A Method for the In-Situ Measurement of the Water Content of Atmospheric Particles

Transcription

1 UNIVERSITY OF PATRAS MASTER THESIS IN CHEMICAL ENGINEERING ENERGY & ENVIRONMENT A Method for the In-Situ Measurement of the Water Content of Atmospheric Particles Author : Supevisor : Epameinondas Tsiligiannis Prof. Spyros Pandis December 21, 2016

2 There is no royal road to science, and only those who do not dread the fatiguing climb of its steep paths have a chance of gaining its luminous summits. Karl Marx, Preface to the French edition of Capital, 1872 A method for the in situ measurement of the water content of atmospheric particles. - ii

3 To my parents, my brother and my sister A method for the in situ measurement of the water content of atmospheric particles. - iii

4 Acknowledgements Herein I would like to express my gratitude and say thanks to all the people I have met and co operated with during my graduate studies in the Laboratory of Air Quality Studies (LAQS) at FORTH/ICE HT and in the Department of Chemical Engineering in Patras. I would especially like to thank: my supervisor, Prof. Spyros Pandis, for introducing me to atmospheric science and giving me the opportunity to work in his laboratory. The fruitful discussions with him and his guidance were an inspiration during my graduate studies. Prof. Petros Koutsoukos and Assoc. Prof. Michael Kornaros for participating in my M.S. committee. Christos Kaltsonoudis, whose technical expertise was priceless and Evangelos Louvaris for sharing the office with me and having so many scientific and not so scientific discussions. One piece of advice, guys, quit smoking it s killing me! Kalliopi Florou for introducing me to the world of Igor and helping me whenever it was needed. Dr. Evangelia Kostenidou for introducing me to the world of the DAASS and providing valuable information. Dr. Dimitrios Papanastasiou for helping me with Labview. Dr. Magdalene Psichoudaki for introducing me to the Water Soluble Organic Carbon (WSOC) measurement techniques and sharing ashtanga yoga classes. Spyros Giorgas for his excellent and very productive collaboration during his diploma thesis. Kalliopi Kiari for the interesting time and discussions during the ACCENT Plus summer school in Italy. A method for the in situ measurement of the water content of atmospheric particles. - iv

5 all the former members of the laboratory team: Giorgos Gkatzelis, Maria Tsiflikiotou and Anna Siampani for their cooperation. all the members of the modeling group, the so called PMCAMx people! my friends for their continuous encouragement, friendship and for always being there for me. my parents, Vasilis and Stavroula, my brother, Spyros, and my sister, Marina for their love and support during my studies in so many ways. A method for the in situ measurement of the water content of atmospheric particles. - v

6 Abstract The hygroscopic behavior of atmospheric aerosols influences their size, composition, lifetime, chemical reactivity, and light scattering. Hygroscopic growth plays an important role in a number of air pollution problems including visibility impairment, climate change, acid deposition, long range transport and the ability of particles to penetrate the human respiratory system. The absorption of water by aerosol particles often exhibits a hysteresis. Thus, the physical state (liquid or solid) of particles and the amount of aerosol water at a specific relative humidity (RH) are uncertain, as they depend on the history of these particles. In this work, the reduced version of the Dry Ambient Aerosol Size Spectrometer (DAASS) that measures the water content of atmospheric aerosols has been redesigned and optimized. The DAASS measures the number distribution of the aerosols at ambient conditions and at low RH thus drying the particles. A comparison of these distributions allows the determination of the physical state of the particles and their water content. The new version of the DAASS is capable of operating at higher RH values than its predecessor. The instrument has been characterized regarding particle wall losses, in a set of smog chamber experiments using (NH 4 ) 2 SO 4 particles. An algorithm checking the consistency of the measurements and the applicability of the assumptions used in the data analysis was developed. The water concentrations observations were compared to the predictions of the aerosol thermodynamics model E AIM. Ambient measurements, using the original and the improved version of DAASS, were conducted during two different time periods in a suburban area in Patras. These tests allowed the testing and assessment of the operation of the DAASS and also the examination of the hygroscopic behavior of particulate matter. The original version was used during a moderate RH period, in which the water content of the aerosol represented 0 50% of the fine aerosol mass. The improved DAASS operated during high RH conditions. The particles retained water throughout the duration of the measurements. The measured volume growth factors were quite higher than the measured ones during the moderate RH period. The water concentrations observations were compared to the predictions of the aerosol thermodynamics model E AIM. A method for the in situ measurement of the water content of atmospheric particles. - vi

7 Table of Contents Acknowledgements... iv Abstract... vi Table of Contents... vii Table of Figures... x List of Abbreviations... xiv CHAPTER Introduction Atmospheric aerosols Aerosol size, sources, and relevant processes Aerosol chemical composition Health effects of atmospheric aerosols Aerosol climate interactions Direct aerosol effect Indirect aerosol effect Aerosol water content Hygroscopic growth Hysteresis deliquescence and efflorescence Measurement of hygroscopic properties Motivation References CHAPTER DAASS Description Introduction A method for the in situ measurement of the water content of atmospheric particles. - vii

8 2.2 Scanning mobility particle sizer (SMPS) Differential mobility analyzer (DMA) Condensation particle counter (CPC) Dry ambient aerosol size spectrometer (DAASS) System design and operation principles Instrument performance Improvement of the DAASS operation References CHAPTER DAASS Characterization DAASS testing Experimental set up Particles losses Data analysis Algorithm consistency check Comparison to a thermodynamic model References CHAPTER DAASS Applications Introduction Ambient measurements with the original DAASS Site and instrumentation Meteorological Conditions Results Ambient measurements with the improved DAASS Site and instrumentation Meteorological Conditions A method for the in situ measurement of the water content of atmospheric particles. - viii

9 4.3.3 Results Conclusions References CHAPTER Conclusions & Future Work APPENDIX A method for the in situ measurement of the water content of atmospheric particles. - ix

10 Table of Figures Figure 1.1: Typical number and volume distributions of atmospheric particles with the different modes (Seinfeld and Pandis, 2006) Figure 1.2: Pictures of typical atmospheric particles (Brasseur et al, 2003) Figure 1.3: PM 1 mass concentration (in μg m 3 ) and mass fractions of non refractory inorganic species and organic components at several remote and urban locations in the Northern Hemisphere. The organic aerosol (OA) is separated by factor analysis of AMS data into SV OOA and LV OOA (in some locations only OOA), HOA and other OA (Jimenez et al., 2009) Figure 1.4: Effects of inhalation of particulate matter on our health based on Pope and Dockery (2006) Figure 1.5: Global mean energy budget under present day climate conditions. Numbers state magnitudes of the individual energy fluxes in W m 2, adjusted within their uncertainty ranges to close the energy budgets. Numbers in parentheses attached to the energy fluxes cover the range of values in line with observational constraints (Wild et al., 2013; IPCC, 2013) Figure 1.6: Overview of interactions between aerosols and solar radiation and their impact on climate. The left panels show the instantaneous radiative effects of aerosols, while the right panels show their overall impact after the climate system has responded to their radiative effects (IPCC, 2013) Figure 1.7: Overview of aerosol cloud interactions and their impact on climate. Panels (a) and (b) represent a clean and a polluted low level cloud, respectively (IPCC 2013) Figure 1.8: Diameter change of (NH 4 ) 2 SO 4, NH 4 HSO 4, and H 2 SO 4 particles as a function of relative humidity. D p0 is the diameter of the particles at 0% RH (Seinfeld and Pandis, 2006) Figure 2.1: Schematic flow for the DMA. Positively charged particles are attracted to the negatively charged rod in the center based upon their electric mobility. Particles of the appropriate size will pass through the narrow opening and continue through the monodisperse outstream Figure 2.2: Schematic flow for the water based CPC Figure 2.3: Flow diagram of the reduced DAASS. The blue, green, and red lines represent the vent, the ambient and the dry mode, respectively Figure 2.4: Example of RH time series in DAASS for the various instruments modes during typical measurements in a smog chamber A method for the in situ measurement of the water content of atmospheric particles. - x

11 Figure 2.5: Revised DAASS flow diagram. An open loop is now used for the ambient sheath air flow allowing the system to reach higher RH values. The blue and red lines represent the ambient and the dry mode, respectively Figure 2.6: The RH of the chamber (red line), inlet flow (black line) and sheath flow (blue line) during an experiment with the improved DAASS Figure 3.1: Experimental set up during chamber experiments Figure 3.2: Average particle loss percentage based on four different experiments as function of particle diameter for dry particles. The black symbols represent the measured losses, the black vertical lines the variability (±1σ) of the average losses correction and the red line is the empirical fit Figure 3.3: Number distributions of ambient (blue line), dry (red line) and corrected dry (black line) aerosol while DAASS was operated at 35% RH, during a chamber experiment with (NH 4 ) 2 SO 4 particles Figure 3.4: Number distributions of ambient (blue line), dry (red line) and corrected dry (black line) aerosol while the DAASS operated at 73% RH. The corrected dry distribution was calculated using Equation Figure 3.5: Flowchart of the DAASS data analysis Figure 3.6: Cumulative ambient (blue symbols) and dry (red symbols) aerosol number distributions during a smog chamber experiment at 73% RH in a probabilitylog graph Figure 3.7: An example of the effect of relative humidity on the (NH 4 ) 2 SO 4 a) number and b) volume size distributions measured at 82% RH. The blue line represents the ambient (wet) distribution, while the red and black lines the dry and the corrected dry distributions, respectively Figure 3.8: Ambient (blue symbols) and corrected dry (red symbols) aerosol number concentrations during a smog chamber experiment at 82% RH. The red line is calculated based on the cubic spline interpolation of the dry data Figure 3.9: Ambient (blue line) and corrected dry (red line) aerosol volume concentrations. The difference is the water volume concentration (green line) Figure 3.10: Time series of the measured (top) and the corrected (bottom) volume growth factors. At the right y axis the sheath RH is shown Figure 3.11: Number distribution of ambient (red line) and measured dried (blue line) (NH 4 ) 2 SO 4 aerosol particles respectively at RH=75%. In addition, the predicted aerosol distribution (black dashed line) accounting for the losses in the DAASS system is shown Figure 3.12: Theoritical (red lines) based on the E AIM model and corrected measured (blue dots) volume growth factors for (NH 4 ) 2 SO 4 particles. The horizontal lines denote the uncertainty (±1σ) of the average RH and the vertical ones of the average measured VGF 1/ A method for the in situ measurement of the water content of atmospheric particles. - xi

12 Figure 4.1: Location of sampling site at the Institute of Chemical Engineering Sciences (ICE HT) Figure 4.2: The inlet (black line) and sheath flow (blue line) temperature as well as the ambient (red line) temperature, during the campaign Figure 4.3: Relative humidity time series during the campaign. The inlet and the ambient RH are depicted by a black and a red line, respectively. The sheath RH is indicated by blue symbols Figure 4.4: Mass concentration of the major PM 1 components measured by the HR ToF AMS and the MAAP during the campaign Figure 4.5: Average composition of the main PM 1 aerosol components during the summer measurements Figure 4.6: Ambient (blue line) and corrected dry (red line) volume concentrations measured by the DAASS. The difference is the water (green line) volume concentration Figure 4.7: Mass fraction of aerosol water (blue symbols) and the corresponding RH (red line) during the campaign Figure 4.8: The a) ambient and b) corrected dry particle number distributions during the campaign. The y axis is the particle diameter, the x axis represents the local time and the color scale indicates the particle number concentration Figure 4.9: The a) ambient and b) corrected dry particle volume distributions during the campaign. The y axis is the particle diameter, the x axis represents the local time and the color scale indicates the particle volume concentration Figure 4.10: Measured volume growth factors during the study Figure 4.11: Time series of the measured volume growth factors (red symbols) by the DAASS. The inlet RH (black line) is indicated at the right y axis Figure 4.12: Cations/anions ratio versus time as calculated from the molar ratio of ammonium over sulfate, nitrate and chloride measured by the AMS Figure 4.13: Water versus time as measured by the DAASS (red symbols) and estimated from E AIM (black line) at sheath RH. The water uptake by the organics is neglected in the simulations Figure 4.14: The inlet flow (black line), sheath flow (blue line) and ambient (red line) temperature, during the ambient measurements with the improved DAASS Figure 4.15: Relative humidity time series during the ambient measurements with the improved DAASS. The inlet and the ambient RH are depicted by a black and a red line, respectively. The sheath RH is indicated by blue symbols Figure 4.16: Mass concentration of the major PM 1 components measured by the HR ToF AMS during the first day of the measurements A method for the in situ measurement of the water content of atmospheric particles. - xii

