Development and Deployment of a Continuous-Flow ... - TSpace

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

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.

ii


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 iii


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.

iv


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 v


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

vi


Glossary Term IN

CCN

Cirrus cloud

Mixed-phase cloud 𝑅𝑅𝐻𝐻𝑤𝑤 𝑅𝑅𝐻𝐻𝑖𝑖

𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 𝑅𝑅𝑅𝑅 CFDC

UT-CFDC Aerosol

Internally mixed Externally mixed PBAP SOA ATOFMS APS FMPS

Definition Ice nucleus (a particle that nucleates ice)

Cloud condensation nucleus (a particle that nucleates water) High-altitude cloud, containing only ice crystals

Mid-level cloud, containing both ice crystals and water droplets Relative humidity with respect to water (PH2O / Psat,water) Relative humidity with respect to ice (PH2O / Psat,ice)

The humidity at which a given type of IN first nucleates ice Continuous flow diffusion chamber

The University of Toronto Continuous Flow Diffusion Chamber A suspension of solid or liquid particles in a gas An aerosol containing similar particles, each a mixture of different substances An aerosol containing different particles, each made up of one substance; i.e., different substances exist as separate particles Primary biological aerosol particles Secondary organic aerosol

Aerosol Time-of-Flight Mass Spectrometer (TSI Inc.) Aerodynamic Particle Sizer

AMS

Fast Mobility Particle Sizer

𝑑𝑑𝑣𝑣𝑣𝑣

Volume-equivalent particle diameter

𝑑𝑑𝑎𝑎

𝑑𝑑𝑣𝑣𝑣𝑣 𝑑𝑑𝑚𝑚

𝜌𝜌𝑒𝑒𝑒𝑒𝑒𝑒 Nd

SAd

Aerosol Mass Spectrometer (Aerodyne Inc.) Aerodynamic diameter (a function of pressure and temperature) Vacuum aerodynamic diameter

Mobility diameter (effective 𝑑𝑑 during drift an electric field) Effective particle density

Number of aerosol particles of diameter 𝑑𝑑 per unit volume Surface area of particles of diameter 𝑑𝑑 per unit volume vii


Residual RSS TSS ESS

RMS 𝑘𝑘

𝑑𝑑𝑑𝑑 R2

adjusted R2 p-value F-ratio b

β

The difference between a measured (yi) and predicted (𝑦𝑦�𝑖𝑖 ) value, i.e. (𝑦𝑦𝑖𝑖 − 𝑦𝑦�𝑖𝑖 ) Residual Sum of Squares. The sum of squared residuals for a given prediction. Total Sum of Squares. The RSS when predicting 𝑦𝑦𝑖𝑖 with mean 𝑦𝑦 (𝑦𝑦�) alone. 𝑇𝑇𝑇𝑇𝑇𝑇 = 𝑅𝑅𝑅𝑅𝑅𝑅 + 𝐸𝐸𝐸𝐸𝐸𝐸. 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�). Root Mean Square Error, the standard deviation of the data about the regression line. 𝑅𝑅𝑅𝑅𝑅𝑅 = 𝑅𝑅𝑅𝑅𝑅𝑅/𝑑𝑑𝑑𝑑. Number of predictors in a regression model. 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 𝑑𝑑𝑑𝑑 = 𝑛𝑛 – 𝑘𝑘 – 1. Multiple correlation coefficient. 𝑅𝑅2 = 𝐸𝐸𝐸𝐸𝐸𝐸/𝑇𝑇𝑇𝑇𝑇𝑇 = 1– 𝑅𝑅𝑅𝑅𝑅𝑅/𝑇𝑇𝑇𝑇𝑇𝑇. Should not be compared when k changes. R2 adjusted to account for artificial increases in R2 when the number of predictors in a multiple regression is changed. 𝑎𝑎𝑎𝑎𝑎𝑎. 𝑅𝑅2 = (𝑅𝑅𝑅𝑅𝑅𝑅/(𝑛𝑛 – 𝑘𝑘 – 1) ) / (𝑇𝑇𝑇𝑇𝑇𝑇/(𝑛𝑛 – 1)). The probability of a given result occurring by chance. The statistical significance of a model prediction. Large values show greater significance. 𝐹𝐹 = (𝐸𝐸𝐸𝐸𝐸𝐸/𝑑𝑑𝑑𝑑) / (𝑇𝑇𝑇𝑇𝑇𝑇/(𝑛𝑛 – 1)). The regression coefficient of a specified parameter, i.e. the “slope”. 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].

