004]. In other words, the slip

correction accounts for the ability of a particle to “slip” through gaps between gas

molecules, thus experiencing a reduced drag. The slip depends on the ratio of the number

of gas molecules per unit volume to the size of the particle in question. This concept is

expressed by the Knudsen number 𝐾𝐾𝑂𝑂 using the gas mean free path 𝜆𝜆 and the particle

radius 𝑑𝑑/2:

𝐾𝐾𝑂𝑂 =2𝜆𝜆𝑑𝑑 (6)

At 293.15 K, 101.325 kPa the mean free path is 66 nm for a typical*

air molecule [Jennings,

1988] and the Cunningham slip is

𝐶𝐶𝑎𝑎(𝐾𝐾𝑂𝑂) = 1 + 𝐾𝐾𝑂𝑂 �𝛼𝛼 + 𝛽𝛽 exp �−𝛾𝛾𝐾𝐾𝑂𝑂�� (7)

𝐶𝐶𝑎𝑎(𝑑𝑑) = 1 + 2𝜆𝜆/𝑑𝑑 �1.252 + 0.399 exp �−1.102𝜆𝜆/𝑑𝑑�� (8)

where the constants 𝛼𝛼,𝛽𝛽, 𝛾𝛾 are from Jennings [1988]. 𝐶𝐶𝑎𝑎 approaches 1 for For 𝐾𝐾𝑂𝑂 < 0.1

(𝑑𝑑 = 1.31 𝜇𝜇𝑚𝑚 when 𝜆𝜆 = 66nm).

Comparison of ATOFMS to APS. The ATOFMS measures particles similarly to the APS,

according to particle transit time between two sizing lasers after acceleration to terminal

velocity. However, because terminal velocity depends on the drag force, which varies with

pressure, the size measured by the ATOFMS is not directly comparable to that of the APS.

As stated above, the aerodynamic diameter 𝑑𝑑𝑎𝑎 is the diameter of a sphere of standard

density (1.0 g/cm3) with the same terminal settling velocity as a given particle. The

terminal settling velocity is the velocity at which the gravitational force 𝐹𝐹𝑔𝑔𝑔𝑔𝑎𝑎𝑣𝑣𝑖𝑖𝑂𝑂𝑦𝑦 is equal to

* This value is “typical” in that it is calculated from the bulk density and viscosity of air at STP.


44

the drag force 𝐹𝐹𝑑𝑑𝑔𝑔𝑎𝑎𝑔𝑔 . The ATOFMS is calibrated using PSL spheres of near-standard density

(1.05 𝑔𝑔/𝑎𝑎𝑚𝑚3) for which 𝑑𝑑𝑎𝑎 = 𝑑𝑑𝑣𝑣𝑂𝑂 . However, for the atmospheric aerosol, with effective

density 𝜌𝜌𝑂𝑂𝑒𝑒𝑒𝑒 = 1.5 g/cm3, a correction must be applied.

From 𝐹𝐹𝑔𝑔𝑔𝑔𝑎𝑎𝑣𝑣𝑖𝑖𝑂𝑂𝑦𝑦 = 𝐹𝐹𝑑𝑑𝑔𝑔𝑎𝑎𝑔𝑔 the following expression can be derived [DeCarlo et al., 2004]:

𝑑𝑑𝑎𝑎 = 𝑑𝑑𝑣𝑣𝑂𝑂�

1𝜒𝜒𝜌𝜌𝑒𝑒𝜌𝜌0

𝐶𝐶𝑎𝑎(𝑑𝑑𝑣𝑣𝑂𝑂)𝐶𝐶𝑎𝑎(𝑑𝑑𝑎𝑎)

(9)

where 𝐶𝐶𝑎𝑎 , as described above, is a function of the mean free path (i.e. different between

ATOFMS and APS measurements). Within the evacuated measurement region of the

ATOFMS, 𝐾𝐾𝑂𝑂 > 10, and particles enter the “free-molecular-regime”. The Cunningham slip

correction 𝐶𝐶𝑎𝑎(𝐾𝐾𝑂𝑂) = 1 + 𝐾𝐾𝑂𝑂(𝛼𝛼 + 𝛽𝛽 exp(−𝛾𝛾/𝐾𝐾𝑂𝑂)) here can be approximated as

𝐶𝐶𝑎𝑎(𝐾𝐾𝑂𝑂 ≫ 1) = 𝐾𝐾𝑂𝑂(𝛼𝛼 + 𝛽𝛽) (10)

𝐶𝐶𝑎𝑎(𝑑𝑑) =2𝜆𝜆𝑑𝑑

(𝛼𝛼 + 𝛽𝛽) = 𝑘𝑘/𝑑𝑑 (11)

where 𝑘𝑘 is a function of gas composition, temperature and pressure but not of 𝑑𝑑.

Substituting equation (11) into equation (9) allows 𝑘𝑘 to be cancelled, giving the following

expression for the vacuum aerodynamic diameter 𝑑𝑑𝑣𝑣𝑎𝑎 [DeCarlo et al., 2004]:

𝑑𝑑𝑣𝑣𝑎𝑎 = 𝑑𝑑𝑣𝑣𝑂𝑂 ∙1𝜒𝜒𝑣𝑣∙𝜌𝜌𝑒𝑒𝜌𝜌0

(12)

Here 𝜒𝜒𝑣𝑣 is the vacuum dynamic shape factor, 𝜌𝜌𝑒𝑒 the particle density, 𝜌𝜌0 the standard

density, and 𝑑𝑑𝑣𝑣𝑂𝑂 is defined above. If 𝜒𝜒 = 1 and 𝜌𝜌𝑒𝑒 = 1.5 𝑔𝑔/𝑎𝑎𝑚𝑚3 ,

𝑑𝑑𝑣𝑣𝑎𝑎 = 𝑑𝑑𝑣𝑣𝑂𝑂 × 1.5 (13)


45

Substituting this into the general expression for 𝑑𝑑𝑎𝑎 yields

𝑑𝑑𝑎𝑎 =

𝑑𝑑𝑣𝑣𝑎𝑎√1.5

�𝐶𝐶𝑎𝑎(𝑑𝑑𝑣𝑣𝑎𝑎 )𝐶𝐶𝑎𝑎(𝑑𝑑𝑎𝑎) ≅

𝑑𝑑𝑣𝑣𝑎𝑎√1.5

(14)

since �𝐶𝐶𝑎𝑎(𝑑𝑑𝑣𝑣𝑎𝑎 )/𝐶𝐶𝑎𝑎(𝑑𝑑𝑎𝑎) ~1. This expression is a consequence of the dependence of

aerodynamic diameter upon 𝐾𝐾𝑂𝑂 .

Thus using equations (5) and (14), the mobility and vacuum aerodynamic diameters

measured by FMPS and ATOFMS respectively were converted to the aerodynamic diameter

measured by APS, i.e.

𝑑𝑑𝑎𝑎 = 𝑑𝑑𝐹𝐹𝑅𝑅𝑃𝑃𝑇𝑇�1.5 ×

𝐶𝐶𝑎𝑎(𝑑𝑑𝐹𝐹𝑅𝑅𝑃𝑃𝑇𝑇 )𝐶𝐶𝑎𝑎(𝑑𝑑𝐴𝐴𝑃𝑃𝑇𝑇) (15)

𝑑𝑑𝑎𝑎 =𝑑𝑑𝐴𝐴𝑇𝑇𝑂𝑂𝐹𝐹𝑅𝑅𝑇𝑇√1.5

(16)

The resulting FMPS distribution ranges from 8.9 – 732 nm across 32 bins. The original 29

APS bins, which range from 523 to 2000 nm, were left untouched. In order to sort the

single-particle ATOFMS data into FMPS and APS size bins, the FMPS and APS bins were

merged together as follows.

The four largest FMPS bins overlapped with the smallest APS bins and were discarded. The

remaining FMPS bins were merged with the APS bins to yield 59 total size bins: 30 FMPS

bins ranging from 8.9 to 523 nm (equal width in log space) and 29 APS bins ranging from

523 nm to 20 µm (equal width in log space, half the width of the FMPS). Particles sized by

ATOFMS were within the range 242– 4217 nm, and were sorted into the FMPS/APS bins

after conversion to 𝑑𝑑𝑎𝑎 . Thus all particles are represented by their equivalent aerodynamic

diameter 𝑑𝑑𝑎𝑎 , and the discussion below refers only to aerodynamic diameter.


46

The largest uncertainties inherent in this comparison are the assumption of spherical

particles with a density of 1.5 g/cm3. The density of elemental or organic particles may be

lower, and dust and salt particles were more likely irregularly shaped with densities on the

order of 2.0–2.5 g/cm3. Applying selective densities to the ATOFMS data would require a

quantitative measurement of particle composition (e.g. the relative amounts of dust and

sulphate in each dust particle), which ATOFMS mass spectra do not provide as noted above.

A density of 2.5 g/cm3 would result in a decrease in 𝑑𝑑𝑎𝑎 of 30% for the ATOFMS

measurements.

5.2.2.3 Analysis of Single Particle Mass Spectra

The ATOFMS obtained size and mass spectral data for 446,527 particles during IN

measurement periods. These data were binned into a matrix of hourly counts segregated

into the 44 size bins of the merged FMPS/APS distribution that spanned from 72 to 4217

nm.

In order to identify different aerosol types, each ATOFMS mass spectrum was analyzed as

follows. First, spectra were processed by integrating peaks at ±250𝑎𝑎𝑚𝑚𝑚𝑚 to the nearest m/z

using the computer program TSI MS Analyze (v5.0). Peaks were retained only if they were

20 arbitrary units above the mass spectral baseline with a minimum area of 20 arbitrary

units, and represented >0.1% of the total peak area. The processed spectra were then

loaded into a modified YAADA data analysis program (Yet Another ATOFMS Data Analyzer

v2.11, http://yaada.org). YAADA was then used to cluster ATOFMS spectra using the ART2a

algorithm described below. The program was modified by Rehbein [2010] to take the

logarithm peak amplitudes before running the algorithm; the distribution of peak areas at

each m/z consequently displayed a Normal distribution.

ART2a works as follows. First, mass spectral signals for each 𝑚𝑚/𝑧𝑧 are transformed to a

standard Normal distribution. The algorithm then chooses two mass spectra at random and

computes their dot product. If the dot product is above a certain “vigilance factor”, here set


47

to 0.4, the particles are grouped together to form a “cluster”, represented by the centroid of

the two. If dissimilar, the particles are separated into two clusters.

This classification is applied to all mass spectra by comparing each spectrum to all existing

clusters and assigning each to the best candidate. Each time a particle is assigned to a

cluster, the cluster centroid is adjusted according to a sensitivity factor termed the

“learning rate”, here 0.05. When all spectra have been classified, cluster memberships are

emptied while the centroids are retained; classification is then repeated to avoid biasing

clusters towards their initial values. Here, 20 iterations were used, yielding an array of

cluster centroids termed the “weight matrix”. The final weight matrix is thus a simple and

complete estimate of typical mass spectra for the dataset.

Rather than create a weight matrix for the short study period described herein, the weight

matrix developed by [Rehbein, 2010] was used for “supervised clustering” with a learning

rate of zero. This matrix was generated using the parameters specified above on a random

sample of 400,000 particles from one year of data measured during SPORT 2007. The

matrix contains 328 clusters and successfully classified 98.6% of the measurements

presented here. The unclassified particles appear to be due to errors in the time-of-flight to

m/z conversion – the entire average mass spectrum would be typical if 1 amu were added

to all m/z peaks. Of the classified spectra, 91% were contained within the 201 largest

clusters. The remainder was discarded.

