implications of ambient ammonia on aerosol acidity and ... · the rst project investigates di...

Implications of Ambient Ammonia on Aerosol Acidity andReactive Nitrogen Measurements

by

Phillip K. Gregoire

A thesis submitted in conformity with the requirementsfor the degree of Master of ScienceGraduate Department of Chemistry

University of Toronto

c© Copyright 2013 by Phillip K. Gregoire

Abstract

Implications of Ambient Ammonia on Aerosol Acidity and Reactive Nitrogen

Measurements

Phillip K. Gregoire

Master of Science

Graduate Department of Chemistry

University of Toronto

2013

This study describes two projects involving recent research on atmospheric ammonia.

The first project investigates differences in modelling techniques of aerosol acidity using

data from two recent field campaigns. Our results show that allowing or disallowing

gas-particle partitioning in the Extended Aerosol Inorganic Model (E-AIM) changed the

average modelled aerosol activity of H+ from one campaign by seven orders of magnitude

and that disallowing gas-particle partitioning may not accurately represent the chemical

state of the aerosols.

The second project investigates the interference of reduced nitrogen in commercial

chemiluminescent nitrogen oxide monitors with molybdenum oxide catalytic converters.

This phenomenon is strongly dependent on the temperature of the catalytic converter.

Our results show these instruments can have high conversion efficiencies of gaseous NH3

and NH4+

salts to NO at typical reported converter temperatures, but conversion efficiency

varies between instruments and may be the result of uncertainty in reported converter

temperature.

ii

Acknowledgements

Jennifer Murphy, my supervisor, has been inspirational in my development as an

academic and as a person. Her guidance has left me optimistic about the prospects of

bond between the physical sciences and the greater society.

Thanks also go to my committee, Jon Abbatt and Jamie Donaldson, who have both

instructed me and engaged with my academic interests. A special thanks to Jon Abbatt

for reviewing and commenting on my thesis.

Jeff Geddes, Milos Markovic, and Trevor Vandenboer facilitated my learning with

patience and enthusiasm that I hope I can share with others. Without Jeff’s instruction

on data processing and Milos’ and Trevor’s assistance with the AIM-IC, I cannot imagine

that this thesis would be possible in its current form.

Greg Wentworth and Alex Tevlin, my CONTACT-2012 colleagues, have truly put

time, energy, and passion into their work. Without their efforts, CONTACT-2012 would

not have existed. Carol Cheyne, Rachel Hems, and Geoff Stupple were also crucial to this

campaign.

I also thank IACPES for funding and creating a unique environment to experience

the research from my peers and be exposed to the social and political ramifications of

scientific research.

Finally, I want to thank Angela Hong for personally supporting me throughout my

research.

iii

Contents

List of Tables vi

List of Figures vii

1 Constraining Aerosol Acidity 1

1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1.1 Accumulation and coarse mode aerosols . . . . . . . . . . . . . . . 3

1.1.2 Methods of characterizing aerosol acidity . . . . . . . . . . . . . . 4

1.1.3 E-AIM: model description . . . . . . . . . . . . . . . . . . . . . . 8

1.1.4 E-AIM and instrumental uncertainty . . . . . . . . . . . . . . . . 11

1.2 Experimental methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

1.2.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

1.2.2 CONTACT-2012 instrumentation . . . . . . . . . . . . . . . . . . 14

1.2.3 Data analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

1.3 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

1.3.1 Particle-only E-AIM analysis for CONTACT-2012 . . . . . . . . . 22

1.3.2 Gas-particle partitioning E-AIM analysis for CONTACT-2012 . . 27

1.3.3 Ad hoc two mode separation from CalNex 2010 . . . . . . . . . . 35

1.3.4 24-hour integration analysis . . . . . . . . . . . . . . . . . . . . . 43

1.3.5 pH distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

1.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

1.5 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

1.6 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

2 Interference in Nitrogen Oxide Monitors 57

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

2.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

2.2.1 Analytical instrumentation . . . . . . . . . . . . . . . . . . . . . . 60

iv

2.2.2 Converter temperature ramping with ammonia gas . . . . . . . . 62

2.2.3 Converter temperature ramping with salt particles . . . . . . . . . 63

2.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

2.3.1 Ammonia gas conversion efficiency trends in commercial nitrogen

dioxide analyzers . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

2.3.2 Ammonia gas conversion efficiency trends in total reactive nitrogen

oxide analyzer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

2.3.3 Salt conversion efficiency trends in total reactive nitrogen oxide

analyzer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

2.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

2.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

2.6 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

2.7 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

A AIM-IC calibration and instrumental information 75

A.1 Gradient eluent chromatographic programs . . . . . . . . . . . . . . . . . 75

A.2 Non-linear organic acids and ammonium . . . . . . . . . . . . . . . . . . 76

A.3 Significant linear inorganic species . . . . . . . . . . . . . . . . . . . . . . 78

A.4 Limits of detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

B E-AIM correlation data 81

v

List of Tables

2.1 Summary of selected literature gaseous NH3 MoOx-CLD conversion effi-

ciencies and associated Tconv . . . . . . . . . . . . . . . . . . . . . . . . . 60

A.1 Cation IC gradient eluent program set points. Flow rate is set to 1.0

mL/min and column temperature is set to 30. . . . . . . . . . . . . . . . 75

A.2 Anion IC gradient eluent program set points. Flow rate is set to 1 mL/min

and column temperature is set to 30 . . . . . . . . . . . . . . . . . . . . 76

A.3 Summary of calibration information for Acetic Acid from the CONTACT-

2012 field campaign for the AIM-IC . . . . . . . . . . . . . . . . . . . . . 76

A.4 Summary of calibration information for Formic Acid from the CONTACT-


A.5 Summary of calibration information for Oxalic Acid from the CONTACT-


A.6 Summary of calibration information for NH4+

from the CONTACT-2012

field campaign for the AIM-IC . . . . . . . . . . . . . . . . . . . . . . . . 77

A.7 Summary of calibration information for Cl-

from the CONTACT-2012 field

campaign for the AIM-IC . . . . . . . . . . . . . . . . . . . . . . . . . . 78

A.8 Summary of calibration information for NO2-



A.9 Summary of calibration information for NO3-



A.10 Summary of calibration information for SO42-



A.11 Summary of calibration information for SO42-



B.1 CONTACT-2012 gas-particle partitioning correlation data . . . . . . . . 81

vi

List of Figures

1.1 Diagram of E-AIM gas-particle partitioning model . . . . . . . . . . . . . 9

1.2 Parameterized particle study . . . . . . . . . . . . . . . . . . . . . . . . . 13

1.3 Layout of campaign field site at CARE . . . . . . . . . . . . . . . . . . . 16

1.4 Photo of campaign field site . . . . . . . . . . . . . . . . . . . . . . . . . 17

1.5 Example calibration curve of particle-phase SO42-

. . . . . . . . . . . . . . 19

1.6 Example calibration curve of gas-phase acetic acid . . . . . . . . . . . . . 21

1.7 Case study of charge balance in CONTACT-2012 . . . . . . . . . . . . . 23

1.8 Particle-only study in situ pH trends in CONTACT-2012 . . . . . . . . . 24

1.9 pH and measured H+strong in CONTACT-2012 . . . . . . . . . . . . . . . . 25

1.10 Case study of particle-only pH in CONTACT-2012 . . . . . . . . . . . . 26

1.11 Time series of NH3(g) particle-only AIM-II predictions and measurements

during CONTACT-2012. . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

1.12 Relationship between in situ pH and the output H+strong . . . . . . . . . . 28

1.13 Diurnal in situ pH coloured for relative humidity in CONTACT-2012 with

gas-particle partitioning enabled . . . . . . . . . . . . . . . . . . . . . . . 29

1.14 Full time series of in situ pH coloured for relative humidity in CONTACT-

2012 with gas-particle partitioning enabled . . . . . . . . . . . . . . . . . 29

1.15 Case study of in situ pH estimated by disallowing and allowing gas-particle

partitioning and including organic acids with gas-particle partitition enabled

in E-AIM in CONTACT-2012 . . . . . . . . . . . . . . . . . . . . . . . . 30

1.16 Differences between partitioning allowed and disallowed in CONTACT-2012 31

1.17 Relationship between in situ pH and XH2O in the gas-particle partitioning

study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

1.18 Relationship between in situ pH and XH2O in the particle-only study . . 32

1.19 Partitioning enabled NH3 predictions . . . . . . . . . . . . . . . . . . . . 33

1.20 Case study of particle-only pH in CONTACT-2012 . . . . . . . . . . . . 35

1.21 AMS/PILS ratio in CalNex 2010 . . . . . . . . . . . . . . . . . . . . . . 37

vii

1.22 HNO3(g) predictions from optimized sea salt mode and accumulation mode

aerosols from CalNex 2010 . . . . . . . . . . . . . . . . . . . . . . . . . . 38

1.23 NH3(g) predictions from optimized sea salt mode and accumulation mode


1.24 HCl(g) predictions from optimized sea salt mode and accumulation mode


1.25 NO3(aq)-

predictions from optimized sea salt mode and accumulation mode

aerosols and input and modelled ratio of sea salt to accumulation mode

from CalNex 2010 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

1.26 NH4(aq)+



from CalNex 2010 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

1.27 Cl(aq)-



from CalNex 2010 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

1.28 Optimized pH values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

1.29 24-hour data integration methodologies from CONTACT-2012 . . . . . . 44

1.30 pH histogram of recent continental North American field campaigns . . . 45

2.1 Schematic of TSI NOx analyzers (adapted from TSI 42i Manual) . . . . . 61

2.2 Conversion efficiencies of NH3(g) in TSI NOx analyzers . . . . . . . . . . 64

2.3 NH3(g) profiles in AQD NOxy . . . . . . . . . . . . . . . . . . . . . . . . . 65

2.4 Conversion efficiencies of NH3(g) and HNO3(g) in AQD NOxy with simulta-

neous AIM-IC monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . 66

2.5 Conversion efficiencies of nitrogen-containing salts in AQD NOxy with

simultaneous AIM-IC monitoring . . . . . . . . . . . . . . . . . . . . . . 67

B.1 Optimized separation comparison between modelled and input NO3(aq)-

. 81

B.2 Optimized separation comparison between modelled and input NH4(aq)+

. 82

B.3 Optimized separation comparison between modelled and input Cl(aq)-

. . . 82

viii

Chapter 1

Constraining Aerosol Acidity

through Ambient Gaseous

Measurements

1.1 Introduction

Atmospheric aerosols are small particles that exist either as suspended solids or liquids in

the gas phase. Aqueous aerosols often contain high concentrations of acids and bases, which

define the acidity of the aerosols. The implications of aerosol acidity include impacts on

both inorganic and organic aerosol formation and effects on the cardio-pulmonary system

and respiration in humans [1–3]. The relationship between aerosol formation, modification

and acidity has become an important area of research for atmospheric chemists in the

past decade as studies have demonstrated new methodologies of characterizing aerosol

acidity and its effects [4, 5].

Aerosol pollution has been known to impact public health, but research has also

investigated whether aerosol acidity exacerbates this effect. Several studies have attempted

to correlate public health data with ambient aerosol acidity. For example, Ostro et al. [3]

noted a significant positive correlation between aerosol acidity and self-reported medication

use and coughing in asthmatics. Another study by Gwynn et al. [6] noted a significant

relationship between periods of elevated aerosol acidity and respiratory hospital admissions

and mortality. Work by Mao et al. [7] determined that inflammation of mucous membranes

in school children was correlated with aerosol acidity. Dockery et al. [2] established that

children living in cities with high particulate acidity were significantly more likely to report

episodes of bronchitis than children living in areas with less acidic aerosols. Raizenne

1

Chapter 1. Constraining Aerosol Acidity 2

et al. [8] reported that the same populations that were observed by Dockery et al. [2]

were more likely to have reduced lung function, growth, and development if individuals

were chronically exposed to high acidity aerosols. As aerosol pollution throughout the

world remains a serious public health concern, understanding how acidity impacts human

health will likely continue to be a relevant area of research.

Another area of aerosol acidity research has focused on halogen activation through

acid-catalyzed pathways in aerosols as an important source of reactive halogen radicals in

the marine boundary layer [9–11]. Brimblecombe and Clegg [12] demonstrated that as

trace acids (e.g. HNO3 and H2SO4) titrate the alkilinity of fresh sea salt particles, the

Cl-

is displaced and volatilized as HCl. HCl can react with gas-phase OH to produce

Cl radicals, and these radicals are important for ozone production and destruction [13].

Recent research by Roberts et al. [14] has demonstrated that increased in situ acidity of

sea salt aerosols may increase the uptake of ClNO2 and subsequent yield of Cl2 gas upon

reaction with particle-phase Cl-. Cl2 may be photolyzed to form Cl radicals. Mozurkewich

[15] and Vogt et al. [16] proposed that dehalogenation of Cl2 and two other important

ozone related reactants, Br2 and BrCl, could occur through acid-catalyzed reactions

with between Cl-/Br

-and HOBr/HOCl. Keene et al. [17] utilized an aerosol chemistry

model and found that modelled aerosols with pH values of 3 and 5.5 produced higher

concentrations of gaseous Br2 and Cl2 than aerosols at pH 8. In order to understand the

extent to which aerosol acidity affects the production of ozone and the general composition

of the atmosphere, it is important to have the ability to produce accurate estimates of

aerosol acidity.

Formation and growth of secondary organic aerosol (SOA) through particle-phase

acid-catalyzed reactions has also driven recent research in aerosol acidity. SOA arises from

the condensation of oxidized volatile organic compounds (VOCs) emitted from the surface

and can comprise significant fractions of total particulate atmospheric organic carbon

[1, 18]. According to Hoyle et al. [1], current mechanisms for describing the partitioning

of atmospheric organics to aerosols does not adequately describe SOA formation without

considering the effects of aerosol acidity on condensed phase formation. Acid-catalyzed

reversible and irreversible reactions can significantly affect the expected growth of particles

that would otherwise be solely defined by thermodynamic partitioning and condensation

[19].

Characterizing aerosol acidity is challenging, and there are differing approaches to

defining and estimating aerosol acidity depending on the purpose of the metric. This work

will describe current methods of determining aerosol acidity as well as test the validity of

different methods with ambient data collected in recent field campaigns.