viii


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, singleparticle 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. 1


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.

2


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 surfacestructural 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 3


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) 4


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 J hf at which a given solute freezes can be expressed as [DeMott, 2002]:

𝐽𝐽ℎ𝑓𝑓 = 𝐶𝐶 𝑒𝑒𝑒𝑒𝑒𝑒 �

−Δ𝐹𝐹𝑎𝑎𝑎𝑎𝑎𝑎 − Δ𝐹𝐹𝑔𝑔 � 𝑘𝑘𝑘𝑘

(1)

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

is the formation energy of the critical ice embryo, and k is the Boltzmann constant. The preexponential factor C is a function of germ radius and the interfacial energy of the ice-

solution interface. Equation 1 can be extended to calculate J hf 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 5


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)

-

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

-

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

6


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, ∆Fg , 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.)

7


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 rg as 35 nm for pure

water at 268 K, with rg decreasing rapidly to ~10 nm for T99% 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 17


mechanisms discussed above, human activity may potentially amplify the IN activity of preexisting 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 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.

*The

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

al. [2009].

18


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 19


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. 20


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.

21


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 𝑃𝑃𝐻𝐻2 𝑂𝑂 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 𝑃𝑃𝑣𝑣𝑣𝑣𝑣𝑣 ,𝑖𝑖𝑖𝑖𝑖𝑖 is a non-linear function of temperature * [Murphy and Koop, 2005].

*

𝑙𝑙𝑙𝑙𝑙𝑙(𝑃𝑃𝑣𝑣𝑣𝑣𝑣𝑣 ,𝑖𝑖𝑖𝑖𝑖𝑖 ) decreases exponentially as inverse temperature increases [Murphy and Koop 2005, Fig. 2].

22


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.

23


3.2 Chamber Design 50.8cm

sample inlet through movable injector 25.4 cm

20.3 cm

45.7 cm

to OPC

outlines of Teflon® spacer d, injector position (cm)

sheath inlet

Teflon® spacer sandwiched between copper plates

O-ring groove

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.

24


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.

25


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].

26


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. 27


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.

28


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 1 μm in

diameter, different aerosol impactors were used to provide 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 39


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 × �� Δ𝐼𝐼 =

Δ𝐼𝐼 =

Δ𝐶𝐶 2 Δ𝑉𝑉 2 � +� � 𝐶𝐶 𝑉𝑉 𝐶𝐶 Δ𝐶𝐶 × 𝑉𝑉 𝐶𝐶

√𝐶𝐶 √𝐼𝐼𝐼𝐼 = 𝑉𝑉 𝑉𝑉 40


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 41


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

During migration, the particle experiences opposing drag and electrical forces. At steady state, these forces can be equated to give

𝑑𝑑𝑚𝑚 = 𝑑𝑑𝑣𝑣𝑣𝑣 ∙ 𝜒𝜒 ∙

𝐶𝐶𝑐𝑐 (𝑑𝑑𝑚𝑚 ) 𝐶𝐶𝑐𝑐 (𝑑𝑑𝑣𝑣𝑣𝑣 )

(3)

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

factor. Here we assume spherical particles, so 𝑑𝑑𝑚𝑚 = 𝑑𝑑𝑣𝑣𝑣𝑣 . 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 𝐹𝐹𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 = 𝐹𝐹𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 ) as a given particle. 𝑑𝑑𝑎𝑎 is given by