The 201 largest ART2a clusters were grouped into 20 sub-types representing relatively

specific chemical compositions according to the classification scheme of Rehbein [2010],

which grouped clusters according to similarities in mass spectra and temporal variation.

Here, roughly 10% of clusters were reclassified based upon their mass spectra, and the 20

amended sub-types were agglomerated into four very general aerosol types: Organic

Carbon (OC, 54%), Dust, including road dust (DUST, 28%), SALT (11%) and Elemental

Carbon or soot (EC, 7%). A fifth category of metallic aerosols was too small to be included.


48

It is worth noting that the OC and EC aerosol types are not sharply defined, and represent a

continuously varying degree of internally mixed components. Similarly, inorganics such as

nitrate and sulphate were invariably mixed with OC, EC or SALT in the ATOFMS data. While

it is likely that SO4- and NO3- did exist as pure inorganic aerosols, they are only detected in

in the ATOFMS in the presence of impurities, as they do not absorb sufficient radiation at

266 nm to be directly ionized by the Nd:YAG laser [e.g. Wenzel and Prather, 2004].

The total number of particles measured in each of these categories was retrieved using

YAADA. Particles were sorted into the 44 bins of the merged FMPS/APS distribution, giving

the APS preference at the 523 nm point of overlap since it measures aerodynamic diameter

directly.

5.2.2.4 Estimation of Chemical Surface Area

The ATOFMS single-particle mass spectra and the APS/FMPS merged size distribution were

combined to provide a quantitative estimate of the surface area attributable to each

ATOFMS aerosol type.

The merged size distribution allows us to correct for the transmission function of the

ATOFMS (a size effect). However, it does not correct for ionization efficiency (a

composition/matrix effect). Ionization efficiencies were not addressed: the challenge of

accounting for matrix effects during laser ablation is well beyond the scope of this work.

The size transmission efficiency of the ATOFMS was corrected as follows. The number of

each aerosol type measured by ATOFMS was divided by the total number of ATOFMS

measurements for a given size, at a given time. The size resolution was dictated by the

FMPS/APS and the time was selected as one hour. The corresponding particle

concentration measured by FMPS/APS was then apportioned between each respective type

to yield an estimated number distribution for each. The resolved number distribution

represents the fraction of aerosol externally mixed as each species. A resolved surface area

distribution was computed similarly. The procedure can be summarized as


49

𝑇𝑇𝐴𝐴𝑘𝑘 = � �nd

k

ndATOFMS × 𝑇𝑇𝐴𝐴𝑑𝑑

𝐴𝐴𝑃𝑃𝑇𝑇/𝐹𝐹𝑅𝑅𝑃𝑃𝑇𝑇�4.22𝜇𝜇𝑚𝑚

𝑑𝑑=0.24𝜇𝜇𝑚𝑚

for surface area, where 𝑘𝑘 = dust, salt, EC, OC or “unclassified”; ndk is the number of particles

with diameter 𝑑𝑑 identified as type 𝑘𝑘; ndATOFMS is the total number of particles of diameter 𝑑𝑑

measured by the ATOFMS in a given hour (including unclassified particles); and

𝑇𝑇𝐴𝐴𝑑𝑑𝐴𝐴𝑃𝑃𝑇𝑇 /𝐹𝐹𝑅𝑅𝑃𝑃𝑇𝑇 is the total surface area for particles of diameter 𝑑𝑑 measured by the APS/FMPS.

𝑇𝑇𝐴𝐴𝑘𝑘 was calculated using 44 bins of ATOFMS data.

5.3 Results I: Aerosol Size and Mass Spectrometry

This section provides a detailed description of the bulk aerosol and its characterization by

single-particle mass spectrometry in preparation for an investigation of the response of ice

nuclei concentrations to these properties in Section 5.4.

5.3.1 ATOFMS Aerosol Types

Four aerosol types were identified using ATOFMS single-particle mass spectra as described

in 5.2.2.3. While the four types, OC, DUST, SALT and EC made up varying degrees of the

total aerosol over time, their overall contribution to the dataset is shown below.

Figure 8 Relative number contribution of each aerosol type to the 𝟒𝟒𝟒𝟒𝟒𝟒,𝟓𝟓𝟓𝟓𝟓𝟓 ATOFMS measurements.

OC and EC are the “organic carbon” and “elemental carbon” aerosol types.

DUST28%

SALT11%

EC7%

OC54%


50

Here, DUST was identified by the presence of Li, Na, Ca, Al, Ba and their oxides/hydroxides.

DUST included both fresh dust (as described) and dust with evidence of nitrate and

sulphate coatings as well as small amounts of organic material.

SALT contained Na and Cl, with K or NO3 typically present and in some cases evidence of an

organic component. Only when a mass spectrum was clearly dominated by series of carbon

peaks �𝐶𝐶𝑂𝑂±� was it labeled EC. Finally, OC may contain any amount of sulphate or nitrate as

long as the remaining mass spectral peaks arose from organic fragments. Mass spectra are

provided in Appendix B.

The DUST aerosol type was originally split into “Road Dust” and “Fresh Dust”, where fresh

dust contains Li, Na, Ca, Al, Ba, but road dust contains these species mixed with significant

amounts of organics and nitrate. Road dust represents crustal minerals that were

resuspended by vehicular traffic outside the building, as confirmed by a direct sample

[Rehbein, 2010]. Unfortunately, not enough fresh dust particles were measured by ATOFMS

to maintain this separation for analysis.

Similarly, OC could be split into fractions of high and low sulphate/nitrate components,

SALT into “fresh” or “nitrate-containing”, and so on. Such subdivisions would represent a

division between continuously changing variables. To maintain simplicity and to increase

the sample size for each type, no such subjective divisions were made.

The contributions of dust and salt aerosols to this dataset were unusually high. The

prevalence of dust, which includes silicates, was possibly enhanced by sanding of the

streetcar tracks outside the sampling building. Similarly, road salting during the winter

very likely enhanced the atmospheric burden of salt aerosol.

There were a few rare particle sub-types that did not fit into the above categories. For

example, a Zn and Pb rich particle type attributed by [Rehbein, 2010] to industrial activity


51

was too rare to be included here. Such sub-types made up on average 27% of ATOFMS

measured particles, but displayed no temporal trend during this study. Similarly, the 9% of

ATOFMS measurements left unclassified by ART2a displayed no apparent trend. The

aerosol types analyzed here represent ~64% of the ATOFMS data set. Some of the

remaining particles may also have been DUST, SALT, EC or OC; most unclassified spectra

simply did not contain sufficient information for classification perhaps due to poor timing

of the ablation laser, insufficient absorption efficiency, etc.

Figure 9. Number distributions of the ATOFMS-derived aerosol types. The size bins used match those

of the FMPS/APS merged distribution.

Histograms of these ATOFMS types as a function of size are shown in Figure 9. OC clearly

dominates the aerosol number below 1 µm. The OC particles were frequently internally

mixed with elemental carbon (as well as sulphate and nitrate). Particles classified as EC

(that is, elemental/black carbon internally mixed only with sulphate or nitrate) were most

dominant at the smallest sizes, as expected for particles formed from fuel combustion

within passing vehicles. SALT and DUST particles displayed a clear tendency towards


52

larger sizes, with dust showing a smaller mode at smaller sizes as well. OC particles

dominated the smaller sizes, and EC particles were restricted to the very smallest.

Unclassified particles generally made up ~20% of the total and did not display a size

dependence comparable to any species, supporting the hypothesis that these particles

were unclassified purely by chance. Since a large number of particles were discarded by the

instrument software due to no measured mass spectrum or instrument busy time, any

further exploration of the unclassified category is unjustified.

The ATOFMS is not equally sensitive to all substances. For example, the high ionization

threshold of sulphate ion (SO42-) means that it is only detected in the presence of threshold-

lowering impurities [Thomson et al., 1997; Wenzel et al., 2003]. On the other hand, the low

ionization potential of metals increases the instrument sensitivity to these species [Gross et

al., 1999] and species such as NH4+ and NO3- are underestimated at larger sizes [Bhave et

al., 2002]. Attempts to account for such matrix effects have so far been unsuccessful [e.g.

Reilly et al., 2000], although the natural separation of each ATOFMS aerosol type by size in

Figure 9 may have allowed us to inadvertently correct for some of these effects. The

correction is obviously imperfect but likely significant. Finally, organic substances may give

pure carbon fragments (𝐶𝐶𝑂𝑂±) [Silva and Prather, 2000], preventing the clear distinction of

organic material from organic-elemental carbon mixtures.

Exemplary mass spectra from each type are presented in Appendix B, as well as a table

describing the key ions used to categorize mass spectra.


53

5.3.2 Aerosol Size and Surface Area

Figure 10. Mean size distributions over the study period for the FMPS, APS and ATOFMS. FMPS/APS

surface area distributions are superimposed. Note that aerosol numbers (red, lower curves) are

plotted on a log scale. FMPS data: dotted lines; APS data: dash-dotted lines; ATOFMS data: solid lines.

The FMPS data from 242 to 523 nm are averaged in Figure 11.

The size-resolved aerosol concentrations measured by the APS, FMPS and ATOFMS are

shown in Figure 10. Recall that the ATOFMS concentrations are affected by the

transmission efficiency of that instrument, while the APS and FMPS data are representative

of the true mean aerosol distribution during the study. The combined FMPS/APS data were

therefore used to scale the ATOFMS data using a one-hour mean as described in Section

5.2.2.2.


54

The four largest FMPS bins (not shown) overlapped with the APS data but did not agree

well and were discarded. The remaining FMPS data did not merge smoothly with the

ATOFMS data. This anomaly is probably due to the uncertainties inherent in estimating

aerodynamic diameter from mobility diameter (Section 5.2.2.2) and was more pronounced

at higher time resolutions. To account for this issue, the largest six FMPS size bins from 242

to 523nm were averaged into one size bin [Jeong et al., 2010]. Below 242nm, no ATOFMS

data were used, and above 523nm, the APS data were used for scaling.

This treatment effectively assigns all data below 523nm an average diameter of 420nm.

The few ATOFMS particles (<0.02 cm-3) measured below 242 nm were discarded. The

merged size and surface area distributions are shown in Figure 11. A graph of the data

prior to combination is shown Appendix A.

Figure 11. Merged number and surface area size distributions as measured by FMPS and APS. The

distribution is cut off below 242 nm, below which no data were used. All FMPS data above the blue

ATOFMS curve in Figure 10 (242 – 523 nm) were averaged here.


55

5.4 Results II: Ice Nuclei

Figure 12. Ice nuclei concentrations measured during the study as a function of time of day.

Concentrations typically decreased during the day (purple times) but not consistently (blue),

indicating an inconsistent diurnal source. The black line shows the overall mean.

Ice nuclei concentrations measured during the study varied from 0–21 L-1. Concentrations

varied throughout the day, as shown in Figure 12. Day-to-day variations were less

pronounced, although the diurnal trend changed on each day.

Temporal trends in the ATOFMS aerosol types (Section 5.3), the aerosol number (N) and

surface area (SA) showed no obvious relationship with IN concentrations. Scatterplots of N

and SA were therefore investigated. In order to identify which aerosol types were most

strongly related to IN concentrations, chemically-resolved number and SA distributions

were estimated from the ATOFMS data and regressed against IN concentrations.

In the discussion below, physical details are added in a stepwise manner. First, we consider

the response of IN to total aerosol number concentrations and surface area. Afterwards, we

separate the aerosol into four types (dust, salt, OC and EC) and consider the degree to

which this additional chemical information improves a linear regression model.