13 Figure 4.17: Average composition of the main PM 1 aerosol components during the first day of the autumn measurements Figure 4.18: Cations/anions ratio versus time as calculated from the ratio of equivalents of ammonium over sulfate, nitrate and chloride measured by the AMS.. 59 Figure 4.19: The a) ambient and b) corrected dry particle number distributions during the ambient measurements in the fall of The y axis is the particle diameter, the x axis represents the local time and the color scale indicates the particle number concentration Figure 4.20: The a) ambient and b) corrected dry particle volume distributions during the ambient measurements in the fall of The y axis is the particle diameter, the x axis represents the local time and the color scale indicates the particle volume concentration Figure 4.21: Ambient (blue line) and corrected dry (red line) volume concentrations measured by the improved DAASS. The difference is the water (green line) volume concentration Figure 4.22: Volume growth factors measured by the improved DAASS during the fall Figure 4.23: Time evolution of the measured volume growth factors (red symbols) by the improved DAASS. The inlet RH (black line) is indicated at the right y axis Figure 4.24: Average measured volume growths factors during the summer campaign (red symbols) and during the fall (blue squares). The error bars indicate one standard deviation and the labels show the number of observations per each point A method for the in situ measurement of the water content of atmospheric particles. - xiii

14 List of Abbreviations AMS BC CCN CPC DAASS DASH SP DMA DRH E AIM ERH FTIR GF HEPA HOA HR ToF AMS H TDMA HULIS ICE HT LAQS LV OOA MAAP Aerosol mass spectrometer Black carbon Cloud condensation nuclei Condensation particle counter Dry ambient aerosol size spectrometer Differential aerosol sizing and hygroscopicity spectrometer probe Differential mobility analyzer Deliquescence relative humidity Extended aerosol inorganics model Efflorescence relative humidity Fourier transform infrared spectroscopy Growth factor High efficiency particulate arrestance (filter) Hydrocarbon like organic aerosol High resolution time of flight aerosol mass spectrometer Hygroscopic tandem differential mobility analyzer Humic like substances Institute of Chemical Engineering Sciences Laboratory of Air Quality Studies Low volatility oxygenated organic aerosol Multi angle absorption photometer A method for the in situ measurement of the water content of atmospheric particles. - xiv

15 OC OA OOA PM PM 0.5 PM 1 PM 10 PTR MS RH SMPS SV OOA VGF VOCs WHOPS WSOM Organic carbon Organic aerosol Oxygenated organic aerosol Particulate matter Particulate matter with diameter < 0.5 μm Particulate matter with diameter < 1 μm Particulate matter with diameter < 10 μm Proton transfer reaction mass spectrometer Relative humidity Scanning mobility particle sizer Semi volatile oxygenated organic aerosol Volume growth factor Volatile organic compounds White light humidified optical spectrometer Water soluble organic matter A method for the in situ measurement of the water content of atmospheric particles. - xv

16 CHAPTER 1 Introduction 1.1 Atmospheric aerosols Atmospheric aerosols are an important and ubiquitous component of the Earth s atmosphere. Aerosols, also known as particulate matter (PM), are solid or liquid particles suspended in a gaseous medium; for atmospheric aerosols this gas is ambient air (Kulkarni et al., 2011). Aerosols have several important effects on the environment and on human health, causing visibility impairment (Charlson, 1969; Sloane and White, 1986; Hand et al., 2002; Watson, 2002) as well as affecting regional and global climate Aerosol size, sources, and relevant processes Aerosols can be classified as primary or secondary. Primary PM is emitted directly, either from anthropogenic sources, through industrial processes, fuel combustion, wood burning, transportation, etc., or from natural sources such as mineral dust, sea salt, pollen, volcanic dust, etc. Secondary PM is produced in the atmosphere via chemical reactions involving different gas phase precursors, such as sulfur dioxide (SO 2 ), nitrogen oxides (NO x ), ammonia (NH 3 ), and volatile organic compounds (VOCs). The gas phase precursors react photochemically, producing new low vapor pressure compounds. These new compounds can condense onto pre existing particles (increase in size) or nucleate forming new ultrafine particles (increase in number). Particles are removed from the atmosphere by two mechanisms: deposition at the Earth s surface (dry deposition) and incorporation into rain droplets precipitation (wet deposition). The typical residence time of particles in the troposphere varies from a few days to several weeks (Seinfeld and Pandis, 2006). Aerosol particles cover a size range over four to five orders of magnitude, from a few nanometers (nm) to tens of micrometers (μm) in diameter. A typical number and volume distribution of aerosol in an urban area, which are often expressed as the sum of lognormal functions, are shown in Figure 1.1. Coarse particles are defined as those having diameters from 2.5 to 10 μm, while those with diameters less than 2.5 μm are A method for the in situ measurement of the water content of atmospheric particles. - 1

17 40 Number (dn/dlogd p ), cm -3 x Nucleation Mode Aitken Mode 0 Volume (dv/dlogd p ), m 3 /cm Condensation Submode Accumulation Mode Droplet Submode Coarse Mode Diameter (micrometers) Figure 1.1: Typical number and volume distributions of atmospheric particles with the different modes (Seinfeld and Pandis, 2006). called fine particles. Also, the fine particles with diameters smaller than 0.1 μm are often called ultrafine. Coarse particles are orders of magnitude fewer in number in comparison to the ultrafine, but have much higher mass concentration. The number distribution includes two modes: the nucleation (< 10 nm) and Aitken ( nm) modes. The nucleation mode is present when new particles are formed in situ during the oxidation of precursor gases. Most of the Aitken nuclei start their atmospheric life as primary particles, and then secondary material condenses on them as they are transported through the atmosphere. The volume distribution is also characterized by two major modes; the accumulation (100 nm 2.5 μm) and coarse (> 2.5 μm) modes, which have different sources and hence different composition and properties. The accumulation mode consists of primary particles and secondary material. It has two overlapping submodes; the condensation and the droplet modes. The coarse mode is formed by mechanical processes and usually includes dust A method for the in situ measurement of the water content of atmospheric particles. - 2

18 Figure 1.2: Pictures of typical atmospheric particles (Brasseur et al, 2003). particles, sea salt, pollen and other mechanically generated particles (Figure 1.2). This difference in sources is also depicted in the composition of the two modes Aerosol chemical composition The chemical composition of aerosols depends on their source and their atmospheric processing. Atmospheric PM components can be categorized as non refractory (e.g. sulfate (SO 2 4 ), nitrate (NO 3 ), ammonium (NH + 4 ), chloride (Cl ), potassium (K + ), organic compounds (alkanes, alkenes, aldehydes, organic acids, ketones, aromatic and polyaromatic species, etc.)) and refractory (e.g. black carbon (BC), mineral dust, some salts). Another useful chemical separation is the grouping according to inorganic and organic identity. The carbonaceous component of aerosols includes black carbon (optical definition) or elemental carbon (thermal definition) and thousands of organic compounds, known as organic carbon (OC) (Penner and Novakov, 1996). There are several approaches to categorize the organic aerosol (OA) according to its properties e.g. water insoluble and water soluble organic matter (WSOM) or organics A method for the in situ measurement of the water content of atmospheric particles. - 3

19 with similarities to humic like substances (HULIS) (Facchini et al., 1999; Decesari et al., 2000; Gysel et al., 2004; Psichoudaki and Pandis, 2013). For the determination of the chemical composition of atmospheric particles aerosol mass spectrometers (AMS) have been increasingly used in the field and laboratory (Cappa and Jimenez, 2010; Lee et al., 2010; Poulain et al., 2010). During the analysis of AMS data a separation of organic aerosol into hydrocarbon like organic (HOA) and oxygenated organic aerosol (OOA) is commonly employed (Zhang et al., 2007a; Lanz et al., 2007). Organic compounds are often the dominant fraction of atmospheric submicrometer PM, with a contribution of 20 to 70%, depending on the location site and season (Figure 1.3) (Kanakidou et al., 2005; Zhang et al., 2007a; Jimenez et al., 2009). Figure 1.3: PM 1 mass concentration (in μg m 3 ) and mass fractions of non refractory inorganic species and organic components at several remote and urban locations in the Northern Hemisphere. The organic aerosol (OA) is separated by factor analysis of AMS data into SV OOA and LV OOA (in some locations only OOA), HOA and other OA (Jimenez et al., 2009). A method for the in situ measurement of the water content of atmospheric particles. - 4

20 1.2 Health effects of atmospheric aerosols One of the most severe incidents of air pollution, involving aerosols, is the Great Smog of 1952 that occured in London from 5 to 9 December 1952, causing several thousands premature deaths (Brimblecombe, 1987; Stegeman et al., 2002). Since then quite a few studies have indicated correlations of elevated particulate matter (PM) and various diseases and mortality (Pope, 1991; Dockery et al., 1993; Hoek et al., 2002; Pope et al., 2002; Kunzli et al., 2005; Pope et al., 2009). The particle size is an important characteristic, but chemical composition and other physical properties might play a significant role as well (Wang et al., 2008; Londahl et al., 2008). The particle size is influenced by hygroscopic growth, which leads to largerr particle size at high relative humidity (RH). On the other hand, volatility of particles might lead to smaller, less volatile particles due to temperature increases. Thus both hygroscopicity and volatility can have an impact by changing the deposition efficiency and location within the respiratory system. Various possible PM effects on human health due to inhalation of particulate matter are shown in Figure 1.4. Figure 1.4: Effects of inhalation of particulate matter on our health based on Pope and Dockery (2006). A method for the in situ measurement of the water content of atmospheric particles. - 5

21 1.3 Aerosol climate interactions Atmospheric particulate matter can also affect the Earth s radiative budget (Figure 1.5) and thus global climate. Aerosols affect climate via their so called direct and indirect radiative effects (Schwartz, 1996). The change in the radiation balance of incoming and outgoing energy is usually expressed as radiative forcing (RF) in W m 2. Radiative forcing is linearly related to the mean surface temperature for small changes; positive forcing increases temperature, while negative forcing tends to reduce it. Figure 1.5: Global mean energy budget under present day climate conditions. Numbers state magnitudes of the individual energy fluxes in W m 2, adjusted within their uncertainty ranges to close the energy budgets. Numbers in parentheses attached to the energy fluxes cover the range of values in line with observational constraints (Wild et al., 2013; IPCC, 2013) Direct aerosol effect The aerosol direct effect on climate (Figure 1.6) is due to scattering and absorption of shortwave and longwave radiation and is now called radiative forcing (RF) due to aerosol radiation interactions (RF ari ) (Fuzzi et al., 2015). Aerosol (e.g. ammonium sulfate) scattering generally makes the planet more reflective, and tends to cool the climate, while aerosol absorption (e.g. by black carbon) has the opposite effect, and A method for the in situ measurement of the water content of atmospheric particles. - 6

22 Figure 1.6: Overview of interactions between aerosols and solar radiation and their impact on climate. The left panels show the instantaneous radiative effects of aerosols, while the right panels show their overall impact after the climate system has responded to their radiative effects (IPCC, 2013). tends to warm the climate system. The balance between cooling and warming depends on aerosol properties and environmental conditions (IPCC, 2013). Water uptake of aerosols clearly influences the size distribution, and thus the ambient relative humidity and the aerosol hygroscopicity have a major impact on the direct effect Indirect aerosol effect The indirect aerosol effect is now referred to as the RF due to aerosol cloud interactions (RF aci ) (Fuzzi et al., 2015). Aerosols serve as cloud condensation nuclei (CCN), on which cloud droplets can form (Figure 1.7). CCN initiate the formation of clouds in the atmosphere, as the atmospheric water vapor supersaturations of 1% or less are never high enough to cause homogeneous water nucleation. The supersaturation needed to form clouds is dramatically reduced if particles are present. Changes in aerosol number, size, chemical composition or mixing state, for example through anthropogenic emissions, influence the number and properties of the nuclei, leading to significant changes in the cloud properties and precipitation (Levin and Cotton, 2009). Quantifying the overall impact of aerosols on clouds is extremely A method for the in situ measurement of the water content of atmospheric particles. - 7

23 Figure 1.7: Overview of aerosol cloud interactions and their impact on climate. Panels (a) and (b) represent a clean and a polluted low level cloud, respectively (IPCC 2013). challenging. Available studies, based on climate models and satellite observations, generally indicate that the net effect of anthropogenic aerosols on clouds is to cool the climate system (IPCC, 2013). 1.4 Aerosol water content Water uptake by atmospheric particles influences their size and thus their lifetime, composition, chemical reactivity, light scattering and consequently the resulting visibility degradation and direct climate forcing (Malm and Day, 2001; Spichtinger and Cziczo, 2008). Water is the most prevalent aerosol component at relative humidities (RHs) above 80% and is often a significant one at lower RHs. The water uptake of aerosols, which consist of single or multiple inorganic salts, such as (NH 4 ) 2 SO 4, KCl, NaCl, etc., is well understood (Tang, 1997; Seinfeld and Pandis, 2006). However, the behavior of complex mixtures including organic and inorganic components remains uncertain Hygroscopic growth The hygroscopic growth of the particles can be described by the diameter growth factor (GF), which is defined as the particle diameter D wet at a certain relative humidity (RH) divided by the particle dry diameter D dry : GF(RH) = D (RH) D (1.1) A method for the in situ measurement of the water content of atmospheric particles. - 8