1.1.1 Accumulation and coarse mode aerosols

Understanding the processes that generate aerosols is crucial to the characterization

of aerosol acidity. Between 25% and 75% of particulate matter less than 2.5 µm in

aerodynamic diameter (PM2.5) is comprised of inorganic salts which primarily consist

of Na+

, Cl-, SO4

2-, NO3

-, and NH4

+[20–23]. The remaining fractions are made up of

organics and crustal species. There are several different dominant particle size-composition

distribution regions (modes) in ambient aerosols including the ultrafine region, which

consists of particles < 0.01 µm in diameter (Dp), accumulation mode (0.01 µm < Dp <

2.5 µm), and coarse mode (Dp > 2.5 µm) [24]. Measurements of PM2.5, therefore, collect

ultrafine and accumulation mode particles and some fraction of the coarse mode due to

the overlap of the modes.

This work focuses on the inorganic component of coarse and accumulation mode

aerosols. Due to their small size, ultrafine aerosols have relatively low atmospheric mass

loadings compared to the larger mode particles. The inorganic component of accumulation

mode aerosols is dominated by SO42-

, NO3-, and NH4

+[20, 21, 25, 26]. Sea salt aerosols

(composed of mostly Na+

and Cl-) are a significant fraction of PM2.5 and make up the

majority of coarse mode aerosols in oceanic environments [27, 28].

Sea salt aerosols are produced through wave action and resulting sea spray [29–31]. As

the sea water droplets are lofted into the lower troposphere they reach equilibrium with

the ambient relative humidity and may decrease in size as the water partitions between

the gas and particle phases. Several modelling studies suggest that the seawater alkalinity

that is transferred to the aerosols is quickly titrated by atmospheric trace acids and results

in acidic aerosols [10, 11, 32].

Unlike sea salt aerosols, secondary aerosols in the accumulation mode (sometimes

referred to as sulphate aerosols) are produced through condensation of low volatility

molecular species in the atmosphere. H2SO4 gas is produced through oxidative reactions of

anthropogenic SO2 or biogenic reduced sulphur compounds like dimethylsulphide (DMS)

[33]. SO2 is produced primarily by coal power in northern mid-latitude regions [13, 33].

H2SO4 has a low vapour pressure and readily condenses to existing particles and may be

neutralized with gaseous NH3, which is produced primarily by agricultural emissions [13].

As the particles grow, they scavenge H2O and HNO3, an important atmospheric pollutant

that is produced in part through anthropogenic emissions [34]. Sulphate aerosols grow

throughout their lifetime but typically remain in the submicron Dp range [35].

In both particle types, the total water content of the particles is determined by the

ambient relative humidity, but also by the hygroscopicity of the constituent species [36].


1.1.2 Methods of characterizing aerosol acidity

The ability to determine aerosol acidity has evolved over the past 30 years. There are

multiple definitions of aerosol acidity that rely on different instrumental and analytical

techniques. Some techniques have been more fully characterized than others, but the

difficulty in creating universal definitions is that each of the techniques has different

advantages and often the disadvantages are poorly understood. Truly direct measurements

are challenging due to the small size and non-ideality of chemical species in solvated

aerosols. Indirect methods that rely on modelling suffer from uncertainty of whether

theoretical estimations accurately represent the physical state of the aerosol.

The in situ aerosol acidity is defined as the pH of the aqueous phase and is calculated

by equation 1.1, where AH+ is defined as the activity of H

+in the aqueous phase. This

definition most accurately represents the chemical state of the aerosols, but it is only

since the advent of modelling and direct measurement techniques that this definition has

been employed [4, 5]

in situ pH = −log(AH+) (1.1)

Indirect measurements

In ambient environments, the acidity must be determined from sampled particles. Early

studies relied on the charge balance of the particulate phase which describes a metric

that this work will refer to as “strong acidity” (H+strong) [17, 37–40]. H

+strong is defined in

equation 1.2 for aerosols by subtracting the sum of the positively charged species (e.g.

NH4+

and Na+

) from the sum of negatively charged species (e.g. SO42-

, NO3-, and Cl

-)

[20, 38, 41].

H+strong = 2 ∗ [SO2−

4 ] + [NO−3 ] + [Cl−]− [NH+4 ]− [Na+] (1.2)

SO42-

, NO3-, etc. are the molar concentrations of the inorganic species per unit volume

of air (nmol m−3). The individual species are often characterized by ion chromatography

(IC) and may also be characterized by other analytical methods including aerosol mass

spectrometry (AMS) [20, 42, 43].

Assuming that all acids are fully deprotonated and bases fully protonated, the value of

H+strong defines what concentration of H

+or OH

-is necessary to maintain charge neutrality.

H+strong>0 signifies excess H

+relative to OH

-in the particle and that the acids in the

particles are not fully neutralized by the bases (primarily NH3). If H+strong is zero or

negative, the acids are considered to be neutralized [13, 44, 45]. This technique has many


limitations and may not adequately describe the acidity of the particles. The technique

does not represent total concentration of free H+

in solvated aerosols, in part because

it ignores the equilibrium between H+

and HSO4-

at low pH. Even in the cases where

HSO4-

is not important, the strong acidity refers to the molar concentration of H+

per

unit volume of air, whereas the pH requires information of the activity of H+

in the

aqueous phase. Therefore, to use measurements of strong acidity to determine particle

pH, one must obtain information on the particle liquid water content and the activity

coefficient for H+

. Another problem is that there may be species contributing to the ion

charge balance that are not represented using the dominant acids and bases in equation

1.2. Work by Keene et al. [17] demonstrates that another limitation of the technique is

that the estimation of H+strong is highly reliant on homogeneity of the composition of the

population of particles. Keene et al. [17] further note that dynamic partitioning of volatile

species between sea salt and sulphate aerosols serves to buffer the H+strong between particles

of differing composition. As a result, bulk H+strong estimates may not effectively capture

differences in the chemistry of differing particle types. Despite these drawbacks, some

researchers still use this technique to characterize particle acidity, although it is more

common for this metric to be used when researchers probe broad atmospheric implications

or public health effects, rather than specific chemical pathways [6, 7, 46].

Another method of estimating the strong acidity of aerosols is to extract particle filters

in small volumes of water and perform acid or base titrations to determine the excess

H+

or OH-

in solution [37, 47, 48]. This technique also provides a concentration of H+

as

number of moles per volume of air sampled. A similar technique to this method utilizes a

pH probe rather than acid-base titration to estimate the H+

concentration [49–52]. The

U.S. Environmental Protection Agency (EPA) considers extraction of filtered particles

into 1 mL of water followed by pH probe analysis to be the standardized method for

estimating aerosol H+strong [53]. These techniques are significantly limited by the difference

in equilibrium concentrations of species in diluted particles from their initial concentrated

state. Keene [54] discusses the potential for orders of magnitude difference between the

concentration of free H+

as a result of the buffering effect of SO42-

and HSO4-

that changes

with dilution. This technique also neglects the activity of the constituents which would

be different when the particles are diluted into solutions many orders of magnitude higher

in water volume than the particles would naturally contain.

Another indirect estimation of acidity is defined as the degree of stoichiometric

neutralization of species in the particle phase, which is the ratio of measured NH4+

molar

concentrations (NH4 meas+

) to the molar concentration of NH4+

needed to fully neutralize

the acidic species in the aerosol (NH4 neu+

) [20]. While equation 1.2 relies on the difference


between anions and cations, this approach uses a ratio, which may be more robust

when measurements are subject to systematic errors, or are close to the detection limit.

Equation 1.3 shows the relationship between acids and bases. The inorganic species on

the right side of the equation were again defined as their respective molar concentrations

(per volume of air).

NH4+meas

NH4+neu

=[NH+

4 ]

2 ∗ [SO2−4 ] + [NO−3 ] + [Cl−]

(1.3)

Direct measurements

Recent work by Li and Jang [5] characterizes a novel method of evaluating in situ aerosol

acidity using colorimetry reflectance UV-visible (C-RUV) spectroscopy. The technique

consists of a sampling pump connected to an indicator-dyed filter. Ambient air is pulled

through the filter and a UV-visible spectrum is taken of the filter. Acidic particles

react with the indicator and produce a signal proportional to the H+

concentration

and mass loading of inorganic particles and inversely proportional to relative humidity.

This technique is advantageous over extraction-based filtration methods because it offers

shortened analysis times, although low mass loadings require longer integration times. It

also is the most direct method available for measuring acidity as it requires no dilution.

An intercomparison study between modelled predictions and results from C-RUV showed

very close correlation across a range of relative humidities. The C-RUV technique is reliant

on modelling to account for water content of the particles and has not been thoroughly

assessed in aerosols with low H+strong. This is a promising new technique, although it

currently is not capable of online sampling or complete independence from modelled

results.

Modelling

In order to probe particle-phase processes and estimate in situ pH, research has focused on

modelling as a methodology for describing the thermodynamic chemical state of aerosols.

Works published by Keene et al. [17] and Keene and Savoie [55] describe an early

methodology which used modelling to estimate aerosol acidity. Keene et al. [17] discusses

using a box model called the Model of Chemistry Considering Aerosols (MOCCA) for

marine boundary layer aerosols. In the study, the model was run five times under different

input conditions with arbitrary steady-state atmospheric concentrations of ambient species.

While this method is not useful for the application of sampled particle data, it did establish

the possibility of using thermodynamically derived data to estimate aerosol pH. Keene


et al. [17] then developed a model for characterizing aerosol pH using measured gas

and particle-phase concentrations. This system relied on the gas-particle partitioning

equilibria of volatile species. The authors rationalized that, given Henry’s law constants

(KH) and acid-base dissociation constants (Kd) of the species in solution, an expression

could be derived to calculate the pH required for a certain particle-phase concentration of

a volatile species. The authors derived aerosol pH by the set of equations 1.4.

AHCl(aq) = KHHCl∗ [HCl(g)] (1.4a)

AHCl(aq) =AH+

(aq)∗ ACl−

(aq)

KaHCl

(1.4b)

ACl−(aq)

=[Cl−(aq)] ∗ γCl−

LWC(1.4c)

By rearranging the above equations, the following AH+ may be calculated

AH+(aq)

=KaHCl

∗KHHCl∗ [HCl(g)]

[Cl−(aq)

]∗γCl−

LWC

(1.4d)

Equation 1.4a relates the atmospheric concentration of gaseous HCl ([HCl(g)]) to the

aqueous activity of HCl (AHCl(aq)) with the Henry’s law constant (KHHCl). Equation

1.4b shows the relationship between AHCl(aq) and the activities of H+(aq) and Cl−(aq) with

the acid dissociation constant (KaHCl). Finally equation 1.4c describes the relationship

between ACl−(aq)

, the activity coefficient (γCl−), measured Cl−(aq) concentration ([Cl−(aq)],

in mol m−3) and the liquid water content (LWC). Liquid water content was estimated

by taking the volume difference between median measured “wet” aerodynamic particle

diameter and the theoretical diameter of a spherical dry salt particle with an assumed

density. The in situ pH was then calculated from AH+(aq)

using equation 1.1. A similar

derivation can be made for HNO3(g)/NO3(aq)-

and other volatile constituents of particles

that participate in gas-particle partitioning.

Other, more complex, modelling methodologies also rely on measurements of particulate

and sometimes gaseous species; the models ISORROPIA, SCAPE2 and E-AIM have all

been used to produce estimations of aerosol acidity [4, 36, 56, 57]. Work by Yao et al.

[4] compares results from the three previously mentioned thermodynamic models using

ambient data. The first method uses E-AIM to determine in situ pH using only particle-

phase thermodynamics without considering gas-particle partitioning. The second approach

was similar to Keene and Savoie [55] and used the relative measured concentrations of


particulate and gaseous NH4+

/NH3, NO3-/HNO3, and Cl

-/HCl to calculate the required

pH for a given ratio to occur. The third approach used the thermodynamic gas-particle

partitioning models, ISORROPIA and SCAPE2, which rely on total gas and particle

concentrations of species which are then repartitioned according to Henry’s law and

dissociation constants. The study concluded that E-AIM produced superior results to

those of the other two approaches, since E-AIM alone was able to reproduce the measured

H+strong [4].

Recent research involving aerosol acidity has focused on using E-AIM as the preferred

estimation technique [4, 20, 44, 45, 58–60]. Input to E-AIM can be restricted to particle

composition only (i.e. the approach used in Yao et al. [4]) or allow both gas and particle

constituents and treat the particles as being in dynamic equilibrium with the gas-phase.

Many current studies have followed the recommendation of Yao et al. [4] and model

aerosol acidity with gas-particle partitioning disabled in E-AIM [20, 44, 45, 58–60]. The

validity of this methodology has not been thoroughly established, and this study will

examine this recommendation with modelled results from different regimes of the E-AIM

model compared with measured data.

In this study, E-AIM was implemented with data taken from recent field campaigns

in order to understand the biases and effects of uncertainty on the different permutations

of the model’s operating parameters. The goal of this research is to examine the quality

of the predictive power of the model with regards to acidity. E-AIM was chosen over

other models because of its widespread usage, flexibility, and global free energy solution

rather than the computationally less expensive, assumption-based methodologies of other

models.

1.1.3 E-AIM: model description

The E-AIM model finds a global minimum free energy state that is defined by the

chemical state of the system. The system involves the dissolution of solid salts, gas-

particle partitioning, acid-base dissociations, and partitioning between the aqueous particle

phase and an organic particle phase. The system may be modified to exclude certain

equilibria depending on assumptions made regarding the nature of the chemical state. In

our study, all modelling analysis of aerosol acidity was performed with E-AIM. The model

has several versions and in this study both the second (AIM-II) and fourth (AIM-IV)

were used [36, 61, 62]. AIM-II processes user defined organic and standardized inorganic

ions including particle-phase H+

, NH4+

, NO3-, and SO4

2-and gaseous organic acids and

inorganic NH3 and HNO3. AIM-IV includes particulate Na+

, Cl-, and gaseous HCl in


addition to the aforementioned species in AIM-II. Relative humidity and temperature are

also included as inputs in both model versions, although AIM-IV is limited to inputs with

high relative humidity (>60%) when all available input species are utilized. The model

then partitions the species using the aformentioned thermodynamic equilibria to produce

moles, mole fractions, and activity coefficients of particulate and gaseous species. The

model also estimates liquid water content based on the hygroscopicity of the particles

and finds activities of all chemical species, including dissociated and undissociated acids

and bases. The in situ pH is estimated from the outputs of the model through equation

1.5. [20, 36].

pH = −log(fH+(aq) ∗ xH

+(aq)) (1.5)

fH+(aq) is the mole fraction-based activity coefficient of aqueous H+, xH+

(aq) is the

mole fraction of aqueous H+ in the particle as predicted by E-AIM. A simple diagram

of the gas-particle partitioning framework is depicted in Fig. 1.1; aqueous/hydrophobic

phase interaction and solid formation are not depicted.