56

5.4.1 Bulk Aerosol Properties as Predictors of IN

As ice nucleation is a surface phenomenon, different materials necessarily vary in their ice

nucleating ability. However, bulk aerosol properties (number, surface area) have been seen

to predict IN concentrations reasonably well under a variety of conditions [DeMott et al.,

2010 and references therein]. Therefore, before introducing any further detail, we

investigated the ability of size-resolved particle number and surface area to predict IN

concentrations.

5.4.1.1 IN vs. Number, Surface Area

IN concentrations were correlated first with aerosol number and surface area in three size

ranges before introducing chemical information. Only surface area in the largest size range

showed a significant correlation, as discussed below.


57

Figure 13. Scatterplots of IN vs number and surface area for the 3 size ranges. See text for an

explanation of the ranges used. Only the best fit line for IN vs. SA>523nm has a significant p-value at the

95% confidence level.

Figure 13 shows the correlation of IN across three ranges of aerosol size. The size ranges

were chosen for their physical significance: the smallest represents sizes below what the

ATOFMS can measure, the middle represents the range of values over which the FMPS data

were averaged, and the largest represents the remainder.

Only surface area of the > 0.5 µm aerosol fraction showed a good correlation with IN. The

relationship with surface area was considerably stronger (R2 = 0.14) than with number (R2

= 0.04) even though the two were quite strongly correlated (R2 = 0.61). For smaller

aerosols, correlations with IN and number and surface area were consistently poor. The

relationship of IN with N and SA over 0.5 µm is highlighted in Figure 14.

Figure 14. Scatterplots of IN vs (left) number and (right) surface area above 0.5 µm. N=85. Note that N

and SA were correlated with R2 of 0.61.

Based on the strong relationship of IN with larger aerosols, only the chemical composition

of aerosol surface area above 0.5 µm was investigated in this work.


58

A similar relationship between IN and >0.5 µm aerosol has been identified by previous

studies [Phillips et al., 2008 and references therein]. Even in a chemically resolved ice

nucleation model, surface area for particles above 0.5 µm has been used to estimate dust

aerosol concentrations [Phillips et al., 2008]. However, at least for this dataset the

assumption that all >0.5 µm aerosol is dust is invalid; ATOFMS mass spectra (Section 5.3.1)

have already demonstrated a strong influence of salt and organics for the >0.5µm regime.*

5.4.1.1 IN vs. n>0.5µm: Comparison to Predictions

In the next section, the parameterization of one such study is quantitatively compared with

the IN concentrations measured here.

Aerosol number is perhaps the simplest predictor of IN. For a size limit of 0.1µm, the

relationship is poor [Richardson et al., 2007], but for aerosol numbers above 0.5µm

(𝑂𝑂>0.5𝜇𝜇𝑚𝑚 ) reasonably good predictions can be made [DeMott et al., 2010; Richardson et al.,

2007]. The success of this simple approach stems from the relationship of 𝑂𝑂>0.5𝜇𝜇𝑚𝑚 to dust

aerosols, which typically dominate aerosol number concentrations at such sizes.

Recently, DeMott et al [2010] were able to predict condensation and immersion mode ice

nuclei concentrations to within a factor of 10 using a simple number and temperature

based parameterization. They parameterized IN concentrations as

𝐼𝐼𝑁𝑁𝑒𝑒𝑔𝑔𝑂𝑂𝑑𝑑𝑖𝑖𝑎𝑎𝑂𝑂𝑂𝑂𝑑𝑑 = 𝑎𝑎 ∙ −𝑇𝑇𝑏𝑏 ∙ �𝑂𝑂>0.5𝜇𝜇𝑚𝑚 �−𝑇𝑇∙𝑎𝑎+𝑑𝑑

where 𝐼𝐼𝑁𝑁𝑒𝑒𝑔𝑔𝑂𝑂𝑑𝑑𝑖𝑖𝑎𝑎𝑂𝑂𝑂𝑂𝑑𝑑 is the predicted concentration of IN (L-1), T is the nucleation temperature

in degrees Celsius (= 𝑇𝑇𝐾𝐾𝑂𝑂𝑙𝑙𝑣𝑣𝑖𝑖𝑂𝑂 – 273.16), and 𝑂𝑂>0.5𝜇𝜇𝑚𝑚 is the number concentration (cm-3) of

particles with diameter over 0.5 µm. Using a wide range of field data, the constants a, b, c, d

were determined as 0.0000594, 3.33, 0.0264, 0.0033 respectively. This model was

developed exclusively for condensation and immersion freezing data, and does not

necessarily apply to our measurements of deposition mode ice nuclei. We estimated ice

*The ATOFMS-estimated proportion of dust at smaller sizes is likely underestimated due to enhanced

sensitivity to organics relative to dusts. Also, the fraction of salt aerosol is probably anomalously high in

Toronto due to winter salting of the road directly outside the sample site.


59

nuclei concentrations according to this relationship based on our APS measurements of

𝑂𝑂>0.5𝜇𝜇𝑚𝑚 .

Figure 15. IN predicted from n>0.5µm according to DeMott et al [2010]. The parameterization predicted

on average 9 times the observed concentration, consistent with the expected factor of 10, however

most values were overpredicted. Dotted line: best fit through origin; dashed line: one to one line.

Figure 15 compares 𝐼𝐼𝑁𝑁𝑒𝑒𝑔𝑔𝑂𝑂𝑑𝑑𝑖𝑖𝑎𝑎𝑂𝑂𝑂𝑂𝑑𝑑 to our measured concentrations. The parameterization

predicted on average 9 times the observed concentration (slope of linear best fit); within

the expected factor of ±10. As our data were measured only at 237 ± 2 𝐾𝐾, 𝐼𝐼𝑁𝑁𝑒𝑒𝑔𝑔𝑂𝑂𝑑𝑑𝑖𝑖𝑎𝑎𝑂𝑂𝑂𝑂𝑑𝑑 was

effectively a function only of 𝑂𝑂>0.5𝜇𝜇𝑚𝑚 . The relationship of both 𝐼𝐼𝑁𝑁𝑒𝑒𝑔𝑔𝑂𝑂𝑑𝑑𝑖𝑖𝑎𝑎𝑂𝑂𝑂𝑂𝑑𝑑 and 𝑂𝑂>0.5𝜇𝜇𝑚𝑚 with

IN was necessarily similar, yet n>0.5µm better accounted for the variability in IN (𝑅𝑅𝐼𝐼𝑁𝑁𝑒𝑒2 =

0.014, 𝑒𝑒 = 0.206; 𝑅𝑅𝑂𝑂>0.5𝜇𝜇𝑚𝑚2 = 0.023, 𝑒𝑒 = 0.129).

The fact that 𝐼𝐼𝑁𝑁𝑒𝑒𝑔𝑔𝑂𝑂𝑑𝑑𝑖𝑖𝑎𝑎𝑂𝑂𝑂𝑂𝑑𝑑 is within its expected error for our data is surprising for two

reasons. First, the model was developed for IN active in the range 101-104% RHw. At such

humidities, ice is expected to form during or after water condensation (condensation or

immersion freezing, respectively). The direct formation of ice during deposition freezing


60

might be expected to depend quite differently on the physical properties of an ice nucleus;

in other words, there was little physical justification for applying this model to our data.

The overprediction by 9 times is reasonable given the different nucleation modes

measured.

Second, laboratory data have shown that coatings of organic and sulphate delay the ice-

nucleation onset temperature for both dust [Chernoff and Bertram, 2010; Eastwood et al.,

2009] and soot [Möhler et al., 2005a] aerosols. While our data do not contradict this effect,

we can generally say that the DeMott et al [2010] parameterization is adequate even for

polluted aerosols under deposition-mode conditions. In other words, our measurements lie

within the range of variability used to generate the parameterization.

5.4.2 ATOFMS Aerosol Types as Predictors of IN

Section 5.4.1.1 identified aerosol surface area above 0.5µm as having the strongest

correlation with IN (R2=0.14, p>0.001). This section investigates which components of the

aerosol contributed most significantly to this relationship. Multiple linear regression of IN

concentrations against a combination of aerosol types was performed to identify the subset

that best predicted ice nuclei concentrations.

There is no physical motivation for dividing our data at the 0.5µm mark, but rather two

practical reasons: (i) this size is the minimum measured by the APS, and has consequently

been used as a minimum in many previous studies, and (ii) the ATOFMS size transmission

maximum is ~0.4 µm, and a higher cutpoint would significantly reduce the available mass

spectral data.

The conclusion from Section 5.4.1.2 that SA>0.5µm (aerosol surface area above 0.5µm)

showed the strongest correlation with IN was used as a reference point during regression.

A regression model was constructed using all five aerosol types, and backwards elimination

was used to determine which aerosol type had the strongest relationship to observed IN


61

concentrations. This approach allows for the identification of a subset of variables that

explain IN concentrations, even if those variables did not do so individually [Dallal, 2010].

Scatterplots comparing all variables to one another were inspected prior to regression (not

shown). No outliers were apparent for any variables, nor were any clear correlations,

indicating that several variables were influential or intercorrelated [Daniel and Wood,

1971]. The results of the regression are shown in Table 1 and discussed below.


62

Table 1. Description of the regression model at each stage. N = 83. “Adj R2” is the adjusted R2, “ESS” is

the explained sum of squared errors, “RSS” is the residual sum of squared errors, “p values” represent

the model significance, and “Residual vs. Predicted” is a description of the residual vs. fitted IN plot

(residual plots are shown in Appendix A). Refer to the Glossary for a detailed definition of terms.

At each stage of the elimination procedure, the least significant regression coefficient was

rejected and the model was reconstructed. “Least significant” was determined as the

coefficient with the highest p-value. Rejections were upheld if the least significant predictor

had a minimal impact on the explanatory power of the model, judged by the adjusted R2

# Predictors (𝑺𝑺𝑨𝑨>0.5 𝜇𝜇𝑚𝑚 )

R2 Adj. R2 F value RMS RSS p value Residual vs. Predicted

Conclusion/ Action

1 SA 0.14 0.13 13.6 5.90 2824 .00040 Strong negative bias at high IN

Add RHw

2 SA with RHw 0.18 0.16 8.93 5.81 2694 .00032 Moderate negative bias, reduced from 1

Try RHw after adding chemical information

3 All aerosol types: DUST, SALT, EC, OC, unclassified

0.25 0.20 5.19 5.66 2467 .00037 Small negative bias

Add RHw after 2

4 3 with RHw

DUST, SALT, EC,

OC, unclassified,

RHw

0.33 0.28 6.34 5.38 2199 .000019 Weak increase in variance with increasing IN

Reject unclassified

5 4, unclassified out (RHw, DUST,

SALT, EC, OC)

0.32 0.27 7.17 5.41 2251 .000015 Little change from 3

Reject EC or OC?

6 5, EC out RHw, DUST, SALT,

OC

0.28 0.24 7.53 5.52 2380 .000035 Many small negatives, fewer large positives

(i) Stop (ii) Try OC

7 5, OC out RHw, DUST, SALT,

EC

0.28 0.24 7.46 5.53 2386 .000038 Improved, largest outliers near mean values

All p-values of predictors <0.02. Try SALT out.

8 7, SALT out RHw, DUST, EC

0.21 0.18 6.93 5.75 2612 .00034 Little change from 7

Reject.


63

and F value. When the model suffered significantly, the elimination procedure was stopped

(step 8 in Table 1).

Residual plots were also inspected at each stage, as noted in Table 1, and are available in

Appendix C. Refer also to Appendix C for regression summaries at each stage of the

regression, as well as correlations of these variables with one another. Refer also to the

Glossary for a definition of regression terms.