24 Other common metrics of growth are the volume, mass or optical scattering growth factors. For a known chemical composition, the GF is dependent on RH and on particle size. The dependence is described by the Köhler theory, which expresses the two effects that determine the vapor pressure over an aqueous solution droplet; the Kelvin effect (curvature effect) that tends to increase vapor pressure and the Raoult effect (solute effect) that tends to decrease vapor pressure. A brief outline of the Köhler theory is given below. Further details about the thermodynamics of atmospheric aerosols may be found in Seinfeld and Pandis (2006). The water vapor content of the atmosphere is usually expressed as the ratio of partial pressure of water vapor, P w, to the saturation vapor pressure of water, P 0 w, at a specific temperature, which is known as relative humidity (RH). RH = P P (1.2) The equilibrium vapor pressure over an ideal aqueous solution, P s, is given by Raoult s law: P P = x (1.3) where x w is the mole fraction of water. The Raoult effect leads to the decrease of the equilibrium RH over an aqueous solution due to solutes. In general, an aqueous solution approaches ideality as it becomes more and more dilute, but this is the case only at very high RH in the atmosphere. Atmospheric aerosols are usually concentrated aqueous solutions that significantly deviate from ideality. The aforementioned deviation is usually described by introducing the activity coefficient, γ w. Hence, the modified Raoult s law can be expressed as: P P = γ x (1.4) The water activity, α w, is defined as the product of γ w and x w, and is equivalent to the effective equilibrium RH over a flat surface. The Kelvin effect describes the enhancement of water vapor pressure over a curved surface. Representing the equilibrium vapor pressure over a curved surface with P K, A method for the in situ measurement of the water content of atmospheric particles. - 9

25 the saturation vapor pressure ratio over a droplet with diameter D is given by the Kelvin equation: P (D) P = exp 4σ M RTρ D (1.5) where σ s is the surface tension of the solution, R the ideal gas constant, T the absolute temperature, ρ w the density of water and M w the molecular mass of water. The Kelvin equation tells us that the vapor pressure over a curved interface always exceeds that of the same substance over a flat surface. The Köhler equation combines the Raoult and the Kelvin effect and describes the relationship between the equilibrium RH and the size of non ideal droplets as: ln P (D) P = 4M σ RTρ D 6n M + lnγ ln 1 + πρ D (1.6) where n s is the number of moles of solute Hysteresis deliquescence and efflorescence Atmospheric particles are able to exist in multiple thermodynamically stable and metastable states. A dry single salt particle, e.g. (NH 4 ) 2 SO 4 (Figure 1.8), will uptake water at a certain relative humidity, which is called deliquescence relative humidity (DRH), grow suddenly, and then continue to grow as RH is increased further. Right after the DRH, the Gibbs free energy of the wet (solute) particle becomes lower than the one of the dry particle and therefore a phase transition takes place. If the RH is then decreased, the particle will release some of the absorbed water, in order to equilibrate with the new conditions. However, as RH is decreased below the DRH, the particle will not immediately crystallize, but it will remain in a metastable supersaturated solution phase until the RH reaches a critical supersaturation, called efflorescence relative humidity (ERH), and crystallization takes place. This results in a hysteresis, a RH range where both solid particles and liquid droplets may exist, depending on the particle s RH history. The RH where a solid particle becomes wet (DRH), is determined by thermodynamics, whereas the RH where crystallization of an aqueous droplet occurs (ERH) depends on nucleation kinetics. Some aerosol components, such as sulfuric acid, do not exhibit deliquescence behavior, but respond smoothly to relative humidity changes (Figure 1.8). A method for the in situ measurement of the water content of atmospheric particles. - 10

26 Figure 1.8: Diameter change of (NH 4 ) 2 SO 4, NH 4 HSO 4, and H 2 SO 4 particles as a function of relative humidity. D p0 is the diameter of the particles at 0% RH (Seinfeld and Pandis, 2006) Measurement of hygroscopic properties A variety of experimental techniques are used to measure the hygroscopic growth of atmospheric particles. The most common is the hygroscopic tandem differential mobility analyzer (H TDMA) (Swietlicki et al., 2008). H TDMAs select a dried narrow size range of particles and expose them to an elevated RH (usually at 90% RH) and then measure the resultant size distribution and thus diameter growth factor. In spite of the significant use of the H TDMA in laboratory and field studies, this technique does not measure the water content of aerosols in their ambient state directly. This is because the particles are dried before sampling and then re wetted. This may lead to evaporation of species other than water during the drying process or to a change in the morphology of the particles, and hence their physical characteristics. In addition, the applicable size range of H TDMA is narrow (approximately up to 250 nm). Another technique devised to measure water uptake is the humidity controlled nephelometer (Rood et al., 1987; Day et al., 2000; Fierz Schmidhauser et al., 2010) A method for the in situ measurement of the water content of atmospheric particles. - 11

27 which measures the aerosol light scattering coefficient at low and ambient RH, and thus the optical scattering growth factors. However, no size specific or mixing state information can be retrieved. FTIR spectroscopy has been used to determine hygroscopic properties of aerosol particles as a function of temperature via infrared absorption spectra (Onasch et al., 1999). Chemical analysis combined with filters weighed at different RHs has also been used to measure the amount of water attributable to the inorganic constituents in filter samples (Speer et al., 2003). Both of these techniques are labor intensive and have low temporal resolution. The differential aerosol sizing and hygroscopicity spectrometer probe (DASH SP) (Sorooshian et al., 2008) combines differential mobility analysis together with multiple humidification and optical sizing steps, in order to determine dry optical size and hygroscopic growth factors for size selected (up to 1 μm) aerosols. The DASH SP has been designed especially for aircraft based measurements, with time resolution as short as a few seconds. Rosati et al. (2015) recently developed the white light humidified optical spectrometer (WHOPS), which provides fast measurements of particle hygroscopicity at subsaturated RH and optical properties. The WHOPS retrieves information of relatively large particles (i.e., diameter > 280 nm). The Dry Ambient Aerosol Size Spectrometer (DAASS), which was developed by Stanier et al. (2004), measures aerosol number size distributions of atmospheric particles at dry and ambient conditions, in order to assess the amount of aerosol water in atmospheric conditions from the difference of integrated volumes. The DAASS is the main instrument used in this work. More technical details can be found in Chapter Motivation The hygroscopic behavior of atmospheric particles affects a number of environmentally important aerosol properties and plays a significant role in a number of air pollution problems. Due to the hysteresis exhibited during the aerosol hygroscopic growth, even for single salts (e.g. ammonium sulfate), the physical state (dry or wet) of ambient particles and the amount of aerosol water are uncertain within a wide range of relative humidities found in the troposphere, leading to uncertainties A method for the in situ measurement of the water content of atmospheric particles. - 12

28 in optical and chemical properties of the aerosol. Furthermore, despite the known importance of water uptake in ambient aerosol particles there is a relative scarcity of water measurements. In this work the reduced Dry Ambient Aerosol Size Spectrometer (DAASS) (Engelhart et al., 2011) is redesigned and optimized. The main goal is to improve its performance and accuracy, as well as to provide data in order to increase our understanding of complex aerosol atmospheric systems and processes. A method for the in situ measurement of the water content of atmospheric particles. - 13

29 1.6 References Brasseur, G.P., Prinn, R.G. and Pszenny A.A.P. (2003). Atmospheric Chemistry in a Changing World. An Integration and Synthesis of a Decade of Tropospheric Chemistry Research, Springer Verlag Berlin Heidelberg. Brimblecombe, P. (1987). The Big Smoke A History of Air Pollution in London Since Medieval Times, London, Methuen & Co. Ltd. Cappa, C. and J. Jimenez (2010). Quantitative estimates of the volatility of ambient organic aerosol, Atmospheric Chemistry and Physics, 10, Charlson, R.J. (1969). Atmospheric visibility related to aerosol mass concentration a review, Environ. Sci. Technol., 3, Day, D. E., Malm, W. C., and Kreidenweis, S. M. (2000). Aerosol light scattering measurements as a function of relative humidity, J. Air Waste Manage, 50, Decesari, S., Facchini, M. C., Fuzzi, S., Tagliavini, E. (2000). Characterization of water soluble organic compounds in atmospheric aerosol: A new approach, J. Geophys. Res., 105, Dockery, D. W., Pope, C. A., Xu, X. P., Spengler, J. D., Ware, J. H., Fay, M. E., Ferris, B. G., and Speizer, F. E. (1993). An association between air pollution and mortality in 6 United States cities, N. Engl. J. Med., 329, Engelhart, G. J., L. Hildebrandt, E. Kostenidou, N. Mihalopoulos, N. M. Donahue, and S. N. Pandis (2011). Water content of aged aerosol, Atmos. Chem. Phys., 11, Facchini, M. C., Fuzzi, S., Zappoli, S., Andracchio, A., Gelencér, A., Kiss, G., Krivácsy, Z., Mészáros, E., Hanson, H. C., Alsberg, T., Zebühr, Y. (1999). Partitioning of the organic aerosol component between fog droplets and interstitial air, J. Geophys. Res., 104, Fierz Schmidhauser, R., Zieger, P., Wehrle, G., Jefferson, A., Ogren, J. A., Baltensperger, U., and Weingartner, E. (2010). Measurement of relative humidity dependent light scattering of aerosols, Atmos. Meas. Tech., 3, A method for the in situ measurement of the water content of atmospheric particles. - 14

30 Fuzzi, S., U. Baltensperger, K. Carslaw, S. Decesari, H. Denier van der Gon, M. C. Facchini, D. Fowler, I. Koren, B. Langford, U. Lohmann, E. Nemitz, S. Pandis, I. Riipinen, Y. Rudich, M. Schaap, J. G. Slowik, D. V. Spracklen, E. Vignati, M. Wild, M. Williams, and S. Gilardoni (2015). Particulate matter, air quality and climate: lessons learned and future needs, Atmos. Chem. Phys., 15, Gysel, M., E. Weingartner, S. Nyeki, D. Paulsen, U. Baltensperger, I. Galambos, and G. Kiss (2004). Hygroscopic properties of water soluble matter and humic like organics in atmospheric fine aerosol, Atmos. Chem. Phys., 4, Hand, J. L., S. M. Kreidenweis, D. E. Sherman, J. L. Collett, S. V. Hering, D. E. Day, and W. C. Malm (2002). Aerosol size distributions and visibility estimates during the Big Bend regional aerosol and visibility observational (BRAVO) study, Atmos. Env., 36, Hoek, G, Brunekreef B, Goldbohm S, Fischer P., Van Den Brandt P. A. (2002). Association between mortality and indicators of traffic related air pollution in the Netherlands: a cohort study, The Lancet, 360, IPCC, (2013): Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G. K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Jimenez, J. L., Canagaratna, M. R., Donahue, N. M., Prevot, A. S. H., Zhang, Q., Kroll, J. H., DeCarlo, P. F., Allan, J. D., Coe, H., Ng, N. L., Aiken, A. C., Docherty, K. S., Ulbrich, I. M., Grieshop, A. P., Robinson, A. L., Duplissy, J., Smith, J. D., Wilson, K. R., Lanz, V. A., Hueglin, C., Sun, Y. L., Tian, J., Laaksonen, A., Raatikainen, T., Rautiainen, J., Vaattovaara, P., Ehn, M., Kulmala, M., Tomlinson, J. M., Collins, D. R., Cubison, M. J., Dunlea, E. J., Huffman, J. A., Onasch, T. B., Alfarra, M. R., Williams, P. I., Bower, K., Kondo, Y., Schneider, J.,Drewnick, F., Borrmann, S., Weimer, S., Demerjian, K., Salcedo, D., Cottrell, L., Griffin, R., Takami, A., Miyoshi, T., A method for the in situ measurement of the water content of atmospheric particles. - 15

