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Measurements of ice nucleating particles in the atmosphere : method development and results from field… Mason, Ryan H. 2015

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MEASUREMENTS OF ICE NUCLEATING PARTICLES IN THE ATMOSPHERE: METHOD DEVELOPMENT AND RESULTS FROM FIELD CAMPAIGNS IN NORTH AMERICA AND EUROPE  by Ryan H. Mason  B.Sc. Hons., St. Francis Xavier University, 2010  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  Doctor of Philosophy in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Chemistry)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  December 2015  © Ryan H. Mason, 2015   ii Abstract  Ice nucleating particles (INPs), which are a small fraction of the total aerosol population, are capable of catalyzing ice formation under atmospheric conditions. INPs may therefore influ-ence the development, albedo, and lifetime of mixed-phase and ice clouds, and ultimately indi-rectly effect climate. As this aerosol indirect effect represents one of the largest sources of uncer-tainty in our understating of climate processes, measurements that quantify and characterize the atmospheric INP population are needed.   The micro-orifice uniform deposit impactor-droplet freezing technique (MOUDI-DFT) was developed to measure INP concentrations in the atmosphere as a function of size and tem-perature in the immersion mode. The first campaign using the MOUDI-DFT was conducted in a Colorado forest. The concentrations of INPs and bioparticles were increased and correlated dur-ing and following rainfall events, and their size distributions were similar. This indicates that rainfall-associated mechanisms of bioparticle release may influence the abundance and efficien-cy of INPs in this region.  The MOUDI-DFT was next used at a coastal site in Western Canada. INP concentrations were strongly correlated with those of fluorescent bioparticles and the size distributions of these particles were similar, suggesting bioparticles were an important source of INPs during this study. Despite the predominance of marine air masses, no evidence of a marine INP source was found. Six parameterizations of ice nucleation were tested and found to be poor predictors of the measured INP concentrations, identifying a need to develop INP parameterizations appropriate for coastal environments.   iii  Finally, size-resolved INP measurements from six ground-level sites in North America and one in Europe were presented, covering Arctic, alpine, coastal, marine, agricultural, and suburban environments. On average, 78 % of INPs were supermicron in size and 53 % were in the coarse mode (> 2.5 micrometers). Large particles were therefore a significant component of the ground-level INP in these diverse locations.  The results presented in this dissertation increase our understanding of atmospheric INP concentrations, composition, and size. This information can be used to constrain INP sources, improve modeling of their long-distance transport and related indirect climate effects, and de-termine the ability of existing instrumentation to capture the full INP population.     iv Preface  Chapters 3, 4, and 5 are co-authored peer-reviewed journal articles, and Chapter 6 is a co-authored journal article under peer review. The details of my contributions to each research chapter are provided below.   Chapter 3 (fifth author on a published journal article): Huffman, J. A., Prenni, A. J., DeMott, P. J., Pöhlker, C., Mason, R. H., Robinson, N. H., Fröhlich-Nowoisky, J., Tobo, Y., Després, V. R., Garcia, E., Gochis, D. J., Harris, E., Müller-Germann, I., Ruzene, C., Schmer, B., Sinha, B., Day, D. A., Andreae, M. O., Jimenez, J. L., Gallagher, M., Kreidenweis, S. M., Bertram, A. K. and Pöschl, U.: High concentrations of biological aerosol particles and ice nuclei during and after rain, Atmos. Chem. Phys., 13, 6151–6164, doi:10.5194/acp-13-6151-2013, 2013. • Performed offline ice nucleation experiments with assistance from A. Sun. • Performed the data analysis of offline ice nucleation experiments. • Writing of the text relating to offline ice nucleation experiments was shared with my su-pervisor. • Additional contributions from co-authors: o Dr. J. A. Huffman and Dr. C. Pöhlker collected the aerosol samples and provided the UV-APS data and fluorescence microscopy images. o Dr. A. J. Prenni, Dr. P. J. DeMott, and Dr. Y. Tobo provided the online measurements of ice nucleating particles. o Dr. D. J. Gochis provided measurements of temperature, relative humidity, precipitation, and leaf wetness state.   v  Chapter 4 (first author on a published journal article): Mason, R. H., Chou, C., McCluskey, C. S., Levin, E. J. T., Schiller, C. L., Hill, T. C. J., Huffman, J. A., DeMott, P. J. and Bertram, A. K.: The micro-orifice uniform deposit impactor-droplet freezing technique (MOUDI-DFT) for measuring concentrations of ice nucleating particles as a function of size: improvements and initial validation, Atmos. Meas. Tech., 8, 2449–2462, doi:10.5194/amt-8-2449-2015, 2015. • Formulated research questions and designed research project with my supervisor. • Performed experiments and analysis of aerosol deposit non-uniformity. • Performed and analyzed the offline ice nucleation experiments. • Prepared all of the figures in this publication. • Writing of the text for this publication was shared primarily with my supervisor. • Additional contributions from co-authors: o C. S. McCluskey and Dr. E. J. T. Levin carried out and analyzed the online ice nucleation experiments.  Chapter 5 (first author on a journal article under peer review): Mason, R. H., Si, M., Li, J., Chou, C., Dickie, R., Toom-Sauntry, D., Pöhlker, C., Yakobi-Hancock, J. D., Ladino, L. A., Jones, K., Leaitch, W. R., Schiller, C. L., Abbatt, J. P. D., Huffman, J. A. and Bertram, A. K.: Ice nucleating particles at a coastal marine boundary layer site: correlations with aerosol type and meteorological conditions, Atmos. Chem. Phys., 15, 12547–12566, 2015. • Formulated research questions and designed research project with my supervisor. • Collected the aerosol samples with M. Si. • Performed ice nucleation experiments with M. Si and Dr. C. Chou.   vi • Completed data analysis with M. Si, Dr. C. Chou, and R. Dickie. • Prepared all of the figures in this publication. • Writing of the text for this publication was shared primarily with my supervisor. • Additional contributions from co-authors: o Dr. J. A. Huffman and J. Li provided online total particle and fluorescence data. o Dr. C. Pöhlker provided images of florescence microscopy. o Dr. W. R. Leaitch and D. Toom-Sauntry provided ion chromatography data. o Dr. C. L. Schiller and K. Jones provided meteorological, black carbon, CO, NOx, and SO2 data. o M. Si, Dr. C. L. Schiller, Dr. J. P. D. Abbatt, J. D. Yakobi-Hancock, Dr. L. A. Ladino, and my supervisor assisted with campaign logistics.  Chapter 6 (first author on a journal article under peer review): Mason, R. H., Si, M., Chou, C., Irish, V. E., Dickie, R., Elizondo, P., Wong, R., Brintnell, M., Elsasser, M., Lassar, W. M., Pierce, K. M., Leaitch, W. R., MacDonald, A. M., Platt, A., Toom-Sauntry, D., Sarda-Estève, R., Schiller, C. L., Suski, K. J., Hill, T. C. J., Abbatt, J. P. D., Huffman, J. A., DeMott, P. J., and Bertram, A. K.: Ice nucleating particles at a coastal marine boundary layer site: correlations with aerosol type and meteorological conditions, Atmos. Chem. Phys. Discuss., 15, 20521–20559, 2015. • Formulated research questions and designed research project with my supervisor. • Collected the aerosol samples with M. Si, Dr. C. Chou, V. E. Irish, M. Brintnell, M. El-sasser, W. Lassar, and K. Pierce.   vii • Performed ice nucleation experiments with M. Si, Dr. C. Chou, V. E. Irish, and P. Eli-zondo.  • Completed data analysis with M. Si, Dr. C. Chou, V. E. Irish, R. Dickie, P. Elizondo, and R. Wong. • Prepared all of the figures in this publication. • Writing of the text for this publication was shared primarily with my supervisor. • Additional contributions from co-authors: o M. Si, Dr. C. Chou, V. E. Irish, Dr. K. J. Suski, Dr. J. P. D. Abbatt, Dr. P. J. DeMott, Dr. T. C. J. Hill, Dr. W. R. Leaitch, A. M. MacDonald, A. Platt, R. Sarda-Estève, Dr. C. L. Schiller, D. Toom-Sauntry, and my supervisor assisted with campaign logistics.    viii Table of Contents Abstract .......................................................................................................................................... ii	  Preface ........................................................................................................................................... iv	  Table of Contents ....................................................................................................................... viii	  List of Tables .............................................................................................................................. xiv	  List of Figures ............................................................................................................................. xvi	  List of Symbols ........................................................................................................................ xxvii	  List of Abbreviations ................................................................................................................ xxx	  Acknowledgements ................................................................................................................. xxxii	  Dedication ............................................................................................................................... xxxiv	  Chapter 1. Introduction ................................................................................................................ 1	  1.1	   Atmospheric aerosols ......................................................................................................... 1	  1.2	   Ice nucleation in the atmosphere ........................................................................................ 3	  1.2.1	   Mechanisms of ice nucleation ..................................................................................... 3	  1.2.2	   Types of ice nucleating particles ................................................................................. 4	  1.3	   How aerosols influence climate ......................................................................................... 5	  1.3.1	   Direct and indirect climate effects .............................................................................. 5	  1.3.2	   The climatic impact of ice nucleating particles .......................................................... 6	  1.4	   Motivation and overview of dissertation ........................................................................... 8	  1.5	   Chapter 1 figures .............................................................................................................. 10	    ix Chapter 2. The micro-orifice uniform deposit impactor-droplet freezing technique (MOUDI-DFT) for measuring concentrations of ice nucleating particles as a function of size: introduction ......................................................................................................................... 14	  2.1	   Motivation ........................................................................................................................ 14	  2.2	   Instrument operation ........................................................................................................ 16	  2.2.1	   Micro-orifice uniform deposit impactor (MOUDI) .................................................. 16	  2.2.2	   Droplet freezing technique (DFT) ............................................................................ 18	  2.3	   Calculating INP number concentrations .......................................................................... 20	  2.4	   Chapter 2 figures .............................................................................................................. 21	  Chapter 3. High concentrations of biological aerosol particles and ice nucleating particles during and after rain .................................................................................................................. 24	  3.1	   Introduction ...................................................................................................................... 24	  3.2	   Experimental .................................................................................................................... 26	  3.2.1	   Site description .......................................................................................................... 26	  3.2.2	   Meteorological and leaf moisture measurements ..................................................... 27	  3.2.3	   Size-resolved INP measurements ............................................................................. 27	  3.2.4	   Real-time INP measurements ................................................................................... 30	  3.2.5	   Online fluorescent measurements ............................................................................. 31	  3.2.6	   Fluorescence microscopy .......................................................................................... 31	  3.3	   Results and discussion ..................................................................................................... 32	  3.4	   Summary and conclusions ............................................................................................... 34	  3.5	   Chapter 3 tables and figures ............................................................................................. 36	    x Chapter 4. The micro-orifice uniform deposit impactor-droplet freezing technique (MOUDI-DFT) for measuring concentrations of ice nucleating particles as a function of size: improvements and initial validation ................................................................................. 42	  4.1	   Introduction ...................................................................................................................... 42	  4.2	   Experimental .................................................................................................................... 43	  4.2.1	   Micro-orifice uniform deposit impactor (MOUDI) .................................................. 43	  4.2.2	   Droplet freezing technique (DFT) ............................................................................ 44	  4.2.3	   Calculating INP concentrations ................................................................................ 44	  4.2.4	   Measurements of MOUDI aerosol deposit non-uniformity ...................................... 46	  4.2.5	   Substrate holders for individual MOUDI stages ....................................................... 48	  4.2.6	   Comparison of MOUDI-DFT and CFDC measurements ......................................... 48	  4.3	   Results and discussion ..................................................................................................... 50	  4.3.1	   MOUDI aerosol deposit non-uniformity and size .................................................... 50	  4.3.2	   Substrate holder design ............................................................................................. 51	  4.3.3	   Correction for aerosol deposit non-uniformity at a spatial resolution of 1 mm ........ 52	  4.3.4	   Correction for aerosol deposit non-uniformity at a spatial resolution of 0.25 and   0.10 mm ................................................................................................................................ 53	  4.3.5	   MOUDI-DFT and CFDC intercomparison ............................................................... 55	  4.4	   Summary and implications for previous measurements .................................................. 57	  4.5	   Chapter 4 tables and figures ............................................................................................. 59	  Chapter 5. Ice nucleating particles at a coastal marine boundary layer site: correlations with aerosol type and meteorological conditions ..................................................................... 67	  5.1	   Introduction ...................................................................................................................... 67	    xi 5.2	   Methods ............................................................................................................................ 69	  5.2.1	   Site description and instrument location ................................................................... 69	  5.2.2	   Ice nucleating particle measurements ....................................................................... 71	  5.2.3	   Total and fluorescent aerosol measurements with sizes ≥ 0.5 µm ............................ 71	  5.2.4	   Fluorescence microscopy .......................................................................................... 72	  5.2.5	   Black carbon (BC) measurements ............................................................................ 73	  5.2.6	   Tracers of anthropogenic aerosols ............................................................................ 74	  5.2.7	   Ion measurements ..................................................................................................... 74	  5.2.8	   Back trajectories ........................................................................................................ 75	  5.3	   Results and discussion ..................................................................................................... 76	  5.3.1	   Site description and instrument location ................................................................... 76	  5.3.2	   Are biological particles a major source of ice nuclei? .............................................. 77	  5.3.3	   Is black carbon a major source of ice nuclei? ........................................................... 79	  5.3.4	   Are particles from the ocean a major source of ice nuclei? ...................................... 79	  5.3.5	   What is the major source of ice nuclei active at -30 °C? .......................................... 81	  5.3.6	   Do the major sources of ice nuclei change with air mass classification? ................. 82	  5.3.7	   Can existing parameterizations accurately predict measured INP concentrations? .. 84	  5.4	   Summary and conclusions ............................................................................................... 86	  5.5	   Chapter 5 tables and figures ............................................................................................. 87	  Chapter 6. Size-resolved measurements of ice nucleating particles at six locations in North America and one in Europe ....................................................................................................... 97	  6.1	   Introduction ...................................................................................................................... 97	  6.2	   Methods ............................................................................................................................ 99	    xii 6.2.1	   Sampling sites ........................................................................................................... 99	  6.2.1.1	   Alert, Nunavut .................................................................................................. 100	  6.2.1.2	   The Labrador Sea ............................................................................................. 100	  6.2.1.3	   Whistler Mountain ........................................................................................... 101	  6.2.1.4	   UBC campus .................................................................................................... 101	  6.2.1.5	   Amphitrite Point ............................................................................................... 101	  6.2.1.6	   Colby, Kansas .................................................................................................. 102	  6.2.1.7	   Saclay, France .................................................................................................. 102	  6.2.2	   Size-resolved INP number concentrations .............................................................. 103	  6.3	   Results and discussion ................................................................................................... 103	  6.3.1	   INP number concentrations ..................................................................................... 103	  6.3.2	   INP size distributions .............................................................................................. 106	  6.4	   Summary and conclusions ............................................................................................. 110	  6.5	   Chapter 6 tables and figures ........................................................................................... 