1 𝜌𝜌𝑝𝑝 𝐶𝐶𝑐𝑐 (𝑑𝑑𝑣𝑣𝑣𝑣 ) 𝑑𝑑𝑎𝑎 = 𝑑𝑑𝑣𝑣𝑣𝑣 � 𝜒𝜒 𝜌𝜌0 𝐶𝐶𝑐𝑐 (𝑑𝑑𝑎𝑎 )

(4)

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

standard density 1.0 𝑔𝑔/𝑐𝑐𝑚𝑚3 , and 𝜌𝜌𝑝𝑝 is the particle density. Here, 𝜌𝜌𝑝𝑝 was taken as 1.5 𝑔𝑔/𝑐𝑐𝑚𝑚3

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 ×

𝐶𝐶𝑐𝑐 (𝑑𝑑𝑚𝑚 ) 𝐶𝐶𝑐𝑐 (𝑑𝑑𝑎𝑎 )

(5)

where the Cunningham slip (defined below) for 𝑑𝑑𝑎𝑎 was found by setting 𝐶𝐶𝑐𝑐 (𝑑𝑑𝑎𝑎 ) = 𝐶𝐶𝑐𝑐 (𝑑𝑑𝑚𝑚 ) initially and solving iteratively until 𝐶𝐶𝑐𝑐 (𝑑𝑑𝑎𝑎 ) converged to a stable value (within 0.001%). 42


Cunningham Slip. The Cunningham slip 𝐶𝐶𝑐𝑐 accounts for the reduction in drag due to nonzero 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 𝐾𝐾𝑛𝑛 using the gas mean free path 𝜆𝜆 and the particle

radius 𝑑𝑑/2:

𝐾𝐾𝑛𝑛 =

2𝜆𝜆 𝑑𝑑

(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

𝐶𝐶𝑐𝑐 (𝐾𝐾𝑛𝑛 ) = 1 + 𝐾𝐾𝑛𝑛 �𝛼𝛼 + 𝛽𝛽 exp �−

𝛾𝛾 �� 𝐾𝐾𝑛𝑛

𝐶𝐶𝑐𝑐 (𝑑𝑑 ) = 1 + 2𝜆𝜆/𝑑𝑑 �1.252 + 0.399 exp �−

1.10 �� 2𝜆𝜆/𝑑𝑑

(7) (8)

where the constants 𝛼𝛼, 𝛽𝛽, 𝛾𝛾 are from Jennings [1988]. 𝐶𝐶𝑐𝑐 approaches 1 for For 𝐾𝐾𝑛𝑛 < 0.1 (𝑑𝑑 = 1.31 𝜇𝜇𝜇𝜇 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 𝐹𝐹𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 is equal to *

This value is “typical” in that it is calculated from the bulk density and viscosity of air at STP.

43


the drag force 𝐹𝐹𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 . The ATOFMS is calibrated using PSL spheres of near-standard density (1.05 𝑔𝑔/𝑐𝑐𝑚𝑚3 ) for which 𝑑𝑑𝑎𝑎 = 𝑑𝑑𝑣𝑣𝑣𝑣 . However, for the atmospheric aerosol, with effective density 𝜌𝜌𝑒𝑒𝑒𝑒𝑒𝑒 = 1.5 g/cm3 , a correction must be applied.

From 𝐹𝐹𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 = 𝐹𝐹𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 the following expression can be derived [DeCarlo et al., 2004]: 1 𝜌𝜌𝑝𝑝 𝐶𝐶𝑐𝑐 (𝑑𝑑𝑣𝑣𝑣𝑣 ) 𝑑𝑑𝑎𝑎 = 𝑑𝑑𝑣𝑣𝑣𝑣 � 𝜒𝜒 𝜌𝜌0 𝐶𝐶𝑐𝑐 (𝑑𝑑𝑎𝑎 )