31 Hatakeyama, S., Shimono, A., Sun, J. Y., Zhang, Y. M., Dzepina, K., Kimmel, J. R., Sueper, D., Jayne, J. T., Herndon, S. C., Trimborn, A. M., Williams, L. R., Wood, E. C., Middlebrook, A. M., Kolb, C. E., Baltensperger, U., and Worsnop, D. R. (2009). Evolution of organic aerosols in the atmosphere, Science, 326, Kanakidou, M., J. H. Seinfeld, S. N. Pandis, I. Barnes, F. J. Dentener, M. C. Facchini, R. Van Dingenen, B. Ervens, A. Nenes, C. J. Nielsen, E. Swietlicki, J. P. Putaud, Y. Balkanski, S. Fuzzi, J. Hjorth, G. K. Moortgat, R. Winterhalter, C. E. L. Myhre, K. Tsigaridis, E. Vignati, E. G. Stephanou, and J. Wilson (2005). Organic aerosol and global climate modeling: A review, Atmospheric Chemistry and Physics, 5, Kulkarni, P., Baron, P.A. and Willeke, K. (2011). Aerosol Measurement: Principles, Techniques and Applications, John Wiley & Sons, Hoboken, New Jersey, 3rd edition. Kunzli, N., M. Jerrett, W. J. Mack, B. Beckerman, L. LaBree, F. Gilliland, D. Thomas, J. Peters, and H. Hodis (2005). Ambient air pollution and atherosclerosis in Los Angeles, Environmental Health Perspectives, 113, Lanz, V. A., Alfarra, M. R., Baltensperger, U., Buchmann, B., Hueglin, C., and Prevot, A. S. H. (2007). Source apportionment of submicron organic aerosols at an urban site by factor analytical modelling of aerosol mass spectra, Atmos. Chem. Phys., 7, Lee, B., E. Kostenidou, L. Hildebrandt, I. Riipinen, G. Engelhart, C. Mohr, P. DeCarlo, N. Mihalopoulos, A. S. H. Prevot, U. Baltensperger, and S. N. Pandis (2010). Measurement of the ambient organic aerosol volatility distribution: Application during the Finokalia aerosol measurement experiment (FAME 2008), Atmospheric Chemistry and Physics, 10, Levin, Z. and Cotton, W. R. (2009). Aerosol Pollution Impact on Precipitation A Scientific Review, Springer. A method for the in situ measurement of the water content of atmospheric particles. - 16

32 Londahl, J., Pagelsb, J., Bomanc, C., Swietlickia, E., Masslinga, A., Risslerad, J., Blomberge, A., Bohgardb, M., and Sandstroumlme, T. (2008). Deposition of biomass combustion aerosol particles in the human respiratory tract, Inhal. Toxicol., 40, , doi: / Malm, W. C. and Day, D. E. (2001). Estimates of aerosol species scattering characteristics as a function of relative humidity, Atmospheric Environment, 35, Onasch, T. B., Siefert, R. L., Brooks, S. D., Prenni, A. J., Murray, B., Wilson, M. A., and Tolbert, M. A. (1999). Infrared spectroscopic study of the deliquescence and efflorescence of ammonium sulfate aerosol as a function of temperature, J. Geophys. Res., 104, Penner, J.E. and Novakov, T. (1996). Carbonaceous particles in the atmosphere: A historical perspective to the fifth international conference on carbonaceous particles in the atmosphere, J. Geophys. Res., 101, Pope, C. A.(1991). Respiratory hospital admission associated with PM10 pollution in Utah, Salt Lake and Cache Valleys, Arch Environ. Health, 7, Pope, C. A., R. T. Burnett, M. J. Thun, E. E. Calle, D. Krewski, K. Ito, and G. D. Thurston (2002). Lung cancer, cardiopulmonary mortality and long term exposure to fine particulate air pollution, JAMA, 287, Pope, C. A. and Dockery, D. W. (2006). Health effects of fine particulate air pollution: Lines that connect, J. Air Waste Manage. Assoc., 56, Pope, C. A., Ezzati, M., and Dockery, D. W. (2009). Fine particulate air pollution and life expectancy in the United States, New Engl. J. Med., 360, Poulain, L., Z. Wu, M. Petters, H. Wex, E. Hallbauer, B. Wehner, A. Massling, S. Kreidenweis, and F. Stratmann (2010). Towards closing the gap between hygroscopic growth and CCN activation for secondary organic aerosols Part 3: Influence of the chemical composition on the hygroscopic properties and volatile fractions of aerosols, Atmospheric Chemistry and Physics, 10, A method for the in situ measurement of the water content of atmospheric particles. - 17

33 Psichoudaki, M. and Pandis, S. N. (2013). Atmospheric aerosol water soluble organic carbon measurement: a theoretical analysis, Environ. Sci. Technol., 47, Rood, M. J., Covert, D. S., and Larson, T. V. (1987). Temperature and humidity controlled nephelometry: Improvements and calibration, Aerosol Sci. Technol., 7, Rosati, B., Wehrle, G., Gysel, M., Zieger, P., Baltensperger, U., and Weingartner, E. (2015). The white light humidified optical particle spectrometer (WHOPS) a novel airborne system to characterize aerosol hygroscopicity, Atmos. Meas. Tech., 8, Schwartz, S. E. (1996). The whitehouse effect short wave radiative forcing of climate by anthropogenic aerosols: An overview, J. Aerosol. Sci., 27, Seinfeld, J. H. and Pandis, S. N. (2006). Atmospheric Chemistry and Physics: From Air Pollution to Climate Change, John Wiley & Sons, Hoboken, New Jersey, 2nd edition. Sloane, C.S., and W.H. White (1986). Visibility: An evolving issue, Environ. Sci. Technol., 20, Sorooshian, A., Hersey, S. P., Brechtel, F. J., Corless, A., Flagan, R. C., and Seinfeld, J. H. (2008). Rapid, size resolved aerosol hygroscopic growth measurements: differential aerosol sizing and hygroscopicity spectrometer probe (DASH SP), Aerosol Sci. Tech., 42, Speer, R. E., Edney, E. O., and Kleindienst, T. E. (2003). Impact of organic compounds on the concentrations of liquid water in ambient PM 2.5, J. Aerosol Sci., 34, Spichtinger, P. and Cziczo, D. J. (2008). Aerosol cloud interactions a challenge for measurements and modeling at the cutting edge of cloud climate interactions, Environ. Res. Lett., 3, doi: / /3/2/ A method for the in situ measurement of the water content of atmospheric particles. - 18

34 Stanier, C. O., Khlystov, A. Y., Chan, W. R., Mandiro, M., and Pandis, S. N. (2004). A method for the in situ measurement of fine aerosol water content of ambient aerosols: The dry ambient aerosol size spectrometer (DAASS), Aerosol Sci. Technol., 38, Stegeman, John J. and Solow, Andrew R. (2002). A Look Back at the London Smog of 1952 and the Half Century Since; A Half Century Later: Recollections of the London Fog, Environmental Health Perspectives, 110, A Swietlicki, E., Hansson, H. C., Hameri, K., Svenningsson, B., Massling, A., McFiggans, G., McMurry, P. H., Petaja, T., Tunved, P., Gysel, M., Topping, D., Weingartner, E., Baltensperger, U., Rissler, J., Wiedensohler, A., and Kulmala, M. (2008). Hygroscopic properties of submicrometer atmospheric aerosol particles measured with H TDMA instruments in various environments a review, Tellus B, 60, , doi: /j x. Tang, I. N. (1997). Thermodynamic and optical properties of mixed salt aerosols of atmospheric importance, J. Geophys. Res., 102, Wang, Z., Hopke, P. K., Ahmadi, G., Cheng, Y. S., and Baron, P. A. (2008). Fibrous particle deposition in human nasal passage: The influence of particle length, flow rate, and geometry of nasal airway, J. Aerosol Sci., 39, Watson, J.G. (2002). Visibility: Science and regulation, J. Air Waste Manage. Assoc., 52, Wild, M., D. Folini, C. Schär, N. Loeb, E. G. Dutton, and G. König Langlo, (2013). The global energy balance from a surface perspective, Clim. Dyn., 40, Zhang, Q., Jimenez, J. L., Canagaratna, M. R., Allan, J. D., Coe, H., Ulbrich, I., Alfarra, M. R., Takami, A., Middlebrook, A. M., Sun, Y. L., Dzepina, K., Dunlea, E., Docherty, K., DeCarlo, P. F., Salcedo, D., Onasch, T., Jayne, J. T., Miyoshi, T., Shimono, A., Hatakeyama, S., Takegawa, N., Kondo, Y., Schneider, J., Drewnick, F., Borrmann, S., Weimer, S., Demerjian, K., Williams, P., Bower, K., Bahreini, R., Cottrell, L., Griffin, R. J., Rautiainen, A method for the in situ measurement of the water content of atmospheric particles. - 19

35 J., Sun, J. Y., Zhang, Y. M., and Worsnop, D. R. (2007a). Ubiquity and dominance of oxygenated species in organic aerosols in anthropogenicallyinfluenced Northern Hemisphere midlatitudes, Geophys. Res. Lett., 34, L13801, doi: /2007GL A method for the in situ measurement of the water content of atmospheric particles. - 20

36 CHAPTER 2 DAASS Description 2.1 Introduction The DAASS was developed to make direct in situ measurements of the water content of the entire distribution of ambient aerosols (Stanier et al., 2004) at the ambient RH automatically. In addition, the DAASS is able to measure the aerosol water concentration of the full PM 0.5, PM 1 or PM 10 and does not focus on the behavior of particles of a given size, in contrast to the H TDMA. Thus, integral information of the desired size range can be gathered with high time resolution, but some of the detailed information provided by an H TDMA, like the mixing state of the particles, is relinquished. The reduced DAASS requires only one differential mobility analyzer (DMA) compared to the two needed by the H TDMA (Engelhart et al., 2011). The DAASS has been until now used only for field monitoring (Khlystov et al., 2005; Engelhart et al., 2011). Due to the fact that the scanning mobility particle sizer (SMPS) is the central part of the DAASS, the basic principles of the SMPS are presented first. Subsequently, the system design and the operation principles of DAASS are shown. Finally, our improvement of the DAASS operation is presented. 2.2 Scanning mobility particle sizer (SMPS) The scanning mobility particle sizer (SMPS) is a system that measures the size (number) distribution of aerosols from as low as 2 nm to up to about 1 μm. The SMPS is actually a combination of a differential mobility analyzer (DMA) and a condensation particle counter (CPC). Specific choice of the DMA geometry, flow settings and CPC determine the actual size range analyzed. In this study, the size distribution covers a range from 10 nm to 500 nm. Specifically, during this study the TSI Classifier model 3080, TSI DMA 3081 and TSI Water CPC 3787 are used Differential mobility analyzer (DMA) The DMA (Figure 2.1) consists of a Kr 85 bipolar charger to neutralize the charges on A method for the in situ measurement of the water content of atmospheric particles. - 21

37 particles, a controller to monitor flows and high voltage and a differential mobility analyzer (DMA) which separates particles based on their electrical mobility. The aerosol first enters an impactor, which removes particles above a known particle size by inertial impaction. The impaction plate deflects the flow forming a 90 o bend in the streamlines. Particles with sufficient inertia are unable to follow the streamlines and impact on the plate. Smaller particles follow the streamlines, avoid contact with the plate and exit the impactor (Hinds, 1999). Figure 2.1: Schematic flow for the DMA. Positively charged particles are attracted to the negatively charged rod in the center based upon their electric mobility. Particles of the appropriate size will pass through the narrow opening and continue through the monodisperse outstream. After that, the aerosol enters a Kr 85 bipolar charger (or neutralizer), which exposes the aerosol particles to high concentrations of bipolar ions. The particles and ions undergo frequent collisions due to the random thermal motion of the ions. The particles quickly reach a state of charge equilibrium, in which the particles carry a known bipolar charge distribution (Fuchs, 1963; Wiedensohler et al., 1986; Wiedensohler, 1988; Kim et al., 2005). The charged aerosol passes from the neutralizer into the main portion of the DMA. Particle free (filtered) air enters the A method for the in situ measurement of the water content of atmospheric particles. - 22

38 sheath flow inlet of the DMA and passes to an annular chamber at the top of the DMA. The polydisperse aerosol flow enters the DMA through an inlet pipe from the top, flows in the axial direction between two narrow concentric cylinders and smoothly merged with the laminar sheath air flow. The inner cylinder, the collector rod, is maintained at a negative voltage, while the outer cylinder is electrically grounded. This creates an electric field between the two cylinders. Particles with negative charge stick to the outer electrode, whereas, neutral particles are removed with the excess flow. Positively charged particles are carried axially downward with the sheath airflow while also being attracted radially toward the center electrode due to the electric field. Particles within a narrow range of electrical mobility exit in the monodisperse air flow through a small slit located at the bottom of the collector rod. The monodisperse distribution that exits the DMA moves to a condensation particle counter (CPC), which measures the particle concentration at that size Condensation particle counter (CPC) The monodisperse particle stream exiting the DMA is counted by a CPC. The aerosol enters the sample inlet (Figure 2.2) at a flow rate of 0.6 L min 1 or 1.5 L min 1, depending upon the setting for the transport flow. The aerosol sample is then pulled through a cool region saturated with water vapor where its temperature is equilibrated. The sample then passes into a growth section where wetted walls are heated to produce an elevated vapor pressure resulting in a thermodynamic supersaturation condition. The particles in the flow stream act as nuclei for condensation and grow into micron sized droplets. The droplets pass through a laser beam and create light pulses. These pulses are detected and counted (Hering et al., 2005). The degree of supersaturation is measured as a saturation ratio, which is defined as the actual vapor partial pressure divided by the saturation vapor pressure for a given temperature. For a given saturation ratio, the particles can grow into droplets only if they are large enough. The minimum particle size capable of acting as a condensation nucleus is called the Kelvin diameter and is evaluated by the following relation: Saturation ratio = P P = exp 4σM ρrtd (2.1) A method for the in situ measurement of the water content of atmospheric particles. - 23