112	  Chapter 7. Conclusions and future work ................................................................................ 119	  7.1	   Development of the micro-orifice uniform deposit impactor-droplet freezing       technique (MOUDI-DFT) ....................................................................................................... 119	  7.2	   Field measurements of INPs: determining size and source ........................................... 120	  7.3	   Directions of future research .......................................................................................... 121	  References .................................................................................................................................. 124	  Appendices ................................................................................................................................. 154	  Appendix A Description of ice nucleation using classical nucleation theory ........................ 154	  Appendix B The impact of MOUDI deposit non-uniformity on forest INP concentrations .. 158	    xiii Appendix C Aerosol measurements at the coastal marine boundary layer site ...................... 165	  C.1	   INP sampling periods and conditions ..................................................................... 165	  C.2	   Correlations involving INPs, aerosol composition, and wind speed ...................... 166	  C.3	   Measurements of CO, NOx, and SO2 ...................................................................... 168	  Appendix D Empirical parameterizations of ice nucleation ................................................... 169	  D.1	   Literature parameterizations ................................................................................... 169	  D.2	   Reducing the MOUDI-DFT size range to match parameterization conditions ...... 171	  Appendix E INP size distributions at sites in North America and Europe ............................. 173	  Appendix F Calculating the percentile size of INPs using binned data .................................. 180	  Appendix G Calculating the fractions of INPs larger than 1, 1.2, or 2.5 µm from previous studies ..................................................................................................................................... 181	  G.1	   Vali (1966) .............................................................................................................. 181	  G.2	   Rosinski et al. (1986) .............................................................................................. 181	  G.3	   Rosinski et al. (1988) .............................................................................................. 182	  G.4	   Berezinski et al. (1988) ........................................................................................... 183	  G.5	   Santachiara et al. (2010) ......................................................................................... 183	  G.6	   Huffman et al. (2013) .............................................................................................. 183	  G.7	   Other studies ........................................................................................................... 184	     xiv List of Tables Table 3.1: Details of the sampling periods used for offline INP measurements. Meteorological parameters have been averaged over the entire sampling duration, and times are reported in Mountain Daylight Time (MDT). ................................................................................. 36	  Table 4.1: CSU sampling conditions. ........................................................................................... 59	  Table 4.2: Deposit diameters and areas, hydrophobic glass cover slip offsets, and non-uniformity correction factors fnu,1mm and fnu,0.25–0.10mm for MOUDI stages 2–8 when using substrate holders. The uncertainty in fnu,1mm is given as the standard deviation. .............................. 59	  Table 5.1: Correlation coefficients (R) for linear regression analyses of INPs versus fluorescent bioparticles, total aerosol particles, eBC, sodium, MSA, and wind speeda. Correlations with statistical significance (P < 0.05) are shown in bold. ............................................... 87	  Table 5.2: Correlation coefficients (R) for linear regression analyses of INPs versus fluorescent bioparticles, total aerosol particles, eBC, and wind speeda within each category of air mass. Correlations with statistical significance (P < 0.05) are shown in bold. ................ 88	  Table 6.1: The seven locations used in this study and conditions during sampling. .................. 112	  Table 6.2: Previous size-resolved INP measurements. ............................................................... 113	  Table C.1: Details of the INP sampling periods. The meteorological parameters given have been averaged over the stated sampling duration and are described in Section 5.2 of Chapter 5. Times are reported as Pacific Daylight Time (PDT). ..................................................... 165	  Table C.2: Correlation coefficients (R) for linear regression analyses of INPs versus CO, NOx, and SO2. No correlations had statistical significance (P < 0.05). ................................... 168	    xv Table C.3: Correlation coefficients (R) for linear regression analyses of INPs versus CO, NOx, and SO2 within category of air mass, which are described in Section 5.2.8 of Chapter 5. Correlations with statistical significance (P < 0.05) are shown in bold. ........................ 168	     xvi List of Figures Figure 1.1: An idealized surface area distribution of atmospheric aerosols as a function of particle size. The sources, formation and removal mechanisms, and modes are indicated. Adapted from Whitby and Cantrell (1976) and Seinfeld and Pandis (2006). ................... 10	  Figure 1.2: Schematic of homogeneous ice nucleation and the four modes of heterogeneous ice nucleation according to Vali (1985) and Pruppacher and Klett (1997). Liquid water, ice crystals, and INPs are represented in the schematic as blue circles, blue hexagons, and amorphous black shapes, respectively. ............................................................................. 11	  Figure 1.3: Radiative forcing by atmospheric aerosols as well as other atmospheric consistuents over the Industrial Era (1750-2011). Uncertainties are reported at the 5–95 % confidence range. Dotted error bars denote the uncertainty in the magnitude of radiative forcing and solid error bars denote the uncertainty in the magnitude of effective radiative forcing. Radiative forcing is defined as the change in net downward radiative flux at the tropopause after allowing only stratospheric temperatures to readjust to radiative equilibrium, while in effective radiative forcing atmospheric temperature, water vapor, clouds, and land surface properties such as temperature, snow and ice cover, and vegetation are also allowed to adjust (IPCC, 2013). Adapted from Figure TS.6 of IPCC (2013). ............................................................................................................................... 12	  Figure 1.4: The probable indirect effect of INPs on climate by ice nucleation in midlevel mixed-phase clouds and upper level cirrus clouds where elements (1) and (3) denote low INP number concentrations and elements (2) and (4) denote increased INP number   xvii concentrations. Arrow thickness indicates the relative intensity of radiation. Adapted from DeMott et al. (2010). ................................................................................................ 13	  Figure 2.1: A simplified schematic of the MOUDI. Only the inlet, two complete stages (nozzle plate with underlying impaction plate and collection substrate), and after filter are shown. ........................................................................................................................................... 21	  Figure 2.2: Schematic diagram of the droplet freezing apparatus used in measurements of INPs: (a) the base of the flow cell with a depression to position the hydrophobic glass cover slip; (b) the body of the flow cell; and (c) the cross section of the flow cell aligned with the optical axis of the microscope. .................................................................................... 22	  Figure 2.3: Images recorded during a droplet freezing experiment: (a) collected particles sitting on the hydrophobic glass cover slip at room temperature; (b) droplets after the condensation of water at 0 °C; (c) droplets after partial evaporation to reduce their size; and (d) frozen droplets after the cell temperature was reduced to -40 °C over a period of 4 minutes. ............................................................................................................................. 23	  Figure 3.1: Satellite images of the sampling site: (a) overview of the surrounding region with the site shown in the white box; (b) the sampling site detailing the layout of instrumentation where (1) was the location of the leaf wetness sensor, disdrometer, and weather transmitter and (2) was the trailer housing the MOUDI, CFDC, and UV-APS. Images were obtained from Bing Maps, 2015 (http://bing.com/maps/). ....................................... 37	  Figure 3.2: Fluorescence microscope images of aerosol impactor samples collected under (a) dry and (b) wet weather conditions. Number concentrations of INPs observed at -25 °C by the CFDC plotted against fluorescent bioparticles < 2.4 µm detected by the UV-APS   xviii during (c) dry and (d) wet weather conditions. Linear correlation coefficients (R) and probability values (P) are given. ....................................................................................... 38	  Figure 3.3: INP activation curves for the four MOUDI samples. Upper panels (a, b) are for samples collected during rain events and lower panels (c, d) are for samples collected during dry periods. Grey traces show stages inlet–2, red traces show stage 3 (3.2–5.6 µm), blue traces show stage 4 (1.8–3.2 µm), and gold traces show stages 5–7. See Table 3.1 for sample details. ....................................................................................................... 39	  Figure 3.4: Size distributions of INPs active at droplet freezing temperatures of (a) -15 °C and (b) -20 °C for samples collected during rain events and dry periods. (c) Size distribution of bioparticles measured during the same periods. Number concentrations have been averaged over both samples within each category. See Table 3.1 for sample details. ..... 40	  Figure 3.5: The fraction of particles that are ice-active at -15 °C in samples collected during rain events (M10, M26), calculated using the INP concentrations from the MOUDI-DFT and total particle concentrations from the UV-APS An exponential curve is shown to guide the eye. .............................................................................................................................. 41	  Figure 4.1: (a) The concentration of aerosol particles on MOUDI stage 8 as a function of distance from the center of the aerosol deposit, measured at a spatial resolution of 0.10 mm. (b) A subsection of the continuous cross section of the aerosol deposit of MOUDI stage 8. The images have been background-corrected by subtracting the sample image from a particle-free image. Background correction was done to remove spots on the image from dust on the optics. When overlapping individual images to produce the continuous image, the individual images do not align perfectly in the vertical dimension   xix because moving the hydrophobic glass cover slip in the x-direction using the XY translational stage of the microscope caused slight movement in the y-direction ............ 60	  Figure 4.2: The deposit profiles for MOUDI stages 2–8 found at a spatial resolution of 1 mm. The normalized particle concentration is the quotient of the particle concentration of a given step divided by the maximum particle concentration. The experimental uncertainty is the standard deviation, and the shaded area is the region of the aerosol deposit in the microscope viewing area of the DFT using the substrate offset given in Table 4.2 with an uncertainty of ± 0.5 mm. ................................................................................................... 61	  Figure 4.3: The same as Figure 4.2 but at a spatial resolution of 0.25 mm. The shaded area is the region of the aerosol deposit in the microscope viewing area of the DFT using the substrate offset given in Table 4.2 with an uncertainty of ± 0.5 mm. .............................. 62	  Figure 4.4: The same as Figure 4.2 but at a spatial resolution of 0.10 mm. The shaded area is the region of the aerosol deposit in the microscope viewing area of the DFT using the substrate offset given in Table 4.2 with an uncertainty of ± 0.5 mm. .............................. 63	  Figure 4.5: General substrate holder design specifications for positioning the hydrophobic glass cover slips in the MOUDI: (a) top-down view of the substrate holder; (b) bottom view; (c) the substrate holder positioned onto the impaction plate of the MOUDI stage. ......... 64	  Figure 4.6: The influence of aerosol deposit non-uniformity on the calculated number of INPs in MOUDI stage 6. Panels (a) and (b) are the #INPs(T) calculated for a non-uniform deposit (solid line) and assuming a uniform aerosol deposit (dashed line). The calculations were carried out for (a) 28 uniformly distributed droplets and (b) 56 uniformly distributed   xx droplets. Panels (c) and (d) show fnu,0.25–0.10mm, calculated by taking the ratio of the solid line to the dashed line in panels (a) and (b), respectively. ................................................ 65	  Figure 4.7: Comparison of INP concentrations found by the MOUDI-DFT and the CFDC under concurrent sampling. The grey shaded region marks the upper and lower bounds to the INP concentration as defined by the experimental uncertainty in the MOUDI-DFT, with points showing median values. The uncertainty in temperature for MOUDI-DFT measurements is not shown but is ± 0.3 °C. The blue shaded region shows the upper and lower bounds to the INP concentrations found in five blank DFT experiments (hydrophobic glass cover slips without atmospheric particles), with points showing median values. Average CFDC values are in red, with uncertainties in the vertical dimension shown as the 95 % confidence interval and in the horizontal dimension as the temperature uncertainty of ± 1 °C. .................................................................................... 66	  Figure 5.1: A satellite image of the sampling site: (1) location of the MOUDIs and the WIBS-4A; (2) location of the MAAP; and (3) Amphitrite Lighthouse where most meteorological data was collected; and (4) a station of the Canadian Coast Guard with supporting infrastructure. The image was modified from Bing Maps, 2014 (http://bing.com/maps/). Inset: the location of the sampling site in British Columbia, Canada. .............................. 89	  Figure 5.2: Seventy-two hour HYSPLIT4 back trajectories of the air masses analyzed at the coastal site (black star) during INP sampling periods. Each back trajectory was initiated from a height of 5.5 m agl and at the midpoint of the sampling period. ........................... 90	  Figure 5.3: INP number concentrations as a function of date determined at ice-activation temperatures of (a) -15 °C, (b) -20 °C, (c) -25 °C, and (d) -30 °C. The symbols are color   xxi coded by air mass category (see Section 5.2.8 for details). Fewer data points are available at -30 °C as INP number concentrations can only be determined to the temperature where all droplets are frozen. ....................................................................................................... 91	  Figure 5.4: Mean INP number concentrations found in each of the four categories of air masses sampled at (a) -15 °C, (b) -20 °C, (c) -25 °C, and (d) -30 °C. The scheme for air mass classification is given in Section 5.2.8. Uncertainties are given as the standard error of the mean. ................................................................................................................................. 92	  Figure 5.5: Number concentrations of INPs active at -25 °C plotted against concentrations of (a) fluorescent bioparticles 0.5–10 µm, (b) total particles 0.5–10 µm, (c) eBC, (d) sodium, (e) MSA, and (f) (wind speed)3.41 based on the power law function of Monahan and Muircheartaigh (1980) where wind speed was in units of m s-1. Linear fits are shown with corresponding correlation coefficients (R) and probability values (P). ............................ 93	  Figure 5.6: Mean number concentrations as a function of size for INPs active at (a) -15 °C, (b) -20 °C, (c) -25 °C, and (d) -30 °C, total particles (e) and fluorescent bioparticles (f) using only samples where both the MOUDI-DFT and WIBS-4A were operating. Uncertainties are given as the standard error of the mean. As INP number concentrations can only be determined at temperatures less than the temperature where all droplets are frozen and Equation (4.1) becomes undefined, fewer samples are represented at -30 °C. Number concentrations below 0.5 µm were not measured by the WIBS-4A for panels (e) and (f) but plot axes are consistent for easier comparison of the size distributions. .................... 94	    xxii Figure 5.7: Fluorescence microscopy images of an aerosol sample collected on August 11, 2013: (a) bright-field image; (b) an overlay of red, green, and blue fluorescence channels. A blue coloration is characteristic of biological material (Pöhlker et al., 2012). ................. 95	  Figure 5.8: Predicted versus measured INP number concentrations based on the parameterizations of (a) Fletcher (1962); (b) Cooper (1986); (c) Meyers et al. (1992); (d) DeMott et al. (2010); and (e–f) Tobo et al. (2013). Details on these parameterizations are given in Appendix D. Data color represents ice nucleation temperatures. This figure uses the format of Figure 9 in Tobo et al. (2013). .................................................................... 96	  Figure 6.1: Sampling locations used in this study: (1) Alert, Nunavut, Canada; (2) the Labrador Sea, Canada; (3) Whistler Mountain, British Columbia, Canada; (4) the University of British Columbia campus, British Columbia, Canada; (5) Amphitrite Point, British Columbia, Canada; (6) Colby, Kansas, USA; and (7) Saclay, France. Site coordinates are given in Table 6.1 with details in Section 6.2.1. The image was modified from Bing Maps, 2014 (http://bing.com/maps). ............................................................................... 114	  Figure 6.2: Mean INP number concentrations at droplet freezing temperatures of -15 °C (dark grey), -20 °C (intermediate grey), and -25 °C (light grey). Uncertainty is given as the standard error of the mean. ............................................................................................. 115	  Figure 6.3: The mean fraction of INPs larger than (a) 1 µm and (b) 2.5 µm. Uncertainty is the standard error of the mean. Shading in the histogram corresponds to INP activation temperature: -15 °C is dark grey, -20 °C is an intermediate grey, and -25 °C is light grey. As 2.5 µm does not align with the size cut of a MOUDI stage, the fraction of INPs larger than 2.5 µm was found by assuming that number concentration of INPs 1.8–3.2 µm in   xxiii size was uniformly distributed over that size range. As only one sample was available from the marine site, no uncertainty is reported. ............................................................ 116	  Figure 6.4: The median size of INPs at ice-activation temperatures of -15 °C (green), -20 °C (blue), and -25 °C (red) when averaged over all analyzed samples. Upper and lower uncertainties are the 75th and 25th percentiles, respectively. ........................................... 117	  Figure 6.5: Fractional INP concentrations as a function of aerosol particle size, location, and activation temperature: (a) -15 °C; (b) -20 °C; and (c) -25 °C. The color bar indicates the fraction of INPs measured in each particle size bin. Aerosol particle sizes correspond to the 50 % cutoff aerodynamic diameters of the MOUDI stages (Marple et al., 1991). Missing sizes for the Whistler Mountain and Amphitrite Point sites are uncolored. ..... 118	  Figure A.1: The Gibbs free energy change (blue trace) associated with the formation and growth of a new phase as a function of cluster size (ri). The green and red traces are calculated from the surface area and volume terms, respectively, of Equation (A.1). The size of the critical cluster needed to overcome the energy barrier to ice nucleation is noted as ri*. 157	  Figure B.1: Average size distributions of INP active at droplet freezing temperatures of (a) -15 °C and (b) -20 °C for samples collected during rain events and dry periods under the assumption of uniform particle deposits. (c) The size distribution of bioparticles measured during the same periods. ................................................................................. 