At stage 1, 𝑇𝑇𝐴𝐴>0.5 𝜇𝜇𝑚𝑚 alone was regressed against IN concentrations. The model residuals

showed a clear negative bias at high IN concentrations. To test the hypothesis that these

values were underpredicted due to variations in chamber humidity, RHw was introduced to

the model. The addition of RHw in 2 resulted in a significant increase in the predictive

power of the model, and reduced the negative bias at high IN to an acceptable level.

In 3, having removed the initial model bias, 𝑇𝑇𝐴𝐴>0.5 𝜇𝜇𝑚𝑚 was divided into the five particle

types DUST, SALT, EC and OC, and “unclassified”. To simplify this step, RH was initially

omitted from the model. Even without RH, the resolved surface area was a much better

predictor of IN, and the original negative bias was reduced. Resolving SA into aerosol types

accounted for as much variability as did introducing RH to the original model; the adjusted

R2 increased from 0.13 to 0.20 compared to 0.13 to 0.16.

The F ratio for 3 is lower than both 1 and 2, implying that the statistical significance of the

five variables is relatively poor. However, little emphasis was placed on the F ratio since

the quality of these data, and consequently the expected quality of the model, is difficult to

evaluate.

The p-value of the regression coefficients in 3 was as high as 0.33 for OC, indicating that OC

had little predictive power in the context of the other four variables. Although this

suggested that OC should be omitted from the model, RHw was first reintroduced in 4.


64

In 4, chemically resolved surface area and RHw were all combined to provide a starting

point for predictor elimination. Upon addition of RHw an improvement almost as great as

seen when adding chemical information was observed; the adj. R2 increased from 0.20 to

0.28.

Elimination was started with model 5. “Unclassified”aerosols showed by far the highest p-

value in 4 (0.18 compared to 0.01 for the second-highest), and so were rejected. Model 5

thus provided objective support for the implicit assumption that these particles were in

general not important ice nuclei.

After elimination of the “unclassified” aerosol, we regressed IN against DUST, SALT, EC, OC

and RHw. The weakest predictor was EC, with the normalized regression coefficient

𝛽𝛽 = 0.35 ± 0.33 (95% CI) and 𝑒𝑒 = 0.039. However, OC was an equally poor candidate, with

𝛽𝛽 = 0.46 ± 0.41, 𝑒𝑒 = 0.035. Steps 6 and 7 therefore excluded either of these variables

(one or the other) with the goal of separating the two.

In step 6, EC was omitted from the model. While some predictive power was lost, with the

adj. R2 decreasing from 0.27 to 0.24, the change was not large. The residuals, however,

were unevenly distributed about zero over the range of IN values measured: positive errors

were typically large, while negative errors were smaller but more numerous.

Step 7 replaced EC while omitting OC, thus regressing IN against DUST, SALT, EC and RHw.

The regression statistics were nearly identical to 6. Before investigating EC vs. OC in detail,

the regression was tested for the potential elimination of any further predictors.

The predictor with highest p-value in Step 7 was SALT, although its p-value was not much

higher than the other predictors (only twice as large as the other two p-values). The drop

in adjusted R2 and explained sum-of-squares for Step 8 shows that the model suffered

significantly, and SALT was therefore restored.


65

The regression model therefore required RH, DUST and EC or OC to best explain variability

in IN. The similarity between EC and OC might have been anticipated from the moderately

strong correlation between the two (R2 = 0.46, p<0.001). However, the residual plots of the

two models were noticeably different. An irregular trend was seen when OC was included,

while for EC the bias in the fit was reduced. The deviation of either set of residuals from

Normality is emphasized by the Normal probability plots in Figure 16. The more normal

spread of residual errors shown by EC suggests that it is a better predictor; however OC

may have suffered due to its complexity. That is, EC was defined very specifically as “pure”

soot, while OC includes multiple types of organic aerosols. On the other hand, organic-

coated EC is a subset of OC, so the relationship between IN and OC may have occurred by

chance. Based on Figure 16, we hypothesize that the OC relationship may be due to the

presence of EC within OC particles, and report the regression coefficients for the EC model

(model 6) in Table 2. Refer to Appendix C for details of all regression models.

-10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18

Residuals

-3

-2

-1

0

1

2

3

Exp

ecte

d N

orm

al V

alue

-12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16

Residuals

-3

-2

-1

0

1

2

3

Exp

ecte

d N

orm

al V

alue

Figure 16. Normal probability plots after fits to IN for DUST, SALT, RHw, and (left) OC or (right) EC. Ideal

residuals would lie along the red line. While the overall goodness of fit is similar for both variables,

the fit for EC shows less bias.


66

Table 2. Regression summary for IN vs. RHw, DUST, SALT and EC 𝑺𝑺𝑨𝑨>0.5𝜇𝜇𝑚𝑚 . Refer to Glossary for term

definitions.

N=78 β σ β b σ b t(78) p-value Intercept -105 49.5 -2.14 0.036

RHW 0.24 0.11 1.14 0.52 2.21 0.030 DUST_SA05 0.33 0.11 0.58 0.20 2.97 0.004 SALT_SA05 -0.38 0.14 -1.51 0.55 -2.72 0.0081

EC_SA05 0.59 0.13 2.15 0.47 4.59 0.00001

The regression coefficients b for the selected model are shown in Table 2. Also shown are

the β values, the regression coefficients that would be obtained if all data were normalized

beforehand to mean zero and standard deviation 1. EC shows a greater significance (larger

β) in the model than DUST, but for this model the β should not be interpreted as showing

that EC was twice as important as DUST because the regression model was developed

specifically to fit these data. The negative response of IN to SALT is interpreted in Section

5.5.

Figure 17 compares the regression model with predicted IN. Prediction errors are

illustrated using the standard error of the regression, however this does not translate to a

confidence interval: the error does not account for the selective procedure by which the

model was developed.


67

Figure 17. Illustration of the regression model, shown as predicted vs. measured IN. Prediction errors

are shown as the standard error of the regression for illustration only. The figure provides an

intuitive picture, to be compared with

Figure 14. Here, 86% of the data were predicted to within a factor of two.

Figure 18. Residuals plotted as a function of predicted IN for the final regression model. No apparent

trend indicates a low bias in the model.


68

5.5 Conclusions

The regression results can be summarized as an observation of a positive relationship

between IN and dust surface area, as well as EC and RHw. At the same time, a negative

relationship was observed between IN and salt surface area.

The negative relationship with salt can be explained by the fact that winter salting of the

roads was a major source of salt aerosol during this study. Road salt is deposited following

snowfall, and snowfall would inhibit the suspension of road dust. The mass spectra

identified as dust have been positively identified as road dust by a roadside sample

[Rehbein, 2010]. The relationship with salt was probably an artifact of the measurement

site not related to ice nucleation.

The regression model identified a positive relationship with IN for dust and EC/OC

concentrations. That increased dust loadings are related to increased IN concentrations is

expected, given that dust is one of the most efficient common ice nucleating aerosols.

Because this model was developed by manually selecting those variables with the greatest

explanatory power, the relative numbers of EC and dust IN cannot be deduced from the

normalized regression coefficients β.

For the relationship of IN with EC, it is possible that the observed relationship is a simple

consequence of increased vehicular traffic simultaneously generating EC (through

combustion) and dust (through resuspension) aerosols. But since the response of IN to salt

suggests that snowfall inhibited dust suspension, the relationship with EC is likely

independent of its relationship with dust. This suggests that EC was in fact an effective IN

during this study. Soot aerosols have indeed been observed to nucleate ice in the

laboratory, but their activity depends strongly on the generation method, and widely

varying results are found in the literature (Section 3.3.2).

OC also showed a positive relationship with IN. However, mass spectra of particles labeled

as OC often contained varying degrees of EC. That is, particles composed of mixed


69

elemental and organic carbon are contained as a subset in OC. Identifying the degree to

which EC and OC were mixed using ATOFMS single-particle mass spectra is not feasible,

since (i) pure organic particles can give mass spectral series of carbon peaks (𝐶𝐶𝑂𝑂±) [Silva

and Prather, 2000] and (ii) the ablation laser does not always fully ionize particles.

Additional single-particle measurements are recommended to elucidate this relationship.

Single-particle mass spectra are not sufficient to identify whether or not dust or EC

nucleation was influenced by coatings of organic material, sulphate, etc. Just one small

uncoated region on the surface of a particle might have allowed ice to nucleate. Single-

particle microscopy studies using samples from this environment would identify sites at

which ice may have nucleated on dust, EC and potentially OC. Such studies should include

surface characterization at the nanometer scale, for example using Scanning Transmission

X-ray Microscopy (STXM) or mapped Raman spectroscopy.

This study represents an attempt to relate the variability in IN concentrations with

variations in aerosol composition. Relationships with dust and carbonaceous aerosols

(organic or elemental carbon) were positively identified, showing that the urban pollution

in Toronto does not prevent aerosols from the region from acting as ice nuclei. Further

work is needed to identify exactly which carbonaceous species nucleated ice. Future

experiments should address this issue and aim to estimate the degree to which these urban

IN might reach the upper troposphere and influence cloud microphysics.


70

6 IN Concentrations during a Biogenic Aerosol Formation Event at Whistler, BC

6.1 Summary

In contrast to the polluted urban environment of Toronto, Whistler provides relatively

pristine conditions under which IN might be measured in the absence of human influences.

Aerosol loadings are much lower, and generally due to the nucleation/condensation of gas-

phase plant emissions (mainly terpenes) to form secondary organic aerosol (SOA), direct

emissions of biological material (e.g. pollen, fungal spores, fragments of plant matter and

suspended bacteria) or long-range transportation.

Directly emitted particles of biological material, or bioaerosols, have been shown to act as

IN at high T and low RH for certain species [Chernoff and Bertram, 2010; Möhler et al.,

2007]. Bioaerosol IN have been directly identified in snowfall [Christner et al., 2008] and

within ice clouds [Pratt et al., 2009].

Secondary organic aerosol represents 18-70% of submicron non-refractory mass [Zhang et

al., 2007]. Although laboratory studies, physical considerations and field measurements

(Section 2.1.2) do not suggest that SOA would act as efficient IN, its ubiquity and

complexity motivate an interest in a direct measurement.

At Whistler, anomalously warm weather resulted in a biogenic SOA event. During this

event, IN concentrations remained below 2.4 L-1 (95% CI) and no change was observed in

IN concentrations when the event began. The warm, dry weather led to elevated levels of

local road dust that were not representative of the regional aerosol, limiting the ability of

the chamber to improve this estimated limit, although average measured concentrations

reached as low as 0.2 L-1.

The unintended measurements of the IN activity of dust in Whistler allowed an estimate of

its IN efficiency as a function of number and size to be estimated. Dust in Whistler was


71

estimated using all aerosol particles >0.5 μm, while Toronto dust was estimated according

to the fraction of particles >0.5 μm identified as dust through single-particle mass

spectrometry. The response of IN concentration to either dust was equal within one

standard deviation, suggesting that the ice-nucleating ability of each was similar regardless

of differences in composition or mixing state.

6.2 Experimental

IN were measured in Whistler, British Columbia, Canada as part of the CARA-II field

campaign. The measurement site was an isolated mountain building (50' 05 06 N, 122' 57

51 W) elevated 1320 m above sea level and about 650 m above Whistler Village. A private

road leading to the site saw about 10 motorized vehicles per day and, during the latter part

of the study, roughly 100 recreational mountain bike riders per day. The majority of

vehicular traffic occurred at 0800 – 1000 or 1600 – 1800.