39 where σ is the surface tension, M the molecular weight, and ρ the density of the condensing fluid, R the ideal gas constant, T the absolute temperature and D p the Kelvin diameter. The higher the saturation ratio, the smaller the Kelvin diameter. Figure 2.2: Schematic flow for the water based CPC. 2.3 Dry ambient aerosol size spectrometer (DAASS) System design and operation principles The DAASS is an automated system that measures the aerosol number distribution at low and ambient RH. A comparison of the dry to ambient distributions provides information on both the amount of water and the growth factor due to the RH changes. A detailed schematic of the reduced DAASS is shown in Figure 2.3. The new edition of DAASS is a compact device (Appendix), which can be transported from the lab to the field effortlessly. The reduced DAASS consists of a scanning mobility particle sizer (SMPS), nafion membrane dryers, temperature and relative humidity sensors, a PC, mechanical servomotors and two way mechanical valves. In A method for the in situ measurement of the water content of atmospheric particles. - 24

40 Figure 2.3: Flow diagram of the reduced DAASS. The blue, green, and red lines represent the vent, the ambient and the dry mode, respectively. contrast to previous configurations, mechanical servomotors and two way mechanical valves are used in this study instead of three way solenoid electrical valves, in order to avoid heat generation, which was a problem in previous studies (Engelhart et al., 2011). Copper, conductive and silicone tubes are used throughout the system. The SMPS is operated at a sheath to aerosol flow ratio of 5:1 for a mobility diameter size range of particle measurements from 10 nm to 500 nm. Each SMPS upscan is 5 min in duration followed by a 5 min downscan. The operation of DAASS includes two sampling modes, dry and ambient, and a vent mode. During the dry measurement the sample flow is directed through a nafion membrane dryer (Permapure MD 110, Toms River, NJ, USA). A single tube dryer with stainless steel housings is used for drying aerosols rather than multitube dryers to limit losses A method for the in situ measurement of the water content of atmospheric particles. - 25

41 (Woo et al., 2001). The sheath air line of the SMPS is also dried in a closed loop (Figure 2.3). A multitube dryer (Permapure PD 200T) is used for the sheath air line of the instrument, due to the higher flow rate. Dry clean air, which has passed through a silica dryer and a HEPA filter, is provided in the sample counter flow direction to the nafion membrane dryers by an oil free compressor at a rate of up to 7 L min 1 for the inlet dryer and up to 15 L min 1 for the sheath dryer depending on the required value of low RH. In the ambient measurement mode, the nafion dryers are bypassed and the aerosol is directed to the SMPS at approximate ambient conditions. The nafion dryers are bypassed using two way mechanical valves, which are controlled by mechanical servomotors (Futaba S3306MG Hi Torque). The vent mode is used in order to rapidly prepare the system for the ambient sampling after the dry sampling mode. Dry air is exhausted from the system and ambient air is drawn into the system. A pump with an orifice is used, in order to drive the ambient air through the system without straining the SMPS pumps. During the vent mode the aerosol flow increases from 1 to 1.3 L min 1 while the sheath flow increases from 5 to approximately 11.2 L min 1. Two sensors (HX93AC, Omega) monitor temperature and relative humidity in the system. The first is located at the inlet of DAASS, while the second is installed in front of the entrance of the sheath flow at the top of the DMA. Tests were conducted by installing one more sensor after the inlet dryer/bypass in order to check the proper function of both nafion dryers. New hardware (NI cdaq 9171, NI 9208, NI 9923, NI PCI 6601) has been used for the purposes of this work and new Labview software was developed, monitoring the temperature and RH, as well as for controlling the servomotors Instrument performance During the dry mode, the measurements do not start at once (Figure 2.4). During the 5 min dry preparation period a rapid decrease in RH is observed. Size distribution data is not collected, but the system is allowed to equilibrate to the low RH. This is followed by a 5 min dry sampling period. Right after the dry sampling mode, the valves switch to the vent mode (4.5 min) and ambient air is rapidly drawn into the A method for the in situ measurement of the water content of atmospheric particles. - 26

42 system causing a rapid increase in the RH. Afterwards, the valves switch to the ambient sampling mode and allow the SMPS pumps and RH sensors to stabilize (0.5 min). After stabilization, the ambient sampling period starts (5 min) and, subsequently, the system enters the dry configuration mode again. Sampling during the dry and ambient modes is synchronized with the SMPS upscan (5 min) and downscan time (5 min). Hence, 3 dry and 3 ambient aerosol size distributions are measured per hour. Figure 2.4: Example of RH time series in DAASS for the various instruments modes during typical measurements in a smog chamber. Figure 2.4 shows a typical time series of RH throughout the DAASS flow configuration during a chamber experiment. Two dry cycles and two ambient cycles are shown. There is a difference between the inlet flow and sheath flow RHs. This difference is either due to insufficient purging of dry air from the system during the vent mode or because of the potential sinks of water vapor inside the DAASS system. During field monitoring, in order to maintain the ambient temperature throughout the system, which is critical for sampling at ambient RH, the whole system has to be placed outdoors, in a shady spot. Also, a fan can be used to increase the air flow around the system. During ambient sampling, the sheath flow RH reaches over 90% of the ambient RH (e.g., inlet RH of 57% yielded a sheath RH of 52%) at low RHs, but for higher RH values the sheath flow RH reaches about 85% of the ambient RH (e.g., inlet RH of 89.8% yielded a sheath RH of 76.9%). In the SMPS systems, the particles are assumed to equilibrate with the sheath flow RH, due to the corresponding A method for the in situ measurement of the water content of atmospheric particles. - 27

43 high exposure time (Stanier et al., 2004). Thus, in the data analysis, it is the average SMPS sheath flow RH that is used to analyze particle size as a function of RH Improvement of the DAASS operation The discrepancy between the inlet and the sheath flow RHs limited the utility of the instrument. Thus, a set of modifications were tested, in order to overcome this problem and reach the critical high relative humidity region (>75%). Initially, the flow rate during the vent mode was increased, in order to purge the dry air from the system faster. The amount of ambient air drawn into the system is limited by the power of the standard sheath and bypass pumps in the SMPS. In this way, the vent mode operated at a flow rate of 15 L min 1 (the maximum sheath flow rate using a long DMA), by swapping the orifice with a needle valve. The use of a needle valve let us manually adjust the sheath flow rate. Although the sheath flow increased, the discrepancy between the inlet and the sheath flow RHs remained almost the same (Giorgas, 2016). In a second attempt the sheath flow loop was modified, in order to avoid the potential sinks of water vapor inside the system. The sheath flow during the ambient mode circulates in a closed loop passing through three HEPA filters. As a result, a part of the water vapor in the stream may be adsorbed on the filters resulting in a reduction of the sheath flow relative humidity. To test this hypothesis the DAASS operation with an open loop for the ambient sheath air flow was tested (Figure 2.5). After the above modification, only two modes are used: the dry and the ambient mode. There is no difference in the operation during the dry mode. As before, there is a dry preparation period and then the dry sampling period starts. The sheath air flow is also dried in a closed loop (Figure 2.5, red line). After the dry mode, the ambient preparation period begins, in which the sheath air flow changes to an open loop (Figure 2.5, blue line). The preparation period lasts 10 min, during which ambient air is drawn into the instrument increasing the RH. Subsequently, the system enters the ambient sampling period. The difference with the previous configuration is that the sheath flow loop remains now open during the ambient sampling period, allowing ambient humid air to enter the system. In this way, the discrepancy between the inlet and the sheath flow RHs becomes negligible. After this, the system enters the dry A method for the in situ measurement of the water content of atmospheric particles. - 28

44 Figure 2.5: Revised DAASS flow diagram. An open loop is now used for the ambient sheath air flow allowing the system to reach higher RH values. The blue and red lines represent the ambient and the dry mode, respectively. mode again. The sheath flow rate is adjusted to 5 L min 1, using the needle valve, during the whole DAASS operation cycle. In order to synchronize the sampling periods with the SMPS upscan (5 min) and downscan time (5 min), the dry and the ambient modes last 15 min each (Figure 2.6). The improved DAASS measures 2 dry and 2 ambient aerosol size distributions per hour. Two typical dry and two ambient cycles are shown in Figure 2.6 during a chamber experiment. The dry cycle starts with a 5 min preparation period, following by the sampling period (5 min) and a waiting 5 min period. The ambient preparation period lasts 10 min and then the sampling period (5 min) starts. These two cycles are repeated continuously. A method for the in situ measurement of the water content of atmospheric particles. - 29

45 Figure 2.6: The RH of the chamber (red line), inlet flow (black line) and sheath flow (blue line) during an experiment with the improved DAASS. The use of an open loop for the ambient sheath flow lets the system reach higher relative humidity values (close to ambient) increasing the RH region in which the DAASS is able to operate. A method for the in situ measurement of the water content of atmospheric particles. - 30

46 2.4 References Engelhart, G. J., L. Hildebrandt, E. Kostenidou, N. Mihalopoulos, N. M. Donahue, and S. N. Pandis (2011). Water content of aged aerosol, Atmos. Chem. Phys., 11, Fuchs, N.A. (1963). On the stationary charge distribution on aerosol particles in a bipolar ionic atmosphere, Geophys. Pura Appl., 56, Giorgas, S. (2016). Experimental study of the effect of relative humidity on atmospheric particles, Diploma thesis, Department of Chemical Engineering, University of Patras. Hering, S. V., Stolzenurg, M. R., Quant, F. R., Oberreit, D., and Keady, P. B. (2005). A laminar flow, water based condensation particle counter (WCPC), Aerosol Science & Technology, 39, Hinds, W. C. (1999). Aerosol Technology: Properties, Behavior, and Measurement of Airborne Particles, John Wiley & Sons, 2nd edition. Khlystov, A., Stanier, C. O., Takahama, S., and Pandis, S. N. (2005). Water content of ambient aerosol during the Pittsburgh Air Quality Study, J. Geophys. Res., 110, D07S10, doi: /2004JD Kim, J.H., G.W. Mulholland, S.R.Kukuck, and D.Y.H. Pui (2005). Slip correction measurements of certified PSL nanoparticles using a nanometer differential mobility analyzer (Nano DMA) for Knudsen number from 0.5 to 83, Journal of Research of the National Institute of Standards and Technology, Stanier, C. O., Khlystov, A. Y., Chan, W. R., Mandiro, M., and Pandis, S. N. (2004). A method for the in situ measurement of fine aerosol water content of ambient aerosols: The dry ambient aerosol size spectrometer (DAASS), Aerosol Sci. Technol., 38, Wiedensohler, A. (1988). Technical note: An approximation of the bipolar charge distribution for particles in the submicron range, Journal of Aerosol Science, 19, A method for the in situ measurement of the water content of atmospheric particles. - 31

47 Wiedensohler, A., E. Lütkemeier, M. Feldpausch, and C. Helsper (1986). Investigation of the bipolar charge distribution at various gas conditions, Journal of Aerosol Science, 17, Woo, K. S., Shi, Q., Sakurai, H., and McMurry, P. H. (2001). A relative humidity conditioner for atmospheric sampling. Presented at the American Association of Aerosol Research. Portland, Oregon, October A method for the in situ measurement of the water content of atmospheric particles. - 32

48 CHAPTER 3 DAASS Characterization 3.1 DAASS testing The instrument performance and accuracy were tested using model particles of known hygroscopic behavior. An algorithm checking the consistency of the measurements and the applicability of the assumptions used in the data analysis was also developed. A comparison of the measured values to the predicted ones by the extended aerosol inorganics model (E AIM) was also performed Experimental set up A set of chamber experiments using (NH 4 ) 2 SO 4 particles at various RH values was performed. Two bubblers were used to increase RH in the chamber (Volume 700L) to the desired value. When the desired RH value was reached the air flow was stopped and the chamber was allowed to equilibrate. Figure 3.1: Experimental set up during chamber experiments. A method for the in situ measurement of the water content of atmospheric particles. - 33