161	  Figure B.2: Average size distributions of INP active at droplet freezing temperatures of (a) -15 °C and (b) -20 °C for samples collected during rain events and dry periods under the assumption that the DFT analyzed the region of the aerosol deposit with the greatest   xxiv particle concentrations. Note that INP concentrations could not be calculated for particles > 10 µm. (c) The size distribution of bioparticles measured during the same periods. .. 162	  Figure B.3: Average size distributions of INP active at droplet freezing temperatures of (a) -15 °C and (b) -20 °C for samples collected during rain events and dry periods under the assumption that the DFT analyzed the region of the aerosol deposit with the lowest particle concentrations. Note that INP concentrations could not be calculated for particles > 10 µm. (c) Size distribution of bioparticles measured during the same periods. ........ 163	  Figure B.4: Average size distributions of INP active at droplet freezing temperatures of (a) -15 °C and (b) -20 °C for samples collected during rain events and dry periods under the assumption that the DFT analyzed the region of the aerosol deposit with the greatest particle concentrations in samples collected during and following rainfall while the DFT analyzed the region of the aerosol deposit with the lowest particle concentrations in samples collected during dry periods. Note that INP concentrations could not be calculated for particles > 10 µm. (c) Size distribution of bioparticles measured during the same periods. ................................................................................................................... 164	  Figure C.1: INP number concentrations from -15 to -30 °C (columns I–IV) plotted against total particle number concentrations (panels a–d), fluorescent bioparticle number concentrations (panels e–h), and eBC mass concentrations (panels i–l). Linear fits are given as solid lines with corresponding correlation coefficients (R) and probability values (P). .................................................................................................................................. 166	  Figure C.2: INP number concentrations from -15 to -30 °C (columns I–IV) plotted against sodium mass concentrations (panels a–d), MSA mass concentrations (panels e–h), and   xxv (wind speed)3.41 based on the power law dependence of whitecap coverage on wind speed by Monahan and Muircheartaigh (1980) with wind speed in m s-1 (panels i–l). Linear fits are given as solid lines with corresponding correlation coefficients (R) and probability values (P). ....................................................................................................................... 167	  Figure E.1: Mean INP size distributions at the Arctic site of Alert, NU, Canada at (a) -15 °C, (b) -20 °C, and (c) -25 °C. Here the fraction of INPs in each MOUDI size bin is reported as the mean of all samples with uncertainty as the standard error of the mean. ................. 173	  Figure E.2: Mean INP size distributions at the alpine site of Whistler, BC, Canada at (a) -15 °C, (b) -20 °C, and (c) -25 °C. Here the fraction of INPs in each MOUDI size bin is reported as the mean of all samples with uncertainty as the standard error of the mean. Number concentrations below 0.18 µm were not measured but plot axes are consistent with the other figures for easier comparison of the size distributions. ......................................... 174	  Figure E.3: Mean INP size distributions at the coastal site of Amphitrite Point, BC, Canada at (a)  -15 °C, (b) -20 °C, and (c) -25 °C. Here the fraction of INPs in each MOUDI size bin is reported as the mean of all samples with uncertainty as the standard error of the mean. Number concentrations below 0.18 µm were not measured but plot axes are consistent with the other figures for easier comparison of the size distributions. ........................... 175	  Figure E.4: Mean INP size distributions at the marine site in the Labrador Sea at (a) -15 °C, (b) -20 °C, and (c) -25 °C. Here the fraction of INPs in each MOUDI size bin is reported as the mean of all samples with uncertainty as the standard error of the mean. As only one sample was collected at this location, no experimental uncertainty is reported. ............ 176	    xxvi Figure E.5: Mean INP size distributions at the suburban France site of Saclay, France at (a) -15 °C, (b) -20 °C, and (c) -25 °C. Here the fraction of INPs in each MOUDI size bin is reported as the mean of all samples with uncertainty as the standard error of the mean.177	  Figure E.6: Mean INP size distributions at the suburban Canada site on the UBC campus, BC, Canada at (a) -15 °C, (b) -20 °C, and (c) -25 °C. Here the fraction of INPs in each MOUDI size bin is reported as the mean of all samples with uncertainty as the standard error of the mean. ............................................................................................................ 178	  Figure E.7: Mean INP size distributions at the agricultural sites in Colby, Kansas, USA at (a) -15 °C, (b) -20 °C, and (c) -25 °C. Here the fraction of INPs in each MOUDI size bin is reported as the mean of all samples with uncertainty as the standard error of the mean.179	     xxvii List of Symbols A  fitting parameter for the F62 parameterization  a  fitting parameter for the D10 parameterization Adeposit  area of aerosol deposit ADFT  area analyzed by the DFT AP>0.5  aerosol particles with an aerodynamic diameter larger than 0.5 µm α  fitting parameter for the T13total parameterization αʹ  fitting parameter for the T13fluorescent parameterization B  fitting parameter for the F62 parameterization b  fitting parameter for the D10 parameterization β  fitting parameter for the T13total parameterization βʹ  fitting parameter for the T13fluorescent parameterization C  elastic strain coefficient c  fitting parameter for the D10 parameterization  D  fitting parameter for the C86 parameterization d  fitting parameter for the D10 parameterization 𝛿   fitting parameter for the T13total parameterization 𝛿ʹ   fitting parameter for the T13fluorescent parameterization 𝛿i  ratio of step concentration to viewing area concentration ΔGhom  Gibbs free energy of homogeneous nucleation 𝜖  elastic strain of the ice lattice E  fitting parameter for the C86 parameterization   xxviii es,i  saturation vapor pressure over ice  es,w  saturation vapor pressure over water F  fitting parameter for the M92 parameterization f  INP concentration in bin f(𝑚)  geometric factor FB>0.5  fluorescent bioparticles with an aerodynamic diameter larger than 0.5 µm fne  Koop correction factor fnu,1mm   non-uniformity factor correction at a spatial resolution of 1 mm fnu,0.25–0.10mm non-uniformity factor correction at a spatial resolution of 0.25–0.10 mm G  fitting parameter for the M92 parameterization γ  fitting parameter for the T13total parameterization γʹ  fitting parameter for the T13fluorescent parameterization I  MOUDI nozzle plate-to-impaction plate distance i  step number #INPs(T) number of INPs active at T  [INPs(T)] INP concentration at T k  Boltzmann constant l  lower size limit of bin λ  wavelength 𝑚  compatibility parameter for ice on a solid substrate Ni  concentration of water molecules in an ice cluster No  total number of droplets Nu(T)  number of unfrozen droplets at T   xxix nm  nanometer P  probability value p  percentile q  cumulative concentration R  correlation coefficient ri  radius of an ice cluster ri*  radius of the critical cluster S  supersaturation σi,n  ice-substrate interfacial energy  σi,w   ice-water interfacial energy σw,n  water-substrate interfacial energy T  temperature t  total INP concentration θ  contact angle µ  ratio of unfrozen to total droplets µm  micrometer V  volume of air sampled W  width of MOUDI nozzle w  bin width   xxx List of Abbreviations agl    above ground level AP    aerosol particles ARL    Air Resources Laboratory asl    above sea level BC    black carbon BEACHON-RoMBAS Bio-hydro-atmosphere interactions of Energy, Aerosols, Carbon,      H2O, Organics, and Nitrogen-Rocky Mountain Biogenic Aerosol      Study C86    Cooper (1986) CCD    charge-coupled device CEA-AS   Commissariat à l’Energie Atomique-Atmospheric Supersite CFDC    continuous flow diffusion chamber CSU    Colorado State University D10    DeMott et al. (2010) DFT    droplet freezing technique eBC    equivalent black carbon F62    Fletcher (1962) FB    fluorescent bioparticles FWHM   full width at half maximum HYSPLIT   Hybrid Single-Particle Lagrangian Integrated Trajectory INP    ice nucleating particle   xxxi M92    Meyers (1992)  MAAP    multi-angle absorption photometer MDT    Mountain Daylight Time MOUDI   micro-orifice uniform deposit impactor MSA    methanesulfonic acid NAD(P)H   nicotinamide adenine dinucleotide phosphate Nd:YAG   neodymium-doped yttrium aluminum garnet NOAA    National Oceanographic and Atmospheric Administration NW    northwest PARSIVEL   particle size and velocity PDT    Pacific Daylight Time PIXE    particle-induced X-ray emission RH    relative humidity SD    standard deviation SE    southeast SSw    supersaturation with respect to water T13fluorescent   Tobo et al. (2013) fluorescent bioparticle INP parameterization T13total    Tobo et al. (2013) total particle INP parameterization TSP    total suspended particulate UBC    University of British Columbia USA    United States of America UV-APS   ultraviolet aerodynamic particle sizer WIBS    waveband integrated bioaerosol sensor   xxxii Acknowledgements  The successful completion of a Ph.D. dissertation is truly an undertaking that cannot be accomplished without a great deal of support. I would first and foremost like to thank my re-search supervisor, Dr. Allan Bertram, for providing me with guidance and sharing his vast knowledge of atmospheric chemistry as I continue to learn about this wonderfully complex field and grow as a scientist. Allan, the level of dedication, kindness, and understanding you show all of your students is deeply appreciated. I also thank Dr. Brian MacLean for giving me the oppor-tunity to become engaged in research as an undergraduate and discover my interest in chemistry.  The Bertram group has provided an ideal environment of support, encouragement, and friendship that has been a pleasure to be a part of. To the past and present members of the group: Donna, Michael, Jason, Rich, Meng, Cédric, Vickie, Yuri, Jacquie, James, Yuan, Mijung, Lind-say, Kaitlin, Matt, Kristina, Stephen, Maki, Amir, Robin, Pablo, Rachel, and Ryan, a sincere thank you. A very special thank you goes to Michael and Donna for showing me the ropes when I first arrived and answering all of my questions along the way, and Meng for her incredible help with field measurements.   I would also like to thank all of the members of the Chemistry department facilities for their assistance. Without the professionalism and hard work of the Mechanical and Electrical Engineering shops and the Chemistry Stores staff, much of this research could not have been ac-complished.   As this dissertation focuses on work in the field, a large number of people and organiza-tions provided expertise and assistance that were vital in helping me carry out this research.   xxxiii While there are too many to list individually, I thank each and every person involved. People and groups of particular note are listed below: • Alex, Yuri, and David from the University of Denver. • Paul, Tom, Christina, Ezra and Kaitlyn from Colorado State University. • Jon, Jacquie, Luis, and Bob from the University of Toronto. • Corinne, Keith, Richard, Desi, Andrew, Miranda, and Michael from Environment Canada. • The Natural Sciences and Engineering Research Council of Canada for financial sup-port and Environment Canada, BC ministry of Environment, Metro Vancouver, the Department of Fisheries and Oceans, and the Canadian Armed Forces for site access.  I am especially grateful to my family and friends, without whom I would have never made it this far. Mom and dad, thank you for always setting a great example, instilling within me the belief that I can achieve any goal I commit to, and providing me with all of the unconditional love and support I could ever ask for. My achievements are as much yours as mine. To Kristy, thank you for always being a great big sister, friend, and confidant.    xxxiv Dedication        To my parents, Raymond and Charlene, and sister, Kristy  1 Chapter 1. Introduction 1.1 Atmospheric aerosols By definition, aerosol is a collective term that refers to both a liquid or solid particle and the gas in which it is suspended (Pöschl, 2005). When used in the context of atmospheric spe-cies, the term aerosol often neglects consideration of the carrier gas, in this case air, and may therefore be used interchangeably with particulates or particulate matter (Seinfeld and Pandis, 2006). This common practice is followed throughout this dissertation.  Due to great variability in their physical properties and chemical composition, it is con-venient to first divide aerosols into general categories. Aerosols may be differentiated by their mechanism of formation; primary aerosols are released directly into the atmosphere as particles, whereas a secondary aerosol is one that forms in the atmosphere from reactions involving gase-ous species by way of gas-to-particle conversion (Seinfeld and Pandis, 2006).   Alternatively, aerosols can be grouped by whether or not they originate from natural or anthropogenic sources. Natural aerosols include mineral dust (primarily from deserts), sea spray (largely salts), volcanic ash, and biogenic emissions such as bacteria, fungal spores, and pollen. Globally, approximately 98 % of aerosol mass flux is thought to be from natural sources, and of these natural sources approximately 84 and 13 % are sea spray and mineral dust, respectively (Gieré and Querol, 2010). The most prevalent anthropogenic emission sources are industrial dust and secondary sulfate production, together accounting for approximately 91 % of all anthropo-genic aerosols (Andreae and Rosenfeld, 2008; Gieré and Querol, 2010). Aerosols are also classified by size into one of four modes as is shown in Figure 1.1 (Seinfeld and Pandis, 2006; Gieré and Querol, 2010). The smallest aerosols belong to the nuclea-  2 tion mode. These small particles are less than 0.01 micrometers (µm) in diameter, forming as the result of vapor condensation and homogeneous nucleation processes. The Aitken mode contains particles 0.01–0.1 µm in size, primarily formed by the coagulation of, or vapor condensation on-to, nucleation mode particles that results in their growth. The next size range is the accumulation mode at 0.1–2.5 µm. Like the Aitken mode, particles in the accumulation mode are often formed by the coagulation of smaller particles or vapor condensation onto them, but may also be directly emitted primary aerosols. These three modes are collectively referred to as the fine-particle mode. Particles larger than 2.5 µm belong to the coarse mode and are typically generated by me-chanical action such as aeolian processes. In general, the nucleation and Aitken modes dominate the aerosol number, the accumulation mode dominates aerosol surface area, and the coarse mode dominates aerosol volume and mass (Seinfeld and Pandis, 2006; Gieré and Querol, 2010).  Number concentrations of aerosols in the atmosphere are typically 102 to 108 cm-3, vary-ing dramatically depending on factors such as location, weather conditions, and level of industri-al and urban activity (Seinfeld and Pandis, 2006). With such high concentrations, both natural and anthropogenic aerosols have wide-reaching impacts on the environment. For example, aero-sols affect human health by degrading air quality. Exposure to aerosols has been linked to ad-verse health effects, most notably asthma, cardiovascular disease, cancer, and an overall increase in mortality (Packe and Ayres, 1985; Dockery et al., 1993; Norris et al., 1999; Pope III et al., 2002; Dales et al., 2003; Simkhovich et al., 2008; Nastos et al., 2010; Samoli et al., 2011; Amarillo and Carreras, 2012; Lepeule et al., 2012). Biological particles in the air can also be a source of allergens and act as a vector of disease (Pöschl, 2005; Peccia et al., 2011; Després et al., 2012). Of particular efficacy to human health are aerosols in the fine mode (Brunekreef, 1997; Pope III, 2000; Pope III et al., 2009) as they may penetrate more deeply into the respirato-  3 ry tract and their chemical composition often involves nitrates, sulfates, ammonium, and transi-tion metals (Seinfeld and Pandis, 2006).  1.2 Ice nucleation in the atmosphere 1.2.1 Mechanisms of ice nucleation  Throughout this dissertation I focus on the ability of aerosol particles to nucleate ice un-der conditions typically found in the atmosphere. The formation of ice in the atmosphere can oc-cur by two primary mechanisms: ice nucleation in solution droplets may occur by a homogene-ous process at temperatures below approximately -37 °C, but at warmer temperatures ice forms by heterogeneous nucleation on solid or partially solid aerosol particles termed ice nucleating particles (INPs). INPs are a small subset of the total aerosol population, often accounting for less than 1 in 106 particles (e.g. Rogers et al., 1998; Prenni et al., 2012), whose unique surface prop-erties make them capable of lowering the energy barrier to ice nucleation and hence cause freez-ing at warmer temperatures or lower supersaturations with respect to ice compared to homoge-neous nucleation.  Heterogeneous ice nucleation can be divided into four subcategories that are briefly de-scribed as follows: deposition nucleation, where ice forms on the surface of the INP directly from the vapor phase without the occurrence of liquid water; condensation freezing, where ice forms as water vapor condenses onto the INP; contact freezing, whereby an INP collides with a supercooled liquid droplet; and immersion freezing, whereby an INP within a supercooled liquid droplet initiates freezing (Vali, 1985; Pruppacher and Klett, 1997). Both heterogeneous and ho-mogeneous ice nucleation are represented schematically in Figure 1.2. A theoretical description   4 of ice nucleation using classical nucleation theory is available in Appendix A to provide addi-tional background for the interested reader. 1.2.2 Types of ice nucleating particles General requirements for a particle to effectively nucleate ice include: (1) insolubility, as a solid structure is needed upon which water can interact; (2) a crystallographic match to ice in order for the particle to serve as a template; and (3) the ability to form intermolecular hydrogen bonds with water molecules as this facilitates the interaction between water and the substrate to orient water molecules into an ice-like structure, therefore lowering the energy barrier to ice formation (Pruppacher and Klett, 1997).  Although these serve as a useful guideline, there are numerous exceptions where particles that do not comply with all of the above requirements still serve as efficient ice nuclei. For ex-ample, several studies have shown that INPs do not have to be completely insoluble given that soluble salts can nucleate ice when they are in a crystalline state (Zuberi et al., 2001; Shilling et al., 2006; Wise et al., 2009; Wagner et al., 2011) and some long chain alcohols and fatty acids can induce freezing at very warm temperatures (e.g. Gavish et al., 1990; Popovitz-Biro et al., 1994). Furthermore, recent simulations of the surface of the ice-active mineral dust kaolinite have identified that, rather than via epitaxial agreement as is commonly assumed, it may be trenches at the mineral surface that induce ice nucleation given that these imperfections increase the molecular order of water (Croteau et al., 2009, 2010). Several reviews are available that detail the types of particles identified as INPs from both laboratory and field measurements (e.g. Szyrmer and Zawadzki, 1997; Ariya et al., 2009; Després et al., 2012; Hoose and Möhler, 2012; Murray et al., 2012). INPs include mineral dust,   5 volcanic ash, black carbon, glassy aerosols, ordered layers of surfactants, and biological particles such as bacteria, lichen, fungal spores, pollen spores, and marine diatoms. Details on ice-active aerosols relevant to this dissertation, namely biological particles, mineral dust, black carbon, and sea-spray aerosols, will be provided in the subsequent chapters. Ambient measurements reveal that ice crystal concentrations can outnumber the concen-tration of INPs by up to several orders of magnitude (e.g. Mossop et al., 1972; Hobbs and Atkinson, 1976; Hobbs et al., 1980; Crosier et al., 2011), indicating the occurrence of ice for-mation processes in addition to those noted in Figure 1.2. Such mechanisms are collectively re-ferred to as secondary ice production or ice multiplication. The most well-known secondary pro-cess is the Hallet-Mossop process (Hallet and Mossop, 1974), where ice splinters may be ejected during riming (the collection of supercooled water by ice crystals). Additional secondary mecha-nisms also include the collision of ice crystals (Vardiman, 1978) and ice crystal breakup during sublimation and melting (Oraltay and Hallet, 1989). Murray et al. (2012) note that INPs active at temperatures where secondary processes occur (above approximately -20 °C; Pruppacher and Klett, 1997) will have a disproportionately large effect on ice formation. 