NH3 NH4

+

Aqueous Particle

HNO3 HNO3

HCl HCl

Gas Phase

H2SO4

HSO4- SO4

2-

NH3

Cl-

NO3-

H2SO4

RH

RH (organic acid)

KH

KH KH

KH

KH

Ka

Ka

Ka Ka

Kb

R- Ka

Figure 1.1: Diagram of E-AIM gas-particle partitioning model

All models currently rely on a number of assumptions that vary slightly between

each. In E-AIM there are three major assumptions that will be addressed below. These


assumptions highlight the difficulty with modelling aerosol acidity. The first assumption

is that aerosols are internally mixed, which means that composition among the analyzed

particles is uniform. The second assumption is that the particles are at or near equilibrium

with surrounding ambient air. Finally, in our use of the model, the particles were assumed

to be deliquesced (i.e. the aerosols were present as aqueous solutions). An organic particle

phase was not incorporated into the model as the data required for such an analysis was

beyond the scope of the available instrumentation. However, ignoring the organic phase

could impact the hygroscopicity and equilibrium relationships of particulate constituents.

Assumptions relating to size, surface tension, particle phases and others were not addressed

in this work, but are considerations which are ongoing areas of research.

The first assumption is that the aerosols are internally, rather than externally mixed.

Internal mixing implies that the composition of the particles is homogeneous whereas

external mixing indicates that the particles are not uniform in composition. This as-

sumption will be discussed in further detail later in this work. Essentially, E-AIM takes

bulk concentration inputs and repartitions the species to produce uniform composition

particles. If particles are known to not be uniform, segregation of particle types must

occur before the data are input into the model. This assumption is of interest because

ambient particle populations are often externally mixed and research has shown that

particles of differing composition tend to become more uniform as they age due to the

equilibrium dynamics among particles through the gas-phase [10, 55].

Work by Meng and Seinfeld [63] discusses the assumption of equilibration time as it

relates to marine and continental aerosols. Submicron aerosols equilibrate very quickly

but larger aerosols may never reach equilibrium and rather reside in nonequilibrium

states [63]. Dassios and Pandis [64] estimate that the equilibration time for submicron

NH4NO3 particles to be on the order of minutes. SO4(aq)2-

and H2SO4(g) are less important

for gas-particle equilibrium considerations than the TNO3 (HNO3(g) and NO-3(aq)) and

TNHx (NH3(g) and NH+4(aq)) because the vapour pressure of H2SO4(g) is very low and

does not partition significantly into the gas phase. Similarly, Ca2+

and Na+

do not

partition significantly into the gas phase. Followup work by Cruz et al. [65] observed a

relatively small decrease in the rate of equilibration due to organic films on dry, inorganic

aerosols. However, work by Shiraiwa et al. [66] suggests that organic coatings may have

a more significant effect than previously thought. Sun and Wexler [67] established that

the equilibration time for H+

is short enough that in systems that are at near acid-

base neutrality, the particulate H+

concentration can be assumed to be in equilibrium

with respect to ambient gaseous species. Work by Zhang et al. [68], determined that the

assumption of thermodynamic equilibrium was met reasonably well over 5 minute sampling


intervals using comparisons of ISORROPIA and experimental data. However, Yao et al.

[4] determined that based on comparisons between the predicted gas concentrations from

the particle-only regime of E-AIM and measured results, the system was not always at

equilibrium.

Finally, the model requires users to decide whether or not to assume that the aerosols

are deliquesced. The deliquescence relative humidity (DRH) is the ambient relative

humidity required for a given solid salt to transition from the solid phase to the aqueous

phase. In cases where the ambient relative humidity is higher than the DRH, the model

assumes that a given species will be deliquesced. However, in cases where the DRH is

higher than the ambient RH, the model provides users with two options. The first option

is to allow the model to leave the salt as a solid while the other allows the particle to exist

in a metastable deliquesced state. The latter option makes the implicit assumption that

the ambient particle had at one point been deliquesced, and that due to the hysteresis

of solubilized particles, the particle remained in the aqueous phase even after the RH

dropped below the DRH. The efflorescence RH (ERH), the RH at which a deliquesced salt

returns to the solid phase, is much lower than the DRH [69]. Furthermore, theoretical and

experimental evidence shows that the DRH of salt mixtures is lower than the DRH of any

of the constituent salts [23, 70, 71]. It is often operationally impossible to determine if the

particles are deliquesced and researchers must acknowledge the limitation of the model

with regards to this assumption. In this work, particles were assumed to be deliquesced

due to the high ambient relative humidities taken throughout the datasets mentioned in

the following section.

The most significant problem with modelling aerosol acidity is that there is no certainty

as to whether the estimates truly correspond with the actual state of the system. If any

of the above assumptions are not met, the model will produce unphysical results, but it

may not be possible to identify which of the assumptions has not be met.

1.1.4 E-AIM and instrumental uncertainty

When gas-particle partitioning is disallowed in E-AIM (called the particle-only mode

of E-AIM), the estimated in situ pH is driven by the balance between acids and bases

in the bulk composition measurements. Particles with excess acid will produce low pH

values, while particles with excess base will produce high pH values. However, since

ambient aerosols are often at or near the equivalence point (i.e. H+strong=0), relatively

small instrumental errors in the measurements of indivual anions and cations can result

in pH values that lie on either side of the pH titration curve.


Although the particle-only mode of E-AIM has been widely used for understanding

aerosol acidity, work by Zhou et al. [44] illustrates the limitations of current acidity

estimations using this technique. Zhou et al. [44] collected hourly measurements of

particle-phase soluble inorganics. Approximately one third of their dataset showed

negative H+strong (i.e. excess NH4

+), and these datapoints were rejected for analysis due to

the authors’ assertion that the model was unable to produce valid output data. Work

by Behera et al. [45] utilized a similar rationale to Zhou et al. [44] for discarding 2/3 of

their data set before processing in E-AIM. Behera et al. [45] argued that when H+strong<0,

the aerosols may be considered neutralized and not considered acidic. When there is an

excess of positively charged species in solution (i.e. H+strong<0), E-AIM tends to produce

results that suggest the particle phase has high concentrations of neutral NH3 in solution.

However, based on Henry’s law, the amount of uncharged NH3 predicted by the model

would not be expected if the NH3 were allowed to partition into the gas phase. Datapoints

with excess acids present a less significant problem for the model because the vapour

pressure of undissociated sulphuric acid is low and the sulphate/bisulphate system acts

as a buffer for free H+

. The average particle-only E-AIM in situ pH of non-neutralized

aerosols in numerous studies has been predicted to be below 2.5 [44, 45, 58, 72]. Since

these studies do not account for neutralized particle data, these results may not represent

the true acidity of the particles due to the limitations of the particle-only regime of

E-AIM.

One reason why researchers might observe aerosols where H+strong is less than zero

is due to instrumental uncertainty. To illustrate the significance of uncertainty that

arises in particle-only E-AIM analysis, a representative parameterized particulate system

was formulated and processed in the particle-only version of E-AIM. In this system the

balance of NH4+

and the sum of NO3-

and SO42-

was allowed to vary from an H+

deficit

to surplus. The modeled activity coefficient and mole fraction of H+

were input into

equation 1.5 to estimate pH. The results of the study are displayed as pH versus H+strong

in Fig. 1.2. As H+strong approaches zero, indicating neutralization, the pH passes through

the titration equivalence point. An absolute concentration difference between the sum of

the acids and bases of 1.0 neq m−3 could result in a difference of free H+

concentration of

approximately 8 orders of magnitude. Given that studies often find ambient aerosols near

charge neutrality, uncertainty in measurements could be strongly affecting the in situ pH

estimates from the particle-only version of E-AIM [20, 44, 45].

This work will endeavor to elucidate the problems with the predictive ability of the

model and provide evidence for why certain modelling methodologies are susceptible to

producing model outputs that are not representative of the measurements. This work will


10

8

6

4

pH

-2 -1 0 1 2

H+

strong (neq m-3

)

Figure 1.2: Parameterized particle with constant SO42-

(2.5 neq mol−1) and NO3-

(5.0 neqmol−1) with linearly increasing NH4

+(8.0-12.0 neq mol−1)

.

also examine the assumption that the particle-only version of E-AIM is the most accurate

representation of the particle phase by investigating whether gas-particle partitioning can

improve the in situ pH estimations. Several studies have suggested that the pH of aerosols

is highly buffered by the gas-particle partitioning effects of expulsion of neutralized volatile

species, [10, 55]. This study will attempt to explore the validity of modelling regimes that

do or do not support this hypothesis.

1.2 Experimental methods

1.2.1 Datasets

The first dataset used in this work was collected during the Characterizing Ontario

Nitrogen Transport and Chemical Transformation (CONTACT-2012) field campaign and

is described in detail in the following subsection of this chapter. The site was located

at the Centre for Atmospheric Research Experiments (CARE, 4413’51”N 7946’58”W)


which is an Environment Canada research site near Egbert, ON.

The second dataset used in this work was collected during the California Research at the

Nexus of Air Quality and Climate Change (CalNex 2010) at the Pasadena, CA, ground site

(348’16”N, 1187’34”W). A compilation of research papers relating to this study may be

found at http://www.esrl.noaa.gov/csd/projects/calnex/papers/. For the compiled data

in this work, a quantum cascade tunable infrared laser diode absorbance spectrometer

(QC-TILDAS) provided ambient NH3 mixing ratios, particle into liquid sampler ion

chromatography (PILS-IC) provided PM2.5 mass loadings of inorganic anions and cations,

AMS provided inorganic anion and cation mass loadings of submicron particulate matter,

and negative ion proton transfer chemical ionization mass spectrometry (NI-PT-CIMS)

provided mixing ratios of gaseous inorganic acids. Meteorological data, including relative

humidity and temperature, was also provided from weather station monitoring. Although

certain instruments were run extensively for weeks during the campaign, only 78 hours of

semi-continuous hourly-averaged data were processed for this study due to instrumental

downtime.

1.2.2 CONTACT-2012 instrumentation

The CONTACT-2012 field campaign relied on instrumentation from the Murphy Research

Group at the University of Toronto, a mobile atmospheric measurement laboratory (the

Mobile Analysis of ParticuLates in the Environment (MAPLE)) from the Southern Ontario

Centre for Atmospheric Aerosol Research (SOCAAR) for transportation and instrumental

housing, and publicly available meteorological data provided by Environment Canada.

The instrumentation included ambient ion monitor ion chromatography (AIM-IC), a

multiple uniform orifice deposition impactor (MOUDI), a sonic anemometer, and QC-

TILDAS, but only AIM-IC data will be discussed in this work. Data were collected from

July 19th, 2012 until October 2nd, 2012.

AIM-IC is a sample analysis and collection system developed by University Research

Glassware Corporation (URG Corp, Chapel Hill, NC). The AIM-IC Model 9000D simul-

taneously measures water-soluble particulate matter and trace gases with hourly time

resolution. The operational details of the instrument are described below and closely

follow those described by Markovic et al. [73]. The particulate species quantified during

the campaign were SO42-

, NO3-, NO2

-, Cl

-, PO4

3-, Li

+, Na

+, NH4

+, K

+, Mg

2+, Ca

2+, acetate,

formate, and oxalate. The trace gases were SO2, HNO3, HONO, HCl, H3PO4, NH3, acetic

acid, formic acid, and oxalic acid.

The modified AIM-IC 9000D in this study consists of three separate components: inlet


system, control box, and ion chromatographs. The inlet draws ambient air at 3.0 L min−1

through an impactor with a size cutoff of 2.5 µm (PM2.5). All debris and particulate

matter larger than PM2.5 is deposited on the impactor frit which was replaced periodically

throughout the campaign to minimize offgassing of volatile constituents that had impacted

on the frit. Prior to the impactor, there is a raincap which blocks precipitation from

entering the inlet. Air is then drawn into a continually renewed parallel plate wet denuder

(PPWD) which dissolves gaseous acids and bases and oxidizes SO2 to SO2−4 . The remaining

flow, which contains non-soluble gases and the atmospheric particles, enters the denuder

into the particle super saturation chamber (PSSC) and the particles hygroscopically grow

and are collected in a cyclone. The remaining air is drawn through a diaphragm pump

and exits the system as exhaust.

The dissolved species from the denuder and PSSC are drawn through 19.2 m of Teflon R©

(FEP) tubing to the control box by two respective sets of two 5 mL gastight syringes

(Hamilton Co, Reno, NV). The liquid from the denuder and PSSC first passes through

two respective 6-port injection valves (Rheodyne LLC, Rhonert Park, CA) before entering

the gastight syringes. The syringes pull liquid for 55 minutes per cycle. The two valves

switch configuration when the syringes are filled to allow injection to four separate 6-port

injection valves (i.e. one valve for each syringe). Two of these valves allow the flow to be

loaded onto two concentrator columns for the gas (denuder) channel, and the other two

valves allow the flow to be loaded onto two concentrator columns for the particle (PSSC)

channel. The injections of the gastight syringes into the four concentrator columns takes

5 minutes; the total collection and injection cycle of the gastight syringes takes one hour.

The system contains one IC which is set up to detect anionic samples and the other for

cationic samples. The particle analytes in the concentrator columns are injected onto the

chromatographic columns of the anion and cation ICs at the beginning of each hour. The

particle samples run for 26 minutes and at 30 minutes after the hour, the gas samples are

injected onto the chromatographic columns and also run for 26 minutes. The total cycle

for the ICs to run both gas and particle samples is one hour to maintain the synchronicity

with the control box gastight syringe cycle.

The denuder solution used in the field campaign contains 2 mM H2O2 in deionized

water (>18 MΩ, DIW, Barnstead, Dubuque, IA and ELGA, High Wycombe, UK), which

was optimized by finding the lowest concentration of H2O2 to oxidize SO2 to SO42-

for

reasonable ambient concentrations.

In this campaign, the PPWD utilized two parallel nylon denuder membranes. Previous

research performed by Markovic et al. [73] demonstrated the dramatic improvement in

response time of nylon membranes to NH3 compared with cellulose membranes. Nylon


QC-TILDAS and Sonic

Anemometer

MAPLE

AIM-IC Inlet

MOUDI

27.5 m

18 m 6.5 m

14.5 m

Figure 1.3: Layout of campaign field site at CARE

membranes also have longer lifetimes and do not need the frequent replacements that

plagued cellulose membranes. The denuder configuration was implemented such that the

denuder solution entered the bottom of the first membrane plate, rose to the top, passed

through a short piece of tubing to the bottom of the second plate, and finally rose to the

top of the second membrane plate and exited the denuder toward the control box. The

denuder setup is an example of countercurrent exchange that is designed to maximize the

diffusion gradient as air flows through the system.