(9)

where 𝐶𝐶𝑐𝑐 , 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, 𝐾𝐾𝑛𝑛 > 10, and particles enter the “free-molecular-regime”. The Cunningham slip correction 𝐶𝐶𝑐𝑐 (𝐾𝐾𝑛𝑛 ) = 1 + 𝐾𝐾𝑛𝑛 (𝛼𝛼 + 𝛽𝛽 exp(−𝛾𝛾/𝐾𝐾𝑛𝑛 )) here can be approximated as 𝐶𝐶𝑐𝑐 (𝐾𝐾𝑛𝑛 ≫ 1) = 𝐾𝐾𝑛𝑛 (𝛼𝛼 + 𝛽𝛽 )

𝐶𝐶𝑐𝑐 (𝑑𝑑 ) =

2𝜆𝜆 (𝛼𝛼 + 𝛽𝛽 ) = 𝑘𝑘/𝑑𝑑 𝑑𝑑

(10) (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 𝑑𝑑𝑣𝑣𝑣𝑣 [DeCarlo et al., 2004]: 𝑑𝑑𝑣𝑣𝑣𝑣 = 𝑑𝑑𝑣𝑣𝑣𝑣 ∙

1 𝜌𝜌𝑝𝑝 ∙ 𝜒𝜒𝑣𝑣 𝜌𝜌0

(12)

Here 𝜒𝜒𝑣𝑣 is the vacuum dynamic shape factor, 𝜌𝜌𝑝𝑝 the particle density, 𝜌𝜌0 the standard density, and 𝑑𝑑𝑣𝑣𝑣𝑣 is defined above. If 𝜒𝜒 = 1 and 𝜌𝜌𝑝𝑝 = 1.5 𝑔𝑔/𝑐𝑐𝑚𝑚3 , 𝑑𝑑𝑣𝑣𝑣𝑣 = 𝑑𝑑𝑣𝑣𝑣𝑣 × 1.5 44

(13)


Substituting this into the general expression for 𝑑𝑑𝑎𝑎 yields 𝑑𝑑𝑎𝑎 =

𝐶𝐶𝑐𝑐 (𝑑𝑑𝑣𝑣𝑣𝑣 ) 𝑑𝑑𝑣𝑣𝑣𝑣 � ≅ √1.5 𝐶𝐶𝑐𝑐 (𝑑𝑑𝑎𝑎 ) √1.5 𝑑𝑑𝑣𝑣𝑣𝑣

(14)

since �𝐶𝐶𝑐𝑐 (𝑑𝑑𝑣𝑣𝑣𝑣 )/𝐶𝐶𝑐𝑐 (𝑑𝑑𝑎𝑎 ) ~1. This expression is a consequence of the dependence of

aerodynamic diameter upon 𝐾𝐾𝑛𝑛 .

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 = 𝑑𝑑𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 �1.5 × 𝑑𝑑𝑎𝑎 =

𝐶𝐶𝑐𝑐 (𝑑𝑑𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 ) 𝐶𝐶𝑐𝑐 (𝑑𝑑𝐴𝐴𝐴𝐴𝐴𝐴 )

𝑑𝑑𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 √1.5

(15) (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. 45


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 46


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. 47


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 48


𝑆𝑆𝐴𝐴𝑘𝑘 =

4.22𝜇𝜇𝜇𝜇

�

𝑑𝑑 =0.24𝜇𝜇𝜇𝜇

�

nkd

𝐴𝐴𝐴𝐴𝐴𝐴 /𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹

× 𝑆𝑆𝐴𝐴𝑑𝑑

nATOFMS d

�

for surface area, where 𝑘𝑘 = dust, salt, EC, OC or “unclassified”; nkd is the number of particles with diameter 𝑑𝑑 identified as type 𝑘𝑘; nATOFMS is the total number of particles of diameter 𝑑𝑑 d measured by the ATOFMS in a given hour (including unclassified particles); and 𝐴𝐴𝐴𝐴𝐴𝐴 /𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹

𝑆𝑆𝐴𝐴𝑑𝑑

is the total surface area for particles of diameter 𝑑𝑑 measured by the APS/FMPS.