1.3 How aerosols influence climate 1.3.1 Direct and indirect climate effects Despite relatively short atmospheric lifetimes on the order of days to weeks, aerosols play an important role in climate by modifying the radiative balance of the Earth-atmosphere system (Finlayson-Pitts and Pitts Jr., 2000). The magnitude of this effect is quantified using radiative forcing, defined as the extent by which an atmospheric constituent alters the balance between incoming solar radiation and outgoing terrestrial radiation over the Industrial Era, typically spec-  6 ified as beginning in the year 1750 (IPCC, 2013). Values of radiative forcing are defined at the tropopause, which is the boundary between the troposphere (the lowest atmospheric layer) and the stratosphere (the second, overlying atmospheric layer). A positive radiative forcing denotes a net warming effect and a negative radiative forcing denotes a net cooling effect. Radiative forcing can be divided into direct and indirect effects. The direct effect occurs when aerosols either scatter incoming radiation (e.g. sulfate particles) to have a negative forcing or absorb radiation (e.g. black carbon particles) to have a positive forcing (Finlayson-Pitts and Pitts Jr., 2000; Andreae and Rosenfeld, 2008; IPCC, 2013). Aerosols can also influence climate indirectly by altering the number and size of water droplets or ice crystals within cloud systems, which can modify the optical properties of clouds in what is termed the first indirect effect or cloud albedo effect (Twomey, 1991). A change in water droplet or ice crystal number and size may also lead to a change in the precipitation rates of clouds in what is termed the second indi-rect effect or cloud lifetime effect (Albrecht, 1989). The magnitude of radiative forcing of at-mospheric aerosols is shown in Figure 1.3, as well as that of other relevant atmospheric constitu-ents. Although aerosols are thought to have a net cooling effect on climate, the large uncertainty coupled with a low level of scientific understanding (IPCC, 2013) indicate that there is still much to learn regarding aerosol-climate interactions. 1.3.2 The climatic impact of ice nucleating particles Following ice formation in the atmosphere, the lower saturation vapor pressure over ice compared to liquid water may result in the growth of ice crystals at the expense of cloud droplets (Korolev, 2007). This mechanism is known as the Wegener-Bergeron-Findeisen process. Vapor transfer, along with the growth of ice crystals by accretion and aggregation,   7 may result in the formation of precipitation. For example, Lau and Wu (2003) determined that most precipitation over tropical oceans originates from ice-phase processes, and in some cases even low concentrations of INPs are sufficient to be the cause of precipitation (Zeng et al., 2009). Through modifying cloud properties, INPs can also indirectly influence Earth’s radiation budget as shown in Figure 1.4.  Due to their cold temperatures, upper-level cirrus clouds trap a greater amount of out-going longwave radiation emitted from the Earth than the amount of incoming solar radiation that they reflect, resulting in a net warming effect on climate (Hartmann et al., 1992; Chen et al., 2000; Mitchell and Finnegan, 2009). At these altitudes, approximately 7–18 km (Lynch et al., 2002), conditions are such that ice nucleation can occur both homogeneously and heterogeneous-ly (e.g. Jensen and Toon, 1997; DeMott et al., 1998; DeMott et al., 2003a; Kärcher and Lohmann, 2003; Cziczo et al., 2004; Barahona and Nenes, 2009). In the presence of INPs, ice crystal formation may occur before the onset of substantial homogeneous freezing, depleting available water vapor and thereby suppressing homogeneous freezing. The net result is a de-crease in the surface area of ice (assuming a constant water content) and a corresponding de-crease in their net warming effect. At lower altitudes where temperatures are too warm for homogeneous nucleation and mixed-phase clouds dominate, ice formation is initiated by the presence of INPs via a heteroge-neous mechanism. This is sometimes referred to as the glaciation indirect effect (Lohmann, 2002; Lohmann and Feichter, 2005). Mixed-phase clouds contain both liquid water and ice crys-tals and are believed to have a net cooling effect on climate (Hartmann et al., 1992; Chen et al., 2000). Increasing the concentration of INPs may lead to increased precipitation as a result of the   8 Wegener-Bergeron-Findeisen process discussed above (DeMott et al., 2010), causing a de-crease in the cloud albedo. While a decrease in ice crystal size caused by increasing INP concen-trations may partially offset the cloud lifetime effect by increasing cloud albedo (Storelvmo et al., 2011), many modeling studies indicate that the increased precipitation and decreased cloud lifetime resulting from an increase in atmospheric INPs will reduce the net cooling effect of mixed-phase clouds (e.g. Lohmann, 2002; Lohmann and Feichter, 2005). 1.4 Motivation and overview of dissertation There is currently much uncertainty in our prediction, in both magnitude and sign, of the role heterogeneous ice nucleation plays in climate (Lohmann and Feichter, 2005; DeMott et al., 2010). This “severe limitation” (Murray et al., 2012) contributes to our low level of scientific understanding of the net aerosol forcing as shown in Figure 1.3. Furthermore, as human activities have increased the total aerosol loading in the atmosphere throughout the industrial age (Andreae, 2007; Andreae and Rosenfeld, 2008), INP concentrations may also be increasing. It is therefore important to identify the types of aerosols that exhibit ice nucleation activity and quan-tify their atmospheric abundance to accurately predict the role of INPs in climate and precipita-tion. For this end, additional laboratory and field studies on heterogeneous ice nucleation is often requested in the literature (e.g. Szyrmer and Zawadzki, 1997; Lohmann and Feichter, 2005; Möhler et al., 2007; Ariya et al., 2009; DeMott and Prenni, 2010; Hoose and Möhler, 2012; Murray et al., 2012).  This dissertation presents research on INP instrumentation and measurement. Focus has been placed on ice nucleation in the immersion mode because both field observations and theo-retical considerations suggest that this is the most important mode for mixed-phase cloud condi-  9 tions (see Young, 1993; Murray et al., 2012; DeMott et al., 2015; and references therein). Chap-ter 1 (this chapter) provides an introduction to atmospheric aerosols and provides the rationale for investigating their ice nucleation properties; Chapters 2–6 are research chapters covering both instrument development and field studies; and Chapter 7 summarizes the work and suggests ave-nues of future research.  Chapter 2 describes the micro-orifice uniform deposit impactor-droplet freezing tech-nique (MOUDI-DFT), a new method for determining INP concentrations as a function of particle size. Chapter 3 presents atmospheric INP measurements that were conducted in a forest in the central United States using both established online and novel offline techniques. Using meas-urements of particle fluorescence, the relationship between precipitation and the concentrations and size distributions of INPs and biological particles was investigated. Chapter 4 further charac-terizes and improves the MOUDI-DFT introduced in Chapter 2. Specifically, aerosol deposit non-uniformity within samples was quantified and non-uniformity correction factors to better constrain the uncertainty in calculated INP concentrations were determined. This chapter also provides an initial validation of the technique through an intercomparison with an established instrument. Chapters 5 and 6 present results from additional field campaigns. In Chapter 5, con-current measurements of INPs, total and fluorescent particles, black carbon, markers of anthro-pogenic and marine aerosols, and meteorological parameters were made at a coastal site in Brit-ish Columbia. Correlations between these variables were used to propose likely sources of INPs as a function of ice-activation temperature and the accuracies of six empirical parameterizations for predicting INP concentrations were examined. Chapter 6 presents size-resolved ambient INP measurements from diverse locations across North America and Europe. The relative contribu-tions of supermicron and coarse mode particles to the total INP population are also discussed.   10 1.5 Chapter 1 figures  Figure 1.1: An idealized surface area distribution of atmospheric aerosols as a function of parti-cle size. The sources, formation and removal mechanisms, and modes are indicated. Adapted from Whitby and Cantrell (1976) and Seinfeld and Pandis (2006).   11  Figure 1.2: Schematic of homogeneous ice nucleation and the four modes of heterogeneous ice nucleation according to Vali (1985) and Pruppacher and Klett (1997). Liquid water, ice crystals, and INPs are represented in the schematic as blue circles, blue hexagons, and amorphous black shapes, respectively.       12  Figure 1.3: Radiative forcing by atmospheric aerosols as well as other atmospheric consistuents over the Industrial Era (1750-2011). Uncertainties are reported at the 5–95 % confidence range. Dotted error bars denote the uncertainty in the magnitude of radiative forcing and solid error bars denote the uncertainty in the magnitude of effective radiative forcing. Radiative forcing is defined as the change in net downward radiative flux at the tropopause after allowing only stratospheric temperatures to readjust to radiative equilibrium, while in effective radiative forcing atmospheric temperature, water vapor, clouds, and land surface properties such as temperature, snow and ice cover, and vegetation are also allowed to adjust (IPCC, 2013). Adapted from Figure TS.6 of IPCC (2013).     13  Figure 1.4: The probable indirect effect of INPs on climate by ice nucleation in midlevel mixed-phase clouds and upper level cirrus clouds where elements (1) and (3) denote low INP number concentrations and elements (2) and (4) denote increased INP number concentrations. Arrow thickness indicates the relative intensity of radiation. Adapted from DeMott et al. (2010).             14 Chapter 2. The micro-orifice uniform deposit impactor-droplet freezing  technique (MOUDI-DFT) for measuring concentrations of ice nucleating   particles as a function of size: introduction 2.1 Motivation Over the past several decades there has been a significant effort to develop instrumenta-tion for measuring INP concentrations in the atmosphere (DeMott et al., 2011). While much of this research has focused on measuring the total concentration of INPs in the atmosphere in real time, determining their concentration as a function of size has also been a subject of interest. Knowing the size of INPs may be useful in identifying their source or modeling their transport in the atmosphere. In addition, size-resolved measurements would be useful to determine if some current techniques for measuring the total concentration of INPs are missing an important frac-tion of the INP population. For example, instruments based on the continuous flow diffusion chamber (CFDC) design of Rogers et al. (2001b) limit the size of particles analyzed to those with an aerodynamic diameter ≤ 0.75 µm in some cases (DeMott et al., 2003a) and ≤ 2.4 µm in others (Garcia et al., 2012). Most approaches to measuring the concentration of INPs as a function of particle size in-volve particle size selection either by inertial separation (Prodi et al., 1980; Rosinski et al., 1986, 1987, 1988; Berezinski et al., 1988; Santachiara et al., 2010) or by filtration (Vali, 1966; Langer and Rodgers, 1975), both followed by freezing measurements. These methods have all been lim-ited to freezing temperatures of approximately -25 °C or greater, likely due to significant back-ground counts of INPs at lower temperatures. Furthermore, the separation of aerosol particles by filter pore size provides only limited size resolution. Another approach for determining the size   15 of INPs involves the analysis of ice crystal residuals as a function of size using single-particle mass spectrometry or electron microscopy (Chen et al., 1998; Petzold et al., 1998; Cziczo, 2004; Targino et al., 2006; Richardson et al., 2007; Pratt et al., 2010).   In addition to the approaches mentioned above, I have developed the micro-orifice uni-form deposit impactor-droplet freezing technique (MOUDI-DFT) for measuring the concentra-tion of INPs as a function of size. This technique addresses some of the limitations of previous size-resolving instrumentation. A rotating MOUDI (MSP Corp., Shoreview, MN, USA) capable of obtaining ten size-fractionated samples spanning 0.056–18 µm (Marple et al., 1991) is used for aerosol collection. The ice nucleating properties of collected particles are then determined in the laboratory by a microscope-based droplet freezing technique (the DFT) that is capable of measuring the concentrations of INPs in the immersion mode to a temperature of approximately -37 °C (Koop et al., 1998; Chernoff and Bertram, 2010; Haga et al., 2013, 2014; Wheeler et al., 2015), which is roughly the homogeneous freezing temperature of water droplets 100 µm in di-ameter (Pruppacher and Klett, 1997).   The MOUDI-DFT permits measurements at a high size resolution and over a wider range of temperatures than most of the size-resolved instrumentation discussed above. As an offline technique, the MOUDI-DFT is also suitable for remote measurements where a dedicated opera-tor may not be available to continuously monitor a real-time instrument. Others have also suc-cessfully used an inertial impactor in conjunction with a microscope-based technique to study ice nucleation by aerosol particles (e.g. Knopf et al., 2010, 2014; Wang et al., 2012a, 2012b). Here I give a brief overview of this new technique.   16 2.2 Instrument operation 2.2.1 Micro-orifice uniform deposit impactor (MOUDI)  The MOUDI is a standard device for sampling aerosol particles (Chow and Watson, 2007), containing a sample inlet to remove particles greater than 18 µm, ten collection stages spanning a size range of 0.056–18 µm, and an after-filter to collect any remaining particles. A simplified schematic of a MOUDI is shown in Figure 2.1. Each stage contains a nozzle plate that consists of a series of nozzles that direct the sample and an impaction plate upon which sub-strates are located for collecting particles. In rotating MOUDIs, such as those used in this work, the impaction plates rotate at approximately 1 revolution per minute to increase particle deposit uniformity and therefore decrease the likelihood of overloading the substrate. A detailed descrip-tion of MOUDI operation and calibration can be found in Marple et al. (1991). Ambient air is drawn into the MOUDI at a constant rate of 30 L min-1. As the total nozzle area decreases from one stage to the next, conservation of mass dictates that the air must acceler-ate as it moves through the instrument. For example, the air velocity at the inlet and stage 8 is approximately 2 and 58 m s-1, respectively. Nozzles within the MOUDI are designed so that their depth, often called the throat length, and conical entrance allow suspended particles to also ac-celerate to the new air velocity (Marple and Liu, 1974). At a given velocity, and therefore a giv-en stage, particles of a particular aerodynamic diameter will have sufficient inertia to reach the impaction plate and be collected while smaller particles remain suspended in the gas and follow the fluid streamlines to the next stage. Several factors govern the collection efficiency of particles at each stage of the MOUDI, one of which is the Stokes number (Marple and Willeke, 1976). The Stokes number, Stk, is a di-  17 mensionless value defined as the ratio of the particle stopping distance to the nozzle radius:  𝑆𝑡𝑘 = 𝜌!𝑉!𝐶𝐷!!𝜇9𝑊   , (2.1) where ρp is the particle density, Vo is the mean air velocity through the nozzle, C is the Cunning-ham slip correction factor, Dp is the particle diameter, µ is the fluid viscosity, and W is the nozzle diameter. Stk50, the value at which 50 % of particles are collected, often defines impactor collec-tion efficiency curves. Particle sizes are reported as the aerodynamic diameter, defined as the di-ameter of a unit density sphere with the same terminal velocity as the particle of interest (DeCarlo et al., 2004). Also influencing particle collection within the MOUDI is the Reynolds number, Re, a dimensionless value defined as the ratio of inertial and viscous forces. The Reynolds number is given by the following equation: 𝑅𝑒 = 𝜌𝑉!𝑊𝜇   , (2.2) where ρ is the fluid density. Optimal values of Re for impactors were previously determined to be between 500 to 3000 as these gave the sharpest cutoff characteristics (Marple and Willeke, 1976). If Re is too low, a thick boundary layer within a nozzle restricts particle motion. If Re is too large, the boundary layer over the impaction plate may be too thin, allowing particles smaller than the stated cut size to be collected (Marple and Willeke, 1976).  A final point of consideration is the distance between the nozzle and impaction plates. In an impactor with round nozzles such as the MOUDI, the ratio of the distance between the nozzle plate and impaction plate (I) and the nozzle diameter (W) should be at least 0.5 (Marple and Liu,   18 1974). If I/W is below 0.5, particles with a smaller aerodynamic diameter than intended may be collected on the MOUDI stage, necessitating a recalibration of the stage cut point. In this work, hydrophobic glass cover slips (HR3-215; Hampton Research, Aliso Viejo, CA, USA) were used as the collection substrates to give a larger contact angle for condensed droplets (described be-low). As the hydrophobic glass cover slips are thicker than the aluminum foils with which the manufacturer calibrated the cut point of each stage (approximately 220 µm versus 25 µm), spac-ers were added between the stages to compensate for a reduced I.  2.2.2 Droplet freezing technique (DFT) Particles collected by the MOUDI are analyzed offline for their ability to act as INPs, typically in the immersion freezing mode. The DFT has been employed previously to study im-mersion freezing by biological particles and mineral dust (Chernoff and Bertram, 2010; Wheeler and Bertram, 2012; Haga et al., 2013, 2014; Wheeler et al., 2015). In this technique, which is based in part on the earlier design of Koop et al. (2000), a flow cell with temperature and hu-midity control is coupled to an Axiolab optical microscope (Zeiss, Oberkochen, Germany) equipped with a charge-coupled device (CCD) camera as illustrated in Figure 2.2.  The flow cell consists of a base, Teflon spacer, body, and top window. A depression is located within the base of the flow cell to position the hydrophobic glass cover slip. The location of the depression is such that the center of the hydrophobic glass cover slip is at the center of the flow cell and can be aligned with the optical axis of the microscope. A Teflon spacer sits on top of the hydrophobic glass cover slip to provide thermal isolation between the base of the flow cell and the body of the flow cell. This ensures that the hydrophobic glass cover slip is the coldest spot within the flow cell and therefore the location where ice will form. The body of the flow cell   19 contains a channel through which humidified air can flow. A resistance temperature detector (Omega model PT100GO1327, Stamford, Connecticut, USA) is located within the base of the flow cell directly beneath the hydrophobic glass cover slip to measure temperature with a preci-sion of ± 0.3 ºC. The resistance temperature detector was first calibrated against the melting point of pure water droplets of approximately 120 µm in diameter, with the measured offset from the expected 0 °C then used to correct all freezing temperatures.   