While the PC, control box, and ICs were installed in MAPLE, the inlet was mounted

on a 3 m tower located 6.5 m from MAPLE. The entrance to the inlet was located 2.75

m above ground. The position of the inlet with relation to other instrumental inlets and

MAPLE is shown below in Fig. 1.3. A photo of the AIM-IC inlet and MAPLE is included

in Fig. 1.4.


Figure 1.4: Photo of campaign field site at CARE. The inlet is housed in the aluminumbox in the foreground and MAPLE is in the background


The two IC systems used for the AIM-IC were Dionex ICS-2000 (Dionex Inc. Sunnyvale,

CA). Both ICs interface with a PC with the Dionex Chromeleon R© 6.8 software that allows

software commands to be transmitted to the ICs and live recording of the chromatographic

runs. Appendix A lists the specific chromatographic componentry used throughout

CONTACT-2012.

The flow rate, column temperature, and gradient eluent programming of the IC

systems were optimized prior to deployment during the campaign. The gradient eluent

program for both systems is tabulated in Table A.1 and Table A.2. The maximum eluent

concentration and eluent flow rate defined by the eluent program is also necessary to

choose the suppressor current. According to the SRS-300 Dionex manual, the suppressor

current is calculated in equation 1.6.

Current (mA) = flow rate (mL min−1) ∗max. eluent conc. (mN) ∗ suppressor factor(1.6)

The suppressor factor for the ASRS-300=2.47 and CSRS-300=2.94

The meteorological data was provided by the Environment Canada, National Climate

Data and Information Archive which is found at climate.weatheroffice.gc.ca. The temper-

ature, relative humidity, and atmospheric pressure are recorded as hourly averages and

posted shortly after acquisition. Since the research site for CONTACT-2012 was at an

Environment Canada research facility, the climate station was located in the immediate

vicinity (<500 m) of the chemical instrumentation.

1.2.3 Data analysis

The methodology for calibrations for AIM-IC are discussed in Markovic et al. [73] and

VandenBoer et al. [74]. Calibrations were run three times on the AIM-IC during the

course of the campaign. The standards were prepared by serial dilution from the inorganic

Dionex 6-cation IC standard and 7-anion IC standard (Sigma-Aldrich, St. Louis, MO).

The 6-cation standard contains Li+, Na+, NH+4 , K+, Mg2+, and Ca2+ while the 7-anion

standard contains F−, Cl−, NO−2 , Br−, NO−3 , SO2−4 , and PO3−

4 (in order of chromatographic

elution). Organic standards were not included in the multiple inorganic standards and were

instead produced from single standards (Sigma-Aldrich, St. Louis, MO) of each. Acetic

acid/acetate, formic acid/formate, and oxalic acid/oxalate were detected and calibrated

for at the same intervals as the inorganic standards. IC dilutions were carefully performed

using powder-free latex gloves and micropipettes with disposable tips. The standard vials

and volumetric flasks were all made from ETFE to reduce risk of contamination. Before


12

10

8

6

4

2

0

Pea

k A

rea

(µS

min

)

14x10-9121086420

Amount Injected (mol)

Coefficient values ± one standard deviation

slope =7.87*108 ± 1.1*10

7

Figure 1.5: Example calibration curve of particle-phase SO42-

usage, the vials and flasks were thoroughly rinsed eight times and dried by mechanical

action (tapping). All steps required to reduce contamination were regularly performed

including working on clean surfaces, ensuring the DIW was of high purity (>18 MΩ), and

that all injection syringes and transfer pipettes were thoroughly cleaned.

The ICs were calibrated with the following offline methodology. The lines from the

control box sample syringes were disconnected from the 6-port injection valves. The

standards and blanks were injected into the vacated ports with 5 mL gastight syringes

which were identical to the control box sample syringes. 5 mL of each standard was

injected at the rate of 1 mL min−1 in order to mimic the conditions of the hourly sample

syringe injections. The chromatographic program that was used for the online AIM-IC

sampling was used for offline calibrations. Blanks were injected prior to the standards

and after the standards in order to ensure minimal background interference. The peak

areas of the standards were quantified by manual integration in Chromeleon R©.

The calibration slopes for most species were linear. The slopes of the linear calibration

curves were determined using linear regression in IgorPro 6. Weak acids and bases tend

to have non-linear calibration slopes as a result of acid-base equilibria in the conductivity

detector, as described by VandenBoer et al. [74]. Calibration for non-linear analytes is

discussed below. The linear analytes were Li+

, Na+

, K+

, Mg2+

, Ca2+

, F-, Cl

-, NO2

-, Br

-,

NO3-, SO4

2-, and PO4

3-. An example of the offline calibration curve from SO4

2-is plotted in

Fig 1.5.


Since the online system may change the background signal detected by the ICs, an

online background was taken by overflowing zero air (Praxair, Mississauga, ON) directly

into the denuder for 24 hours while instrument was in online operation. The average of

this measurement was taken as the online background since it accounted for impurities

and interferences from the denuder membrane, denuder solution, or other impurities in

the lines or instrumentation. The average peak area from the online background was

subtracted from each ambient data point before using the calibration slope to calculate the

moles of analyte. The limit of detection was calculated by using the standard deviation

of background measurement with equation 1.7. The limits of detection for all quantified

species are tabulated in Table A.11. The collection efficiency of the online system must

also be taken into account when converting peak area values into concentrations. Cellulose

membranes are known to have collection efficiencies at or around 90%, and preliminary

work from the our research group and URG [75] indicates that nylon membranes also

have very high collection efficiency (>99% for SO2), so an assumption of 100% was used

in this work [73].

Limit of Detection = 3 ∗ σbackground (1.7)

While the calculation of concentration from peak area was well established for the linear

species, determination of concentration of weak acids and bases was more complicated.

Work by VandenBoer et al. [74] established a methodology for quantifying weak acids and

bases which was implemented during CONTACT-2012. While it is possible to fit a curve

to the calibration data with exponential functions supplied by IgorPro, VandenBoer et al.

[74] showed that, based on the acid dissociation constants of the species, the physical

process is more accurately represented with a quadratic function and is briefly discussed

below.

The AIM-IC conductivity detector is only able to detect ions, but with weak acids

and bases, the protonated acids or deprotonated bases are invisible to the detector. After

the eluent is removed by the suppressor, weak acids and bases are often concentrated

enough to prevent either full deprotonation (for acids) or protonation (for bases) which

results in neutralized molecules. Equation 1.8a shows the derived relationship between

ntotal analyte (the sum of the number of moles of detected ion and undetected uncharged

species) and nanalyte ion (the detected analyte ion). In equation 1.8a, v is a scalar to

convert between nanalyte ion and the detected peak area. A = KD

2and B = KD, where KD

is the acid or base dissociation constant. volume is the theoretical volume of solution that

would result in a given peak area of a homogeneous solution of analyte in the detector.

Equation 1.8b simplifies the equation for practical usage with IgorPro’s curve fitting


12

10

8

6

4

2

0

Pea

k A

rea

(µS

min

)

200x10-9150100500

Amount Injected (mol)

Coefficient values ± one standard deviationA'=2.37 ± 0.82

B'=7.03*108 ± 9.6*10

7

Figure 1.6: Example calibration curve of gas-phase acetic acid

application. In equation 1.8b A′ is the product of A and v while B′ is the product of B, v,

and the reciprocal of volume. Equation 1.8b was used for organic acid calibrations while

equation 1.8a was used for the ammonium calibrations. Equation 1.8a is advantageous

in instances where a single estimated volume can be constrained as a constant between

multiple calibrations and result in reasonable curve fitting, because it contains a single

variable rather than two in equation 1.8b. An example of the organic acid calibrations is

shown is Fig. 1.6.

Peak area = v ∗ nanalyte ion = v ∗[(−A) +

√A2 +B ∗ ntotal analyte

volume

](1.8a)

Peak area = (−A′) +√A′2 +B′ ∗ ntotal analyte (1.8b)

One difficulty with non-linear calibrations that may increase the uncertainty of con-

centration values is caused by differences between the calibration methodology and actual

ambient sampling. The methodology assumes that the background signal of the standards

(i.e. the DIW signal) is the same as the background signal of the ambient samples taken

with zero air flowing into the inlet. However, comparing the background signals of DIW

injections during calibration and zero-air samples for the ambient background reveals

substantial differences. Non-linear calibrations are complicated since the subtraction of a

single background value from each of the standard points on the curve improperly implies

that the background signal is constant at all concentrations. For the purposes of this


study, since both background signals were relatively small for NH3 and NH4+

, the impact

of the non-linear background effect was minimal. The organic acids had significantly

higher zero air background peak areas by about one order of magnitude, and thus the

calibrations may be less reliable than those of other species.

Three calibrations throughout the campaign served to constrain the degradation of the

concentrators over time. Unlike simple sample loops, concentrators have a limited number

of binding sites which degrade over time and thus change the ability of the concentrators

to retain analytes. Between each calibration run, the percent difference between slopes

of linear species and the differences in curve shape were evaluated to establish if the

concentrators were failing. After the second calibration, the anion gas concentrator was

replaced and calibrated prior to usage for the remainder of the campaign. In order to

account for the variation in calibration curves, the calibration curve data of the linear

species and NH3 were linearly interpolated between calibration runs and concentration

values were calculated using the interpolated calibrations. The concentration values for

non-linear species were calculated using each of the calibration curves and then weighting

the concentration values depending on the sample’s position between two calibration runs.

For example, an online concentration value taken a quarter of the way between the first

and second calibration would be composed of 75% of the concentration calculated from the

first calibration and 25% of the concentration calculated from the second calibration. The

difference between the two interpolating methods was necessitated by the way non-linear

regression program allowed A′ and B′ to vary.

The overall relative uncertainty for all species in this analysis was estimated as 15%

and the limit of detection for each respective quantified species. Markovic et al. [73]

and Zhou et al. [44] estimated the relative uncertainty of AIM-IC istrumentation to

be approximately 10%, but work by this research group indicates that the uncertainty

may be somewhat higher. Further work is needed to ensure the best represenatation of

instrumental uncertainty with this technique.

A summary of calibration information, including limits of detection, is provided in

Appendix A.

1.3 Results and discussion

1.3.1 Particle-only E-AIM analysis for CONTACT-2012

The composition of the particles collected during the CONTACT-2012 campaign consisted

mostly of SO42-

, NH4+

, and NO3-. H

+strong was calculated for the CONTACT-2012 dataset


using the mole equivalents of the dominant particulate species throughout the campaign. A

case study of charge balance in the campaign shown in Fig. 1.7 illustrates the comparison

between the near equivalent concentration of anions and cations in the aerosols. At certain

times, there are excess cations, but much of the campaign features excess NH4+

. The

average H+strong for the entire campaign was -3.84±5.80 nmol m−3.

200

150

100

50

0

Con

cent

ratio

n (n

eq m

-3)

3:00 PM8/24/2012

6:00 AM8/25/2012

9:00 PM 12:00 PM8/26/2012

3:00 AM8/27/2012

6:00 PM 9:00 AM8/28/2012

Date

4020

0

H+

stro

ng (

nmol

m-3

)

Cl-

NO3-

SO42-

NH4+

Figure 1.7: Case study of charge balance in CONTACT-2012.

For CONTACT-2012 data analysis, AIM-II was used to obtain the mole fraction and

activity coefficient of H+

required for calculated in situ pH as described in equation 1.5.

The particles were assumed to be internally mixed and consist of only one deliquesced,

aqueous phase. Since the field campaign found only low levels of HCl/Cl-, Na

+and other

detectable inorganic ions, it was unnecessary to utilize the more inflexible AIM-IV. In

order to obtain the activity coefficient and mole fraction of H+

needed for estimating pH,

concentrations of particulate NO3-, SO4

2-, and NH4

+and, in some cases, gaseous NH3 and

HNO3 were input into the batch mode of AIM-II. Any of the hourly data that did not

contain all of the essential measurements was removed from the finalized dataset that was

input into the model. Negative concentration data points below the limit of detection

were set to zero to avoid model errors. A small number of other hourly measurements

were discarded due to the concentrations of individual species exceeding the parameters

of the model.


The first iteration of the model experiments involved disallowing the gas phase to

interact with the particle phase. This model configuration (called the “particle-only

study”) simulates other studies which only collected particle data and were unable to

allow gas-particle partitioning [20, 59, 60]. Based on the near-zero average of H+strong

throughout the campaign and the results from previous studies, we expect the in situ pH

calculated with this method to fluctuate between acidic and basic values [44, 45].

The in situ pH values in the particle-only study exhibited a bimodal trend with the

majority of modeled values predicted to be >9 or <5. Fig. 1.8 shows the time series of

the in situ pH estimations and a histogram of number of values at each pH unit. The

average in situ pH was 10.45±2.91.

14

12

10

8

6

4

2

0

pH

7/21/2012 8/10/2012 8/30/2012 9/19/2012Date

6004002000Number of Values

Figure 1.8: Particle-only study in situ pH trends in CONTACT-2012

The results from this approach may be explained by acid-base titration of the species

in solution. Since this approach does not permit the buffering effect of neutralized species

partitioning into the gas phase, the acidity of the particles is controlled by the measured

balance between acids and bases. This effect is clearly demonstrated in Fig. 1.9, which

shows the in situ pH estimated by AIM-II plotted against the H+strong. As expected from

the parameterized particle study (Fig. 1.2), the in situ pH is never basic when H+strong is

positive and never acidic when H+strong is negative.

Fig. 1.10 shows a subset of the overall time series with associated error. Error was

estimated by rerunning the model twice as sensitivity runs. The sensitivity runs were

created by estimating the maximum H+strong due to instrumental uncertainty as increasing

the anions by 15% and the detection limit while decreasing the cations by 15% and the

detection limit and then performing the reverse for the low estimate of H+strong. As can be

seen from this subset of the data, the relatively small error in measurement can result in


14

12

10

8

6

4

2

0

pH

40200-20-40

H+

strong (nmol m-3

)

Figure 1.9: pH and measured H+strong in CONTACT-2012

several orders of magnitude error in free H+

concentration. The bimodal trend in Fig.

1.8 may be the result of the instrumental uncertainty interpreted by AIM-II as moving

across the equivalence point of the acid-base titration. Qualitatively, the balance of acids

and bases (signified as H+strong) plotted in Fig. 1.10 suggests that periods when the NH4

+

has neutralized or nearly neutralized the acids results in strongly basic aerosols, while

the periods of increased anions relative to cations result in strongly acidic aerosols. One

interpretation of Fig. 1.10 is that the precision and accuracy of the particle constituent

measurements from the AIM-IC is not sufficient to determine with certainty if the strong

acidity is different than zero. As a result, a broad distribution of values is possible at

each time point.