𝑆𝑆𝐴𝐴𝑘𝑘 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. DUST 28%

OC 54%

EC 7%

SALT 11%

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.

49


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 �𝐶𝐶𝑛𝑛 ± � 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 50


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 51


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 (𝐶𝐶𝑛𝑛± ) [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.

52


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.

53


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 (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. 57


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. *

In the next section, the parameterization of one such study is quantitatively compared with the IN concentrations measured here.

5.4.1.1 IN vs. n>0.5µm: Comparison to Predictions

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

(𝑛𝑛>0.5𝜇𝜇𝜇𝜇 ) 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 𝑛𝑛>0.5𝜇𝜇𝜇𝜇 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 −𝑇𝑇∙𝑐𝑐+𝑑𝑑

𝐼𝐼𝑁𝑁𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 = 𝑎𝑎 ∙ −𝑇𝑇 𝑏𝑏 ∙ �𝑛𝑛>0.5𝜇𝜇𝜇𝜇 �

where 𝐼𝐼𝑁𝑁𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 is the predicted concentration of IN (L-1), T is the nucleation temperature in degrees Celsius (= 𝑇𝑇𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾 – 273.16), and 𝑛𝑛>0.5𝜇𝜇𝜇𝜇 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.

58


nuclei concentrations according to this relationship based on our APS measurements of 𝑛𝑛>0.5𝜇𝜇𝜇𝜇 .

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 𝐼𝐼𝑁𝑁𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 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, 𝐼𝐼𝑁𝑁𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 was

effectively a function only of 𝑛𝑛>0.5𝜇𝜇𝜇𝜇 . The relationship of both 𝐼𝐼𝑁𝑁𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 and 𝑛𝑛>0.5𝜇𝜇𝜇𝜇 with 2 IN was necessarily similar, yet n>0.5µm better accounted for the variability in IN (𝑅𝑅𝐼𝐼𝑁𝑁 = 𝑝𝑝 2 0.014, 𝑝𝑝 = 0.206; 𝑅𝑅𝑛𝑛>0.5𝜇𝜇𝜇𝜇 = 0.023, 𝑝𝑝 = 0.129).

The fact that 𝐼𝐼𝑁𝑁𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 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 59


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 60


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.

61


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.

#

Predictors (𝑺𝑺𝑨𝑨>0.5 𝜇𝜇𝜇𝜇 )

R2

Adj. R2

F value

RMS

RSS

p value

Residual vs. Predicted

Conclusion/ Action

Strong negative bias at high IN Moderate negative bias, reduced from 1 Small negative bias

Add RHw

Weak increase in variance with increasing IN Little change from 3

Reject unclassified

1 SA

0.14

0.13

13.6

5.90

2824

.00040

2 SA with RHw

0.18

0.16

8.93

5.81

2694

.00032

3 All aerosol types: DUST, SALT, EC, OC, unclassified 4 3 with RHw

0.25

0.20

5.19

5.66

2467

.00037

0.33

0.28

6.34

5.38

2199

.000019

0.32

0.27

7.17

5.41

2251

.000015

0.28

0.24

7.53

5.52

2380

.000035

0.28

0.24

7.46

5.53

2386

.000038

0.21

0.18

6.93

5.75

2612

.00034

DUST, SALT, EC, OC, unclassified, RHw

5 4, unclassified out (RHw, DUST,

Try RHw after adding chemical information Add RHw after 2

Reject EC or OC?

SALT, EC, OC)

6 5, EC out RHw, DUST, SALT, OC

7 5, OC out RHw, DUST, SALT, EC

8 7, SALT out RHw, DUST, EC

Many small negatives, fewer large positives Improved, largest outliers near mean values Little change from 7

(i) Stop (ii) Try OC All p-values of predictors 0.5 𝜇𝜇𝜇𝜇 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, 𝑆𝑆𝐴𝐴>0.5 𝜇𝜇𝜇𝜇 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. 63


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 pvalue 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 𝑝𝑝 = 0.039. However, OC was an equally poor candidate, with 𝛽𝛽 = 0.46 ± 0.41, 𝑝𝑝 = 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.