Images from a DFT experiment are shown in Figure 2.3. In the DFT, a hydrophobic glass cover slip that contained particles collected with the MOUDI was placed on the base of the flow cell, the rest of the components of the flow cell were assembled, the center of the flow cell was aligned with the optical axis of the microscope, and a video recording of the particles was initiat-ed (Figure 2.3a). Next, the temperature of the flow cell was decreased to 0 °C, and a humidified gas flow with a dew point of approximately 3 °C was passed over the hydrophobic glass cover slip to condense water onto the collected particles and grow droplets (Figure 2.3b). After reach-ing a size of approximately 140 µm, the relative humidity (RH) was lowered to partially evapo-rate the droplets and increase the spacing between adjacent droplets (Figure 2.3c). Upon reaching the desired droplet size, the cell was isolated by closing valves upstream and downstream of the cell. The cell temperature was then lowered at a constant rate of -10 °C min-1 to a temperature of -40 °C. During the condensation, evaporation, and cooling processes, a digital video was contin-uously recorded. The freezing of each droplet was manually identified by an increase in the drop-let’s opacity in the digital video (Figure 2.3d), and its corresponding freezing temperature was retrieved using the video timestamp. As there is a stochastic component to immersion freezing (Vali and Stansbury, 1966), the   20 cooling rate used may influence the measured number of ice-active particles at a given tempera-ture. In the DFT, the sample is cooled at a relatively fast rate of -10 °C min-1 versus the -1 °C min-1 or slower rate often used in droplet freezing assays. An increase in the cooling rate by an order of magnitude can shift the median freezing temperature of a sample to colder temperatures by approximately 0.5–2 °C (Murray et al., 2011; Welti et al., 2012; Wright and Petters, 2013; Wright et al., 2013; Wheeler et al., 2015). While this influence has not been explicitly considered when interpreting the results, it is not expected to alter the conclusions of the data presented. 2.3 Calculating INP number concentrations The number of INPs active at a given temperature, #INPs(T), in each freezing experiment can be determined using the following equation based on the method of Vali (1971): #INPs(𝑇) = −ln 𝑁!(𝑇)𝑁! 𝑁!  , (2.3) where Nu(T) is the number of unfrozen droplets at temperature T, and No is the total number of droplets. Equation (2.3) takes into account the possibility of multiple INPs being contained in a single droplet (Vali, 1971).   The atmospheric concentration of INPs, [INPs(T)] in units of L-1, is then found using the following equation: INPs 𝑇 = #INPs 𝑇 𝐴!"#$%&'𝐴!"#𝑉   , (2.4) where Adeposit is the total area of the aerosol deposit on the MOUDI impaction plate, ADFT is the area of the hydrophobic glass cover slip analyzed in the DFT experiments, and V is the total vol-ume of air sampled.    21 2.4 Chapter 2 figures   Figure 2.1: A simplified schematic of the MOUDI. Only the inlet, two complete stages (nozzle plate with underlying impaction plate and collection substrate), and after filter are shown.         22  Figure 2.2: Schematic diagram of the droplet freezing apparatus used in measurements of INPs: (a) the base of the flow cell with a depression to position the hydrophobic glass cover slip; (b) the body of the flow cell; and (c) the cross section of the flow cell aligned with the optical axis of the microscope.    23  Figure 2.3: Images recorded during a droplet freezing experiment: (a) collected particles sitting on the hydrophobic glass cover slip at room temperature; (b) droplets after the condensation of water at 0 °C; (c) droplets after partial evaporation to reduce their size; and (d) frozen droplets after the cell temperature was reduced to -40 °C over a period of 4 minutes.   24 Chapter 3. High concentrations of biological aerosol particles and ice nucle-ating particles during and after rain 3.1 Introduction  Primary biological aerosol particles are a ubiquitous component of the atmosphere, where the dissemination of viable or reproductive units by way of atmospheric transport is a critical part in the life cycle of many species (Després et al., 2012). These biological aerosol particles, hereafter bioparticles, may reach high altitudes (Pady and Kelly, 1953; Pady and Kapica, 1955; Fulton, 1966; Imshenetsky et al., 1978; Narlikar et al., 2003; Wainwright et al., 2003; Griffin, 2004; Amato et al., 2007; Bowers et al., 2009; Smith et al., 2010; DeLeon-Rodriguez et al., 2013) or be transported on continental to global scales (e.g. Griffin et al., 2001; Brown and Hovmøller, 2002; Kellogg et al., 2004; Prospero et al., 2005; Burrows et al., 2009). Airborne bioparticles may have a role in climate processes by acting as a source of INPs (Szyrmer and Zawadzki, 1997; Möhler et al., 2007; Garcia et al., 2012), thereby influencing cloud microphysical properties and lifetime. Certain species of bacteria, fungi, and pollen have been shown to efficiently nucleate ice (e.g. Lindow et al., 1978a; Maki and Willoughby, 1978; Tsumuki et al., 1992; Diehl et al., 2001; Pummer et al., 2012; Morris et al., 2013), in some cases at temperatures as warm as -1 °C (Lindow et al., 1989; Richard et al., 1996). Biological particles have also been observed in ice-crystal residuals of mixed-phase clouds (Pratt et al., 2009) and snow samples (Christner et al., 2008; Morris et al., 2008). Model studies have shown that biolog-ical particles may not be important for ice nucleation on a global and annual scale (Hoose et al., 2010a; Sesartic et al., 2013; Spracklen and Heald, 2014), but may be important on regional and seasonal scales, especially if concentrations of biological particles are high or concentrations of   25 other types of INPs are low (Phillips et al., 2009; Sun et al., 2012; Burrows et al., 2013; Creamean et al., 2013; Yun and Penner, 2013; Costa et al., 2014; Spracklen and Heald, 2014).  A variety of mechanisms for the release of bioparticles into the atmosphere have been identified. As an example, agricultural processes such as combine harvesting may release large amounts of bioparticles from soil and plant surfaces into the atmosphere (Lighthart, 1984; Lee et al., 2006), in some cases with corresponding increases in INP concentrations (Bowers et al., 2011; Garcia et al., 2012). Once suspended, rising plumes of thermally heated air can carry the bioparticles to high altitudes where they can initiate ice nucleation.  Several important mechanisms of bioparticle aerosolization involve precipitation. Fungal spores and epiphytic bacteria may be released through the passive process of splash dispersal during rainfall (Hirst, 1953; Gregory et al., 1959; Butterworth and McCartney, 1991; Madden, 1997; Huber et al., 1998; Allitt, 2000), and in periods of elevated relative humidity (RH) active processes such as surface-tension catapults (Webster et al., 1989; Pringle et al., 2005) may also contribute to fungal spore release. Rainfall and its associated increase in RH have been identified as factors in the growth of INP populations on plants (Hirano et al., 1996), increased INP con-centrations in the atmosphere (Bigg and Miles, 1964; Wright et al., 2014), and a downward flux of INP bacteria (Constantinidou et al., 1990).   This apparent connection between precipitation and bioparticles has resulted in a pro-posed “bioprecipitation feedback cycle” whereby bioparticles from vegetation and soils become airborne and induce precipitation via cloud condensation or ice nucleation activity, in turn lead-ing to surface conditions favorable for continued plant and microbe growth (Sands et al., 1982; Morris et al., 2014; Bigg et al., 2015). However, evidence of a direct connection between elevat-  26 ed bioparticle concentrations associated with rainfall and INP concentrations has thus far been limited.  To investigate the relationship between bioparticles, INPs, and precipitation, a series of aerosol measurements were conducted during the BEACHON-RoMBAS (Bio-hydro-atmosphere interactions of Energy, Aerosols, Carbon, H2O, Organics, and Nitrogen - Rocky Mountain Bio-genic Aerosol Study; http://web3.acd.ucar.edu/beachon/) field campaign. Online and offline techniques were utilized to quantify bioparticle and INP concentrations during both dry weather conditions and during the onset, continuation, and aftermath of rainfall. A very strong correlation between INPs and bioparticles during and after rain events, together with agreement in the size of these particles, suggests that local bioparticles aerosolized via a rain-dependent mechanism are an important source of INPs in this region.  3.2 Experimental 3.2.1 Site description  Measurements were conducted at Manitou Experimental Forest, Colorado, USA (39.10° N, 105.10° W, 2370 m asl), a site representative of the semi-arid montane pine zone of the cen-tral Rocky Mountains, between July 20 and August 23, 2011. Satellite images of the site are shown in Figure 3.1. The site, located 15 km north of the small community of Woodland Park and 35 km northwest of Colorado Springs, is dominated by clean continental air (Ortega et al., 2014). The local forest canopy is sparse, with ponderosa pine clusters standing 10–20 m in height and covering roughly 60 % of the site. Tree clusters are separated by large areas of grass and forb, producing a highly variable leaf area index with a mean of 1.9 (DiGangi et al., 2011). Additional site information is available at http://web3.acd.ucar.edu/beachon/site-info.shtml.   27 3.2.2 Meteorological and leaf moisture measurements  Wind speed, air temperature, and RH were measured using a model WXT520 weather transmitter (Vaisala Inc., Helsinki, Finland) to an accuracy of ± 3 %, ± 0.3 °C, and ± 3 %, re-spectively. The weather transmitter was located within 100 m of the other instrumentation dis-cussed below.   Precipitation occurrence, rate, and microphysical state were measured using a laser-optical disdrometer (PARticle SIze and VELocity “PARSIVEL” sensor; OTT Hydromet GmbH, Kempton, Germany) that was located in a clearing with a polar angle of more than 45° (labeled 1 in Figure 3.1). The disdrometer senses rainfall by measuring the magnitude and duration of sig-nal attenuation as the hydrometer falls through a continuous two-dimensional laser beam of 780 nanometer (nm) wavelength. The precipitation rates were measured to an accuracy of ± 5 %.  Leaf wetness state was identified using a dielectric leaf wetness sensor (Decagon Devic-es, Inc., Pullman, WA, USA) that was positioned next to the disdrometer at a height approxi-mately 1 m agl. As the dielectric constant of water is much higher than that of air, water that has fallen or condensed onto the sensor will result in a voltage change that is proportional to the amount of water present. Sharp increases in voltage concurrent with precipitation events are characterized as “rainfall wetness” while slow changes in voltage that are unaccompanied by precipitation events are characterized as dew formation. 3.2.3 Size-resolved INP measurements  Size-resolved measurements of INP concentrations were made using the MOUDI-DFT described in Chapter 2. Only details differing from, or not provided in, Chapter 2 will be dis-cussed here. Samples were collected using a MOUDI model 110R that was located in a climate-  28 controlled trailer (labeled 2 in Figure 3.1) with air sampled through a total suspended particulate (TSP) inlet at 4 m agl. Four sets of MOUDI samples were collected onto hydrophobic glass cov-er slips, with each set consisting of the inlet and stages 1–7 (> 18 to 0.32 µm aerodynamic diam-eter; Marple et al., 1991). Samples M10 and M26 were collected during and following rain events while samples M1 and M27 were collected during dry periods. Average conditions during each offline INP sampling period are given in Table 3.1. Samples were stored in the dark at 4 °C for a period of 29–105 days prior to analysis.   Samples were analyzed by the DFT as detailed in Chapter 2, where the sample was placed in a flow cell, water droplets were condensed onto the sample, and the sample was cooled at a rate of -10 °C min-1 to determine droplet freezing temperatures. In these experiments, the droplets were grown to approximately 100 µm in diameter, where they each contained between 30 and 100 particles, and between 11 and 66 droplets (an average of 36) were monitored with the CCD camera. The number of INPs active in a freezing experiment, #INPs(T), was calculated from the freezing data using the following equation: #INPs(𝑇) = −ln 𝑁! 𝑇𝑁! 𝑁!  , (3.1) where Nu(T) is the number of unfrozen droplets at temperature T and No is the total number of droplets. Equation (3.1) takes into account the possibility of multiple INPs being contained in a single droplet (Vali, 1971).   The atmospheric number concentration of INPs as a function of temperature, [INPs(T)], was then found using the following equation:    29 INPs 𝑇 = #INPs 𝑇 𝐴!"#$%&'𝐴!"#𝑉   , (3.2) where Adeposit is the total area of the aerosol deposit on the MOUDI impaction plate, ADFT is the area of the hydrophobic glass cover slip analyzed in the DFT experiments, and V is the total vol-ume of air sampled by the MOUDI. Values of Adeposit were taken from Marple et al. (1991) and Maenhaut et al. (1993). Equation (3.2) assumes that particles are uniformly distributed over the MOUDI stages. Previous work by Maenhaut et al. (1993) indicates that this assumption is valid for MOUDI stage 7 at a spatial resolution of 2 mm. For the other stages, Maenhaut et al. (1993) noted the following variations in particle concentration with location on the stage at a spatial res-olution of 2 mm: stage 6 = +30 %, -30 %; stages 4 and 5 = +30 %, -50 %; stage 3 = +25 %, -20 %; and stage 2 = +40 %, -20%.  In the freezing experiments, a majority of the droplets froze by immersion freezing while a minority froze by contact freezing. Here immersion freezing refers to freezing of droplets by INPs immersed in the liquid droplets, and contact freezing refers to freezing of liquid droplets by contact with neighboring frozen droplets that grow as a result of vapor transfer via the Bergeron-Wegener-Findeisen process. Droplets that froze by contact freezing were not considered in this analysis. Deposition nucleation, which is the formation of ice on a particle not immersed in a so-lution droplet, also occasionally occurred during the freezing experiments. Deposition nucleation was included in the calculation of [INPs(T)] by adding the number of deposition nucleation events to #INPs(T) calculated with Equation (3.1) above.  Depending on the number of droplets condensed in an experiment and the total volume of air sampled by the MOUDI, the maximum concentrations of INPs that could be detected for any   30 given sample (i.e. the size interval sampled with the MOUDI) with the DFT was roughly 0.6–0.9 L-1. As a result, the maximum concentration of INPs determined offline is small compared to the maximum concentration determined with the online CFDC method described below. 3.2.4 Real-time INP measurements  Online measurements of INP concentrations were made using the continuous flow diffu-sion chamber (CFDC) of Colorado State University (Rogers et al., 2001b). Within the CFDC is an annular chamber containing ice-coated walls held at different temperatures. As sampled air was drawn through the chamber between two particle-free sheath flows, aerosols were exposed to a temperature of -25 ± 1 ºC and a supersaturation with respect to water of 103–106 ± 3 %. Un-der these temperature and humidity conditions, particles may activate as INPs in the immersion and condensation freeing modes to form ice crystals that grow to several µm in size (Sullivan et al., 2010). As aerosol particles > 2.4 µm in size were removed upstream of the CFDC by physi-cal impaction and water droplets without INPs were evaporated in the lower third of the instru-ment, INPs were detected at the bottom of the annular chamber by an optical particle counter based on their large size. While the removal of particles > 2.4 µm upstream of the impactor may remove some INPs, field data suggests that the INP concentrations are underestimated by a fac-tor of 2 under most sampling conditions (Garcia et al., 2012).  For the CFDC data presented here, upstream particle concentrations were enhanced using an MSP model 4240 aerosol concentrator (labeled 2 in Figure 3.1) positioned 1 m agl. Measure-ments using the concentrator have been corrected using the manufacturer’s specifications for par-ticles 1 µm in aerodynamic diameter and the sampling conditions at the site, which were deter-mined from direct measurements made roughly every two days. INP number concentrations   31 measured with the CFDC are reported at standard temperature and pressure. 3.2.5 Online fluorescent measurements  An ultraviolet aerodynamic particle sizer (UV-APS; TSI Inc. Model 3314, St. Paul, MN, USA) was used for the measurement of total and fluorescent aerosol particle concentrations over the size range of 0.3–20 µm (Huffman et al., 2010, 2012). Particle fluorescence was measured using pulsed excitation by a 355 nm Nd:YAG laser and emission detection between 420 and 575 nm (non-wavelength-dispersed). Measurements were initiated every 5 minutes and integrated over a sample length of 285 s. Since particles smaller than 1 µm have lower transmission effi-ciencies through the instrument, the reported number concentrations of submicron particles should be considered as lower limits (Huffman et al., 2010).  The UV-APS probes fluorophores characteristic of bioparticles, such as NAD(P)H, ribo-flavin, and tryptophan. Although some non-biological particles do fluoresce at the investigated wavelengths (Pan et al., 1999; Sivaprakasam et al., 2004; Bones et al., 2010; Pöhlker et al., 2012; Lee et al., 2013), the number of fluorescent particles often represents a lower limit to the number of primary biological aerosol particles (Huffman et al., 2010; Pöhlker et al., 2012, 2013). There-fore, fluorescent particles are hereafter referred to as fluorescent bioparticles. 3.2.6 Fluorescence microscopy  Aerosol samples were collected onto glass cover slips using a custom-made single-stage impactor with a 50 % aerodynamic diameter cutoff of 0.5 µm when operating at a flow rate of 1.2 L min-1. The collection substrates were coated with a thin layer of high-viscosity silicone grease (Baysilone grease; Bayer, Germany) to reduce particle bounce. This impactor was con-nected to the same TSP inlet as the MOUDI (location 2 in Figure 3.1).   32 Fluorescence microscopy of the aerosol samples was performed using a BZ-9000 fluo-rescence microscope (Keyence, Inc., Osaka, Japan) equipped with a 120 W super high-compression mercury lamp and a 1.5 megapixel monochrome CCD camera. Images were ob-tained using the following fluorescence filters: OP-66834 DAPI-BP (λex = 360/20 nm, λdichroic = 400 nm, λabs = 460/25 nm), OP-66836 GFP-BP (λex = 470/20 nm, λdichroic = 495 nm, λabs = 535/25 nm), and OP-66838 TexasRed (λex = 560/20 nm, λdichroic = 595 nm, λabs = 630/30 nm). Filter spec-ifications are given as wavelength of maximum absorbance or excitation and full width at half maximum (λ/FWHM).  3.3 Results and discussion  Variations in fluorescent bioparticle concentrations during the campaign were found to closely follow precipitation patterns. During dry background conditions, the percentage of su-permicron particles with fluorescence as found by the UV-APS (not shown) was approximately 2–6 %, but rapidly increased to 20 % at the onset of rainfall. The relative contribution of super-micron particles with fluorescence further increased to 40 % during post-rain periods of high RH and leaf wetness. The fluorescence microscopy images of Figure 3.2 are characteristic of the campaign and support these real-time findings. In the dry particle sample of Figure 3.2a, the ir-regular morphology and weak red fluorescence of most particles suggest a high abundance of mineral dust (Bozlee et al., 2005). In contrast, Figure 3.2b has a high abundance of bioparticles, identified by their more uniform morphology and intense green and blue fluorescence (Pöhlker et al., 2012).   Figure 3.2 also shows the real-time measurements of INPs plotted against the concentra-tions of fluorescent bioparticles. The INP concentrations measured using the CFDC were more   33 than an order of magnitude higher during rain events (Figure 3.2d) than during dry periods (Figure 3.2c). Furthermore, INPs and fluorescent bioparticle concentrations are strongly correlat-ed during and following rainfall with a linear correlation coefficient (R) of 0.94. This is markedly different from the findings during dry conditions, where R was -0.06.   INP activation curves found using the microscope technique are shown as a function of size for each of the four MOUDI samples; two collected during rain events (Figure 3.3a and b) and two collected during dry periods (Figure 3.3c and d). Data from stages 3 (3.2–5.6 µm) and 4 (1.8–3.2 µm) are shown in red and blue to highlight particles in the 1.8–5.6 µm size range be-cause these were found to efficiently nucleate ice at temperatures above -20 °C in both of the samples collected during rain events. Droplet freezing when using dry samples predominately occurred below -25 °C, indicating differences in INP concentrations and possibly INP sources between wet and dry weather.  The size distributions of INPs and bioparticles are shown in Figure 3.4, where data col-lected during rain events and dry weather conditions are shown in blue and brown, respectively. For rain events, the concentration of INPs active at -15 °C was on average 0.96 L-1 and ~1 % of supermicron particles were ice active (Figure 3.5). These warm-temperature INPs had sizes around 1.8–5.6 µm. In contrast, INP concentrations were below 0.02 L-1 during dry periods and no obvious relationship exists between INP concentration and size at either -15 or -20 °C. During these dry periods the percentage of supermicron particles with ice activity was also much lower (< 0.01 %, not shown).  