12

8

4

0

pH

12:00 AM8/25/2012

12:00 AM8/26/2012

12:00 AM8/27/2012

Date

806040200

-20H

+st

rong

(nm

ol m

-3)

Particle-only Mode

H+

strong

Figure 1.10: Case study of particle-only pH in CONTACT-2012

E-AIM is also able to provide theoretical trace gas concentrations predicted to be

in equilibrium with measured particulate species that would be required to produce the

measured aerosol values. In order to assess the physical plausibility of the calculated

strong acidity, the modelled atmospheric concentrations of the gases were compared

with the concentrations measured with the AIM-IC. As can be seen if Fig. 1.11, the

modelled mixing ratios were frequently orders of magnitude higher than the measured

values. The mean measured and predicted mixing ratios of NH3 were 2.42±1.98 ppb

and 5.76 ∗ 107 ± 7.20 ∗ 107 ppb, respectively. This discrepancy is a result of the model

predicting excess aqueous NH3 due to the near equivalency of acids and bases throughout

the campaign. The model is unable to protonate the excess NH3 due to insufficient

quantities of acids and is forced to leave unprotonated NH3 at concentrations which could

only partition into the condensed phase at very high ambient gas concentrations. The

prediction of unrealistically high gaseous NH3 suggests that particle-only inputs are not

sufficient to constrain the pH predictions.


10-4

10-2

100

102

104

106

108

Mix

ing

Rat

io (

ppb)

7/21/2012 8/10/2012 8/30/2012 9/19/2012Date

Measured NH3(g)

AIM-II Predicted NH3(g)

Figure 1.11: Time series of NH3(g) particle-only AIM-II predictions and measurementsduring CONTACT-2012.

1.3.2 Gas-particle partitioning E-AIM analysis for CONTACT-

2012

Since the particle-only regime produces in situ pH values that are inconsistent with

observed gas-phase species, gas-particle partitioning was enabled in AIM-II (called the

“partitioning study”) to establish if this regime could better characterize the in situ pH and

chemical state of the system. The model was allowed to repartition volatile species and

was then evaluated to determine if the thermodynamic predictions of both particle and

gas phase species matched measurements. The sensitivity of the model to instrumental

uncertainty was tested as in the previous section. The model was run with inorganic

species followed by inclusion of the organic acids.

It was determined that allowing gas-particle partitioning in E-AIM dramatically

changes the model predictions of gas and particle phase concentrations. In addition, E-

AIM produced in situ pH outputs with a reduced range. Fig. 1.12 shows the relationship

between the repartitioned (output) H+strong and in situ pH. The results show that unlike

the relationship in Fig. 1.9, the output H+strong values are never negative, and the pH

values are higher than 6 or lower than 2. Fig. 1.13 shows the diurnal trend of pH averaged

over the field campaign, and Fig. 1.14 shows the time series of in situ pH over the course

of the field campaign. The pH increased at night with the increased relative humidity.

The liquid water content of the particle was similarly correlated with periods of elevated

pH. During the day, as the relative humidity decreases and the temperature increases, the


pH decreased. The campaign average for the pH was 3.84±0.56 and no evidence of basic

aerosols was observed. This result matches those predicted by Keene and Savoie [55] and

suggests that the acidity of the particle is continually buffered by the volatility of NH3.

5.5

5.0

4.5

4.0

3.5

3.0

2.5

pH

1.6x10-91.41.21.00.80.60.40.20.0

Output H+

strong (nmol m-3

)

0.90.80.70.60.5Mole Fraction of H2O

Figure 1.12: Relationship between in situ pH and the output H+strong

Fig. 1.15 depicts a subset of the in situ pH data and illustrates the differences between

particle-only and partitioning studies. As can be observed, the uncertainty of the pH

in the partitioning study was very small compared to the uncertainty of the pH in the

particle-only study. The in situ pH estimated for the partitioning study was generally

within the uncertainty of the particle-only study.


5.5

5.0

4.5

4.0

3.5

3.0

2.5

pH

00:00 06:00 12:00 18:00Time of Day

pH Mean Median0.90.80.70.60.50.4

Relative Humidity

Figure 1.13: Diurnal in situ pH coloured for relative humidity in CONTACT-2012 withgas-particle partitioning enabled

6

5

4

3

2

pH

7/21/2012 8/10/2012 8/30/2012 9/19/2012Date

4003002001000Number of Values

0.90.80.70.60.50.4Relative Humidity

Figure 1.14: Full time series of in situ pH coloured for relative humidity in CONTACT-2012with gas-particle partitioning enabled


14

12

10

8

6

4

2

0

pH

12:00 AM8/25/2012

12:00 PM 12:00 AM8/26/2012

12:00 PM 12:00 AM8/27/2012

Date

80

40

0

-40

H+

stro

ng (

nmol

m-3

)

Particle-only Study Partitioning Study Partitioning Study w/ Organics

H+

strong

Figure 1.15: Case study of in situ pH estimated by disallowing and allowing gas-particlepartitioning and including organic acids with gas-particle partitition enabled in E-AIM inCONTACT-2012

However, at times the uncertainty of the measurements was unable to account for the

differences between the particle-only pH and the partitioning allowed pH predictions. In

Fig. 1.16, the particle-only pH followed the extremes of H+strong. It is possible that the

instrumental uncertainty was underestimated, which could result in a maximum H+strong

sensitivity run that still has a negative H+strong value. Also, periods where the pH values

were significantly different often occured during hours with low ambient mass loadings,

which could indicate that the limit of detection estimates had been low. Ultimately,

these results indicate that the in situ pH estimated by the gas-particle partitioning study

was far less sensitive to instrumental uncertainty and was generally not significantly

different than the pH estimated by the particle-only study due to its high sensitivity to

instrumental uncertainty.


14

12

10

8

6

4

2

0

pH

12:00 AM8/27/2012

12:00 PM 12:00 AM8/28/2012

12:00 PM 12:00 AM8/29/2012

Date

40

20

0

-20H+

stro

ng (

nmol

m-3

) Particle-only Study Partitioning Study Partitioning Study w/ Organics

H+

strong

Figure 1.16: Case study of significant difference between gas-particle partitioning allowedand disallowed regimes in CONTACT-2012

Another important figure for qualitative analysis is the relationship between aqueous

mole fraction and in situ pH. Theoretically, for equivalent mass loadings of solutes, as

the particle becomes more dilute, the pH should trend towards higher values. Fig. 1.17

depicts the relationship between calculated in situ pH in the gas-particle partitioning

study and the predicted XH2O. The pH of particles at a given XH2O varies depending on

composition of the particle but generally increases with increasing XH2O (slope=2.89±0.05,

r2 =0.660). However, in the particle-only study, there was no relationship between pH and

XH2O as seen if Fig. 1.18, which further suggests that the results from the particle-only

regime are not physically representative of the ambient particles.


5.5

5.0

4.5

4.0

3.5

3.0

2.5

pH

1.00.90.80.70.60.50.40.3Mole fraction of H2O

Figure 1.17: Relationship between in situ pH and XH2O in the gas-particle partitioningstudy

14

12

10

8

6

4

2

0

pH

0.80.60.4Mole fraction of H2O

Figure 1.18: Relationship between in situ pH and XH2O in the particle-only study

Predictions of volatile species versus measured results using AIM-II gas-

particle partitioning

In order to assess the quality of the predicted gas-particle repartitioning, the predicted

gaseous and particulate concentrations of volatile species were compared to the measured


values. As seen in the particle-only regime, dramatic differences between the predicted

and measured values would indicate that the model is failing to replicate the conditions

measured during the campaign. However, the modelled and measured values for most

species closely correlated for the gas-particle partitioning enabled model. Fig. 1.19 shows

a subset of the NH3(g) time series with modeled and measured concentrations of both

particulate and gaseous species. Predicted NH3(g) and NH4(aq)+

values closely correlate to

measured concentrations. Correlation data for all major species are tabulated in Table

B.1.

200

150

100

50

0Con

cent

ratio

n (n

mol

m-3

)

12:00 AM8/25/2012

12:00 AM8/26/2012

12:00 AM8/27/2012

12:00 AM8/28/2012

Date

400

300

200

100

0

Con

cent

ratio

n (n

mol

m-3

)

NH3(g) Measured

NH4+

(aq) Measured Modelled Concentration

Figure 1.19: NH3(g) predictions from AIM-II upon allowing gas-particle partitioning andmeasured concentrations in CONTACT-2012

AIM-II predictions of NO3(aq)-

and HNO3(g) were also relatively well correlated with the

measured concentrations, although outliers significantly changed the r2 values. Generally

the modelled concentration was different in absolute value than the measured concentration.

Fig. 1.20 shows that while the trends for both of these species were matched by the

predictions, the absolute values tend to lie outside of the instrumental uncertainty. The


reason for the discrepancy is possibly explained by the underestimation of uncertainty.

Since the absolute concentrations of NO3(aq)-

and absolute instrumental uncertainties were

much lower than those of NH4(aq)+

or SO4(aq)2-

, the sensitivity runs will be dominated by

the changes in NH4(aq)+

or SO4(aq)2-

. Small deviations between predicted and measured NH4(aq)+

or SO4(aq)2-

may result in relatively large deviations in NO3(aq)-

. The difference between

measured and modelled NH4(aq)+

and NH3(g) was 5.78±8.23 nmol m−3 and -5.53±12.05

nmol m−3, respectively, while the difference between measured and modelled NO3(aq)-

and

HNO3(g) was 1.07±6.08 nmol m−3 and -1.06±6.08 nmol m−3. These results indicate that

while the relative difference between modelled and measured NO3(aq)-

and HNO3(g) is

large compared to NH4(aq)+

and NH3(g), the model is producing results that vary from the

measured value by roughly the same absolute amount for both species.

The discrepancy between modelled and measured values may also be explained by the

ambient aerosols not meeting the assumptions of the model. The aerosols may have been

composed of phases other than the simple deliquesced aqueous phase utilized in the model.

For example, an organic phase could have impacted the ability of the particles to uptake

water and changed the rate at which the particles equilibrated with the gas phase. The

particles also may have been externally mixed and some of the NO3(aq)-

may have been

associated with coarse mode particles or the system may have not reached equilibrium.

In the following section we investigate E-AIM analysis with externally mixed aerosols.

Overall, the evidence of these studies suggests that the particle-only version of E-AIM

may not accurately portray the in situ pH and that enabling gas-particle partitioning in

the model results in an improved relationship between the modelled and measured gas

and particulate composition.


50

40

30

20

10

0Con

cent

ratio

n (n

mol

m-3

)

12:00 AM8/25/2012

12:00 AM8/26/2012

12:00 AM8/27/2012

12:00 AM8/28/2012

Date

50

40

30

20

10

0Con

cent

ratio

n (n

mol

m-3

) HNO3(g) Measured

NO3-(aq) Measured

Modelled Concentration

Figure 1.20: Case study of particle-only pH in CONTACT-2012

1.3.3 Ad hoc two mode separation from CalNex 2010

In order to probe whether the assumption of internal mixing is leading to the poor

HNO3(g)/NO3(aq)-

predictions, data from CalNex 2010 were used to examine the plausibility

of bimodal acidity and chemical system prediction improvement on results from Ellis

et al. [76] (in prep). The PILS-IC (PM2.5) and AMS (PM1.0) used during CalNex 2010

provided the opportunity to examine differences in particle acidity between sea salt and

accumulation mode aerosols. The coarse mode was assumed to be dominated by sea

salt particles since Pasadena, CA is located less than 30 km from the Pacific Ocean

[31]. In this study, PM2.5 data were segregated as the “accumulation mode” and “sea

salt mode.” It was assumed that the AMS measurements reflect the composition of the

accumulation mode. For reasons discussed later in this section, the AMS data were used

only for qualitative analysis, while the PILS-IC (PM2.5) data were segregated based on

assumptions about the composition of each mode.

The particles were separated into sea salt and accumulation mode by inputting a


uniform percentage of each species measured with PILS-IC across all hourly datapoints

into AIM-IV (e.g. if 40% of measured particulate NH4+

was input as the sea salt mode,

60% was input as accumulation mode). Since both particle populations were assumed to

be in equilibrium with the gas phase, the gas-phase concentration results from the “sea

salt” and “accumulation” model runs were compared with the measured values as a way

of checking whether the mode segregation had adequately represented the system.

In the simplest separation scenario, the particles were treated as being internally

mixed and run in AIM-IV with allowed gas-particle partitioning. Next, the PILS-IC

particulate concentration data were separated to produced a sea salt aerosol which was

defined as being purely Na+

and Cl-

while the accumulation mode was defined as NH4+

,

NO3-, and SO4

2-. The model was then run iteratively to produce optimized input sea salt

and accumulation mode particles which were set percentages of all five species. Studies

have shown that SO42-

is almost exclusively found in accumulation mode aerosols, while

Na+

is found primarily in sea salt aerosols [35, 77]. Ultimately an accumulation/sea salt

aerosol input ratio was optimized from PILS-IC data at a sea salt value of 30% NH4+

,

100% Na+

, 10% SO42-

, 60% NO3-, and 90% Cl

-and all remaining particulate composition

was designated as accumulation mode.

One significant assumption of creating this ad hoc separation of sea salt and accumu-

lation mode aerosols from bulk data is that the relative ratios of each species between

the two modes is constant throughout the dataset. An approximate assessment of this

assumption was made by plotting the relative ratio throughout the campaign of PILS-IC

and AMS data (see Fig. 1.21). Since the two instruments collect different size fractions,

the data were evaluated to determine whether there was a constant relative amount of each

species in each mode throughout the campaign. Ideally, the two instruments would have

been able to provide mode resolved composition, but this is not possible due, most likely,

to the variability of the size of populations of particles and problems with the accuracy

of the instrumentation. In Fig. 1.21 the ratio of AMS to PILS-IC concentrations of

sulphate remained above 1.5 throughout the campaign. Since the PILS-IC samples PM2.5

while the AMS samples approximately PM1.0, the result indicates a possible calibration

error as the theoretical ratios should always remain at or below 1.0 [78]. This is likely

the result of the uncertainty in the collection efficiency of the AMS, a known and well

documented problem [73, 79]. In addition, the uncertainty of the AMS measurements was

estimated at 30% and the PILS-IC uncertainty was estimated to be 13% [80]. However,

despite the calibration problem, the ratio remains relatively constant throughout the

campaign. Although the data does not provide exact answers as to the relative amounts

between particle populations, it does suggest the relative composition of the modes remain


approximately the same throughout the campaign.