The building was surrounded by coniferous rainforest, and the local aerosol saw almost no

anthropogenic influence. Any such influences were due to the few service vehicles

mentioned above or were rare plumes from Whistler Village (population 10,000; density

~57 km-2). From 1100–1900, westerly winds of about 4 ms-1 brought air up from the

valley. From 1900–0000 at night, wind speeds generally fell to zero. From 0000–1100,

easterly winds moved air down the mountain at about 3 ms-1 (see Figure 1, Appendix B).

A stainless steel sampling inlet (1.27 cm internal diameter) extended 30 cm above the

building, approximately 5 m above ground level. The inlet forked into an Aerodynamic

Particle Sizer (APS, TSI 3321) and the CFDC. A pump pulled 2.78 L min-1 past the CFDC to

reduce residence time in the inlet. The CFDC was operated from June 19th to July 10th 2010.

A suite of additional instruments were in operation during CARA-II from a separate inlet

(0.63 cm internal diameter) including a High Resolution Aerosol Mass Spectrometer (HR-

AMS, Aerodyne Inc.). The HR-AMS measures non-refractory aerosol mass by electron


72

impact ionization after vapourization of aerosol particles by a 600°C filament. The AMS is

described in detail in [Jimenez et al., 2003].

6.3 Results

Two distinct periods were observed during the study. The first is termed the “wet period”,

and saw temperatures generally below 10°C, with the measurement site frequently within

liquid-water clouds rising from the valley.

Following the wet period, temperatures rose past 20°C at the site (30°C in the valley)

triggering a significant increase in terpene emissions from the forest, as shown by the

increase in organic aerosol mass due to the formation of secondary organic aerosol (SOA,

Figure 19). The second period is thus termed the “biogenic SOA period.”

6.3.1 IN during the biogenic period

Figure 19. Temperature, humidity, organic aerosol mass and aerosol surface area evolution during

CARA-II. Note that the AMS organic mass is not corrected for instrument collection efficiency. IN

activity during the periods labeled A and B is discussed in detail below.


73

The heat wave that caused the biogenic SOA period also caused dryness, shown by the

rapid variation in aerosol surface area above 0.5 μm (SA>0.5μm). As well as short-lived

spikes in concentration (due to bikers, vehicular traffic and wind gusts), a longer-term

increase in dust loading was observed. Dust is discussed in Section 6.3.2. In this section it

is argued that no contribution to IN concentrations was made by biogenic SOA, by

contrasting the wet and biogenic periods.

Figure 20. (A) IN concentrations during the wet period were below 1 L-1. (B) IN concentrations during

the biogenic SOA event (high organic mass) were highly variable in response to the rapidly varying

dust concentrations (shown by SA>0.5μm). The minimum concentration, at 1405 hrs, was 1.0 ± 1.4 L-1.

Refer to Figure 19 for context.

Figure 20A shows typical IN concentrations during the wet period. The site was

intermittently immersed in clouds during this day, but the air was clear during the

presented measurements. IN concentrations during the wet period never exceeded 1 L-1.

Note that no aerosols >0.5 μm were detected and the concentration of aerosol particles

above 10 nm (not shown) was generally less than 1× 103 particles cm-3.


74

While IN concentrations during the biogenic SOA event varied rapidly, these variations

reflected variations in SA>0.5μm due to dust. The highest measured concentrations reflect

dust IN while the lowest reflect the constant organic background. Based on periods where

SA>0.5μm was lowest, biogenic SOA was estimated to contribute ≤ 2.4 L-1 to IN concentrations

during the day.

The uncertainty in IN counts above is mainly due to the rapidly varying dust background.

Four overnight measurements were attempted in order to avoid the wind, bicycle and

motor vehicle sources of dust. During these measurements SOA mass remained high

(Figure 19) while dust (i.e. SA>0.5μm) dropped significantly. IN levels reached as low as 0.2 L-

1 (Figure 21).

Each overnight run required the chamber to be in operation from 1800 because the

measurement site was not accessible at night. Unfortunately, after about 6 hours, frosting

within the chamber created a significant background of ice crystal counts. Because SA>0.5μm

remained high for the first 5-6 hours (until 0000) each night, by the time the dust

background was reduced frosting prevented an improved precision in the reported IN

concentrations. The data is shown in Figure 21. Although the wind generally originated

from higher up the mountain after 0000, SOA concentrations remained high, indicating a

continued influence of the biogenic aerosol.

The overnight measurements show that IN concentrations do indeed drop to near zero in

the absence of dust, though the issues noted above prevented a precise estimate of their

magnitude. Nonetheless, these overnight data support the hypothesis that increased IN

during the biogenic period were due only to increased dust loadings, and that biogenic SOA

in Whistler did not act as IN.

That biogenic SOA was an unimportant IN in a forested environment is similar to the

observations of Pratt et al. [2009], who found that IN in the Amazon rainforest were only


75

weakly related to AMS organic mass (R2 = 0.07). No other studies have reported IN

concentrations in similar environments.

Figure 21. Overnight IN concentrations for three nights. No data are shown before 0000, when SA>0.5μm

remained high. No data were available on July 10th due to severe frosting.

6.3.2 IN response to dust

The high dust loadings observed during the biogenic SOA event were unrepresentative of

the regional aerosol, since they originated from an isolated unpaved road. Nevertheless,

this dust was very efficient at nucleating ice. This section compares the ice-nucleating

ability of dust in Toronto with dust in Whistler.

No single-particle chemical information was available for the Whistler data. However, the

observations noted above suggest that the aerosol particles >0.5 μm were entirely made up

of dust. In particular, the observation that visible dust clouds travelled from the road to the

sampling inlet prior to spikes in >0.5 μm concentrations, and the observation that

concentrations fell to zero at night support the assumption that these aerosols were dust.


76

All >0.5 μm aerosol at Whistler was therefore considered dust, and compared with dust

estimated for Toronto using single-particle mass spectrometry.

Figure 22. (upper) IN vs. N and SA > 0.5μm (dust) for the data shown in Figure 20 (n = 13). N and SA

were averaged over 30 minutes for the comparison. (lower) Toronto data from

Figure 14 reproduced for comparison.

Figure 22 shows the response of IN to N>0.5μm and SA>0.5μm for both Whistler and Toronto.

For these few Whistler data (n=13), SA no longer clearly outperforms N. However, this

comparison is between the response of IN to dust (Whistler) and the response to an


77

externally and internally mixed aerosol (Toronto). A better comparison is between

Whistler dust and Toronto dust. Controlling for RH, salt and EC in Toronto, 5.8 ± 2.0 × 10-4

IN were observed per μm2 of dust SA. For Whistler, the slope was 9.4 ± 3.1 × 10-4 IN μm-2

dust SA. These slopes are equal within one standard deviation, suggesting that the dust at

either location possessed a similar number of active sites per unit surface area at 238 K and

134% RHi. Such a response was unexpected given that these two dust aerosols have very

different physical sources. Furthermore, the Whistler dust was probably clean since it was

measured directly after its suspension, while the dust in Toronto was internally mixed with

sulphate and nitrate, which depress the ice-nucleating activity of dust (Section 2.3.1).

6.3.2.1 Whistler dust in context of previous studies

The number of observed IN per unit surface area of dust discussed in Section 6.3.2 can be

compared to the results of previous studies [DeMott et al., 2003b; DeMott et al., 2003c;

DeMott et al., 2009; Richardson et al., 2007] all of which used the University of Colorado

Continuous-Flow Diffusion Chamber. For reasons similar to those above, these studies

assumed all >0.5 µm particulate surface area to represent dust. As for the measurements

presented here, these data were collected using an Aerodynamic Particle Sizer (TSI, Inc.).

The number of IN per dust SA in Whistler can therefore be directly compared to the data

from these earlier studies, as shown in Figure 23 (adapted from Phillips et al. [2008]). For

Toronto, the calculated response of IN to dust SA based on the backwards-elimination

regression model is shown. The figure shows that dust in Whistler, BC showed a similar

response to that observed in Mt. Werner, Colorado, U.S.A.*

This similarity supports the use

of a single parameterization for the ice-nucleating ability of mineral dust, as proposed by

Phillips et al. [2008]. However, the two order-of-magnitude spread in the data remains

unexplained.

* While the Whistler site was 1320 m above sea level, the Mt Werner site altitude was 3200 m. Whistler is

near the western coast of North America while Mt Werner is near central North America.


78

Figure 23. The response of IN concentrations to dust surface area at Mt Werner, CO (INSPECT1, open

squares, DeMott et al. 2003c; INSPECT2, filled squares, Richardson et al. 2007), above Florida during a

Saharan dust storm (CRYSTAL-FACE, triangles, DeMott et al. 2003b, 2009) and in a laboratory study

using milled dust surrogates (circles; Archuleta et al. 2005). Local dust at Whistler behaved similarly

to dust observed at the Mt Werner site. Adapted from Phillips et al. [2008] Fig. 1.

The fact that the milled dust surrogates in Figure 23 (open circles) lie outside the general

trend suggests that such dust should be used only for mechanistic studies, as mentioned in

Section 2.3.1.

6.4 Discussion and Conclusions

No change in IN concentration was detected during a biogenic SOA event in the coniferous

rainforest of Whistler, BC. While a strong background signal due to dust prevented a

precise estimation of biogenic IN, concentrations were at least below 2.4 L-1 (95% CI) and

fell as low as 0.2 L-1 even though organic aerosol mass did not change. More measurements

of IN concentrations, free of interference from local sources, would also allow a better

constraint on these IN concentrations.


79

Assuming that the heat wave that triggered the measured SOA event was accompanied by

elevated emissions of primary biological aerosol particles*

The similarity of deposition-mode IN activity of fresh dust at Whistler with road dust in

Toronto is an interesting result that suggests the IN ability of dust in Toronto is not

significantly inhibited by sulphate or nitrate coatings. It is possible that dust particles in

Toronto were not completely coated, leaving active sites available for nucleation. It is also

possible that Toronto dust was initially a more efficient IN prior to coating, or more likely

(PBAP), the Whistler forest

appears to generate neither IN active SOA nor PBAP. However, no direct measurements of

PBAP were made, and direct measurements of their presence in Whistler would be

informative.

SOA and PBAP aerosols are of interest due to their ubiquity and (in certain cases) excellent

IN ability, respectively. While certain PBAP are some of the best known IN (in terms of

activation at high T and low RH) it is uncertain what significance these have to climate

[Mohler et al., 2007]. DNA-containing IN have been identified in snowfall across the planet

[Christner et al., 2008] but in very low numbers. Pratt et al. [2008] and Prenni et al. [2009]

have identified biological IN at ground level and within-cloud, respectively. Conversely,

Hoose et al. [2010] argue that IN active PBAP are insignificant to global climate given that

they do not show appreciable effects in a global climate model. However, the inability of a

global model to detect an effect does not eliminate the possibility of important regional

effects. Secondary organic aerosol is of interest since it represents 18-70% of submicron

non-refractory mass [Zhang et al., 2007].

While PBAP in Whistler may have been IN active in the deposition mode at temperatures

lower than 238 K, such activity would likely not be significant for cloud formation given the

very low concentrations of such particles. However, it remains possible that PBAP at

Whistler would have been more active in the immersion rather than deposition mode.

* Recall from Section 2.3.3 that PBAP are typically pollen, fungal spores, fragments of plant matter and

suspended bacteria.


80

that a more careful study would reveal a small difference between the two. Further study

might involve studies of the critical IN conditions for either dust, as well as an investigation

of the surface characteristics of the two.


81

7 Summary and Future

Atmospheric ice crystals play a major role in cloud microphysics, affecting precipitation

and cloud reflectivity in the atmosphere. The sensitivity of these effects to atmospheric

perturbations remains one of the greatest uncertainties in current cloud and climate

models.