Also given in Figure 3.4 are the average size distributions of fluorescent bioparticles measured by the UV-APS during the same wet and dry periods as MOUDI sampling. The peak   34 in the INP distribution at 1.8–5.6 µm corresponds with the peak of the size distribution of fluo-rescent bioparticles measured during rain events (Figure 3.4c). A similar enhancement was also identified using florescence microscopy, where the number fraction of bioparticles 1.8–5.6 µm in size increased from 0.24 in the dry sample of M28 to 0.73 in the wet sample of M10. The corre-lation and size- and temperature-dependent data of Figures 2.2–2.4 suggest that bioparticles ac-count for changes in INP concentrations.  The bioparticles 1.8–3.2 µm in size were likely bacteria or fungal spores given off by lo-cal vegetation surfaces as the result of droplet impact (e.g. Hirst, 1953; Gregory et al., 1959; Hirst and Stedman, 1963; Butterworth and McCartney, 1991; Madden, 1997; Huber et al., 1998; Allitt, 2000; Paul et al., 2004; Boyer, 2008; Penet et al., 2014). This mechanism is consistent with the observed strong initial increase bioparticle concentrations and lesser enhancement with continued rainfall. Bioparticles between 3.2 and 5.6 µm often appeared roughly 8 hours after the onset of rainfall and the enhancement of atmospheric concentrations could persist for up to 12 additional hours. Under these conditions, emission mechanisms such as the active wet ejection of fungal spores (Hirst and Stedman, 1963; Elbert et al., 2007; Després et al., 2012) or pollen frag-mentation by hygroscopic swelling (Taylor et al., 2004; Miguel et al., 2006; Pummer et al., 2012; Augustin et al., 2013) may have been active. 3.4 Summary and conclusions  INP and bioparticle concentrations were measured under varying weather conditions at a site representative of the central USA pine zone. Several key findings indicate that these two par-ticle types were closely coupled to both each other and rainfall during this study: (1) the absolute and relative concentrations of INPs and fluorescent bioparticles were both greatly enhanced by   35 rain; (2) the INP size distribution measured for samples collected during rain events was domi-nated by particles 1.8–5.6 µm in diameter, which is typical for biological ice nucleators such as bacteria and fungal spores (e.g. Maki and Willoughby, 1978; Iannone et al., 2011; Haga et al., 2014); (3) the size distributions of rain-enhanced fluorescent bioparticles and INPs and were similar at droplet freezing temperatures of -15 and -20 °C; and (4) a strong linear correlation was found between INP concentrations and fluorescent bioparticle concentrations during rainfall-associated sampling. It is therefore likely that ground-level INPs in this region were primary bi-oparticles aerosolized via a rain-mediated dispersal mechanism.  To determine the impact of these ice-active bioparticles on cloud properties and precipita-tion, additional measurements or calculations detailing their vertical convective transport to alti-tudes relevant to mixed-phase cloud formation are needed. Furthermore, follow-up studies are necessary to determine if the close relationship between INPs, bioparticles, and rainfall identified in this study is evident in other terrestrial regions.         36 3.5 Chapter 3 tables and figures Table 3.1: Details of the sampling periods used for offline INP measurements. Meteorological parameters have been averaged over the entire sampling duration, and times are reported in Mountain Daylight Time (MDT). Sample ID Start date and time (MDT) End date and time (MDT) Sampling duration (min) Temperature (°C) Relative Humidity (%) Wind Speed  (m s-1) Period type M01 07/22 14:29 07/23 09:41 1152 17.8 51 0.47 Dry M10 08/02 05:55 08/03 05:55 1440 14.3 84 0.55 Rain M26 08/16 20:26 08/17 06:32 606 9.7 82 0.35 Rain M27 08/17 06:35 08/17 19:46 791 22.3 39 0.67 Dry             37  Figure 3.1: Satellite images of the sampling site: (a) overview of the surrounding region with the site shown in the white box; (b) the sampling site detailing the layout of instrumentation where (1) was the location of the leaf wetness sensor, disdrometer, and weather transmitter and (2) was the trailer housing the MOUDI, CFDC, and UV-APS. Images were obtained from Bing Maps, 2015 (http://bing.com/maps/).   38  Figure 3.2: Fluorescence microscope images of aerosol impactor samples collected under (a) dry and (b) wet weather conditions. Number concentrations of INPs observed at -25 °C by the CFDC plotted against fluorescent bioparticles < 2.4 µm detected by the UV-APS during (c) dry and (d) wet weather conditions. Linear correlation coefficients (R) and probability values (P) are given.         39   Figure 3.3: INP activation curves for the four MOUDI samples where INP concentrations are plotted against freezing temperature for each MOUDI stage. Upper panels (a, b) are for samples collected during rain events and lower panels (c, d) are for samples collected during dry periods. Grey traces show stages inlet–2, red traces show stage 3 (3.2–5.6 µm), blue traces show stage 4 (1.8–3.2 µm), and gold traces show stages 5–7. See Table 3.1 for sample details.         40  Figure 3.4: Size distributions of INPs active at droplet freezing temperatures of (a) -15 °C and (b) -20 °C for samples collected during rain events and dry periods. (c) Size distribution of bi-oparticles measured during the same periods. Number concentrations have been averaged over both samples within each category. See Table 3.1 for sample details.   41  Figure 3.5: The fraction of particles that are ice-active at -15 °C versus particle aerodynamic di-ameter in samples collected during rain events (M10, M26), calculated using the INP concentra-tions from the MOUDI-DFT and total particle concentrations from the UV-APS. A curve is shown to guide the eye.   42 Chapter 4. The micro-orifice uniform deposit impactor-droplet freezing  technique (MOUDI-DFT) for measuring concentrations of ice nucleating   particles as a function of size: improvements and initial validation 4.1 Introduction  A new experimental technique for measuring INP concentrations as a function of size, the micro-orifice uniform deposit impactor-droplet freezing technique (MOUDI-DFT), was intro-duced in Chapter 2 and subsequently deployed in the field in Chapter 3. The MOUDI-DFT per-mits measurements over a wider temperature range and at a higher size resolution than most pre-vious size-resolving methods.   Despite the name, when particles are collected with a rotating MOUDI the concentration of particles on a collection substrate is not uniform; rather the concentration varies with distance from the center of the aerosol deposit. For example, Maenhaut et al. (1993) analyzed the uni-formity of MOUDI samples using particle-induced X-ray emission (PIXE) and showed that par-ticle concentrations on the MOUDI aerosol deposits varied by at least 25 % at a spatial resolution of 2 mm. Since a MOUDI aerosol deposit covers an area of 425 mm2 to 605 mm2 (depending on the stage) while the area of the MOUDI aerosol deposit analyzed by the DFT using a 5× magni-fication objective lens is only 1.2 mm2, non-uniformity can lead to significant uncertainty in measured atmospheric concentrations of INPs. In Chapter 3 I used the non-uniformity results of Maenhaut et al. (1993) to estimate uncertainties in the INP concentrations determined with the MOUDI-DFT. However, since the non-uniformity was not known at a sufficient spatial resolu-tion (e.g. 0.25–1 mm), questions remain regarding the impact of non-uniformity on measured INP concentrations.   43  In the following chapter I improve on the MOUDI-DFT approach. The concentration of particles on the MOUDI aerosol deposits is first measured as a function of distance from the cen-ter of the deposits to determine aerosol deposit non-uniformity. I then use these non-uniformity measurements to build substrate holders for the different MOUDI stages and calculate correction factors to be used when determining INP concentrations using the new substrate holders for in-creased experimental accuracy and precision.  In addition to improving the MOUDI-DFT, for method validation results from the MOUDI-DFT using the new substrate holders are compare with results from a CFDC operated by Colorado State University (CSU) during a measurement campaign at CSU. The CFDC tech-nique is a well-accepted approach for quantifying INP concentrations in the atmosphere. When comparing results from the two instruments, only particles collected onto MOUDI stages with an upper range ≤ 2.4 µm are considered to ensure that the particle size ranges measured by the two instruments overlapped as much as possible. As highlighted by DeMott et al. (2011), intercom-parison studies of INP instrumentation are important for finding potential biases or deficiencies present in the methods, relating independent data sets, and identifying where efforts for instru-ment improvement should be focused. 4.2 Experimental 4.2.1 Micro-orifice uniform deposit impactor (MOUDI)  As the MOUDI-DFT was introduced in Chapter 2, only differences from the description given in Chapter 2 are noted here. Samples were collected using a model 120R MOUDI II onto hydrophobic glass cover slips. To determine the aerosol deposit non-uniformity, the collection substrates were located roughly in the center of the impaction plates and held in place by a small   44 piece of tape running along one edge of the hydrophobic glass cover slip. For the field measure-ments at CSU, substrate holders were used to position the sampling substrate at a location on the impaction plate where particle concentrations varied by a relatively small amount (see Section 4.2.5 for details on the design of the substrate holders). 4.2.2 Droplet freezing technique (DFT) Particles collected by the MOUDI were analyzed for their ability to act as INPs in the immersion freezing mode. The DFT used here follows the description provided in Chapter 2. The microscope objective used in the experiments was an EC Plan-Neofluar 5× objective (Zeiss). This resulted in a viewing area in the DFT of 1.2 mm2. Based on the accuracy of the substrate holders, the location of the depression in the base of the flow cell, and the alignment of the hy-drophobic glass cover slip with the optical axis of the microscope, the center of the microscope viewing area in the DFT experiment was at the center of the hydrophobic glass cover slip ± 0.5 mm.  4.2.3 Calculating INP concentrations The number of INPs active at a given temperature, #INPs(T), in each freezing experiment was determined using the following equation based on the method of Vali (1971): #INPs(𝑇) = −ln 𝑁! 𝑇𝑁! 𝑁!𝑓!",!.!"!!.!"##𝑓!!  , (4.1) where Nu(T) is the number of unfrozen droplets at temperature T, No is the total number of drop-lets, fnu,0.25–0.10mm is a non-uniformity factor which corrects for aerosol deposit inhomogeneity at a scale of 0.25–0.10 mm (see Section 4.3.4 for details), and fne is a correction factor to account for uncertainty associated with the number of nucleation events in each experiment where fewer fro-  45 zen droplets result in a greater experimental uncertainty. Equation (4.1) takes into account the possibility of multiple INPs being contained in a single droplet (Vali, 1971).   The atmospheric concentration of INPs, [INPs(T)], was then found using the following equation: [INPs 𝑇 ] = #INPs(𝑇) 𝐴!"#$%&'𝐴!"#V 𝑓!",!""  , (4.2) where Adeposit is the total area of the aerosol deposit on the MOUDI impaction plate, ADFT is the area of the hydrophobic glass cover slip analyzed in the DFT experiments, V is the total volume of air sampled, and fnu,1mm  is a non-uniformity factor which corrects for aerosol deposit inhomo-geneity at the 1 mm scale (see Section 4.3.3 for more details). Values of the non-uniformity cor-rection factors fnu,0.25–0.10mm and fnu,1mm were based on the non-uniformity of particle concentra-tions on the hydrophobic glass cover slips, and fne was calculated following the error analysis of Koop et al. (1997) at the 95 % confidence level. During an ice nucleation experiment, after a droplet froze it could grow by vapor diffu-sion at the expense of surrounding liquid droplets because of the lower saturation vapor pressure over ice compared to liquid water. If given sufficient time, the growing ice crystal can come into contact with a neighboring liquid droplet, causing it to freeze. Alternatively, a neighboring liquid droplet may completely evaporate since it can lose water to the growing ice crystal. These two processes were accounted for during data analysis by (i) calculating an upper limit to the concen-tration of INPs active in the immersion mode as a function of temperature by assuming that all droplets which underwent the processes discussed above froze by immersion freezing, and by (ii) calculating a lower limit to the INP concentration by assuming that all droplets which underwent   46 the processes discussed above remained liquid until the homogeneous freezing temperature of approximately -37 °C (Wheeler et al., 2015). To minimize the occurrence of these contact and evaporation events in the DFT, which can introduce large uncertainties to the INP concentration, the spacing between droplets was increased by partial evaporation and a rapid cooling rate of -10 °C min-1 was used.  4.2.4 Measurements of MOUDI aerosol deposit non-uniformity For measurements of non-uniformity of the MOUDI aerosol deposits, particle collection was done at Amphitrite Point near Ucluelet, British Columbia, Canada (48.92° N, 125.54° W, approximately 20 m asl) during August of 2013 as part of the larger NETCARE (NETwork on Climate and Aerosols: addressing key uncertainties in Remote Canadian Environments) project. Environment Canada, the British Columbia Ministry of Environment, and Metro Vancouver op-erate this site for the continuous monitoring of aerosols and trace gases. Four MOUDI samples were collected through a louvered TSP inlet (Mesa Labs Inc., Butler, NJ, USA) and mast extend-ing 5.5 m agl.  In the laboratory, the hydrophobic glass cover slips were mounted on an optical micro-scope with an XY translational stage (Zeiss LSM). Images were recorded with one of three ob-jective lenses depending on the MOUDI stage: an EC Plan-Neofluar 20× for stages 2–4 (particle sizes of 10–1.8 µm); an LD Plan-Neofluar 40× for stages 5–6 (particle sizes of 1.8–0.56 µm); and an EC Plan-Neofluar 63× for stages 7–8 (particle sizes of 0.56–0.18 µm). Aerosol deposit non-uniformity was not measured for the inlet or stages 1, 9, and 10 as the inlet and stage 1 con-tained insufficient particles for quantitative analysis, and individual particles could not be identi-fied with the threshold method in stages 9 and 10.    47 Once the hydrophobic glass cover slips were mounted on the optical microscope, images were taken along a line passing through the center of the MOUDI aerosol deposit. These images were recorded in steps, with the dimensions of the steps dependent on the magnification used to record the images. The dimensions (x-length by y-length) of these steps were 520 µm × 690 µm for stages 2–4, 260 µm × 340 µm for stages 5–6, and 170 µm × 230 µm for stages 7–8. Images were recorded in such a manner that they could be superimposed to produce continuous images of the particle concentration across the MOUDI aerosol deposits. Shown in Figure 4.1 is part of the aerosol deposit of stage 8 as an example of a subsection of a continuous image, where lighter regions show zones where more particle deposition occurred. Using the continuous images, particle concentrations as a function of distance from the center of the MOUDI aerosol deposit were determined with the threshold function of the image processing software ImageJ (Rasband, 2014). Concentrations were found using step sizes of 1 and 0.25 mm for all stages analyzed. A spatial resolution of 1 mm was used since this is roughly equal to the dimensions of the area analyzed in DFT experiments, and a spatial resolution of 0.25 mm was used to determine if there is non-uniformity at a spatial resolution smaller than the area analyzed in the DFT. The normalized particle concentration, which is the quotient of the particle concentration of a given step divided by the maximum particle concentration, was calculated as a function of distance from the center of the MOUDI aerosol deposit for each hydrophobic glass cover slip at spatial resolutions of 1 and 0.25 mm. Visual inspection of aerosol deposits showed that there was spatial variability of the particle concentrations at a spatial resolution as low as 0.10 mm for MOUDI stages 6–8, so these stages were also analyzed at this spatial resolution. A total of three hydrophobic glass cover slips were analyzed for stages 2 and 8, and four hydropho-bic glass cover slips were analyzed for stages 3–7.   48 4.2.5 Substrate holders for individual MOUDI stages For each MOUDI stage a substrate holder was constructed to position the hydrophobic glass cover slip in a unique and reproducible position on the MOUDI impaction plate. The loca-tion of the hydrophobic glass cover slip was chosen based on the non-uniformity results such that the region analyzed in the droplet freezing experiment had minimal variation in the particle con-centration at the 0.25 mm spatial resolution. Substrate holders were constructed out of 6061-T561, an aluminum alloy, and had a thickness of 0.41 mm.  4.2.6 Comparison of MOUDI-DFT and CFDC measurements For method validation I compared INP concentrations found using the MOUDI-DFT with INP concentrations found using the CFDC operated by CSU during a measurement campaign at CSU. Detailed descriptions of the CFDC design and operation can be found in Rogers (1988), Rogers et al. (2001b), and Eidhammer et al. (2010). Briefly, air sampled by the instrument was first dried and passed through a two-stage impactor to remove large particles. For the experi-ments described here a two-stage impactor with a 50 % cutoff aerodynamic diameter of 2.4 µm (the same for each stage) was used. After the two-stage impactor the sampled air entered an an-nular chamber where the particles were exposed to a specific temperature and supersaturation with respect to water (SSw). Under the conditions used, any ice will quickly grow to sizes be-tween 3 and 10 µm. The sample then entered a region of reduced relative humidity to evaporate any liquid droplets that formed but did not contain an INP. At the chamber outlet, ice was dis-criminated from other particles using an optical particle counter where particles exceeding 3 µm in size were classified as ice.  The measurements for intercomparison involved sampling ambient aerosols at the De-  49 partment of Atmospheric Science’s Atmospheric Chemistry building of CSU in Fort Collins, Colorado, USA (40.59° N, 105.14° W) over 3 days in November 2013. The MOUDI was located directly outside the building, while the CFDC was located in an adjacent laboratory (approxi-mately 5 m away) with ambient air drawn through conductive rubber tubing (Simolex, Plymouth, MI, USA). The MOUDI and CFDC were operated simultaneously to ensure any variations in INP concentrations would be captured by both techniques. The CFDC temperature and SSw were kept constant throughout the sampling period to obtain an average INP concentration for later comparison to the INP concentration obtained offline by the MOUDI-DFT.  Two sampling periods from the CSU campaign were chosen for comparison purposes (Table 4.1). An additional sampling period was carried out during this campaign, but it was not included because of poor temperature overlap between the CFDC and the DFT. In sample CSU-1 the average CFDC temperature and SSw with an uncertainty of 1 standard deviation (SD) were -21.7 ± 0.3 °C and 5.5 ± 0.6 %, respectively, while in CSU-2 the CFDC conditions were -26.6 ± 0.2 °C and 5.8 ± 0.6 % SSw. MOUDI samples were collected for stages 2–8 (particle sizes of 10–0.18 µm), stored at 4 °C, and analyzed using the DFT within 2 weeks of collection. INP concen-trations were not found for samples collected on the inlet and stages 1, 9, and 10 of the MOUDI, as aerosol deposit non-uniformity could not be measure for these stages (see Section 4.2.4).  DeMott et al. (2015) found that CFDC measurements of natural mineral dust where parti-cles were exposed to an SSw of approximately 5 %, as was used in this study, resulted in an un-der-prediction of INP concentrations by a factor of 3 when compared to the use of a higher SSw (approximately 9 %). It was therefore suggested that a correction factor of 3 be applied to INP concentrations of mineral dust samples determined by the CFDC when using an SSw of 5 %.   