2.0

1.5

1.0

0.5

0.0

Rat

io [A

MS

]/[P

ILS

]

6/9/2010 6/10/2010 6/11/2010 6/12/2010 6/13/2010 6/14/2010

Date

NH4+

Cl-

NO3-

SO42-

Figure 1.21: AMS/PILS ratio in CalNex 2010

The input values and modelled results from the optimized separation study demonstrate

that the technique was able to produce modelled gas concentrations from the two different

particle types that reasonably matched the measured value. Figs. 1.22, 1.23 and 1.24 show

the measured and modelled gaseous concentrations of the three dominant volatile gases,

HNO3, NH3, and HCl, from the optimized sea salt and accumulation mode experiments.

The modelled values are generally within the uncertainty of the measurements for all

of the species. Figs. 1.25, 1.26 and 1.27 show the comparison between the optimized

estimations of the total sea salt and accumulation mode concentrations and the AIM-IV

modelled predictions as well as the differences between the input and modelled ratio of sea

salt and accumulation mode. Figs. B.1, B.2 and B.3 show the relationship between the

individual optimized input and output particle-phase data for each mode. Although the

results are often outside of the instrumental uncertainty of the PILS-IC, the concentration

trends were generally captured. Given the uncertainty of the uniformity of the sea

salt/accumulation mode ratio, the results, while imperfect, do appear to be representing

the chemical state of the particles. The deviations in particle concentrations are likely

significantly affected by the inaccuracy of the fixed percentage input method, since the

relative amounts of any given species likely varies throughout the campaign.


250x10-9

200

150

100

50

0

Con

cent

ratio

n (m

ol m

-3)

6/9/2010 6/10/2010 6/11/2010 6/12/2010 6/13/2010 6/14/2010Date

Measured HNO3(g)

Modelled HNO3(g) from Accumulation Mode Modelled HNO3(g) from Sea Salt Mode

Figure 1.22: HNO3(g) predictions from optimized sea salt mode and accumulation modeaerosols from CalNex 2010

200x10-9

150

100

50

0

Con

cent

raito

n (m

ol m

-3)

6/9/2010 6/10/2010 6/11/2010 6/12/2010 6/13/2010 6/14/2010Date

Measured NH3(g)

Modelled NH3(g) from Accumulation Mode Modelled NH3(g) from Sea Salt Mode

Figure 1.23: NH3(g) predictions from optimized sea salt mode and accumulation modeaerosols from CalNex 2010


160x10-9

140

120

100

80

60

40

20

0

Con

cent

ratio

n (m

ol m

-3)

6/9/2010 6/10/2010 6/11/2010 6/12/2010 6/13/2010 6/14/2010Date

Measured HCl(g)

Modelled HCl(g) from Accumulation Mode Modelled HCl(g) from Sea Salt Mode

Figure 1.24: HCl(g) predictions from optimized sea salt mode and accumulation modeaerosols from CalNex 2010

200x10-9

150

100

50

0

Con

cent

ratio

n (m

ol m

-3)

6/9/2010 6/10/2010 6/11/2010 6/12/2010 6/13/2010 6/14/2010Date

6

4

2

0Rat

io [S

ea S

alt]/

[Acc

umul

atio

n]

Modelled Sea Salt Mode NO3-(aq)

Modelled Accumulation Mode NO3-(aq)

Measured Total NO3-(aq)

Input Concentration Ratio Modelled Output Concentration Ratio

Figure 1.25: NO3(aq)-

predictions from optimized sea salt mode and accumulation modeaerosols and input and modelled ratio of sea salt to accumulation mode from CalNex 2010


250x10-9

200

150

100

50

Con

cent

ratio

n (m

ol m

-3)

6/9/2010 6/10/2010 6/11/2010 6/12/2010 6/13/2010 6/14/2010Date

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0Rat

io [S

ea S

alt]/

[Acc

umul

atio

n]

Modelled Sea Salt Mode NH4+

(aq)

Modelled Accumulation Mode NH4+

(aq)

Measured Total NH4+

(aq)

Input Concentration Ratio Modelled Output Concentration Ratio

Figure 1.26: NH4(aq)+



50x10-9

40

30

20

10

0Con

cent

ratio

n (m

ol m

-3)

6/9/2010 6/10/2010 6/11/2010 6/12/2010 6/13/2010 6/14/2010Date

12

10

8

6

4

2

0Rat

io [S

ea S

alt]/

[Acc

umul

atio

n]

Modelled Sea Salt Mode Cl-(aq)

Modelled Accumulation Mode Cl-(aq)

Measured Total Cl-(aq)

Input Concentration Ratio Modelled Output

Concentration Ratio

Figure 1.27: Cl(aq)-


Fig. 1.28 shows the optimized and bulk pH values calculated for each hourly datapoint

throughout the campaign. Despite the significant differences in input composition of the

two aerosol modes the magnitude of the difference in pH was relatively small throughout

the campaign. This result agrees with findings by Keene and Savoie [55] and Erickson et al.

[10] who concluded that partitioning drives the pH of sea salt and accumulation modes

toward a common value. Our results show that the average pH values for the optimized

accumulation and sea salt modes were 3.95±0.27 and 4.21±0.26, respectively, while the

modelled bulk pH was 4.10±0.27. Keene et al. [81] found that for size resolved marine

aerosols with Dp <1 µm were approximately 1 pH unit lower at pH=3, on average, than

supermicron aerosols ranging up to 10 µm at pH=4. Despite the dramatically different

composition of the particles, the pH in our study between the two input modes was not

significantly different.

This study illustrates a proof of concept regarding the use of the E-AIM to characterize

the pH of different size fractions of aerosols. However, since the size fractions were not

physically separated during the study, the results cannot be independently verified. As

a consequence, it is possible that the model found the “optimized” modes to produce


5.5

5.0

4.5

4.0

3.5

pH

6/9/2010 6/11/2010 6/13/2010Date

Accumulation Mode Sea Salt Mode Bulk

Figure 1.28: The pH values for the accumulation and sea salt modes of the optimizedseparation and the bulk pH from CalNex 2010

predictions that approximately matched the arbitrarily defined modes, but the possibility

that the model found results that coincidentally matched the inputs cannot be precluded.

Furthermore, if the aerosols are not at equilibrium, the ad hoc separation could produce a

system that is attempting to replicate conditions that do not exist. A more sophisticated

model that accounts for the effect of size and composition dependence of mass transfer

rates on equilibration time could be developed to produce more accurate results. Given

the uncertainty of the instrumentation and the inability to know the fractionated con-

centrations independently, the uncertainty in the fractionated segregation should not be

understated.

This analysis demonstrates that the pH difference between the two modes is strongly

buffered by gas-particle partitioning. However, the bimodal system simplifies the reality

that atmospheric aerosol composition is often very heterogeneous. For example, small,

unaged, nucleating particles found in coal power plant exhaust plumes may be highly

acidic since they have not yet been neutralized by ambient concentrations of NH3 [82].

However, since their mass is significantly less than the rest of the accumulation mode,

their contribution to the acidity of the particles may be underrepresented.

The study also used a relatively approximative methodology of estimating the frac-

tionation of the individual aerosol constituents. Analysis time could be shortened by


automating the model processing to enable rapid optimization in future studies. This

would allow for more exact segregation of species into the two modes that could be

performed on each datapoint rather than inputting a constant fractionation throughout

the campaign.

1.3.4 24-hour integration analysis

Numerous aerosol acidity studies have been conducted that integrate particle measurements

over 24-hour or longer sampling periods [4, 59, 60, 83]. One of the drawbacks of this

method is that the processes that affect particulate composition may change dramatically

over the course of 24 hours. Temperature, relative humidity, mass loading, and ambient

gas concentration all vary on relatively short timescales [84]. As a result, long integration

times may not allow for the necessary resolution to accurately estimate acidity. Since

the CONTACT-2012 dataset provided hourly gas and particle measurements, model

experiments were performed to determine the extent to which 24-hour integration impacted

acidity predictions.

In order to probe the question of whether differing integration will impact in situ

pH, four model runs were performed in AIM-II. The first run averaged the concentration

values of particulate and gaseous species for 24 hours (starting at 00:00). This run was

called the “bulk average.” The temperature and relative humidity were averaged over the

same time period. The model was run in the particle-only configuration with gas-particle

partitioning disallowed. The second run used the particle-only AIM-II regime to process

hourly measurements and find hourly activities of H+

. The AH+ values were averaged over

24 hours (also starting at 00:00) and the pH calculated from each 24-hour AH+ integration

was called the “model average.” pH values from the bulk and model averages were plotted

in Fig. 1.29. Both model and bulk averaging methodologies were also applied to the

gas-particle partitioning regime of AIM-II and the results are also plotted in Fig. 1.29.

All 24-hour periods that lacked complete hourly measurements were disregarded.

Several averaged 24-hour periods produced errors in the model and were unable to be

processed. These errors were the result of conditions being outside of the ranges that

the model was designed to operate at and usually were caused by very low relative

concentrations of NH4+

to other constituents.

The results showed that there were substantial differences between the pH values

predicted by the two averaging methods in the particle-only experiments, while the

gas-particle partitioning experiments showed a close relationship between bulk and model

averages. The results demonstrate that the variability throughout a 24-hour period may


strongly influence the particle-only predictions. An explanation for this effect is that

if a single hour from the model average has a pH that falls on the acidic (pH<7) side

of the pH titration curve, the model average will be strongly influenced by this single

value since the scale is logarithmic. If particles generally are on the pH>7 side of the pH

titration curve, the bulk average will be less influenced by a single hour with pH<7 since

the overall 24-hour balance between acid and base may still produce excess base.

12

10

8

6

4

pH

7/21/2012 7/31/2012 8/10/2012 8/20/2012 8/30/2012 9/9/2012 9/19/2012 9/29/2012Date

Bulk Average (Particle-only) Model Average (Particle-only) Bulk Average (Partitioning allowed) Model Average (Partitioning allowed)

Figure 1.29: 24-hour data integration methodologies from CONTACT-2012

1.3.5 pH distribution

The pH values throughout the CONTACT-2012 campaign were plotted against other

datasets which apply the same E-AIM gas-particle partitioning pH estimation in Fig.

1.30. These studies suggest that the aerosol pH measured in the boundary layer at urban

and rural sites in continental North America lies approximately in the pH 3-5 range.

These studies also confirm the buffering effect observed by Keene and Savoie [55] and

Erickson et al. [10] that suggests the pH of ambient aerosols is strongly regulated by the

partitioning of acids and bases and that the particle-gas equilibrium exerts a dominating

effect on acidity.


0.35

0.30

0.25

0.20

0.15

0.10

0.05

0.00

Nor

mal

ized

Fre

quen

cy o

f Obs

erva

tion

7654321

pH

Location n= Pasadena 475 Bakersfield 547 BAQS-Met 309 Toronto 139 CONTACT 1394

Figure 1.30: pH histogram of recent continental North American field campaigns, where“n” is the number of measurements taken at each site

1.4 Conclusions

These results demonstrate the importance of constraining aerosol acidity measurements

with ambient gaseous data. While uncertainty regarding Henry’s law partitioning constants

is avoided by disabling gas-particle partitioning, the errors in the particulate measurements

can have magnified, deleterious effects on the model’s accuracy. Aerosol acidity methods

which rely solely on sampled particle concentrations and thermodynamic modelling

may be inadequate for providing accurate pH estimates. Model predictions may not

be representative of a real atmospheric system. Future studies that use E-AIM and

disallow partitioning should evaluate the discrepancies between modelled and measured

ambient gas concentrations to establish if the pH estimates are reliable. This analysis

is not commonly done in recent literature and may explain the findings of papers that

observe dramatic fluctuations of in situ pH and H+strong [44, 45, 85]. Despite inadequacies

in the predictive powers of HNO3/NO3-

from the gas-particle partitioning study from

the CONTACT-2012 campaign, the model is generally able to produce repartitioned

results that closely correlate with the measured values of both gas and particle species.

Furthermore, size-resolved analysis may aid the model’s ability to accurately estimate pH

of chemically dissimilar aerosols by reducing the effects of the assumption of particulate


internal mixing. Future research that collects size-resolved concentration data of inorganic

species may verify the findings of this study.

The results from this study also suggest that the difference in pH between the sea

salt and the accumulation mode may be more similar and more invariable than has been

previously suggested [85]. The results support the assertions from Keene [54] and Erickson

et al. [10] that pH values of sea salt and accumulation mode tend to be similar due to the

buffering effect of intermode partitioning.

Laboratory studies, especially those relating to recent SOA experiments, must constrain

the acidity of the particles. The current methodologies of evaluating SOA growth as a

function of H+strong or the in situ pH estimated from the particle-only mode of E-AIM may

fail to accurately probe the aerosol system. Furthermore, the results from this and other

recent field studies found average in situ pH values that are higher than those required by

previous laboratory studies to produce significant VOC uptake [19, 50, 51]. Ambient air

quality and public health studies may find novel implications of aerosol acidity if they are

able to correlate in situ pH with respiratory and pulmonary illness rather than H+strong.

1.5 Acknowledgements

CalNex 2010 NH3(g) data was collected and processed by Raluca Ellis using the QC-

TILDAS. Rodney Weber provided PILS-IC data and Patrick Hayes provided AMS data.

Raluca Ellis also compiled the dataset prior to the E-AIM analysis and provided guidance

regarding the model’s usage. Greg Wentworth contributed to much of the AIM-IC data

collection and analysis. Alex Tevlin provided CONTACT-2012 QC-TILDAS data as well

as E-AIM support. Jennifer Murphy contributed much insight into theoretical background

of the analysis; she also compiled pH data from recent campaigns. Carol Cheyne, Rachel

Hems, and Geoff Stupple also helped with instrumentation throughout CONTACT-2012.

Finally, the author thanks the NSERC CREATE program, IACPES, for continued funding

throughout this research.

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90069-M.

Chapter 2

Reduced Nitrogen Interference in

Chemiluminescent Nitrogen Oxide

Monitors

2.1 Introduction

Reactive nitrogen oxides have wide-ranging implications for air quality by influencing par-

ticulate matter formation, ozone production and the oxidative capacity of the troposphere

[1, 2]. Nitrogen dioxide (NO2) is designated as a criteria pollutant by the United States

Environmental Protection Agency (EPA) and Environment Canada due to its health and

environmental effects and is regulated according to national air quality standards (CFR 40

part 50.11 and CEPA Schedule 1, 63). NO2 readily interconverts with NO, and the sum

of both species is defined as NOx. NOx is primarily formed as a byproduct of combustion,

e.g. from fossil fuel use by mobile sources and in energy production [3]. The lifetime of

NOx in the atmosphere is governed by its oxidation to higher order oxides, which are

subsequently deposited and lost from the atmosphere. The sum of NOx and more highly

oxidized products such as N2O5, peroxyacyl nitrates (PANs), nitric acid (HNO3), nitrous

acid (HONO), alkyl nitrates (RONO2) and particulate nitrate (NO3-), is defined as total

reactive nitrogen oxides (NOy). Non-NOy nitrogen-containing atmospheric species include

nitriles, ammonia, amines, N2O, and HCN.