49 An aqueous (NH 4 ) 2 SO 4 solution of concentration in the range between 1 and 1.5 g L 1 ((NH 4 ) 2 SO 4 purity 99%, Sigma Aldrich) was provided to a constant output atomizer using a syringe pump. The produced (NH 4 ) 2 SO 4 droplets flowed through a silica dryer and then the dry particles entered the chamber. After a few minutes the aerosol generator stopped and allowed the chamber to equilibrate. Finally, the DAASS started measuring. A flow schematic of the experimental set up is shown in Figure 3.1. In a few experiments an aerosol mass spectrometer (AMS) was also used, in order to verify the aerosol composition during experiments testing for potential impurities. A very small fraction (less than 3%) of organics was measured, probably due to impurities in the water or inside the chamber. The rest of the aerosol mass consisted of ammonium sulfate Particles losses Two processes are responsible for particle losses inside the DAASS: losses because of collisions with wall tubes and coagulation. Losses due to coagulation were negligible at the low concentrations used in these experiments, so the analysis focused on particle wall losses. Particles may be lost in the tubing and fittings between the inlet and the measuring device or inside the sampling probe, because of the curving streamlines entering the inlet or along the tubing (Hinds, 1999). Use of copper and stainless steel tubing throughout the system minimizes particle losses. Different fractional losses were observed for dry and wet particles. So, two approaches were used, in order to estimate the wall losses in the instrument. For dry particles a size dependent correction was applied. On the other hand, for wet particles a size independent correction has been used. For dry particles, losses were determined in a set of chamber experiments at low RH values, using (NH 4 ) 2 SO 4 particles. The RH was kept below the deliquescence relative humidity (DRH) of (NH 4 ) 2 SO 4, which is 79.9 ± 0.5 at 298 K (Seinfeld and Pandis, 2006), in order to avoid water absorption, and thus particle growth. The particle losses were calculated as a function of size using: n(d ) n(d ) Losses D = n(d ) (3.1) A method for the in situ measurement of the water content of atmospheric particles. - 34

50 where, D p is the particle diameter in nm, n(d p ) ambient and n(d p ) dry are the ambient and dry number distribution respectively. Figure 3.2: Average particle loss percentage based on four different experiments as function of particle diameter for dry particles. The black symbols represent the measured losses, the black vertical lines the variability (±1σ) of the average losses correction and the red line is the empirical fit. The average result from four experiments at low RH conditions is depicted in Figure 3.2. An exponential fit (Figure 3.2, red line) was used to describe the average particle wall losses and is given by the following function: (%)Losses(D ) = exp 18.1 D (3.2) where D p is the diameter of the particles in nm. The function was tested with ambient particles with diameters up to 350 nm, during low RH ambient conditions (<30%) and a similar behavior was noticed. The losses for particles larger than 40 nm are low and around 4 5%. For smaller particles losses increase reaching 30% at 20 nm due to Brownian diffusion. This loss function can be used to correct the measurements in the dry DAASS mode. A typical example is shown in Figure 3.3. The corrected dry and ambient number distributions are in good agreement. The dry number concentration increased from 8201 cm 3 to 8582 cm 3 due to the correction of Equation 3.2. The ambient A method for the in situ measurement of the water content of atmospheric particles. - 35

51 Figure 3.3: Number distributions of ambient (blue line), dry (red line) and corrected dry (black line) aerosol while DAASS was operated at 35% RH, during a chamber experiment with (NH 4 ) 2 SO 4 particles. concentration was 8600 cm 3. Losses of wet particles were found to depend on both particle size and relative humidity. Experiments using model aerosol indicated that the RH dependence of the losses was much stronger than their size dependence. Based on these results, a size independent but RH dependent correction is used for each sample and RH. Thus, the losses were calculated as: Losses(%) = N N N 100% (3.3) where, N ambient and N dry are the ambient and the dry number concentrations, respectively. An example using this correction is shown in Figure 3.4. The effect of relative humidity on the aerosol size distributions is clear. The dry number distribution is shifted to the left relatively to the ambient one, as the particles lose water and decrease in size. The corrected dry number concentration was cm 3 and the ambient one was cm 3. The measured dry value was cm 3. Significantly higher losses were observed at high RH values than at low ones. This behavior is due to water absorption by the particles and the resulting changes of their physical state leading to an increase of the wall losses. Wet particles stick to the tube A method for the in situ measurement of the water content of atmospheric particles. - 36

52 Figure 3.4: Number distributions of ambient (blue line), dry (red line) and corrected dry (black line) aerosol while the DAASS operated at 73% RH. The corrected dry distribution was calculated using Equation 3.3. surfaces more efficiently. A 90 0 bend at the beginning of the dry flow (see Figure 2.5) appears to be a main reason for the losses, when the particles have absorbed water. A couple of tests were conducted to test this hypothesis and the results confirmed that the 90 0 bend plays a significant role in the losses of wet particles (Giorgas, 2016). It is recommended in future DAASS development to replace the 90 0 bend with a Y splitter Data analysis Using the method of Stanier et al. (2004) the total measured volumes of the ambient and dry distributions are given by: V = π 6 D n (D)dD (3.4) V = π 6 D n (D)dD (3.5) where V ambient and V dry are the total ambient and the total dry particle volume concentration, respectively. D is the particle mobility diameter, D a1 and D a2 are the lower and upper integration limits respectively of the ambient distribution, and n ambient (D) is the ambient aerosol number distribution. D d1 and D d2 are the lower and upper integration limits respectively of the dry distribution, and n dry (D) is the dry A method for the in situ measurement of the water content of atmospheric particles. - 37

53 aerosol number distribution. The upper integration limit, D a2, is 500 nm the same as the upper limit of the SMPS. D d2 is adjusted so that the same number particles are measured in both the ambient and dry distributions. The lower integration limits D a1 and D d1 are set to be both equal to 10 nm the same as the lower limit of the SMPS. D d1 is set to be equal to D a1 for two reasons. First, the smallest particles (below 10 nm) in the distribution have a negligible contribution to the aerosol volume. Second, very few particles smaller than 10 nm were present during our experiments. So, the error introduced by this simplification is insignificant. The hygroscopic growth factor is calculated for pairs of ambient and dry volume size distributions, assuming a single, size independent growth factor. The volume growth factor (VGF) is defined as: VGF = V V = D n (D)dD D n (D)dD (3.6) Assuming the same growth factor for all the particles, the volume growth factor is also equal to: VGF = D D (3.7) Solving iteratively Equations 3.6 and 3.7 the upper integration limit, D d2, is obtained. The internal mixture assumption is used to determine D d2. Stanier et al. (2004) have shown that in case of an externally mixed population the growth factor in Eqs. 3.6 and 3.7 is approximately the volume weighted average growth factor of the various externally mixed aerosol subpopulations. The aerosol water concentration can be estimated from the ambient and dry volume concentration. Assuming water is the only semivolatile species causing a volume change and volume additivity between aerosol water and nonvolatile aerosol components, then the mass of absorbed water can be calculated as: m = ρ V V (3.8) where ρ w is the density of liquid water. The assumption of volume additivity may introduce a small error, because the actual solution may deviate from ideality. A method for the in situ measurement of the water content of atmospheric particles. - 38

54 However, the error due to the assumption of additivity is negligibly small (Dick et al., 2000). As discussed in Section 3.1.2, two approaches were used to estimate the wall losses in the instrument. Subsequently, two different methods of data analysis have been developed for dry and wet particles. First, VGF values are calculated from the raw data. If their values are smaller or equal to 1.05, then the correction for dry particles is applied. Namely, dry number and volume distributions are corrected using Equation 3.2, and finally the corrected VGF is estimated. On the other hand, if the raw VGF is larger than 1.05, the Equation 3.3 is used for correction of dry number and volume distributions. The value 1.05 is selected as a boundary between dry and wet particles in order to include possible errors during measurements. Figure 3.5: Flowchart of the DAASS data analysis. The correction of VGF for wet particles involves an additional challenge, due to the change in particle size as the aerosol moves from the wet to the dry state. The DAASS measures the aerosol distribution from 10 nm to 500 nm, but in many cases the A method for the in situ measurement of the water content of atmospheric particles. - 39

55 ambient volume distribution is extends above the limit of 500 nm. In order to estimate the VGF for wet particles, we need to correct for this missing part of the distribution. An easy way to do this is to assume that the aerosol volume distribution is lognormal. The new upper limit, D d2, must also be calculated for the dry volume concentration. Solving iteratively Equations 3.6 and 3.7 the upper integration limit, D d2, is obtained and finally the corrected dry volume concentration is evaluated. Thus, the corrected VGF is determined. Figure 3.5 summarizes the steps of data analysis. Figure 3.6: Cumulative ambient (blue symbols) and dry (red symbols) aerosol number distributions during a smog chamber experiment at 73% RH in a probabilitylog graph. In order to assess the validity of the lognormal distribution assumption, the cumulative number distribution function is plotted on a log probability graph. In this plot the x axis is logarithmic and the y axis is scaled like the error function, erf(y). This scaling compresses the scale near the median (50% point) and expands the scale near the ends (further details in Seinfeld and Pandis (2006)). In these diagrams the cumulative distribution function of a lognormal distribution is a straight line. The cumulative number distributions of an ambient and a dry sample during an experiment at 73% RH are shown in Figure 3.6. The data for both fall on a practically straight line. If the number distribution is lognormal, the volume distribution is also lognormal A method for the in situ measurement of the water content of atmospheric particles. - 40

56 with the same geometric Pandis, 2006). Therefore data in this case. standard deviation as the parent distribution (Seinfeld and the assumption of lognormal distributions is valid for our Results from typical measurements are shown in Figure 3.7. The dry number distribution is shifted to the left relatively to the wet distribution, because of the loss of water. The corrected dry distribution is obtained using Equation 3.3. The dry number concentration increased from cm 3 to cm 3. The ambient concentration was cm 3. The volume size distributions indicate that the volume concentration of the ambient aerosol was, as expected, much higher than that of the dry one. The difference is equal to the absorbed water. A small fraction of the change in the volume is due to particle losses and not from evaporation. So, the dry distribution is corrected using Equation 3.3. Figure 3.7: An examplee of the effect of relative humidity on the (NH 4 ) 2 SO 4 a) number and b) volume size distributions measured at 82% RH. The blue line represents the ambient (wet) distribution, while the red and black lines the dry and the corrected dry distributions, respectively. The dry and ambient distributions are not measured at the same time. To minimize the errors due to this temporal difference a cubic spline interpolation is used, so the corrected dry distributions can be estimated at the same time as the ambient ones. Typical measurements of the temporal evolution of the number and volume concentrations in an experiment are shown in Figures 3.8 and 3.9 together with the estimated water concentration. Equation 3.8 is used for the water calculation. In both figures the dry data have been corrected for particle losses. The concentration A method for the in situ measurement of the water content of atmospheric particles. - 41

57 Figure 3.8: Ambient (blue symbols) and corrected dry (red symbols) aerosol number concentrations during a smog chamber experiment at 82% RH. The red line is calculated based on the cubic spline interpolation of the dry data. Figure 3.9: Ambient (blue line) and corrected dry (red line) aerosol volume concentrations. The difference is the water volume concentration (green line). decreases with time, due to losses of the particles to the walls of the smog chamber. Figure 3.10 shows the measured volume growth factors as a function of time. The estimated VGF is quite stable, as the RH remained practically constant during the experiment. Using the method described above the corrected volume growth factors are calculated, which as expected are higher after the corrections. A method for the in situ measurement of the water content of atmospheric particles. - 42

58 Figure 3.10: Time seriess of the measured (top) and the corrected (bottom) volume growth factors. At the right y axis the sheath RH is shown. 3.2 Algorithm consistency check An algorithm checking the consistency of the measurements and the applicability of the assumptions used in the data analysis was developed, based on the consistency test initially proposed by Lee et al. (2010). This check is made by calculating the number and volume distributions from the corresponding ambient measurements and then comparing them to the dried ones. Specifically, the corrected VGF is obtained, and then the dry diameter is estimated as: D = D VGF (3.9) The predicted remaining number distribution, n n (D,dry), assuming spherical particles, is given by: A method for the in situ measurement of the water content of atmospheric particles. - 43

59 n (D, dry) = n (D, ambient) (1 Losses) 6 VGF pi D (3.10) where n v (D,ambient) is the ambient volume distribution from the DAASS. The ambient and measured dry number distributions, as well as the predicted aerosol distribution accounting for the losses in the DAASS are shown in Figure 3.11 for a typical measurement. The predicted distribution is in good agreement with the measured dry one. This confirms that the losses are not strongly size dependent for this measurement, the particles have a practically spherical shape and the method for the data analysis is reasonable. Figure 3.11: Number distribution of ambient (red line) and measured dried (blue line) (NH 4 ) 2 SO 4 aerosol particles respectively at RH=75%. In addition, the predicted aerosol distribution (black dashed line) accounting for the losses in the DAASS system is shown. 3.3 Comparison to a thermodynamic model The measured volume growth factors by the DAASS were compared to the Extended Aerosol Inorganics Model (E AIM) (Clegg et al., 1998; Wexler and Clegg, 2002). This thermodynamic model determines the state of an aerosol system containing water and two or more ions at equilibrium, at a specific temperature and relative humidity. The E AIM Model II was used, in order to obtain the hygroscopic behavior of (NH 4 ) 2 SO 4. A method for the in situ measurement of the water content of atmospheric particles. - 44

60 The measured volume growth factors were corrected for losses depending on the physical state of the particles, as explained in Section The results are presented in Figure The comparison of the measured volume growth factor (VGF) of (NH 4 ) 2 SO 4 aerosol particles to the predicted VGF by E AIM confirmed the reliability of the operation of the improved DAASS. Figure 3.12: Theoritical (red lines) based on the E AIM model and corrected measured (blue dots) volume growth factors for (NH 4 ) 2 SO 4 particles. The horizontal lines denote the uncertainty (±1σ) of the average RH and the vertical ones of the average measured VGF 1/3. A method for the in situ measurement of the water content of atmospheric particles. - 45