50 More work is needed to determine if INP concentrations are similarly underestimated in general ambient aerosol samples such as those of this study, but the potential impact of this factor of 3 on the intercomparison results is discussed in Section 4.3.5. As mentioned above, the CFDC used here measures INP concentrations for particle sizes ≤ 2.4 µm. When comparing the MOUDI-DFT and CFDC results only MOUDI stages 4–8 were included, covering a size range of 3.2–0.18 µm. In addition, the INP concentrations measured in stage 4 (particle sizes of 1.8–3.2 µm) were multiplied by a factor of 3/7, the fraction of the parti-cle size range of stage 4 which overlaps with the size range measured by the CFDC, to ensure the size range covered by the MOUDI-DFT was as close as possible to the size range covered by the CFDC. In all cases the CFDC measured smaller particles than the MOUDI-DFT, which could result in differences between the two instruments. 4.3 Results and discussion 4.3.1 MOUDI aerosol deposit non-uniformity and size Shown in Figures 4.2, 4.3, and 4.4 are the normalized concentrations of aerosol particles as a function of distance from the center of the MOUDI aerosol deposit for spatial resolutions of 1, 0.25, and 0.10 mm, respectively, when averaged over all analyzed samples. The uncertainty in Figures 4.2–4.4 is the standard deviation of these samples. Particle concentrations have been normalized to the maximum particle concentration measured at the stated spatial resolution. Par-ticle concentrations at a spatial resolution of 0.10 mm are shown only for stages 6–8 and only for the region of the aerosol deposit that corresponds to the region analyzed in the DFT experiments when using substrate holders in the MOUDI. Figures 4.2 and 4.3 illustrate that the particle con-centration can vary by more than 2 orders of magnitude across the aerosol deposit. In compari-  51 son, the particle concentration measured in the PIXE analysis of Maenhaut et al. (1993) varied by less than an order of magnitude. To calculate atmospheric concentrations of INPs using Equation (4.2), the total area of the MOUDI aerosol deposit is needed. In their instrument paper describing the MOUDI, Marple et al. (1991) state that a surface with a diameter of 27 mm is required for sample collection in stages 2–8, but no other details were provided and some deposits were found to be larger than 27 mm in this study. Aerosol deposit sizes were reported in Maenhaut et al. (1993), but the criteria used to define the deposit edge were not given. Here, the area of each aerosol deposit was deter-mined using the normalized particle concentrations of Figure 4.3, where the edge of the deposit was defined as the point where the normalized particle concentration transitioned from above to below the detection limit of the technique (the average plus 3 SDs of the normalized particle concentration in non-deposit regions of the hydrophobic glass cover slip). Aerosol deposit diam-eters and areas are reported in Table 4.2. 4.3.2 Substrate holder design  As the concentration profiles found using the microscope analysis revealed that MOUDI deposits can be highly non-uniform, substrate holders were designed to position the hydrophobic glass cover slips at specific places on the MOUDI impaction plates. Details of the dimensions of the substrate holders are given in Figure 4.5. Each holder has the same diameter, height, and thickness to fit securely onto the impaction plate of the MOUDI. In addition, each holder had a square piece of the material of the same dimensions as the hydrophobic glass cover slip removed. When the substrate holder was secured onto the impaction plate, this region of removed material created a square well where the hydrophobic glass cover slip could be precisely located (see Fig-  52 ure 4.5c). The dimensions of the substrate holder were chosen such that the aerosol deposit at the center of the hydrophobic glass cover slip (once the cover slip was located in the substrate hold-er) had a relatively small variation in particle concentrations at the 0.25 and 0.10 mm spatial res-olution. The distances from the center of the hydrophobic glass cover slip to the center of the substrate holder when the hydrophobic glass cover slip is located in the holder, termed the offset, are listed for MOUDI stages 2–8 in Table 4.2 and are also represented by the shaded regions in Figures 4.2–4.4. 4.3.3 Correction for aerosol deposit non-uniformity at a spatial resolution of 1 mm Figure 4.2 shows that the particle concentrations across the MOUDI aerosol deposits can vary by more than an order of magnitude at a spatial resolution of 1 mm. This variation in parti-cle concentration at the 1 mm scale is taken into account when calculating INP concentrations using the non-uniformity correction factor fnu,1mm, which was determined using the following equation: 𝑓!",!"" = average  particle  concentration  over  the  entire  aerosol  depositaverage  particle  concentration  in  the  microscope  viewing  area  . (4.3)  Since the substrate holders position the hydrophobic glass cover slips in a known and re-peatable position, and the region of the sample analyzed by the DFT is always within 0.5 mm of the center of the hydrophobic glass cover slip due to the design of the flow cell, the correction factor in this case always remains the same for each MOUDI stage. The fnu,1mm correction factors that are applicable when using the substrate holders mentioned above are listed in Table 4.2. The stated uncertainty in fnu,1mm is due to the uncertainty in the location of the hydrophobic glass cov-  53 er slip in both the DFT experiments and sample collection with the MOUDI, and the uncertain-ties in the normalized particle concentrations shown in Figures 4.2 and 4.3. 4.3.4 Correction for aerosol deposit non-uniformity at a spatial resolution of 0.25 and   0.10 mm The second correction factor needed when calculating INP concentrations is fnu,0.25–0.10mm, which corrects for aerosol deposit non-uniformity at the 0.25 and 0.10 mm scale. Equation (4.1) with fnu,0.25–0.10mm = 1 assumes that the particles are deposited uniformly in the area analyzed in the DFT experiments, and the distribution of INPs within the droplets can be described using Poisson statistics. Shown in Figure 4.6 is the relationship between the #INPs(T) and the fraction of droplets unfrozen in the DFT experiment (Nu(T)/No) if these conditions hold (i.e. particles are deposited uniformly in the area analyzed in the DFT experiments and INPs within the droplets can be described using Poisson statistics). The range in droplet number used in Figure 4.6, 28 to 56, covers 1 SD from the average number of droplets in a DFT experiment.  Figures 4.3 and 4.4 show that in experiments using MOUDI samples the particles are not always uniformly deposited in the viewing area of the DFT, even when substrate holders are used. For example, Figure 4.4a illustrates that for stage 6 the particle concentration can vary by a factor of 3.4 in the microscope viewing area of the DFT.  To quantify the effect of non-uniformity within the area analyzed by the DFT, I first cal-culated the relationship between #INPs(T) and Nu(T)/No using the measured aerosol deposit non-uniformity within the microscope viewing area for each stage when using the substrate holders. For stages 2–5 non-uniformity was considered at a spatial resolution of 0.25 mm and for stages 6–8 non-uniformity was considered at a spatial resolution of 0.10 mm. A resolution of 0.10 mm   54 was used for stages 6–8 as some aerosol deposit non-uniformity is not captured at a spatial reso-lution of 0.25 mm for these stages as discussed above. The following is an example of how I cal-culated the relationship between #INPs(T) and Nu(T)/No for the case of non-uniform aerosol de-posits. For stages 2–5 the microscope viewing area was divided into four equal sections with a width of 0.25 mm (consistent with the spatial resolution of non-uniformity measurements in Fig-ure 4.3) and a height of 1.3 mm. These sections are labeled 1–4. I also assumed that the droplets were uniformly distributed over the viewing area and the number of INPs in each 0.25 mm wide section was #INPs(T)𝛿i/4, where 𝛿i was given by the following equation: 𝛿! = average  particle  concentration  in  the  0.25  mm  wide  section  𝑖average  particle  concentration  in  the  microscope  viewing  area  , (4.4) with i varying from 1 to 4. To get the relationship between #INPs(T) and Nu(T)/No for the entire microscope viewing area, the following equation was applied to each section of the slide to cal-culate the fraction of droplets unfrozen for each section:  𝑁!(𝑇)𝑁! ! = exp −#INPs𝛿!𝑁!   , (4.5) again with i varying from 1 to 4. Equation (4.5) is based on Equation (4.1) but with fnu,0.25–0.10mm set to 1. (Nu(T)/No)i from each section was then used to calculate Nu(T)/No for the entire micro-scope viewing area. To determine the relationship between #INPs(T) and Nu(T)/No for stages 6–8, I applied a similar procedure as described above for stages 2–5, but the microscope viewing area was divided into ten equal sections with a width of 0.10 mm and the non-uniformity meas-urements shown in Figure 4.4 were used to determine 𝛿i. The number of sections used to divide the microscope viewing area was selected for each MOUDI stage such that the section width was   55 smaller than or equal to the spatial scale of non-uniformity. If fewer (i.e. wider) sections are used, non-uniformity is not sufficiently captured and fnu,0.25-0.10mm is underestimated. However, using more (i.e. narrower) sections does not change fnu,0.25-0.10mm. The results of these calculations for MOUDI stage 6 for different values of #INPs(T) are shown Figure 4.6a and b. Figure 4.6 shows that, if fnu,0.25–0.10mm is not applied when calculating #INPs(T), the #INPs(T) will be under-predicted, and this under-prediction increases in magnitude as Nu(T)/No decreases.  To calculate the correction factor fnu,0.25–0.10mm for use in Equation (4.1), the relationship between #INPs(T) and Nu(T)/No determined for a non-uniform sample was divided by the rela-tionship between #INPs(T) and Nu(T)/No determined under the assumption of a uniform aerosol deposit. For example, for stage 6 this involved dividing the solid lines of Figure 4.6a and b by the dashed lines. These corrections for stage 6 are plotted in Figure 4.6c and d for 28 and 56 droplets in the microscope viewing area, respectively. These panels illustrate that the correction factors are a function of Nu(T)/No but are independent of the number of droplets used in the cal-culation. The above procedure together with the non-uniformity information shown in Figures 4.3 and 4.4 was used to determine the correction factors for the different substrate holders. The fnu,0.25–0.10mm correction factor for each substrate holder is given in Table 4.2. 4.3.5 MOUDI-DFT and CFDC intercomparison INP concentrations found using the MOUDI-DFT were compared with those detected in real-time by the CFDC during the CSU measurement campaign. INP concentrations found by the two instruments are shown in Figure 4.7. Also included in Figure 4.7 are the INP concentrations determined using blank hydrophobic glass cover slips. In this case, new hydrophobic glass cover   56 slips were processed the same way as samples collected during CSU measurements except they were not exposed to atmospheric particles. The blanks illustrate that heterogeneous ice nuclea-tion by the hydrophobic glass cover slip was not observed above -33.7 °C and therefore did not contribute to the measured INP concentrations in CSU samples. Figure 4.7 shows that during CSU-1 the average value of the INP concentration obtained by the CFDC was a factor of approximately 3.8 larger than the median value determined with the MOUDI-DFT at a temperature of -21.7 °C. However, the two values are not in disagreement if the uncertainties in the measurements are considered. During CSU-2, the median INP concentra-tion of the MOUDI-DFT was a factor of approximately 1.1 larger than the average value from the CFDC at a temperature of -26.6 °C. Again, the two measurements are not in disagreement if the uncertainties in the measurements are considered. If a correction factor of 3 was applied to the CFDC data due to this technique underestimating the INP concentration (DeMott et al., 2015), a possibility noted in Section 4.2.6 although not established for the sampling conditions, then the average INP concentration found by the CFDC would be greater than that of the MOUDI-DFT by a factor of 11.5 in sample CSU-1 and 2.6 in sample CSU-2. The agreement observed in Figure 4.7 is comparable to results of previous intercompari-son studies of INP instrumentation. For example, during the 2007 International Workshop on Comparing Ice Nucleation Measuring Systems (ICIS-2007) in Germany (DeMott et al., 2008; Möhler et al., 2008a), instruments encompassing continuous flow diffusion chambers (e.g. the CFDC of CSU), static diffusion chambers, mixing chambers, and expansion chambers were used to investigate different particle types including mineral dust and bacteria (Snomax®, hereafter Snomax). In general, the fraction of aerosols serving as INPs as a function of temperature and   57 RH between all instruments agreed within a factor of 4–5 (DeMott et al., 2008, 2011, 2015; Jones et al., 2011). Similar differences were observed between the Aerosol Interactions and Dy-namics in the Atmosphere (AIDA) cloud expansion chamber (Möhler et al., 2006) and the CFDC of CSU during the Third Aerosol-Cloud Interaction (ACI03) campaign with samples of ambient aerosols and coated and uncoated Asian dust (DeMott et al., 2015). Additional intercomparison studies by Hiranuma et al. (2015a) using the mineral dust illite NX and Wex et al. (2015) using Snomax found that instruments measuring INP concentrations could disagree by more than an order of magnitude. 4.4 Summary and implications for previous measurements  The MOUDI-DFT is a recent approach to measuring concentrations of INPs as a function of size in the atmosphere. Here I have improved on the technique as presented in Chapter 2. First, the non-uniformity of the MOUDI aerosol deposits has been characterized for stages 2–8 using optical microscopy. The results show that the particle concentrations can vary by more than 2 orders of magnitude across the aerosol deposit. In comparison, the particle concentrations measured in the PIXE analysis of Maenhaut et al. (1993) varied by less than an order of magni-tude due to the lower spatial resolution used in their experiments. Second, using these non-uniformity measurements, substrate holders were designed to position the hydrophobic glass cover slips in a known and reproducible position in the MOUDI that has a relatively uniform concentration profile to improve experimental precision. Lastly, using the non-uniformity results, correction factors were calculated to reduce the uncertainty in INP concentrations found using the MOUDI-DFT.  An intercomparison between the MOUDI-DFT and the CFDC was conducted using sam-  58 ples from a campaign measuring ambient continental aerosols. Results from this study indicate a reasonable agreement between the two techniques for the limited conditions examined thus far, as INP concentrations agreed within experimental uncertainty in both of the samples investigat-ed. The agreement observed here is similar to or better than the agreement observed in other in-tercomparison studies of INP instrumentation. This reasonable agreement and consistency with a currently used method suggests that the MOUDI-DFT is a promising technique for measuring INP concentrations as a function of size in the atmosphere, although additional validation exper-iments are warranted. As different levels of agreement have been observed in past intercompari-son studies depending on aerosol type (Hiranuma et al., 2015a; Wex et al., 2015), additional in-tercomparison studies are needed with different aerosol types.    When calculating the MOUDI-DFT INP concentrations presented in Chapter 3, it was assumed that aerosol deposit non-uniformity was relatively minor based on the analysis of Maenhaut et al. (1993) which found that particle concentrations varied by at most 50 % across the deposit. However, from the MOUDI characterization presented in this chapter it is now know that aerosol deposit non-uniformity at spatial resolutions more appropriate for the DFT can be greater than 50 %. Therefore, I have reassessed the forest INP data presented in Chapter 3 using the non-uniformity corrections presented in this chapter (see Appendix B).       59 4.5 Chapter 4 tables and figures Table 4.1: CSU sampling conditions. Sample ID Sample  composition MOUDI sampling (min) MOUDI size range (µm) Number of CFDC    measurements Mean CFDC temperature (°C) Mean CFDC SSwa (%) Temporal overlap (%) CSU-1 Ambient aerosols 410 0.18-10 66 -21.7 ± 0.3 5.5 ± 0.6 90 CSU-2 Ambient aerosols 256 0.18-10 52 -26.6 ± 0.2 5.8 ± 0.6 98 aSSw: Supersaturation with respect to water in the sample region of the CFDC.   Table 4.2: Deposit diameters and areas, hydrophobic glass cover slip offsets, and non-uniformity correction factors fnu,1mm and fnu,0.25–0.10mm for MOUDI stages 2–8 when using substrate holders. The uncertainty in fnu,1mm is given as the standard deviation. MOUDI stage Deposit diameter (mm) Deposit area (mm2) Hydrophobic          glass cover slip offset (mm) fnu,1mm with   uncertainty fnu,0.25–0.10mm 2 23.25 424.6 9.13 ± 0.50 0.74, +0.20, -0.13 0.1225exp(-11.29µ) + 1.065exp(-0.06412µ) 3 26.25 541.2 6.38 ± 0.50 0.72, +0.08, -0.08 0.04718exp(-14.15µ) + 1.023exp(-0.02347µ) 4 26.25 541.2 3.25 ± 0.50 1.18, +0.10, -0.15 0.04252exp(-13.06µ) + 1.024exp(-0.02386µ) 5 26.25 541.2 8.25 ± 0.50 0.97, +0.03, -0.10 0.03023exp(-14.97µ) + 1.015exp(-0.01515µ) 6 27.75 604.8 7.50 ± 0.50 0.75, +0.19, -0.02 0.5799exp(-10.57µ) + 1.148exp(-0.1408µ) 7 27.25 583.2 7.00 ± 0.50 0.84, +0.07, -0.11 0.1151exp(-10.66µ) + 1.072exp(-0.07029µ) 8 27.25 583.2 5.63 ± 0.50 1.01, +0.03, -0.12 1.03exp(-12.79µ) + 1.268exp(-0.2422µ) µ = Nu(T)/No. Nu(T) is the number of unfrozen droplets at temperature T in the freezing experiment. No is the total number of droplets in the freezing experiment.      60  Figure 4.1: (a) The concentration of aerosol particles on MOUDI stage 8 as a function of dis-tance from the center of the aerosol deposit, measured at a spatial resolution of 0.10 mm. (b) A subsection of the continuous cross section of the aerosol deposit of MOUDI stage 8. The images have been background-corrected by subtracting the sample image from a particle-free image. Background correction was done to remove spots on the image from dust on the optics. When overlapping individual images to produce the continuous image, the individual images do not align perfectly in the vertical dimension because moving the hydrophobic glass cover slip in the x-direction using the XY translational stage of the microscope caused slight movement in the y-direction        61  Figure 4.2: The deposit profiles for MOUDI stages 2–8 found at a spatial resolution of 1 mm. The normalized particle concentration is the quotient of the particle concentration of a given step divided by the maximum particle concentration. The experimental uncertainty is the standard deviation, and the shaded area is the region of the aerosol deposit in the microscope viewing area of the DFT using the substrate offset given in Table 4.2 with an uncertainty of ± 0.5 mm.    62  Figure 4.3: The same as Figure 4.2 but at a spatial resolution of 0.25 mm. The shaded area is the region of the aerosol deposit in the microscope viewing area of the DFT using the substrate off-set given in Table 4.2 with an uncertainty of ± 0.5 mm.   63  Figure 4.4: The same as Figure 4.2 but at a spatial resolution of 0.10 mm. The shaded area is the region of the aerosol deposit in the microscope viewing area of the DFT using the substrate off-set given in Table 4.2 with an uncertainty of ± 0.5 mm.       64  Figure 4.5: General substrate holder design specifications for positioning the hydrophobic glass cover slips in the MOUDI: (a) top-down view of the substrate holder; (b) bottom view; (c) the substrate holder positioned onto the impaction plate of the MOUDI stage.   65  Figure 4.6: The influence of aerosol deposit non-uniformity on the calculated number of INPs in MOUDI stage 6. Panels (a) and (b) are the #INPs(T) calculated for a non-uniform deposit (solid line) and assuming a uniform aerosol deposit (dashed line) as a function of the fraction of drop-lets unfrozen. The calculations were carried out for (a) 28 uniformly distributed droplets and (b) 56 uniformly distributed droplets. Panels (c) and (d) show fnu,0.25–0.10mm, calculated by taking the ratio of the solid line to the dashed line in panels (a) and (b), respectively.    