Due to the interconnected chemistry of NO2 and other nitrogen oxides, accurate NOx

and NOy measurements have remained important elements of understanding processes that

affect air quality. Air quality models rely on accurate and precise in situ measurements

of NO2, and accurate measurements are also necessary for the validation of satellite

57

Chapter 2. Interference in Nitrogen Oxide Monitors 58

measurements and for some public health epidemiological studies [4, 5].

Publicly funded air quality networks, including the Canadian Air and Precipitation

Monitoring Network (CAPMoN) and National Air Pollution Surveillance Program (NAPS)

in Canada and the California Air Resources Board (CARB) and the State or Local Air

Monitoring Stations (SLAMS) in the U.S.A., often use commercial NOx or NOy analyzers

which rely on the chemiluminescent reaction of NO with O3 [6–8]. The primary channel

in these instruments draws ambient air and an O3 stream into the reaction chamber to

quantify the NO concentrations by the light emitted from the chemiluminescent reaction

of NO and O3. The secondary channel reduces oxidized reactive nitrogen species to NO

before detection in the reaction chamber as either NOx, if the converter only reduces NO2,

or NOy, if the converter also oxidizes higher order nitrogen oxides.

Most chemiluminescent NOy instruments use heated molybdenum oxide (MoOx) cata-

lysts to convert nitrogen oxides to NO, and many chemiluminescent NOx instruments use

the same catalysts in an altered configuration to convert NO2 to NO [7, 9, 10]. Since both

NOx and NOy are operationally defined by these instruments as the concentration of NO

found through chemiluminescence in the converter channel, other species that degrade

or convert to NO through non-specific conversion will interfere with the desired signal

[4]. MoOx-based chemiluminescence detectors (MoOx-CLD) have been shown to system-

atically overestimate NOx concentrations when compared with collocated spectroscopic

instruments [9, 11–13]. Both NOx and NOy instruments have also been shown to convert

non-NOy species—including NH3, HCN, and N2O—to NO and such interferences have

been extensively characterized over the past four decades [10, 14–18].

The interference of gaseous ammonia (NH3) and particulate ammonium (NH4+

) from

oxidation within the converter is poorly understood. NH3 is a trace gas that is emitted

from both natural and agricultural sources and NH4+

results from the gas to particle

partitioning of NH3 in the presence of atmospheric trace acids [3]. The interferences of

MoOx-CLD instruments have been considered to be relatively less significant in cities and

other high-NOx environments due to the relatively small concentrations of interfering

species compared with NOx and NOy [9, 19]. However, Bishop et al. [20] showed that

modern three-way catalytic converters may be changing the relative amounts of reduced

and oxidized nitrogen emitted from mobile sources [20]. The implications of increasing

NHx relative to NOx and NOy could mean that in urban environments, NHx interferences

may be more significant than previously thought.

Some studies have suggested a minimal interference of NHx [14, 15], while others have

reported conversions of NH3 as high as 38% under normal operating conditions [21, 22].

Unfortunately, the confusion over the findings has led some researchers to either claim


that NHx interference is negligible based on prior literature or to neglect to investigate its

role when considering biases in reactive nitrogen oxide measurements [9, 23? ]. Several

studies have observed a gap between measurements from MoOx-CLD measurements and

the sum of individual NOy constituents [24–26]. In NOy chemiluminescent analyzers

with gold catalytic converters, NHx interference has been observed to account for 85%

of the “NOy gap” and a similar phenomenon could be occurring in molybdenum-based

instruments if significant NHx is converting to NO [27].

The variability of NH3 conversion of has been considered a consequence of converter

age—the result of increased oxidation of the molybdenum surface—and converter tem-

perature (Tconv) [10, 21]. The oxidative aging of the converter is a known problem that

instrument manufacturers recommend resolving using a reductive regeneration procedure.

However, the current characterization of the relationship between Tconv and conversion

efficiency is incomplete. Typically, Tconv in NO2 instruments is set at 325C, while Tconv

for NOy is higher, usually up to 350C. However, these values fluctuate throughout the

literature. For example, the Canadian Council of Ministers of the Environment recom-

mends that Tconv for NO2 be set at 325C, while the Environment Canada CAPMoN

program sets Tconv at 325C for NOy instruements [28, 29]. Breitenbach and Shelef [22]

noted that ammonia conversion increased in a molybdenum-carbon composite converter

as they increased the Tconv [22]. Xue et al. [21] observed a decrease in NH3 MoOx-CLD

conversion efficiency from 38% to 11% when lowering Tconv from 350C to 325C [21].

Fehsenfeld et al. [30] developed a MoOx-CLD technique to measure NHx as the difference

between MoOx-CLD measurements taken with a converter at 350 and another at 450

[30]. Their results demonstrate that at a reasonably low Tconv, a MoOx converter can

quantitatively convert NH3 and NH4+

to NO.

Despite the evidence of a strong temperature dependence on the NHx interference,

a survey of the literature results in dramatically different conversion efficiencies as a

function of reported converter temperatures. Work by Fitz et al. [10] attempted to isolate

this problem by first adjusting the Tconv of 14 new analogous converters to a minimal

value while still maintaining a high conversion efficiency of NO2 [10]. The researchers

noted that not only did the optimal temperature for NO2 conversion vary from 327C

to 370C between 14 analogous converters, but the NH3 conversion rate at the optimal

temperature ranged from 2.5% to 26.8% with an average of 11.2% and was not correlated

with the Tconv [10]. Conversion efficiencies from selected previous studies with reported

MoOx Tconv are shown in Table 2.1. The relationship of temperature and NH3 conversion

remains unclear, and the influence of NH4+

also has not been thoroughly studied.

In this work we investigate the relationship between Tconv and NH3 and NH4+

with


Table 2.1: Summary of selected literature gaseous NH3 MoOx-CLD conversion efficienciesand associated Tconv

Reference Tconv (C) Conversion EfficiencyXue et al. [21] 350 3 converters: 0-14%

350 1 New converter: 38%325 Same new converter: 11%

Fitz et al. [10] 327-370: 14 Converters 2.5%-26.8%(no correlation with Tconv)

Fehsenfeld et al. [30] 350 negligible450 100%

Williams et al. [15] 350: Instrument 1 0%375: Instrument 2 5%340: Instrument 3 8%

Dickerson et al. [31] 425 <0.1%Williams et al. [32] 400 37%Fehsenfeld et al. [14] 400 2%

500 Substantial ConversionBreitenbach and Shelef [22] 525 7%? ] Unreported <1%Minarro and Ferradas [33] Unreported <1%Dunlea et al. [11] Unreported No correlation between ambient

NH3 and excess NOx

three MoOx-CLD instruments. Our objective is to elucidate the causes of the confusion

in the existing literature regarding this problem and offer practical recommendations to

minimize the effects of the interference.

2.2 Methods

2.2.1 Analytical instrumentation

The experimental setup consisted of an Ambient Ion Monitor-Ion Chromatograph (AIM-

IC, URG Corp., Chapel Hill, NC), a custom-built NO chemiluminescence analyzer with a

2-channel NOx and NOy inlet system (AQD NOxy, Air Quality Design Inc., Wheat Ridge,

CO) and two commercial chemiluminescence NO-NO2-NOx analyzers (TSI NOx, Thermo

Scientific, Model 42i, Franklin, MA). AIM-IC provided simultaneous measurements of gas

and particle phase NHx and other water-soluble species while the AQD NOxy and TSI

NOx analyzer were utilized to characterize molybdenum converter response to NH3 and


MoOx Output

Ozone Generator

NO2 to NO Converter

Flow Sensor

NO Channel

NO2 Channel

Pressure Transducer

Dry Air

Sample

Exhaust

Reaction Chamber

Filter

PMT

Pump Ozone Scrubber

Flow

Flow

Flow

Figure 2.1: Schematic of TSI NOx analyzers (adapted from TSI 42i Manual)

NH4+

.

The AQD NOxy inlet consists of two inlet channels that separately measure NOx and

NOy by conversion to NO. The NOx channel utilizes a photolytic converter (Blue Light

Converter, Air Quality Design, Wheat Ridge, CO) to selectively convert NO2 to NO,

while the NOy channel relies on a MoOx converter to reduce NOy species to NO. The

converted air flows at 1.5 standard L min−1 through the ozone reaction chamber and

subsequent chemiluminescence is detected by a PMT. The TSI NOx instruments (Fig.

2.1) are functionally analogous to the NOy channel of the NOxy as ambient air enters

the MoOx converter and is detected in an ozone reaction chamber. The flow through

each channel in the TSI NOx instruments was 0.6 volumetric L min−1. The Air Quality

Design and Thermo Scientific instruments utilized equivalent, commercially available

MoOx converters (PN: 9269, Thermo Environmental, Inc.).

AIM-IC simultaneously measures atmospheric gases, including NH3, SO2, HNO3, and

HONO, and particulate species, including NH4+

, SO42-

, NO3-, and NO2

-, at 3.00 volumetric

L min−1 with hourly time resolution. A parallel-plate wet denuder strips water-soluble

gases from the air sample and oxidizes SO2 to SO42-

with dilute (1 mmol) H2O2 solution.

The remaining particles and non-soluble gases are drawn into a particle supersaturation

chamber (PSSC) and condense into a liquid phase. The effluents from the denuder and


PSSC are drawn into two respective sets of 5 mL syringes over the course of an hour

before injection onto cation and anion ion chromatographs.

Permeation sources of HNO3 and NH3 (Type HRT for HNO3 and EL-SRT-2 for NH3,

Kin-Tek, La Marque, TX) were utilized to produce constant signals for assessing interfer-

ences. The permeation rate for the NH3 source may have varied between experiments and

it was necessary to evaluate the permeation output using AIM-IC to quantify conversion

efficiency. The methodology of quantitation is described in further detail in Markovic

et al. [34].

2.2.2 Converter temperature ramping with ammonia gas

The first set of experiments was designed to isolate the relationship of Tconv and NH3

conversion. Temperature ramping experiments were performed on the AQD NOxy by

diluting NH3 from a permeation source into zero air and observing the relationship between

the NOy mixing ratios and converter temperature. Tconv was increased stepwise between

275C and 375C and was held constant at each step for approximately 30 minutes. This

experiment was replicated five times with the aged converter which had been originally

installed on the AQD NOxy and once with a previously unused replacement converter from

one of the TSI NOx instruments. Between each set of experiments, the physical placement

of the thermocouple and heating unit changed due to heating insulation being unpacked

and repacked. The AIM-IC was only implemented to simultaneously monitor the NH3

gas concentration during the final experiment as it became clear that the permeation rate

of NH3 had not remained constant between the first five sets of temperature ramping

experiments.

A similar temperature ramping experiment was performed once on both TSI NOx

instruments. In these instruments Tconv is electronically limited between 310C and

340C. Tconv was increased stepwise by 5C and held at each step for approximately 30

minutes. Both commercial NOx analyzers (henceforth denoted as TSI NOx–A and –B)

were purchased approximately one year prior to the experiments, but TSI NOx–A had

been operated continuously over that time while TSI NOx–B had been operated for <1

week prior to the ramping experiments. While the AIM-IC was not used during this

experiment to simultaneously measure NH3 mixing ratios, the NH3 permeation source was

calibrated shortly before use and was assumed to have remained at the same permeation

rate.


2.2.3 Converter temperature ramping with salt particles

In order to analyze the conversion efficiency of particle NH4+

and NO3-, solutions of

(NH4)2SO4, NH4NO3 and Ca(NO3)2, were atomized (Aerosol Generator ATM 226, Topas

GmbH, Dresden, Germany) and dried through a drying tube (DDU 570/L, Topas GmbH,

Dresden, Germany) packed with silica beads (P077.1, Carl Roth GmbH, Karlsruhe,

Germany) to produce polydisperse, internally mixed particles with a maximum number

concentration between 300 and 500 nm (Topas ATM 226 Manual). These salts were

chosen to isolate conversion behaviour between salts with NOy and non-NOy particulate

nitrogenous species. In addition, high purity deionized water (DIW, >18 MΩ, Easypure

RoDI II, Barnstead Inc. Dubuque, IA) was atomized to provide a background measurement.

The particles were directed into a teflon T-junction and split through PFA tubing toward

the AQD NOxy and AIM–IC. A temperature ramp was performed on the AQD NOxy

while the AIM-IC monitored the mass loadings of particulate species. No particle size

selection device was implemented on either the AQD NOxy or AIM-IC. 80% particle

collection efficiency was estimated for the AIM-IC due to high mass loadings of particles

and based on measurements taken from the exhaust of the inlet.

2.3 Results

2.3.1 Ammonia gas conversion efficiency trends in commercial

nitrogen dioxide analyzers

The temperature ramping experiments for NH3 showed a strong positive linear correlation

between conversion efficiency and Tconv in the TSI NOx instruments (Fig. 2.2). Within the

electronically limited temperature range of 310-340C, there was also significant difference

between the absolute conversion efficiencies of the two instruments. TSI NOx–A, which

had been operated continuously for a year, had an elevated conversion efficiency compared

to TSI NOx–B, which had been operated for less than a week. Furthermore, immediately

after TSI NOx–B was first calibrated after purchase, it was noted that addition of ammonia

resulted in negligible conversion. Halfway through the temperature ramping experiment,

TSI NOx–B experienced an unrelated instrumental failure and was unavailable for further

analysis.


0.30

0.25

0.20

0.15

0.10

Con

vers

ion

Effi

cien

cy

340335330325320315310Converter Temperature (°C)

TSI NOx-A TSI NOx-B

Figure 2.2: Conversion efficiencies of NH3(g) in TSI NOx analyzers

2.3.2 Ammonia gas conversion efficiency trends in total reactive

nitrogen oxide analyzer

The temperature conversion experiment was repeated six times with the AQD NOxy and

each experiment is plotted as a function of the fraction of the maximum signal against

the reported converter temperature in Fig. 2.3. The sigmoidal curve shape observed

in these experiments contrasts with the linear relationship in the TSI NOx instruments.

This result may be explained by the relatively narrow temperature range in the TSI NOx

instruments that does not allow observation of the non-linear relationship at the extremes

of Tconv.