This thesis presented measurements of atmospheric ice nuclei concentrations using a

Continuous Flow Diffusion Chamber (CFDC) at 238 K and 134% RHi for two contrasting

Canadian sites: Toronto, a major city, and Whistler, a pristine coniferous rainforest. IN

concentrations were measured in conjunction with detailed measurements of aerosol

properties in order to identify important ice-nucleating aerosols at the two sites. Prior to

these two field campaigns, the CFDC was modified to increase portability and allow for

unsupervised operation.

In Toronto, IN concentrations of 0–20 IN L-1 were observed over a three-week period. The

temporal variation of these IN concentrations was related to variations in carbonaceous

(elemental and/or organic carbon) and dust aerosols, as shown by a regression model.

More specifically, IN numbers were related to the surface area of these two aerosols as

estimated by single-particle aerosol time-of-flight mass spectrometry combined with

quantitative measurements of aerosol surface area.

The regression model was unable to separate the relationships of EC and OC to IN. This

suggests that both EC and/or OC particles may have acted as IN, however the possibility of

a small amount of ice-nucleating EC present within OC particles (and vice versa) prevents a

definitive identification of one or both of these carbonaceous aerosols as important IN.

Future studies should use different techniques to better determine the surface

characteristics of EC and OC particles at the time of measurement. Detailed surface

measurements may be able to differentiate OC-dominated particles with exposed EC


82

inclusions from true organic IN. However, such measurements would require either a

three-dimensional nanoscale-resolution map of the particle surface or a sensitive and

specific chemical test for exposed EC. Three dimensional maps have been achieved with X-

ray microscopy, but are time-consuming and inappropriate for a correlation analysis as

presented here. Individual IN should be analyzed in detail to determine the critical surface

features for nucleation. These IN should be directly sampled from the atmosphere, as

laboratory surrogates for both OC and EC are too dissimilar from atmospheric aerosols to

be meaningful.

As well as EC/OC, the surface area of dust aerosols in Toronto showed a significant

relationship with IN concentrations. This response to dust surface area was similar for both

Toronto and Whistler, suggesting that coatings of sulphate and/or nitrate in Toronto did

not strongly affect the IN ability of dust at that site. It is possible that inhibition was not

observed because of the relatively low temperature and high humidity at which IN

concentrations were measured, or alternatively because the irregular morphology of dust

particles allowed small regions of the particle to remain uncoated and thus uninhibited

from nucleation. X-ray or Raman microscopy might eliminate the latter possibility, while

chemical characterization of dust from either site should be performed to determine how

comparable the two are. Measurements of IN concentrations at a variety of temperature

and RH conditions should be performed to characterize the behavior of Toronto dust. Since

the majority of dust had mass spectra similar to a dust sample collected directly from the

road, controlled ice-nucleation experiments could be performed on this sample.

As well as the dust measurements made at the Whistler site, biogenic IN were studied with

a maximum concentration of 2.4 L-1 (95% CI). The signal did not change during an

unusually large biogenic SOA event, suggesting that the forest was not a source of ice-

nucleating particles. It is assumed that emissions of primary biological aerosol particles

(PBAP) increased along with SOA during this heat-wave triggered event, however no direct

measurements were made. Future studies should address this directly. These studies might

simultaneously measure IN and PBAP concentrations in the field, or determine the major


83

PBAP within the forest before performing controlled laboratory studies on their IN ability.

This latter approach is preferred, as it would allow the evaluation of T and RH response in

detail.

While PBAP in Whistler may have been IN active in the deposition mode at temperatures

lower than 238 K, such activity would likely not be significant for cloud formation given the

very low concentrations of such particles. However, it remains possible that PBAP at

Whistler would have been more active in the immersion rather than deposition mode.

For both Whistler and Toronto, simultaneous characterization of the atmospheric aerosol

and measurement of IN concentrations allowed insight into the nature of IN at the site.

Future studies should become both more detailed and more general. For detail, future

studies should investigate the mechanism of ice nucleation upon these particles by

characterizing the surface features of ice-nucleating particles. For general applicability, the

variation of IN concentrations with measurement temperature and humidity should be

investigated in order to provide a picture of which IN would first become important in

cloud formation.

For both sites all IN concentrations were measured at ground level. Given the importance

of larger particles as IN, sedimentation of these IN would likely be relatively rapid, and the

significance of the measured concentrations at altitude is uncertain. Measurements at cloud

height above the forested site and within air masses downwind of the urban site should be

made to provide an estimate of the impact of these IN on cloud formation.


84

8 References

Lynch, D. K., Sassen, K., Starr, D. O., Stephens, G. (2002), Cirrus, Oxford University Press US, 2002.

Baklanova, A. M., et al. (1990), The influence of lead iodide aerosol dispersity on its ice-forming activity, Journal of Aerosol Science, 22.

Baustian, K. J., et al. (2009), Depositional ice nucleation on solid ammonium sulfate and glutaric acid particles, Atmos. Chem. Phys. Discuss., 9(5), 20949-20977.

Bertram, A. K., et al. (1999), Ice Formation in (NH4)2SO4−H2O Particles, The Journal of Physical Chemistry A, 104(3), 584-588.

Bhave, P. V., et al. (2002), A Field-Based Approach for Determining ATOFMS Instrument Sensitivities to Ammonium and Nitrate, Environmental Science & Technology, 36(22), 4868-4879.

Cantrell, W., and C. Robinson (2006), Heterogeneous freezing of ammonium sulfate and sodium chloride solutions by long chain alcohols, Geophys. Res. Lett., 33.

Chen, J. P., et al. (2008), Parameterizing ice nucleation rates using contact angle and activation energy derived from laboratory data, Atmos. Chem. Phys., 8(24), 7431-7449.

Chernoff, D. I., and A. K. Bertram (2010), Effects of sulfate coatings on the ice nucleation properties of a biological ice nucleus and several types of minerals, J. Geophys. Res., 115(D20), D20205.

Christner, B. C., et al. (2008), Ubiquity of Biological Ice Nucleators in Snowfall, Science, 319(5867), 1214-.

Cziczo, D. J., et al. (2009), Inadvertent climate modification due to anthropogenic lead, Nature Geosci, 2(5), 333-336.

Dallal, J. (2010), The Little Handbook of Statistical Practice, edited by J. Dallal, Tufts University. http://www.tufts.edu/~gdallal/LHSP.HTM Accessed November 20, 2010.

Daniel, C., and F. S. Wood (1971), Fitting Equations to Data: Computer Analysis of Multifactor Data, Wiley-InterScience.

DeCarlo, P., et al. (2004), Particle morphology and density characterization by combined mobility and aerodynamic diameter measurements. Part 1: Theory, Aerosol Science and Technology, 38, 1185-1205.

http://www.tufts.edu/~gdallal/LHSP.HTM�


85

DeMott, P. J., et al. (1999), Ice formation by black carbon particles, Geophys. Res. Lett., 26(16), 2429-2432.

DeMott, P. J., et al. (2003a), Measurements of the concentration and composition of nuclei for cirrus formation, Proceedings of the National Academy of Sciences of the United States of America, 100(25), 14655-14660.

DeMott, P. J., et al. (2003b), Measurements of the concentration and composition of nuclei for cirrus formation, Proc. Natl. Acad. Sci. U.S.A., 100(25), 14655-14660.

DeMott, P. J., et al. (2003c), African dust aerosols as atmospheric ice nuclei, Geophys. Res. Lett., 30(14), 1732.

DeMott, P. J. (2008), Progress and Issues in Quantifying Ice Nucleation Involving Atmospheric Aerosols, edited, pp. 405-417.

DeMott, P. J., et al. (2009), Correction to: African dust aerosols as atmospheric ice nuclei, Geophys. Res. Lett., 36(7), L07808.

DeMott, P. J., et al. (2010), Predicting global atmospheric ice nuclei distributions and their impacts on climate, Proceedings of the National Academy of Sciences.

Durant, A. J., and R. A. Shaw (2005), Evaporation freezing by contact nucleation inside-out, Geophys. Res. Lett., 32(20), L20814.

Eastwood, M. L., et al. (2009), Effects of sulfuric acid and ammonium sulfate coatings on the ice nucleation properties of kaolinite particles, Geophys. Res. Lett., 36.

Fergenson, D. P., et al. (2003), Reagentless Detection and Classification of Individual Bioaerosol Particles in Seconds, Analytical Chemistry, 76(2), 373-378.

Field, P. R., et al. (2006), Some ice nucleation characteristics of Asian and Saharan desert dust, Atmos. Chem. Phys., 6(10), 2991-3006.

Froyd, K. D., et al. (2009), Aerosol composition of the tropical upper troposphere, Atmos. Chem. Phys., 9(13), 4363-4385.

Gallavardin, S. J., et al. (2008), Single Particle Laser Mass Spectrometry Applied to Differential Ice Nucleation Experiments at the AIDA Chamber, Aerosol Science and Technology, 42(9), 773 - 791.

Gross, D. S., et al. (1999), Relative Sensitivity Factors for Alkali Metal and Ammonium Cations in Single-Particle Aerosol Time-of-Flight Mass Spectra, Analytical Chemistry, 72(2), 416-422.


86

Hairston, P. P., et al. (1997), Design of an instrument for real-time detection of bioaerosols using simultaneous measurement of particle aerodynamic size and intrinsic fluorescence, Journal of Aerosol Science, 28(3), 471-482.

Hand, J. L., and S. M. Kreidenweis (2002), A New Method for Retrieving Particle Refractive Index and Effective Density from Aerosol Size Distribution Data, Aerosol Science and Technology, 36(10), 1012-1026.

Harris, W. A., et al. (2006), Aerosol MALDI of peptides and proteins in an ion trap mass spectrometer: Trapping, resolution and signal-to-noise, International Journal of Mass Spectrometry, 258(1-3), 113-119.

Hoose, C., et al. (2010), A Classical-Theory-Based Parameterization of Heterogeneous Ice Nucleation by Mineral Dust, Soot, and Biological Particles in a Global Climate Model, Journal of the Atmospheric Sciences, 67(8), 2483-2503.

Jennings, S. G. (1988), The mean free path in air, Journal of Aerosol Science, 19(2), 159-166.

Jeong, C.-H., and G. Evans (2009), Inter-Comparison of a Fast Mobility Particle Sizer and a Scanning Mobility Particle Sizer Incorporating an Ultrafine Water-Based Condensation Particle Counter, Aerosol Science and Technology, 43(4), 364-373.

Jeong, C.-H., et al. (2010), Quantification Of Aerosol Chemical Composition Using Continuous Single Particle Measurements, edited, Submitted.

Jimenez, J. L., et al. (2003), Ambient aerosol sampling using the Aerodyne Aerosol Mass Spectrometer, J. Geophys. Res., 108(D7), 8425.

Kamphus, M., et al. (2010), Chemical composition of ambient aerosol, ice residues and cloud droplet residues in mixed-phase clouds: single particle analysis during the Cloud and Aerosol Characterization Experiment (CLACE 6), Atmos. Chem. Phys., 10(16), 8077-8095.

Kanji, Z. A., et al. (2008), Ice formation via deposition nucleation on mineral dust and organics: dependence of onset relative humidity on total particulate surface area, Environmental Research Letters(2), 025004.

Kanji, Z. A., and J. P. D. Abbatt (2009), The University of Toronto Continuous Flow Diffusion Chamber (UT-CFDC): A Simple Design for Ice Nucleation Studies, Aerosol Science and Technology, 43(7), 730 - 738.