64


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, p0.5𝜇𝜇𝜇𝜇 . Refer to Glossary for term definitions.

N=78 Intercept RHW DUST_SA05 SALT_SA05 EC_SA05

β

σβ

0.24 0.33 -0.38 0.59

0.11 0.11 0.14 0.13

b -105 1.14 0.58 -1.51 2.15

σb 49.5 0.52 0.20 0.55 0.47

t(78) -2.14 2.21 2.97 -2.72 4.59

p-value 0.036 0.030 0.004 0.0081 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.

66


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.

67


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 68


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 (𝐶𝐶𝑛𝑛± ) [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.

69


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 gasphase 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 70


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 (HRAMS, Aerodyne Inc.). The HR-AMS measures non-refractory aerosol mass by electron 71


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.

72


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. 73


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 L1

(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 74


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. 75


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 76


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.

77


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.

78


Assuming that the heat wave that triggered the measured SOA event was accompanied by elevated emissions of primary biological aerosol particles* (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. 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 *

Recall from Section 2.3.3 that PBAP are typically pollen, fungal spores, fragments of plant matter and

suspended bacteria.

79


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.

80


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 81


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 Xray 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 82


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.

83


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 iceforming 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), 48684879. 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. 84


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. 85


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. 86


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. 87


Niedermeier, D., et al. (2010), Heterogeneous freezing of droplets with immersed mineral dust particles – measurements and parameterization, Atmos. Chem. Phys., 10(8), 36013614. 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. 88


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. 89


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.

90


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.

91


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 FMPSmeasured size). Colours indicate surface area on a linear scale.

92


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.

93


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).

94


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.

95


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)).

96


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).

97


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+).

98


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+.

99


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.

•

variance 1.

• •

β : regression coefficient when all predictors are first normalized to mean zero and 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: Statistic Multiple R Multiple R² Adjusted R² F(1,81) p Std.Err. of Estimate

Summary Statistics; DV: IN (RegressionData.sta) Value 0.37953 0.14404 0.13347 13.63078 0.00040 5.90482

100


N=83 Intercept SA0.5-20µm

Regression Summary for Dependent Variable: IN (RegressionData.sta) R= .37952823 R²= .14404168 Adjusted R²= .13347429 F(1,81)=13.631 p

Development and Deployment of a Continuous-Flow ... - TSpace

Development and Deployment of a Continuous-Flow ... - TSpace

Suggest Documents

DEVELOPMENT AND CHARACTERIZATION OF A ... - TSpace

DEVELOPMENT AND CHARACTERIZATION OF A ... - TSpace

A GENERIC DEVELOPMENT AND DEPLOYMENT ...

Development and Deployment of an Ethnobotanical and

E-management development and deployment

Development, Characterization and Validation of ... - TSpace

Development, Characterization and Validation of ... - TSpace

Heterogeneously Catalyzed ContinuousFlow ... - Semantic Scholar

Development and Deployment of VoiceXML-Based Banking ...

Development, management, and deployment of ... - Semantic Scholar

Development and Deployment of VoiceXML ... - Semantic Scholar

Development and Deployment of VoiceXML-Based Banking ...

Fostering Development, Evaluation, and Deployment of ... - CiteSeerX

Development and deployment of a line of sight ... - Semantic Scholar

Comparing the development and deployment of ...

Development and Deployment of VoiceXML-Based Banking ...

Development of a knowledge scale about breast cancer and ... - TSpace

Development and Deployment of VoiceXML-Based Banking ...

Development and Deployment of a Large-Scale Flower Recognition ...

LNCS 2869 - The Real-Time Development and Deployment of a ...

Development, Validation and Deployment of a Real Time 30 ... - PLOS

The development and deployment of a field-based loop ... - Plos

Development and deployment of a new robotics toolbox ... - ARVC-UMH

A framework for development, teaching and deployment of inference ...