Throughout the AQD NOxy temperature ramping experiments, the sigmoidal shape

of the conversion curve remained the same while the temperatures that corresponded to

given fractions of maximum signal varied. We hypothesize that these results are due to the

irreproducibility of position of the converter’s temperature probe and local environment

upon physical disturbance of the converter and its housing unit due to adjustments to the

insulation in the converter housing throughout the experiments. Due to the fluctuation

of the permeation source over the course of the six temperature ramping experiments, the

fraction of maximum detected signal (rather than conversion efficiency) as a function of

reported temperature in the AQD NOxy is displayed in Fig. 2.3. The February 7, 2012


temperature ramp experiment was performed with a previously unused converter taken

from the TSI NOx–B instrument while the rest of the runs were performed with the aged

converter from the AQD NOxy. The AIM-IC was implemented during the April, 16 2012

temperature ramp experiment to give the estimated conversion efficiency as a function

of reported converter temperature as seen in Fig. 2.4. We also noted that there was no

temperature dependence of HNO3—an important NOy species—within the Tconv range,

as shown in Fig. 2.4.

1.0

0.8

0.6

0.4

0.2

0.0

Fra

ctio

n of

Max

imum

Sig

nal

380360340320300280260Reported Converter Temperature (°C)

June 27, 2011 January 20, 2012 January 23, 2012 February 7, 2012 February 24, 2012 April 16, 2012

Figure 2.3: NH3(g) profiles in AQD NOxy

2.3.3 Salt conversion efficiency trends in total reactive nitrogen

oxide analyzer

The conversion efficiency of NO3-

and NH4+

salts were plotted as functions of reported

temperature in Fig. 2.5. The (NH4)2SO4 curve exhibits a similar conversion profile

to that of NH3; at the lowest temperatures the conversion efficiency is very low, but

reaches near unity at the highest temperatures. Ca(NO3)2 exhibits 50% conversion at

low temperatures followed by a linear increase to near unity at the highest temperatures.

Finally, NH4NO3 also exhibits the sigmoidal trend, but the conversion efficiency begins at

50% at the lowest temperature. Due to the low degradation temperature of NH4NO3, the

high initial conversion may be due to the complete conversion of NO3-

at low temperatures


1.0

0.8

0.6

0.4

0.2

0.0

Con

vers

ion

Effi

cien

cy


HNO3(g)

NH3(g)

Figure 2.4: Conversion efficiencies of NH3(g) and HNO3(g) in AQD NOxy with simultaneousAIM-IC monitoring

followed by the increasing conversion of NH4+

with temperature [35]. The difference

between the low-temperature conversion of particulate NO3-

in NH4NO3 and Ca(NO3)2

may be due to the relative susceptibility of NH4NO3 to thermal degradation compared

Ca(NO3)2 [36, 37].


1.0

0.8

0.6

0.4

0.2

Con

vers

ion

Effi

cien

cy


NH4NO3

Ca(NO3)2

(NH4)2SO4

Figure 2.5: Conversion efficiencies of nitrogen-containing salts in AQD NOxy with simul-taneous AIM-IC monitoring

2.4 Discussion

There are several major results which highlight the importance of understanding of

the NHx interference in MoOx-CLD reactive nitrogen instruments. The first is that

the absolute conversion efficiencies between two effectively identical commercial NOx

analyzers at any given temperature were significantly different. This result is consistent

with previous studies that reported variations between operationally similar instruments.

While the exact cause of the variations in conversion efficiency could not be isolated, we

speculate that the converter age, local environment in or around the converter, or the

calibration of the temperature probe could all be responsible.

The second major result is that the trend of increasing NH3 conversion efficiency with

increasing temperature was consistently observed. However, the AQD NOxy exhibited

substantial variability in the temperature required to produce maximum conversion

between the runs. This internal variability may be explained by the high sensitivity of

conversion efficiency to small changes in temperature between 300C and 360C. Error

in the temperature readout (perhaps caused by changes to the local environment of the

converter) could create the broad sensitivity range that allowed the AQD NOxy to convert

between 20% and >95% of the maximum signal at a reported temperature of 325C (a


typical operating temperature). Although uncertainty in Tconv may lead to the differences

in reported conversion efficiencies, it is also possible that manufacturing inadequacies that

lead to so-called “hot spots” within the converter or converter aging is impacting the

absolute conversion efficiency of the instrument [14]. This study does not preclude the

possibility of these factors but rather suggests that the effect of all of these problems may

only be accounted for by direct measurement of NH3 conversion efficiency.

The results obtained in this study contradict the interpretation of prior literature that

the conversion efficiency of NH3 is not temperature dependent [21]. Although prior studies

have observed dramatically different conversion efficiencies across a range of Tconv, this

study illustrates a critical shortcoming of the approach of reporting a single conversion

efficiency value at a given Tconv. If the position of the conversion efficiency curve on the

Tconv axis varies between instruments or converters due to bias or uncertainty in the readout

temperature, then previous studies may have observed accurate conversion efficiencies, but

either the absolute converter temperature was incorrect or converter aging had changed the

converter’s oxidizing ability. Since no study systematically characterized the relationship

between conversion efficiency and Tconv of a single converter and instrument, the results

could justifiably conflict.

Finally, NH4+

also exhibited similar conversion efficiency trends to NH3. Fehsenfeld

et al. [30] indirectly observed NH4+

interference, but this study directly confirms the

NH4+

interference and suggests that NH4+

must be considered along with NH3 to fully

characterize NHx interference.

2.5 Conclusions

Modern photolytic converters are replacing MoOx converters for chemiluminescent NOx

instrumentation and have been shown to closely correlate with spectroscopic NO2 measure-

ments via cavity ring-down spectroscopy (CARDS), laser-induced fluorescence (LIF), and

differential absorption spectroscopy (DOAS) [38–40]. However, many molybdenum con-

verter NOx instruments remain in usage and on the market, and MoOx-CLD instruments

are the principle method for determining NOy. Consequently, understanding the influence

of interferences is crucial to obtaining accurate measurements from these instruments.

Conventional analysis of field measurements taken by MoOx chemiluminescent instru-

ments characterizes MoOx conversion of NHx as being at or around a fixed percentage.

However, this approach may cause researchers to underestimate the true variability of NHx

conversion efficiency through neglecting to individually characterize their instruments.

Our research demonstrates the importance of regular characterization of NHx interference


and keeping the converter temperature as low as possible while still maintaining high

conversion efficiency of desired species (e.g. NO2, HNO3, etc.). Furthermore, the high

sensitivity of the conversion efficiency of NHx and uncertainty of instrumental reported

temperature helps explain the disagreements in prior literature. For NOx analyzers,

intoducing particle filters and NH3 denuders may effectively reduce the NHx interference,

however these devices may be impractical for NOy instrumentation since they may supress

particulate NO3-

and gas-phase HNO3. Further studies should be performed to determine

the best methods for reducing the amount of NHx entering the analyzers.

2.6 Acknowledgements

The author wishes to acknowledge the Abbatt and Evans research groups at the University

of Toronto for usage of the TSI NOx instruments. Jeff Geddes processed and analyzed all

of the AQD NOxy data and Jennifer Murphy provided guidance and support throughout

the experiments. The authors also wish to thank Jon Wang, Alex Tevlin, Greg Wentworth,

Angela Hong, and Stephanie Pugliese for assistance with various instrumental operations

throughout the experiment. Finally, the author acknowledges the NSERC CREATE

program, IACPES, for continued financial support.

2.7 Bibliography

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Appendix A

AIM-IC calibration and

instrumental information

The following lists the models of various Dionex IC components that were used specifically

for the AIM-IC operation. The eluent is produced from eluent generator cartridges (EGC)

and is recycled through the suppressor and continuously regenerated trap column (CR-TC).

In CONTACT-2012, both anion and cation systems used the EGC model III. The eluent

in the cation IC was methanesulphonic acid (MSA) while the anion IC used potassium

hydroxide (KOH). The cation concentrators, guard columns, and analytical columns

were models TCC-ULP1, CG17, and CS17, respectively, while the anion analogues were

TAC-ULP1, AG19, and AS19, respectively. The suppressor model was the respective

anion and cation versions of the self-regenerating suppressor (ASRS-300 and CSRS-300).

A.1 Gradient eluent chromatographic programs

Table A.1: Cation IC gradient eluent program set points. Flow rate is set to 1.0 mL/minand column temperature is set to 30.

Time (min) MSA Concentration (mM)0.0 2.009.0 2.0011.0 6.0016.0 10.0022.0 10.0027.0 2.00

75

Appendix A. AIM-IC calibration and instrumental information 76

Table A.2: Anion IC gradient eluent program set points. Flow rate is set to 1 mL/minand column temperature is set to 30

Time KOH Concentration (mM)0.0 1.0012.1 20.0018.0 85.0025.0 1.00

A.2 Non-linear organic acids and ammonium

Table A.3: Summary of calibration information for Acetic Acid from the CONTACT-2012field campaign for the AIM-IC

Date A’ value A’ σ B’ value B’ σ χ2

Gas StandardsJuly 24 1.06 0.257 6.56 ∗ 108 3.7 ∗ 107 0.0523September 7 2.37 0.82 7.03 ∗ 108 9.6 ∗ 107 0.164October 10 2.11 0.96 4.68 ∗ 108 1.4 ∗ 108 0.0123Particle StandardsJuly 24 1.75 0.30 8.26 ∗ 108 4.3 ∗ 107 0.0424September 7 2.27 1.01 8.02 ∗ 108 1.32 ∗ 108 0.309October 10 1.76 0.27 7.64 ∗ 108 6.0 ∗ 107 0.00394

Table A.4: Summary of calibration information for Formic Acid from the CONTACT-2012field campaign for the AIM-IC


Gas StandardsJuly 24 31.94 1.02 1.80 ∗ 1010 3.6 ∗ 108 0.0219September 7 47.53 13.9 2.29 ∗ 1010 4.71 ∗ 109 1.23October 10 475.1 497 1.51 ∗ 1011 1.63 ∗ 1011 0.0759Particle StandardsJuly 24 52.97 5.23 2.64 ∗ 1010 1.86 ∗ 109 0.146September 7 55.68 17.8 2.97 ∗ 1010 6.75 ∗ 109 1.74October 10 77.03 43.8 3.66 ∗ 1010 1.94 ∗ 1010 0.0245


Table A.5: Summary of calibration information for Oxalic Acid from the CONTACT-2012field campaign for the AIM-IC


Gas StandardsJuly 24 64.74 7.12 7.11 ∗ 1010 6.37 ∗ 109 0.0330September 7 52.22 3.89 6.00 ∗ 1010 3.5 ∗ 109 0.0207October 10 38.46 8.54 4.39 ∗ 1010 8.98 ∗ 109 0.00206Particle StandardsJuly 24 44.38 2.66 4.90 ∗ 1010 2.25 ∗ 109 0.0135September 7 46.756 3.02 5.41 ∗ 1010 2.68 ∗ 109 0.0174October 10 41.64 11.1 4.697 ∗ 1010 1.16 ∗ 1010 0.00257

Table A.6: Summary of calibration information for NH4+

from the CONTACT-2012 fieldcampaign for the AIM-IC

Date v v σ χ2

volume = 2.41mLGas StandardsJuly 24 4.33 ∗ 105 6.46 ∗ 103 0.02214September 7 4.25 ∗ 105 6.00 ∗ 103 0.0190October 10 3.82 ∗ 105 3.62 ∗ 103 0.0070Particle StandardsJuly 24 4.11 ∗ 105 2.63 ∗ 103 0.00367September 7 4.14 ∗ 105 1.90 ∗ 103 0.00192October 10 3.85 ∗ 105 3.95 ∗ 103 0.00828


A.3 Significant linear inorganic species

Table A.7: Summary of calibration information for Cl-


Date slope (µS min mol−1) slope σ (µS min mol−1) r2

Gas StandardsJuly 24 4.46 ∗ 108 3.21 ∗ 107 0.985September 7 4.33 ∗ 108 4.34 ∗ 107 0.971New ConcentratorSeptember 10 4.31 ∗ 108 4.60 ∗ 107 0.967October 10 2.41 ∗ 108 9.91 ∗ 106 0.995Particle StandardsJuly 24 4.21 ∗ 108 8.08 ∗ 107 0.900September 7 4.21 ∗ 108 2.88 ∗ 107 0.986October 10 4.22 ∗ 108 7.88 ∗ 106 0.999

Table A.8: Summary of calibration information for NO2-





Table A.9: Summary of calibration information for NO3-




Table A.10: Summary of calibration information for SO42-





A.4 Limits of detection

Table A.11: Summary of calibration information for SO42-


Species Limit of Detection (pptv)

Gas StandardsNH3 200.HCl 12.1HONO 2.79HNO3 8.43SO2 2.68Acetic Acid 566Formic Acid 488Oxalic Acid 7.39

Species Limits of Detection (ng m−3)

Particle Standards

Na+

150.

NH4+

25.0

K+

3.57

Mg2+

17.1

Ca2+

527Cl

-8.42

NO2-

3.52NO3

-36.1

SO42-

38.1Acetate 145.Formate 27.0Oxalate 5.43

Appendix B

E-AIM correlation data

Table B.1: CONTACT-2012 gas-particle partitioning correlation data

Species Slope r2

NH3 0.969 0.975

NH4+

1.031 0.940HNO3 0.974 0.349NO3

-0.429 0.288

SO42-

1.11 0.979

120x10-9

100

80

60

40

20

Con

cent

ratio

n (m

ol m

-3)

6/9/2010 6/10/2010 6/11/2010 6/12/2010 6/13/2010 6/14/2010Date

80x10-9

60

40

20

0Con

cent

ratio

n (m

ol m

-3) Input Accumulation Mode

Modelled Sea Salt Mode Input Sea Salt Mode Input Accumulation Mode

Figure B.1: Optimized separation comparison between modelled and input NO3(aq)-

81

Appendix B. E-AIM correlation data 82

160x10-9

120

80

40

Con

cent

ratio

n (m

ol m

-3)

6/9/2010 6/10/2010 6/11/2010 6/12/2010 6/13/2010 6/14/2010Date

100x10-9

80

60

40

20

0 Con

cent

ratio

n (m

ol m

-3)

Input Accumulation Mode Input Sea Salt Mode Modelled Accumulation Mode Modelled Sea Salt

Figure B.2: Optimized separation comparison between modelled and input NH4(aq)+

20x10-9

15

10

5

0Con

cent

ratio

n (m

ol m

-3)

6/9/2010 6/10/2010 6/11/2010 6/12/2010 6/13/2010 6/14/2010Date

50x10-9

40

30

20

10

0Con

cent

ratio

n (m

ol m

-3)

Input Accumulation Mode Input Sea Salt Mode Modelled Accumulation Mode Modelled Sea Salt

Figure B.3: Optimized separation comparison between modelled and input Cl(aq)-

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