Khlystov, A., et al. (2004), An Algorithm for Combining Electrical Mobility and Aerodynamic Size Distributions Data when Measuring Ambient Aerosol, Aerosol Science and Technology, 38(12 supp 1), 229-238.

Knopf, D. A., et al. (2010), Heterogeneous nucleation of ice on anthropogenic organic particles collected in Mexico City, Geophys. Res. Lett., 37(11), L11803.


87

Kärcher, B., and P. Spichtinger (2009), Clouds in the Perturbed Climate System, MIT Press.

Lin, J. C., et al. (2006), Effects of biomass-burning-derived aerosols on precipitation and clouds in the Amazon Basin: a satellite-based empirical study, J. Geophys. Res., 111.

Lohmann, U., and K. Diehl (2006), Sensitivity Studies of the Importance of Dust Ice Nuclei for the Indirect Aerosol Effect on Stratiform Mixed-Phase Clouds, Journal of the Atmospheric Sciences, 63(3), 968-982.

Lohmann, U., et al. (2007), Cloud microphysics and aerosol indirect effects in the global climate model ECHAM5-HAM, Atmos. Chem. Phys., 7(13), 3425-3446.

Lynch, D. K., et al. (2002), Cirrus, Oxford University Press US, 2002.

Mahowald, N. M., and L. M. Kiehl (2003), Mineral aerosol and cloud interactions, Geophys. Res. Lett., 30(9), 1475.

Marcolli, C., et al. (2007), Efficiency of immersion mode ice nucleation on surrogates of mineral dust, Atmos. Chem. Phys., 7(19), 5081-5091.

Mertes, S., et al. (2007), Counterflow Virtual Impactor Based Collection of Small Ice Particles in Mixed-Phase Clouds for the Physico-Chemical Characterization of Tropospheric Ice Nuclei: Sampler Description and First Case Study, Aerosol Science and Technology, 41(9), 848 - 864.

Meyers, M. P., et al. (1992), New Primary Ice-Nucleation Parameterizations in an Explicit Cloud Model, Journal of Applied Meteorology, 31(7), 708-721.

Murphy, D. M., and T. Koop (2005), Review of the vapour pressures of ice and supercooled water for atmospheric applications, Quarterly Journal of the Royal Meteorological Society, 131(608), 1539-1565.

Murray, B. J., et al. (2010a), Heterogeneous freezing of water droplets containing kaolinite and montmorillonite particles, Atmos. Chem. Phys. Discuss., 10(4), 9695-9729.

Murray, B. J., et al. (2010b), Heterogeneous nucleation of ice particles on glassy aerosols under cirrus conditions, Nature Geosci, 3(4), 233-237.

Möhler, O., et al. (2005a), Effect of sulfuric acid coating on heterogeneous ice nucleation by soot aerosol particles, J. Geophys. Res., 110(D11), D11210.

Möhler, O., et al. (2005b), Ice nucleation on flame soot aerosol of different organic carbon content, Meteorologische Zeitschrift, 14, 477-484.

Möhler, O., et al. (2007), Microbiology and atmospheric processes: the role of biological particles in cloud physics, Biogeosciences, 4(6), 1059-1071.


88

Niedermeier, D., et al. (2010), Heterogeneous freezing of droplets with immersed mineral dust particles – measurements and parameterization, Atmos. Chem. Phys., 10(8), 3601-3614.

Niemand, M., et al. (2010), Parameterization of heterogeneous ice nucleation on mineral dust particles: An application in a regional scale model.

Noble, C. A., and K. A. Prather (2000), Real-time single particle mass spectrometry: A historical review of a quarter century of the chemical analysis of aerosols, Mass Spectrometry Reviews, 19(4), 248-274.

Nriagu, J. O. (1990), Science of the Total Environment, edited, pp. 13-28, Science of the Total Environment.

Petters, M. D., et al. (2009), Ice nuclei emissions from biomass burning, J. Geophys. Res., 114(D7), D07209.

Phillips, V. T. J., et al. (2008), An Empirical Parameterization of Heterogeneous Ice Nucleation for Multiple Chemical Species of Aerosol, Journal of the Atmospheric Sciences, 65(9), 2757-2783.

Pitter, R. L., and H. R. Pruppacher (1973), A wind tunnel investigation of freezing of small water drops falling at terminal velocity in air, Quarterly Journal of the Royal Meteorological Society, 99(421), 540-550.

Popovicheva, O., et al. (2008), Effect of soot on immersion freezing of water and possible atmospheric implications, Atmospheric Research, 90(2-4), 326-337.

Pratt, K. A., et al. (2009), In situ detection of biological particles in cloud ice-crystals, Nature Geosci, 2(6), 398-401.

Prenni, A. J., et al. (2009), Relative roles of biogenic emissions and Saharan dust as ice nuclei in the Amazon basin, Nature Geosci, 2(6), 402-405.

Prospero, J. M. (1999), Long-range transport of mineral dust in the global atmosphere: Impact of African dust on the environment of the southeastern United States, Proceedings of the National Academy of Sciences of the United States of America, 96(7), 3396-3403.

Pruppacher, H. R., and J. Klett, D. (1996), Microphysics of Clouds and Precipitation (Atmospheric and Oceanographic Sciences Library), 976 pp., Springer.

Rehbein, P. (2010), The Characterization of Fine Particulate Matter in Toronto Using Single Particle Mass Spectrometry, MASc Thesis thesis, University of Toronto.

Reilly, P. T. A., et al. (2000), The Elucidation of Charge-Transfer-Induced Matrix Effects in Environmental Aerosols Via Real-Time Aerosol Mass Spectral Analysis of Individual Airborne Particles, Aerosol Science and Technology, 33(1), 135-152.


89

Reischela, M. T. (1975), Ice nuclei from reactions involving lead, ammonia and iodine, Atmospheric Environment (1967), 9.

Richardson, M. S., et al. (2007), Measurements of heterogeneous ice nuclei in the western United States in springtime and their relation to aerosol characteristics, J. Geophys. Res., 112.

Rosenfeld, D., et al. (2001), Desert dust suppressing precipitation: A possible desertification feedback loop, Proceedings of the National Academy of Sciences of the United States of America, 98(11), 5975-5980.

Sassen, K., et al. (2003), Saharan dust storms and indirect aerosol effects on clouds: CRYSTAL-FACE results, Geophys. Res. Lett., 30.

Sassen, K., and V. I. Khvorostyanov (2008), Cloud effects from boreal forest fire smoke: evidence for ice nucleation from polarization lidar data and cloud model simulations, Environmental Research Letters(2), 025006.

Schaefer, V. J. (1966), Ice Nuclei from Automobile Exhaust and Iodine Vapor, Science, 154(3756), 1555-a-1557.

Silva, P. J., and K. A. Prather (2000), Interpretation of Mass Spectra from Organic Compounds in Aerosol Time-of-Flight Mass Spectrometry, Analytical Chemistry, 72(15), 3553-3562.

Sullivan, R. C., et al. (2010a), Chemical processing does not always impair heterogeneous ice nucleation of mineral dust particles, Geophys. Res. Lett., 37(24), L24805.

Sullivan, R. C., et al. (2010b), Irreversible loss of ice nucleation active sites in mineral dust particles caused by sulphuric acid condensation, Atmos. Chem. Phys., 10(23), 11471-11487.

Thomson, D. S., et al. (1997), Thresholds for Laser-Induced Ion Formation from Aerosols in a Vacuum Using Ultraviolet and Vacuum-Ultraviolet Laser Wavelengths, Aerosol Science and Technology, 26(6), 544-559.

Virtanen, A., et al. (2010), An amorphous solid state of biogenic secondary organic aerosol particles, Nature, 467(7317), 824-827.

Wenzel, R. J., et al. (2003), Aerosol time-of-flight mass spectrometry during the Atlanta Supersite Experiment: 2. Scaling procedures, J. Geophys. Res., 108(D7), 8427.

Wylie, D. P., and W. P. Menzel (1999), Eight Years of High Cloud Statistics Using HIRS, Journal of Climate.

Zhang, Q., et al. (2007), Ubiquity and dominance of oxygenated species in organic aerosols in anthropogenically-influenced Northern Hemisphere midlatitudes, Geophys. Res. Lett., 34(13), L13801.


90

Zobrist, B., et al. (2008a), Do atmospheric aerosols form glasses?, Atmos. Chem. Phys., 8(17), 5221-5244.

Zobrist, B., et al. (2008b), Heterogeneous Ice Nucleation in Aqueous Solutions: the Role of Water Activity, The Journal of Physical Chemistry A, 112(17), 3965-3975.

Zuberi, B., et al. (2002), Heterogeneous nucleation of ice in (NH4)2SO4-H2O particles with mineral dust immersions, Geophys. Res. Lett., 29.


91

9 Appendix A: Complete Aerosol Distributions for Toronto (Chapter 5)

This Appendix contains additional figures of the aerosol distribution in Toronto during the

measurements reported in Chapter 5. Both corrections to the FMPS data described in

5.2.1.1 have been applied.

Figure 24. Number distributions measured by FMPS and APS plotted on a single graph. Colours

indicate logarithmic changes in concentration. White areas are missing data.


92

Figure 25. Aerosol surface area throughout the study (hourly average). A discontinuity is seen at the

point where the FMPS and APS data come together (the low-point near 500 nm is the highest FMPS-

measured size). Colours indicate surface area on a linear scale.


93

Figure 26. The data from Figure 10 with the axes extended to show all measured sizes. Note the log

scale for FMPS/APS number concentrations. FMPS: dotted lines, APS: dash-dotted lines, ATOFMS: solid

blue line.


94

10 Appendix B: ATOFMS Mass Spectra (Chapter 5)

This appendix contains exemplary average mass spectra of the ATOFMS clusters identified

in Chapter 5. The following table identifies important m/z ratios used for identification.

Figure 27. Cluster identified as DUST based on +7 (Li), +23 (Na), +39 (K), +56 (Fe), +73 (FeOH), +27

(Al). The negative spectrum contains Cl (-35), O (-16), NO2 (-46), NO3 (-62), NH3NO2 (-79) and H(NO3)2

(-125).


95

Figure 28. DUST cluster identified similarly to the previous figure. Here, Fe (56Fe+, 73FeOH+) is replaced

by Ca (40Ca+, 56CaOH+) and traces of barium (154BaO+) are seen. The peak at -1 appears to be a hydride

ion.

Figure 29. A DUST cluster similar to the previous figure but with fewer contaminants. Not enough of

these “clean” particles were observed for separate analysis.


96

Figure 30. SALT cluster. 23Na+, 39K+, 35Cl-, 46NO2-, 62NO3- identify this particle type as salt.

Figure 31. A SALT cluster mixed with other material includeing nitrates and possibly dust. The Na and

Cl peaks dominate the possible dust peaks (which are +7 (Li) and +56 (Fe)).


97

Figure 32. Organic (OC) particle type. This was the most abundant particle type. Major peaks are -97

(HSO42-), -62 and -46 (nitrate), +43 (C2H3O) and +12/+24/+36/60 (C1/2/3/4+). Note that Cn+ peaks are

not necessarily due to soot and may form from organics.

Figure 33. OC particle type similar to the previous figure. Nitrate (-46 NO2, -62 NO3, -125 H(NO3)2) and

sulphate (-97 HSO42-, -195 H(HSO4)2-) are dominant, while the positive spectrum shows organic

fragments (+27 C2H3, +43 C2H3O).


98

Figure 34. EC particle type. Almost all peaks are due to carbon (multiples of 12) with only sulphate (-

97) and nitrate (-46, -62, -125) as major non-carbon components.