61 3.4 References Clegg, S. L., Brimblecombe P., and Wexler A. S. (1998). A thermodynamic model of the system H + NH + 4 SO 2 4 NO 3 H 2 O at tropospheric temperatures, J. Phys. Chem. A, 102, Dick, W. D., P. Saxena, and P. H. McMurry (2000). Estimation of water uptake by organic compounds in submicron aerosols measured during the Southeastern Aerosol and Visibility Study, J. Geophys. Res., 105, Giorgas, S. (2016). Experimental study of the effect of relative humidity on atmospheric particles, Diploma thesis, Department of Chemical Engineering, University of Patras. Hinds, W. C. (1999). Aerosol Technology: Properties, Behavior, and Measurement of Airborne Particles, John Wiley & Sons, 2nd edition. Lee, B. H., E. Kostenidou, L. Hildebrandt, I. Riipinen, G. J. Engelhart, C. Mohr, P. F. DeCarlo, N. Mihalopoulos, A. S. H. Prevot, U. Baltensperger and S. N. Pandis (2010). Measurement of the ambient organic aerosol volatility distribution: application during the Finokalia Aerosol Measurement Experiment (FAME 2008), Atmos. Chem. Phys., 10, Seinfeld, J. H. and Pandis, S. N. (2006). Atmospheric Chemistry and Physics: From Air Pollution to Climate Change, John Wiley & Sons, Hoboken, New Jersey, 2nd edition. Stanier, C. O., Khlystov, A. Y., Chan, W. R., Mandiro, M., and Pandis, S. N. (2004). A method for the in situ measurement of fine aerosol water content of ambient aerosols: The dry ambient aerosol size spectrometer (DAASS), Aerosol Sci. Technol., 38, Wexler, A. S. and Clegg S. L. (2002). Atmospheric aerosol models for systems including the ions H +, NH + 4, Na +, SO 2 4, NO 3, Cl, Br and H 2 O, J. Geophys. Res. 107, doi: /2001JD A method for the in situ measurement of the water content of atmospheric particles. - 46

62 CHAPTER 4 DAASS Applications 4.1 Introduction Ambient measurements, using both the original and the improved version of DAASS, were conducted during two different time periods in a suburban area in Patras. The original version of DAASS in conjunction with other instruments was used to monitor and characterize the local and regional aerosol during a summer period. The improved DAASS operated during a rainy period in autumn. The ambient measurements are a first application of the DAASS to monitor and characterize the ambient aerosol. The major objectives were to test and assess the operation of the improved DAASS system. 4.2 Ambient measurements with the original DAASS Site and instrumentation A mini summer campaign was conducted at the Institute of Chemical Engineering Sciences (ICE HT) from July 29 to August 3 in The ICE HT (38⁰ N, 21⁰ E) is located in Platani, Patras, which is a suburban area (Figure 4.1). The measurements took place at the facilities of the Laboratory of Air Quality Studies (LAQS). Figure 4.1: Location of sampling site at the Institute of Chemical Engineering Sciences (ICE HT). A method for the in situ measurement of the water content of atmospheric particles. - 47

63 A variety of instruments was used to characterize both the particulate and gas phases. Briefly, the instrumentation used to characterize the aerosol phase included the DAASS, an Aerodyne high resolution time of flight aerosol mass spectrometer (HR ToF AMS), a SMPS, a multi angle absorption photometer (MAAP), and a Thermodenuder. VOCs were measured using a proton transfer reaction mass spectrometer (PTR MS), while online monitors were measuring NO x, O 3, CO, CO 2, SO 2 concentrations. For this work the data from the DAASS, the AMS, the SMPS and the MAAP were used. The multi angle absorption photometer (MAAP, model 5012, Thermo scientific; Petzold and Schönlinner, 2004) measures black carbon mass loadings based on aerosol optical absorption. The MAAP makes this measurement on particles collected on a filter substrate. MAAP s inlet was heated to 70⁰ C before analysis, in order to remove water and organics. In this work a PM 1 inlet was used, thus the PM 1 black carbon mass concentration was measured. The DAASS system was placed outside and was shielded from sunlight by an awning (Appendix). In order to increase air flow around the system a fan was used. The inlet copper tube of the DAASS was insulated Meteorological Conditions Temperature and RH were monitored by the sensors of DAASS, one at the inlet and one before the entrance of the sheath flow at the top of the DMA (see Figure 2.3). An additional sensor was placed outside (outdoors sensor) to measure the ambient conditions. Due to technical problems this outdoors sensor did not operate continuously throughout the campaign. High temperatures (Figure 4.2) prevailed throughout the campaign, whereas RH fluctuated from high RH (up to 80%) to low (< 30%) values (Figure 4.3). For the entire period, both the inlet and the sheath flow of the DAASS had the same temperature. The ambient temperature was also very close to both of them. There was good agreement between the ambient RH and the inlet one. The sheath RH deviated from the inlet one as expected. This discrepancy was larger for high RH values and smaller for low ones. In general the time period with high RH (above 60%) was not long; therefore despite the RH discrepancy the original DAASS was expected to have A method for the in situ measurement of the water content of atmospheric particles. - 48

64 Figure 4.2: The inlet (black line) and sheath flow (blue line) temperature as well as the ambient (red line) temperature, during the campaign. Figure 4.3: Relative humidity time series during the campaign. The inlet and the ambient RH are depicted by a black and a red line, respectively. The sheath RH is indicated by blue symbols. reasonable performance Results The PM 1 mass concentration (in μg m 3 ) of organic aerosol (OA), ammonium, sulfate, nitrate, and chloride was measured by the AMS, whereas black carbon via the MAAP. The time series of the corresponding PM 1 mass concentrations throughout the campaign are shown in Figure 4.4. The dry total PM 1 concentration ranged from 4.9 A method for the in situ measurement of the water content of atmospheric particles. - 49

65 μg m 3 to 11.1 μg m 3 with an average of 7.7 μg m 3. Organics were the dominant species (50.3%), followed by sulfate (28.5%), ammonium (11.1%) and black carbon (7.9%), while nitrate and chloride were responsible for less than 2.5% of the PM 1 (Figure 4.5). Figure 4.4: Mass concentration of the major PM 1 components measured by the HR ToF AMS and the MAAP during the campaign. Figure 4.5: Average composition of the main PM 1 aerosol components during the summer measurements. A method for the in situ measurement of the water content of atmospheric particles. - 50

66 Particle size distributions ranging from 10 to 500 nm were monitored by the DAASS and the SMPS. The ambient and the corrected aerosol dry volume concentrations during the campaign measured by the DAASS are shown in Figure 4.6. The volume concentration of the ambient (wet) aerosol is, as expected, higher than that of the dry one. The difference is the amount of absorbed water, i.e., the aerosol water content. Figure 4.6: Ambient (blue line) and corrected dry (red line) volume concentrations measured by the DAASS. The difference is the water (green line) volume concentration. Figure 4.7: Mass fraction of aerosol water (blue symbols) and the corresponding RH (red line) during the campaign. A method for the in situ measurement of the water content of atmospheric particles. - 51

67 The mass fraction of aerosol water during the campaign is depicted in Figure 4.7. During this moderate RH period the aerosol water represented 0 50% of the fine aerosol mass. Figure 4.8: The a) ambient and b) corrected dry particle number distributions during the campaign. The y axis is the particle diameter, the x axis represents the local time and the color scale indicates the particle number concentration. The time series of ambient and corrected dry number as well as volume size distributions measured by the DAASS are depicted in Figures 4.8 and 4.9 respectively. Most of the particles had diameters in the range from around 50 to 300 nm. During the afternoon and evening of August 1, smaller particles with sizes in the nm were present probably due to some local source. The dry distribution shifted A method for the in situ measurement of the water content of atmospheric particles. - 52

68 slightly towards smaller particle diameters due to the loss of water (Figure 4.8 b). The volume size distributions (Figure 4.9) were quite different with most of the volume being in the nm size range. The dry particle volume concentrations were lower than the ambient ones, indicating the loss of the absorbed water once again. Figure 4.9: The a) ambient and b) corrected dry particle volume distributions during the campaign. The y axis is the particle diameter, the x axis represents the local time and the color scale indicates the particle volume concentration. The volume based growth factors, VGF, that is the aerosol volume at ambient conditions divided by the volume at low RH as a function of RH (sheath RH) during the campaign are presented in Figure A growth factor larger than one indicates A method for the in situ measurement of the water content of atmospheric particles. - 53

69 that aerosol contains water. In general low values of VGF were measured, mainly due to low RH conditions during the measurements. The aerosol always contained water at RH values above 50%, whereas at RH values below 50% there were periods when the particles were dry and periods when they contained water. The aerosol water content shows a strong dependence on the relative humidity. However, the time series of the measured VGF (Figure 4.11) shows that the volume growth factors were not just a simple function of RH, but they changed rapidly with changing composition and meteorology. For example, at midnight, August 2, the RH increased, but the VGF remained almost the same. Figure 4.10: Measured volume growth factors during the study. Figure 4.11: Time series of the measured volume growth factors (red symbols) by the DAASS. The inlet RH (black line) is indicated at the right y axis. A method for the in situ measurement of the water content of atmospheric particles. - 54

70 The presence of water, even at low RH values, may be explained by the acidity of the particles. The cations/anions ratio of the PM 1 as calculated by the AMS measurements for the time periods of the campaign are depicted in Figure The cations/anions ratio is estimated according to: mol Cations/Anions Ratio = 2mol + mol + mol (4.1) where mol NH ₄ +, mol SO ₄ 2, mol NO ₃ and mol Cl are the molar concentrations of ammonium, sulfate, nitrate and chloride, respectively. The particles were slightly acidic during most of the campaign, however the difference from unity is during most of the study within experimental error. Figure 4.12: Cations/anions ratio versus time as calculated from the molar ratio of ammonium over sulfate, nitrate and chloride measured by the AMS. The measured water mass concentrations by the DAASS were compared to the estimated ones from the thermodynamic Extended Aerosol Inorganics Model (E AIM). The inorganic concentrations for sulfate, ammonium, and nitrate measured by the AMS were used as inputs for E AIM (Clegg et al., 1998) in conjunction with the DAASS sheath RH and the sheath temperature. The DAASS RH and temperature were averaged over the sampling interval of the AMS. Production of solids and organic particulate related water were neglected in these calculations. The time series of the measured and predicted aerosol water are shown in Figure The theoretical predictions are quite consistent with the measurements even if the A method for the in situ measurement of the water content of atmospheric particles. - 55

71 water content of the organics is neglected in this calculation. The impact of organics on water uptake is still largely unknown. Multiple studies (e.g., Cruz and Pandis, 2000; Choi and Chan, 2002b; Prenni et al., 2003; Gysel et al., 2004; Duplissy et al., 2011; Hodas et al. 2015) have tried to elucidate the hygroscopic properties of organic aerosol, as well as the influence of organic aerosol components on the hygroscopic behavior and phase transitions of inorganic salts. This body of research has demonstrated that the water uptake behavior of organic aerosol components and their influence on the phase transitions of inorganics can be positive or negative depending on multiple factors, including the composition and relative amounts of the organic and inorganic aerosol fractions, the physiochemical properties of the organic components, and ambient conditions. The deviation between the measured water mass and the predicted one during some periods may be explained by the positive or negative effect of organic aerosol on the water uptake behavior of atmospheric particles. Figure 4.13: Water versus time as measured by the DAASS (red symbols) and estimated from E AIM (black line) at sheath RH. The water uptake by the organics is neglected in the simulations. A method for the in situ measurement of the water content of atmospheric particles. - 56

72 4.3 Ambient measurements with the improved DAASS Site and instrumentation Additional ambient measurements were conducted, using this time the improved DAASS, from 19 to 23 September The location of the measurements was the facilities of ICE HT in Platani, Patras. The DAASS system, in contrast to the previous ambient measurements, was located inside the lab due to heavy rain conditions. Only the sampling inlet was located outside. An Aerodyne high resolution time of flight aerosol mass spectrometer (HR ToF AMS) and a SMPS were also used to monitor and characterize the aerosol phase. Unfortunately, the AMS operated only during the first day of the measurements, because of technical problems Meteorological Conditions The sensors of the DAASS (see Figure 2.5) monitored temperature and RH. An extra sensor was placed outside to measure the ambient conditions. The temperature as well as the RH time series are presented in Figures 4.14 and Figure 4.14: The inlet flow (black line), sheath flow (blue line) and ambient (red line) temperature, during the ambient measurements with the improved DAASS. The inlet and the sheath flow temperatures were almost the same for the entire period. They were lower than the ambient values during midday and higher during early morning because the DAASS was located inside the lab. The temperature was controlled by an air conditioning system in the lab and remained steady during the period of the measurements. This discrepancy in temperature conditions had an A method for the in situ measurement of the water content of atmospheric particles. - 57

73 impact on RH. A small deviation from the ambient conditions was present. On the other hand, the sheath RH during sampling was in very good agreement with the inlet RH demonstrating the improvement in DAASS operation. Figure 4.15: Relative humidity time series during the ambient measurements with the improved DAASS. The inlet and the ambient RH are depicted by a black and a red line, respectively. The sheath RH is indicated by blue symbols Results PM 1 composition data are available only for the first day of the measurements. During that day the average dry PM 1 mass concentration measured via the AMS was 2.4 μg m 3 and ranged from 1.4 μg m 3 to 4.5 μg m 3. Figure 4.16: Mass concentration of the major PM 1 components measured by the HR ToF AMS during the first day of the measurements. A method for the in situ measurement of the water content of atmospheric particles. - 58

74 The time series of PM 1 mass concentration is depicted in Figure The average composition of PM 1 is presented in Figure Organics (43.5%) and sulfate (42.2%) were the dominant species, followed by ammonium (11.8%), nitrate (1.9%) and chloride (0.6%). The cations/anions ratio of the particles was calculated using Equation 4.1 and is shown in Figure Highly acidic conditions prevailed during that day as a result of high sulfate concentrations. Figure 4.17: Average composition of the main PM 1 aerosol components during the first day of the autumn measurements. Figure 4.18: Cations/anions ratio versus time as calculated from the ratio of equivalents of ammonium over sulfate, nitrate and chloride measured by the AMS. A method for the in situ measurement of the water content of atmospheric particles. - 59

75 The time evolution of ambient and corrected dry number as well as volume size distributions are presented in Figures 4.19 and 4.20, respectively. The presence of smaller particles (range 20 to 300 nm) is noticed in Figure 4.19, in contrast to the summer period measurements (range 50 to 300 nm). The dry distribution shifted slightly towards lower particle diameters, as expected, indicating the loss of water content. The difference between the ambient and the dry volume distribution (Figure 4.20) is large implying significant amounts of absorbed water. Figure 4.19: The a) ambient and b) corrected dry particle number distributions during the ambient measurements in the fall of The y axis is the particle diameter, the x axis represents the local time and the color scale indicates the particle number concentration. A method for the in situ measurement of the water content of atmospheric particles. - 60

76 Figure 4.20: The a) ambient and b) corrected dry particle volume distributions during the ambient measurements in the fall of The y axis is the particle diameter, the x axis represents the local time and the color scale indicates the particle volume concentration. The measured ambient and corrected dry volume concentrations are shown in Figure In general the measured volume concentrations were quite lower than the ones measured during the summer campaign, mainly due to higher wet deposition. The average ambient and dry volume concentrations measured via the improved DAASS were 3.75 μm 3 cm 3 and 2.20 μm 3 cm 3 respectively. As a comparison, the average ambient and dry volume concentrations during the summer campaign were 6.67 μm 3 cm 3 and 5.27 μm 3 cm 3 respectively. However the average water volume A method for the in situ measurement of the water content of atmospheric particles. - 61

77 concentration (Figure 4.21) measured during September was higher than the one during summer. The average values were 1.56 μm 3 cm 3 during the fall s measurements and 1.40 μm 3 cm 3 during the summer. The measured volume growth factors are shown in Figure RH was moderate to high (50 to 80%) during the measurements and the particles contained water throughout the measurement period. The temporal evolution of volume growth factors in conjunction with RH is shown in Figure 4.23 indicating the dependence on RH. Figure 4.21: Ambient (blue line) and corrected dry (red line) volume concentrations measured by the improved DAASS. The difference is the water (green line) volume concentration. Figure 4.22: Volume growth factors measured by the improved DAASS during the fall. A method for the in situ measurement of the water content of atmospheric particles. - 62

78 Figure 4.23: Time evolution of the measured volume growth factors (red symbols) by the improved DAASS. The inlet RH (black line) is indicated at the right y axis. Figure 4.24 shows the average measured volume growths factors during the summer campaign as well as during the measurements with the improved DAASS. Higher VGF values were measured during September s measurements than during the summer period, indicating that particles absorbed more water during autumn for these datasets. Figure 4.24: Average measured volume growths factors during the summer campaign (red symbols) and during the fall (blue squares). The error bars indicate one standard deviation and the labels show the number of observations per each point. A method for the in situ measurement of the water content of atmospheric particles. - 63

79 4.4 Conclusions A short measurement campaign took place at the facilities of ICE HT in Platani, Patras, from July 29 to August 3 in The aim of this campaign was primarily to test and assess the stability and reliability of the DAASS system for long time measurements and secondly, the characterization of hygroscopic behavior of ambient particulate matter in this suburban area during the summer. During the campaign high temperatures prevailed. 217 measurements were made at RH values below 50%, and 106 above 50%. The average dry PM 1 mass concentration was 7.7 μg m 3 and the major component of PM 1 was organic matter, followed by sulfate, ammonium and black carbon. The absorbed water of the particles represented 0 50% (17.9% on average) of the fine aerosol mass during that period. The measured values of VGF were generally low, mainly due to the low RH during the measurements. The particles always had some water at RH values above 50%. At RH values below 50% there were periods when the particles were dry and periods when they contained water. The slightly acidic environment during the longest period of the campaign or the absorption by the organics probably explains the aerosol water even at low RH values. A comparison between the measured aerosol water by the DAASS to the calculated one from the thermodynamic model E AIM is encouraging. The average measured water (for periods when the particles were wet) was 1.6 μg m 3 while the predicted water for the same periods was 1.2 μg m 3. The positive or negative effect of organic aerosol on the water uptake behavior of atmospheric particles may explain deviations between the measured water mass and the predicted one, as the water content of organics is neglected in this calculation. The DAASS operated throughout the campaign without any significant problems despite the high temperatures. The operation of the improved DAASS system was tested with ambient measurements from 19 to 23 September High RH conditions prevailed throughout the duration of the measurements. The excellent agreement between the inlet and the sheath RH confirms the improvement in the DAASS operation. Composition data are available only for the first day of the measurements. During that day the average dry PM 1 mass concentration was 2.4 μg m 3 and the major A method for the in situ measurement of the water content of atmospheric particles. - 64

80 components of PM 1 were organics (43.5%) and sulfate (42.2%), followed by ammonium (11.8%), nitrate (1.9%) and chloride (0.6%). Highly acidic conditions prevailed during that day of the measurements, as a result of high sulfate concentrations. All the observations (178) were made at RH values above 50% and the particles always contained water. Quite higher volume growth factors were measured during the autumn compared to the summer. A method for the in situ measurement of the water content of atmospheric particles. - 65

81 4.5 References Choi, M. Y. and Chan, C. K. (2002b). The effects of organic species on the hygroscopic behaviors of inorganic aerosols, Environ. Sci. Technol., 36, Clegg, S. L., Brimblecombe P., and Wexler A. S. (1998). A thermodynamic model of the system H + NH + 4 SO 2 4 NO 3 H 2 O at tropospheric temperatures, J. Phys. Chem. A, 102, Cruz C. N. and S. N. Pandis (2000). Deliquescence and hygroscopic growth of mixed inorganic organic atmospheric aerosol, Environ. Sci. Technol., 34, Duplissy, J., DeCarlo, P. F., Dommen, J., Alfarra, M. R., Metzger, A., Barmpadimos, I., Prevot, A. S. H., Weingartner, E., Tritscher, T., Gysel, M., Aiken, A. C., Jimenez, J. L., Canagaratna, M. R., Worsnop, D. R., Collins, D. R., Tomlinson, J., and Baltensperger, U. (2011). Relating hygroscopicity and composition of organic aerosol particulate matter, Atmos. Chem. Phys., 11, Gysel, M., E. Weingartner, S. Nyeki, D. Paulsen, U. Baltensperger, I. Galambos, and G. Kiss (2004). Hygroscopic properties of water soluble matter and humic like organics in atmospheric fine aerosol, Atmos. Chem. Phys., 4, Hodas N., A. Zuend, W. Mui, R. C. Flagan, and J. H. Seinfeld (2015). Influence of particle phase state on the hygroscopic behavior of mixed organic inorganic aerosols, Atmos. Chem. Phys., 15, Petzold, A. and Schönlinner, K. (2004). Multi angle absorption photometry a new method for the measurement of aerosol light absorption and atmospheric black carbon, Aerosol Science., 35, Pilinis C., P. E. Charalampidis, N. Mihalopoulos, and S. N. Pandis (2014). Contribution of particulate water to the measured aerosol optical properties of aged aerosol, Atmos. Environ., 82, A method for the in situ measurement of the water content of atmospheric particles. - 66

82 Prenni, A. J., DeMott, P. J., and Kreidenweis, S. M. (2003). Water uptake of internally mixed particles containing ammonium sulfate and dicarboxylic acids, Atmos. Environ., 37, A method for the in situ measurement of the water content of atmospheric particles. - 67

83 CHAPTER 5 Conclusions & Future Work A new improved version of the reduced Dry Ambient Aerosol Size Spectrometer (DAASS) has been developed able to operate at relative humidity values above 75%. An open loop is now used for the ambient sheath air flow, in contrast to previous configurations, avoiding adsorption of part of the water vapor in the particle filters. Mechanical servomotors and two way mechanical valves, instead of three way solenoid electrical valves, were used eliminating the heat generation due to the operation of the electrical valves. The new version of DAASS is compact, and is a lot easier to transport to field sites. The instrument was characterized in a set of smog chamber experiments at a wide range of RH values, using (NH 4 ) 2 SO 4 particles. Two approaches were used to estimate particle wall losses in the instrument. For dry particles a size dependent correction was applied. On the other hand, for wet particles the RH dependence of the losses was much stronger than their size dependence. As a result a size independent correction was used, different for each sample and RH. An algorithm consistency check was also developed to assess the quality of the measurements and the validity of the assumptions used in the data analysis. In addition a comparison of the measured volume growth factors (VGF) of (NH 4 ) 2 SO 4 aerosol particles to the predicted VGF by the thermodynamic model E AIM confirmed the reliability of the operation of the improved DAASS. Two ambient tests took place one using the original and one using the improved DAASS. The original version of the DAASS was used at a suburban area during a summer mini campaign from July 29 to August 3 in 2015 with moderate RH. The average dry PM 1 mass concentration was 7.7 μg m 3 and the major component of PM 1 was organic matter, followed by sulfate, ammonium and black carbon. The water content of the aerosol represented 17.9% on average of the fine aerosol mass with values ranging from 0 to 50%. The particles contained water at RH values above 50%, whereas when the RH was below 50% the particles were sometimes dry and A method for the in situ measurement of the water content of atmospheric particles. - 68

84 sometimes they retained water. The presence of water, at low RH values, may be explained by the slightly acidic particles or absorption by the organics. The measured aerosol water by the DAASS was compared to the predictions of the thermodynamic model E AIM with encouraging results. The water content of organics was neglected in this calculation; hence the positive or negative effect of organic aerosol on the water uptake behavior of atmospheric particles may explain deviations between the measured water mass and the predicted one. The DAASS operated for several days without any significant problems, in spite of the high prevailing temperatures. Ambient measurements, using the improved version of DAASS, were conducted at the same place during a rainy period from 19 to 23 September During the first day, the average dry PM 1 mass concentration was 2.4 μg m 3 and the major components of PM 1 were organics (43.5%) and sulfate (42.2%), followed by ammonium (11.8%), nitrate (1.9%) and chloride (0.6%). The particles were highly acidic during that day of the measurements, as a result of high sulfate concentrations. High RH conditions (>50%) prevailed throughout the duration of the measurements and the particles always contained water. The measured volume growth factors (VGF) were quite higher during this period, than the measured ones during the summer. In future work, more ambient measurements at high RH conditions (>75%) must be conducted, in order to examine the hygroscopic behavior of particles at this RH range. Finally, it is recommended in future deployments to replace the 90 0 bend with a Y splitter at the beginning of the dry flow line, in order to minimize the particle losses inside the instrument. A method for the in situ measurement of the water content of atmospheric particles. - 69

85 Laboratory and field photographs APPENDIX Evolution of the Dry Ambient Aerosol Size Spectrometer (DAASS) Figure S1: Photo of the complete DAASS system in the Carnegie Mellon laboratory during testing and development phase (Stanier et al., 2004). Figure S2: Photo of the reduced DAASS set up in the Carnegie Mellon laboratory for testing and development (Engelhart et al., 2011). A method for the in situ measurement of the water content of atmospheric particles. - 70

86 Figure S3: Photographs of the improved reduced DAASS in ICE HT laboratory during testing. Figure S4: The reduced DAASS system operating during the summer mini campaign outside the ICE HT building. A method for the in situ measurement of the water content of atmospheric particles. - 71

87 Figure S5: The sampling inlet during the summer mini campaign. A method for the in situ measurement of the water content of atmospheric particles. - 72