Figure 35. A second example of an EC cluster. The series of carbon peaks extend to very high masses,

possible due to a small amount of laser energy hitting the particle (allowing larger fragments to

remain). Trace organic material is seen (27C2H3O+).


99

Figure 36. An unclassified particle type. Insufficient information is available from the spectrum. The

major peak at +38 was most likely due to 39K+.

Figure 37. Another unclassified particle. Again, adding 1 amu to all major peaks would identify them

as common species: 96SO4-, 62NO3-, 46NO2-, 39K+.


100

11 Appendix C: Regression Results (Chapter 5)

This appendix provides detailed results from each regression stage in Section 5.4.2 (page

60). Each figure is titled according to its number in Table 2, and the variables used in the

regression are listed.

While uncertainties should be taken into account when interpreting the values below,

regression parameters are reported with a fixed number of decimal points for ease of

comparison between tables.

Variables are defined in the Glossary section, except for the following:

• Multiple R: The square root of Multiple R2.

• Multiple R2: The R2 (see Glossary) for a multiple regression model.

• F(x,y): The F-value defined in the Glossary with x being the number of predictors

and y being the number of data cases.

• b : regression coefficient.

• β : regression coefficient when all predictors are first normalized to mean zero and

variance 1.

• Residual vs. predicted plots should show a Normal distribution about zero.

• Normal probability plots should lie along the red line.

• Red highlighting indicates p-value below 0.05, i.e. 95% confidence.

1 SA:

Summary Statistics; DV: IN (RegressionData.sta)Statistic ValueMultiple RMultiple R²Adjusted R²F(1,81)pStd.Err. of Estimate

0.379530.144040.13347

13.630780.000405.90482


101

Regression Summary for Dependent Variable: IN (RegressionData.sta)R= .37952823 R²= .14404168 Adjusted R²= .13347429F(1,81)=13.631 p<.00040 Std.Error of estimate: 5.9048

N=83Beta Std.Err.

of BetaB Std.Err.

of Bt(81) p-level

InterceptSA0.5-20µm

0.964126 1.504007 0.641038 0.5233070.379528 0.102798 0.111100 0.030092 3.691988 0.000402

Analysis of Variance; DV: IN (RegressionData.sta)

EffectSums ofSquares

df MeanSquares

F p-level

Regress.ResidualTotal

475.263 1 475.2630 13.63078 0.0004022824.219 81 34.86693299.482

Predicted vs. Residual ScoresDependent variable: IN

0 2 4 6 8 10 12 14 16Predicted Values

0 2 4 6 8 10 12 14 16

-12

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

Res

idua

ls

-12

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

95% confidence

Normal Probability Plot of Residuals

-12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16

Residuals

-12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16

-3

-2

-1

0

1

2

3

Exp

ecte

d N

orm

al V

alue

-3

-2

-1

0

1

2

3


102

2 RHw SA:


0.4272180.1825150.1620788.9305830.0003165.806544


N=83Beta Std.Err.

of BetaB Std.Err.

of Bt(80) p-level

InterceptRHWSA0.5-20µm

-90.5713 47.19711 -1.91900 0.0585510.196202 0.101115 0.9500 0.48962 1.94038 0.0558560.374869 0.101115 0.1097 0.02960 3.70734 0.000385



df MeanSquares

F p-level


602.206 2 301.1031 8.930583 0.0003162697.276 80 33.71603299.482

Predicted vs. Residual Scores

Dependent variable: IN

-2 0 2 4 6 8 10 12 14

Predicted Values

-2 0 2 4 6 8 10 12 14

-12

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

Res

idua

ls

-12

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

95% confidence


103


-12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16

Residuals

-12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16

-3

-2

-1

0

1

2

3

Exp

ecte

d N

orm

al V

alue

-3

-2

-1

0

1

2

3

3 DUST, SALT, EC, OC, other:


0.5020410.2520450.2034765.1894700.0003715.661289


N=83Beta Std.Err.

of BetaB Std.Err.

of Bt(77) p-level

InterceptDUST_SA05SALT_SA05EC_SA05OC_SA05other_SA05

3.39964 1.870657 1.81735 0.0730530.503100 0.157359 0.89590 0.280219 3.19716 0.002014

-0.236672 0.150173 -0.93314 0.592092 -1.57600 0.1191260.690222 0.230622 2.53115 0.845727 2.99287 0.0037130.198670 0.202167 0.26812 0.272836 0.98270 0.328831

-0.381690 0.276847 -1.44289 1.046554 -1.37870 0.171979



df MeanSquares

F p-level


831.618 5 166.3235 5.189470 0.0003712467.865 77 32.05023299.482


104


-2 0 2 4 6 8 10 12 14 16

Predicted Values

-2 0 2 4 6 8 10 12 14 16

-20

-15

-10

-5

0

5

10

15

Res

idua

ls

-20

-15

-10

-5

0

5

10

15

95% confidence


-20 -15 -10 -5 0 5 10 15

Residuals

-20 -15 -10 -5 0 5 10 15

-3

-2

-1

0

1

2

3

Exp

ecte

d N

orm

al V

alue

-3

-2

-1

0

1

2

3

4 RHw, DUST, SALT, EC, OC, other:


0.5774970.3335030.2808856.3381780.0000195.379168


105


N=83Beta Std.Err.

of BetaB Std.Err.

of Bt(76) p-level

InterceptRHWDUST_SA05SALT_SA05EC_SA05OC_SA05other_SA05

-177.750 59.46424 -2.98919 0.0037670.386396 0.126782 1.871 0.61390 3.04772 0.0031710.605451 0.153242 1.078 0.27289 3.95095 0.000173

-0.478804 0.163316 -1.888 0.64391 -2.93177 0.0044510.551806 0.223786 2.024 0.82066 2.46577 0.0159260.561039 0.225912 0.757 0.30488 2.48344 0.015215

-0.353546 0.263213 -1.336 0.99501 -1.34319 0.183207



df MeanSquares

F p-level


1100.388 6 183.3980 6.338178 0.0000192199.094 76 28.93543299.482


-2 0 2 4 6 8 10 12 14

Predicted Values

-2 0 2 4 6 8 10 12 14

-12

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

18

Res

idua

ls

-12

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

18

95% confidence


106


-12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18

Residuals

-12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18

-3

-2

-1

0

1

2

3

Exp

ecte

d N

orm

al V

alue

-3

-2

-1

0

1

2

3

5 RHw, DUST, SALT, EC, OC:


0.5636320.3176810.2733757.1700970.0000155.407184


N=83Beta Std.Err.

of BetaB Std.Err.

of Bt(77) p-level

InterceptRHWDUST_SA05SALT_SA05EC_SA05OC_SA05

-181.050 59.72292 -3.03149 0.0033140.392370 0.127364 1.900 0.61671 3.08071 0.0028630.517534 0.139282 0.922 0.24803 3.71574 0.000382

-0.543972 0.156755 -2.145 0.61804 -3.47021 0.0008550.349371 0.166289 1.281 0.60981 2.10099 0.0389140.461203 0.214446 0.622 0.28941 2.15068 0.034639



df MeanSquares

F p-level


1048.184 5 209.6367 7.170097 0.0000152251.299 77 29.23763299.482


107


-2 0 2 4 6 8 10 12 14 16

Predicted Values

-2 0 2 4 6 8 10 12 14 16

-12

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

18

Res

idua

ls

-12

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

18

95% confidence


-12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18

Residuals

-12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18

-3

-2

-1

0

1

2

3

Exp

ecte

d N

orm

al V

alue

-3

-2

-1

0

1

2

3

6 RHw, DUST, SALT, OC:


0.5277940.2785660.2415707.5295060.0000355.524257


108


N=83Beta Std.Err.

of BetaB Std.Err.

of Bt(78) p-level

InterceptRHWDUST_SA05SALT_SA05OC_SA05

-212.917 59.01547 -3.60781 0.0005430.457321 0.126230 2.214 0.61122 3.62292 0.0005170.595420 0.137164 1.060 0.24426 4.34092 0.000042

-0.506165 0.159090 -1.996 0.62725 -3.18163 0.0021030.758965 0.164423 1.024 0.22190 4.61593 0.000015



df MeanSquares

F p-level


919.124 4 229.7810 7.529506 0.0000352380.358 78 30.51743299.482

Predicted vs. Residual Scores

Dependent variable: IN

-4 -2 0 2 4 6 8 10 12 14

Predicted Values

-4 -2 0 2 4 6 8 10 12 14

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

18

Res

idua

ls

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

18

95% confidence


109


-10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18

Residuals

-10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18

-3

-2

-1

0

1

2

3

Exp

ecte

d N

orm

al V

alue

-3

-2

-1

0

1

2

3

7 RHw, DUST, SALT, EC:


0.5260170.2766940.2396027.4595520.0000385.531419


N=83Beta Std.Err.

of BetaB Std.Err.

of Bt(78) p-level

InterceptRHWDUST_SA05SALT_SA05EC_SA05

-105.878 49.53931 -2.13725 0.0357110.236263 0.107060 1.144 0.51840 2.20682 0.0302680.327440 0.110116 0.583 0.19609 2.97360 0.003915

-0.382162 0.140679 -1.507 0.55466 -2.71656 0.0081220.585726 0.127665 2.148 0.46817 4.58800 0.000017



df MeanSquares

F p-level


912.948 4 228.2369 7.459552 0.0000382386.535 78 30.59663299.482


110


-2 0 2 4 6 8 10 12 14

Predicted Values

-2 0 2 4 6 8 10 12 14

-12

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

Res

idua

ls

-12

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

95% confidence


-12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16

Residuals

-12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16

-3

-2

-1

0

1

2

3

Exp

ecte

d N

orm

al V

alue

-3

-2

-1

0

1

2

3

8 RHw, DUST, EC:

Summary Statistics; DV: IN (RegressionDaStatistic ValueMultiple RMultiple R²Adjusted R²F(3,79)pStd.Err. of Estimate

0.4563560.2082610.1781956.9267910.0003375.750430


111


N=83Beta Std.Err.

of BetaB Std.Err.

of Bt(79) p-level

InterceptRHWDUST_SA05EC_SA05

-65.8314 49.16772 -1.33892 0.1844380.145358 0.105723 0.7038 0.51193 1.37489 0.1730510.212736 0.105725 0.3788 0.18827 2.01216 0.0476120.358170 0.100155 1.3135 0.36728 3.57616 0.000599



df MeanSquares

F p-level


687.154 3 229.0513 6.926791 0.0003372612.328 79 33.06743299.482


0 2 4 6 8 10 12 14

Predicted Values

0 2 4 6 8 10 12 14

-12

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

Res

idua

ls

-12

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

95% confidence


-12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14

Residuals

-12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14

-3

-2

-1

0

1

2

3

Exp

ecte

d N

orm

al V

alue

-3

-2

-1

0

1

2

3


112

Figure C1. Scatterplot matrix of regression data. Each histogram (centre) can be considered the axis label for the x-axis of all plots in that column, or the y-axis of all plots in that row.

IN

DUST_SA05

SALT_SA05

EC_SA05

OC_SA05

other_SA05


113

12 Appendix D: Meteorological data for Whistler (Chapter 6)

Figure 38. Wind speed and direction during the Whistler study. Each measurement is plotted as a dot

according to time of day and wind direction. Colour intensity indicates wind speed. The plot

background is coloured as zero wind speed to highlight higher values. East = downhill; west = uphill.

Figure 39. Different view of the previous figure. Wind speed plotted against wind direction.

Development and Deployment of a Continuous-Flow …...Discussions with Maygan McGuire on aerosol...

Documents

Transcript of Development and Deployment of a Continuous-Flow …...Discussions with Maygan McGuire on aerosol...