66  Figure 4.7: INP concentrations found by the MOUDI-DFT and the CFDC as a function of tem-perature under concurrent sampling for samples (a) CSU-1 and (b) CSU-2 (see Section 4.2.6 and Table 4.1 for details). The grey shaded region marks the upper and lower bounds to the INP con-centration as defined by the experimental uncertainty in the MOUDI-DFT, with points showing median values. The uncertainty in temperature for MOUDI-DFT measurements is not shown but is ± 0.3 °C. The blue shaded region shows the upper and lower bounds to the INP concentrations found in five blank DFT experiments (hydrophobic glass cover slips without atmospheric parti-cles), with points showing median values. Average CFDC values are in red, with uncertainties in the vertical dimension shown as the 95 % confidence interval and in the horizontal dimension as the temperature uncertainty of ± 1 °C.     67 Chapter 5. Ice nucleating particles at a coastal marine boundary layer site:        correlations with aerosol type and meteorological conditions 5.1 Introduction  The presence of INPs in the atmosphere can lead to changes in the microphysical proper-ties and lifetime of clouds. As a result, a change in INP concentrations can indirectly modify climate by changing cloud optical properties, lifetime, and cloud extent (e.g. Baker, 1997; Lohmann, 2002; Storelvmo et al., 2011; Creamean et al., 2013). Currently, the role of INPs in climate change is highly uncertain (Boucher et al., 2013). To predict the role of INPs in climate change and precipitation, information on what particle types are the major sources of INPs in the atmosphere is needed. Particle types that were focused on as possible candidates for INPs in-cluded primary biological particles, black carbon (BC), and primary marine particles. As noted in Chapter 3, primary biological particles have been identified as a possible source of INPs (e.g. Szyrmer and Zawadzki, 1997; Möhler et al., 2007; Garcia et al., 2012; Hiranuma et al., 2015b). For example, certain species of bacteria (e.g. Maki et al., 1974; Lindow et al., 1978a; Maki and Willoughby, 1978; Kozloff et al., 1983), fungi (e.g. Jayaweera and Flanagan, 1982; Tsumuki et al., 1992; Richard et al., 1996; Iannone et al., 2011; Haga et al., 2013; Morris et al., 2013), and pollen (Augustin et al., 2013; von Blohn et al., 2005; Diehl et al., 2001, 2002; Pummer et al., 2012) can nucleate ice. Strong correlations between number concen-trations of INPs and primary biological particles have been found during field studies (Prenni et al., 2009b, 2013; Huffman et al., 2013; Tobo et al., 2013), and biological particles have been ob-served in ice-crystal residuals of mixed-phase clouds (e.g. Pratt et al., 2009), cloud water (e.g. Joly et al., 2014), and snow samples (e.g. Christner et al., 2008; Morris et al., 2008; Hill et al.,   68 2014). Ice-active biological particles have also been associated with soils (Conen et al., 2011; O’Sullivan et al., 2014; Tobo et al., 2014; Fröhlich-Nowoisky et al., 2015).  Another potential source of INPs in the atmosphere is BC particles (Kärcher et al., 2007). Laboratory studies have given varying results on whether BC particles act as efficient INPs un-der atmospherically-relevant conditions (e.g. Gorbunov et al., 2001; Möhler et al., 2005; Dymarska et al., 2006; Kärcher et al., 2007; DeMott et al., 2009; Friedman et al., 2011; Cziczo et al., 2013; Brooks et al., 2014), although BC is generally considered to be less efficient than min-eral dust in the immersion mode (Hoose and Möhler, 2012; Murray et al., 2012; and references therein). Field studies have also produced varying results (e.g. Lin et al., 2006; Cozic et al., 2008; Kamphus et al., 2010; Twohy et al., 2010; Ebert et al., 2011; Corbin et al., 2012; Cziczo et al., 2013; Knopf et al., 2014; McCluskey et al., 2014). Models have suggested that carbonaceous aerosols may have a significant indirect effect on climate if they efficiently nucleate ice (e.g. Lohmann, 2002; Liu et al., 2009; Penner et al., 2009; Yun and Penner, 2013). Ambient measurements have found INPs in air masses above oceans and coastal sites (e.g. Bigg, 1973; Schnell, 1977; Flyger and Heidam, 1978; Saxena, 1983; Rosinski et al., 1995; Rogers et al., 2001a; Prenni et al., 2009a), with some of the INPs identified as biological (e.g. Jayaweera and Flanagan, 1982). Studies have also indicated the potential of marine particles as a source of ice nuclei, including bacteria and phytoplankton found in seawater and sea ice (Schnell, 1975, 1977; Schnell and Vali, 1975; Parker et al., 1985; Knopf et al., 2011; Alpert et al., 2011), synthetic sea-salt or sodium chloride particles (Wise et al., 2012; Wagner and Möhler, 2013; Schill and Tolbert, 2014), generated sea spray aerosol using ocean water (DeMott et al., 2013; Prather et al., 2013), and the sea surface microlayer (Wilson et al., 2015). It is therefore   69 possible that the release of biological particles from the oceans may be a source of INPs in the atmosphere. The modeling work of Burrows et al. (2013) indicates that marine INPs may be par-ticularly important in remote regions such as the Southern Ocean. To determine which aerosol particles are the major source of INPs in the immersion mode at a coastal site in Western Canada, correlations between INP number concentrations and both concentrations of different atmospheric particle types and meteorological conditions were investigated. Measurements were conducted in August 2013 as part of the NETwork on Climate and Aerosols: addressing key uncertainties in Remote Canadian Environments (NETCARE) pro-ject (http://netcare-project.ca/). A primary goal of the study was to investigate whether primary biological particles, BC particles, or primary marine particles are a major source of INPs at this coastal site. In addition, I also test the ability of parameterizations reported in the literature at predicting the INP number concentrations measured at this coastal site.  5.2 Methods 5.2.1 Site description and instrument location  Measurements were performed at Amphitrite Point (48.92° N, 125.54° W) on the west coast of Vancouver Island in British Columbia, Canada from August 6–27, 2013. This was also the location of studies on ozone (McKendry et al., 2014) and cloud condensation nuclei (Yakobi-Hancock et al., 2014). Amphitrite Point (Figure 5.1) is located approximately 2.2 km south of the town of Ucluelet (population of 1627 in 2011; Statistics Canada, 2012). The largest nearby popu-lation centers are Nanaimo 120 km to the east, Victoria 170 km to the southeast, and Vancouver 180 km to the east. This region has a temperate maritime climate, falling under the Cfb climate type of the Köppen-Geiger classification scheme (Kottek et al., 2006) which is characterized by   70 warm summers, mild winters, and relatively high levels of cloud cover and precipitation. Local forests contain predominantly coniferous tree species including western hemlock, western redce-dar, and Douglas-fir that is characteristic of most low-elevation sites along the west coast of Canada (Austin et al., 2008). The Pacific Ocean is west and south of the site, where the mixing of iron-rich coastal waters with nitrate-rich oceanic waters produces a zone of high primary productivity (Whitney et al., 2005; Ribalet et al., 2010).   Aerosol instrumentation was located in one of two mobile laboratories; one specific to the NETCARE project (labeled 1 in Figure 5.1) and one operated by Environment Canada, the British Columbia Ministry of Environment, and Metro Vancouver (labeled 2 in Figure 5.1). Aer-osols were sampled through louvered total suspended particulate inlets (Mesa Labs Inc., Butler, NJ, USA) or louvered PM10 inlets (Thermo Scientific, Waltham, MA, USA) atop masts extend-ing 5.5 m agl. The two mobile laboratories were approximately 20 m above mean sea level and 100 m from the high tide line of the Pacific Ocean (McKendry et al., 2014). A row of trees and shrubs approximately 2–10 m in height stood between the laboratories and the rocky shoreline. Adjacent to the laboratories on their seaward side was Amphitrite Lighthouse (labeled 3 in Fig-ure 5.1) and Wild Pacific Trail, local tourist attractions and a source of foot traffic during fair weather. Immediately north and east of the site was a station of the Canadian Coast Guard (la-beled 4 in Figure 5.1).  The meteorological parameters reported in this study were measured at Amphitrite Light-house, located approximately halfway between the mobile laboratories and the ocean. Relative humidity and temperature were monitored using an HMP45C probe (Campbell Scientific, Logan, UT, USA) with accuracies of ± 3 % and ± 0.2 °C, respectively. Wind direction and wind speed   71 were determined by a model 05305L Wind Monitor (R. M. Young, Traverse City, Michigan, USA) to a respective accuracy of ± 3° and ± 0.2 m s-1. 5.2.2 Ice nucleating particle measurements INP number concentrations in the immersion mode were measured using the micro-orifice uniform deposit impactor-droplet freezing technique (MOUDI-DFT) detailed in Chapters 2 and 4, including the use of templates and non-uniformity correction factors. Samples from MOUDI stages 2–8 were used in this study, corresponding to a particle size range of 10–0.18 µm (50 % cutoff aerodynamic diameter). Thirty-four sets of MOUDI samples were collected; 18 dur-ing the day and 16 at night. The average collection time of a MOUDI sample was 7.8 hours. De-tails of each INP sampling period are available in Table C.1 of Appendix C. Here INP data is reported between -15 and -30 °C as few (1.3 %) of droplets froze at warmer temperatures and droplet saturation occurred at colder temperatures. INP number concentrations have been adjust-ed to standard temperature and pressure. 5.2.3 Total and fluorescent aerosol measurements with sizes ≥ 0.5 µm A model-4A waveband integrated bioaerosol sensor (WIBS-4A; Droplet Measurement Technologies, Boulder, CO, USA) was used to find both the total and fluorescent aerosol number concentrations with sizes ≥ 0.5 µm. Particles that enter the WIBS-4A first transect a continuous-wave 635 nm diode laser. The forward-scattered light from the continuous-wave laser is detected with a quadrant photomultiplier tube for the determination of particle size and asymmetry factor based on the signal intensity and asymmetry, respectively. The detected forward-scattered light also triggers excitation pulses from xenon lamps, the first at a wavelength of 280 nm and the se-cond at 370 nm. The excitation pulses may lead to fluorescent emission from the particle, which   72 is then collected in two wavelength ranges: 310–400 nm (short wavelength region) and 420–650 nm (long wavelength region). This results in sample information provided for each particle in three fluorescence channels: excitation at 280 nm, emission in the short wavelength region (FL1); excitation at 280 nm, emission in the long wavelength region (FL2); and excitation at 370 nm, emission in the long wavelength region (FL3). Detailed descriptions of the instrument can be found in Kaye et al. (2005), Gabey et al. (2010), and Healy et al. (2012a). The sample and total flow rates of the WIBS-4A were 0.63 and 2.3 L min-1, respectively, and number concentrations have been adjusted to standard temperature and pressure. The fluorescent channels used in the WIBS-4A allow for the detection of fluorophores characteristic of biological activity. These fluorophores include the amino acid tryptophan, the cofactor NAD(P)H, and the micronutrient riboflavin. While some non-biological species such as soot, mineral dusts, polycyclic aromatic hydrocarbons, secondary organic aerosols, and humic-like substances can produce a fluorescent signal (Pan et al., 1999; Sivaprakasam et al., 2004; Bones et al., 2010; Pöhlker et al., 2012; Lee et al., 2013), the number of fluorescent particles is generally considered to be a lower limit to the number of primary biological particles (Huffman et al., 2010, 2012; Pöhlker et al., 2012). In addition, fluorescence microscopy measurements of samples collected during this field study show high concentrations of fluorescent biological par-ticles (see below). Therefore, fluorescent particles detected using the WIBS-4A are hereafter re-ferred to as fluorescent bioparticles. 5.2.4 Fluorescence microscopy Aerosol samples were collected onto glass cover slips using a custom single-stage im-pactor operating at a flow rate of 1.2 L min-1 with a 50 % cutoff aerodynamic diameter of 0.5   73 µm. Prior to sample collection, the substrates were coated with a thin layer of high viscosity grease (Baysilone grease, Bayer, Germany) to reduce particle bounce. Fluorescence microscopy images were taken on a BZ-9000 fluorescence microscope (Keyence, Inc., Osaka, Japan) equipped with a 120 W super high-compression mercury lamp and a 1.5 megapixel monochrome CCD camera. Images were obtained using the following fluores-cence filters: OP-66834 DAPI-BP (λex = 360/20 nm, λdichroic = 400 nm, λabs = 460/25 nm), OP-66836 GFP-BP (λex = 470/20 nm, λdichroic = 495 nm, λabs = 535/25 nm), and OP-66838 TexasRed (λex = 560/20 nm, λdichroic = 595 nm, λabs = 630/30 nm). Filter specifications are given as wave-length of maximum absorbance or excitation and full width at half maximum (λ/FWHM).  5.2.5 Black carbon (BC) measurements BC mass concentrations were measured using a multi-angle absorption photometer (MAAP model 5012; Thermo Scientific, Franklin, MA, USA). Detailed descriptions of the MAAP are available in Petzold et al. (2002), Petzold and Schönlinner (2004), and Petzold et al. (2005). Within the MAAP, particles are continuously collected on a glass fiber filter. The inten-sity of transmitted and forward-scattered light through the aerosol particle layer and filter matrix is measured by a photodetector located beneath the filter at a frequency of 1 Hz. The signal strength is attenuated by the presence of both light-absorbing particles and particles that cause backscattering. As the angular distribution of back-scattered light is related to the fraction of non-absorbing particles (Petzold and Schönlinner, 2004), four additional photodetectors located above the filter are used to quantify the non-absorbing component of the sample. The absorbance by the collected aerosol is then related to a mass of BC using a mass-specific absorption coeffi-cient of 6.6 m2 g-1. Mass concentrations have been adjusted to standard temperature and pressure.   74 Non-BC material such as mineral dusts and brown carbon can also absorb 670 nm wave-length light used in the MAAP, albeit with smaller absorption coefficients than BC (Yang et al., 2009). I follow the recommendation of Petzold et al. (2013) for BC data derived from optical ab-sorption methods and hereafter refer to MAAP data as measurements of equivalent black carbon (eBC).  5.2.6 Tracers of anthropogenic aerosols Measurements of CO, NOx, and SO2 were used to identify anthropogenic contributions to the sampled air masses as their sources include fossil fuel combustion and biomass burning (Galanter et al., 2000; Gadi et al., 2003; United States Environmental Protection Agency, 2014). CO concentrations were monitored using a Thermo Fisher Scientific 48i-TLE (Waltham, MA, USA), an absorbance-based analyzer using infrared light at a wavelength of 4.6 µm. NOx con-centrations were monitored using chemiluminescence with a Thermo Fisher Scientific 42i. This instrument first converts NO2 to NO, which then reacts with ozone to produce luminescence of intensity in proportion to the level of NOx. A Teledyne API T100U (San Diego, CA, USA), us-ing fluorescence emitted by SO2 under excitation by ultraviolet light, monitored SO2 concentra-tions. Data were collected for each instrument at a frequency of 1 min-1. 5.2.7 Ion measurements Size-resolved aerosol samples were collected on Teflon® filters (Pall Corporation, Port Washington, NY, USA) using a second MOUDI (model 110R). Samples were collected on the inlet, stage 1, and stages 7–10 of the MOUDI with stages 2–6 being removed prior to collection. The flow rate through the MOUDI was on average 24 L min-1, resulting in a collected size range of 0.068 µm to > 20 µm (50 % cutoff aerodynamic diameter). Collection times ranged from ap-  75 proximately 45–49 hours and samples were stored at 4 °C for a period of one month before anal-ysis. Mass concentrations of sodium and methanesulfonic acid (MSA) were found using cati-onic and anionic chromatography following the method of Phinney et al. (2006). Briefly, filters were extracted with sonication in 10 mL of deionized water for 1 hour, and samples were ana-lyzed with a Dionex DX600 ion chromatograph using an AS11-HC column and a CS12 column for anions and cations, respectively. Filter blanks were measured to be below the limit of detec-tion for both analytes. Mass concentrations were adjusted to standard temperature and pressure. 5.2.8 Back trajectories Back trajectories spanning a period of 72 hours were calculated for each sampling period using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT4) model of the National Oceanographic and Atmospheric Administration and the GDAS1 meteorological data archive (Draxler and Rolph, 2014). To determine if the air mass changed during a sampling peri-od, back trajectories were initiated at the beginning of the sampling period and every 2 hours un-til the end of the sampling period. Back trajectories were used to assign each sampling period to one of four general air mass categories: (i) coastal NW where boundary layer air, defined here as an altitude below 1000 m, has traversed land northwest of the sampling site during its approach; (ii) coastal SE where boundary layer air has traversed land southeast of the sampling site during its approach; (iii) Pacific Ocean where boundary layer air has approached directly from the ocean and has not encountered land prior to arrival at the sampling site; and (iv) free troposphere where the air mass has spent more than 50 % of the 72 hour back trajectory in the free tropo-sphere. In four sampling periods, back trajectories initiated at different times in the sampling pe-  76 riod indicated that the air mass changed during sampling, such as a change in the predominant altitude of the air mass from the free troposphere to the marine boundary layer. In these situa-tions, the air mass category to which the majority of the back trajectories belonged was selected as the air mass category of the sample. 5.3 Results and discussion 5.3.1 Site description and instrument location Back trajectories initiated at the midpoint of each INP sampling period are shown in Fig-ure 5.2. The back trajectories indicate that 88 % of the air masses sampled spent the majority of their 72 hours prior to reaching the site over the Pacific Ocean within the marine boundary layer (est. < 1000 m). Furthermore, air masses approached the sampling site from an onshore direction with minimal flow over land apart from coastal regions. Average local wind directions of 89° to 297° during INP sampling support this finding.  Shown in Figure 5.3 is the number concentration of INPs as a function of time, color-coded by the classification of the air mass. At ice-activation temperatures between -15 and -25 °C there is no obvious trend with the type of the air mass. At -30 °C, INP number concentrations associated with air masses from the coastal SE (red points) appear to be higher than INP number concentrations associated with other air masses, but the statistics are low for the coastal SE air masses, especially at -30 °C. Figure 5.4 shows that the mean values for the different air mass types vary by less than a factor of 2.6. I therefore conclude that INP number concentrations did not exhibit a strong dependence on the type of air mass sampled. The correlation analysis pre-sented in Sections 5.3.2–5.3.5 uses the entire dataset (i.e. the data were not differentiated based on air mass type). The dependence on air mass type is further explored in Section 5.3.6.   77 5.3.2 Are biological particles a major source of ice nuclei? To investigate if biological particles are an important source of INPs at the coastal site, correlations between INPs and fluorescent bioparticles were determined. In the following corre-lation analysis, WIBS-4A data are limited to particle sizes of 10 µm or smaller to better match the size range of the MOUDI-DFT. The correlation coefficients (R) of linear fits to the data are presented in Table 5.1 with correlation plots at a freezing temperature of -25 °C shown in Figure 5.5 and plots at -15, -20, and -30 °C given in Appendix C. Here the scheme of Dancey and Reidy (2011) is used where correlations with an R value of 0.1–0.3, 0.4–0.6, and 0.7–0.9 are classified as weak, moderate, and strong, respectively. While focus is placed on correlations with statistical significance (P value < 0.05), cases where no correlations of statistical significance were found over the analyzed temperature range are also noted. With values of R between 0.74 and 0.83, INP number concentrations are strongly corre-lated with the number concentrations of fluorescent bioparticles for INPs active between -15 and -25 °C (Figure 5.5a and A.1, Table 5.1). At these temperatures, fluorescent bioparticles have the largest correlation coefficients with INPs compared to all of the other parameters investigated. This suggests that biological particles are an important component of the INP population. Using similar fluorescence techniques, others have also noted strong correlations between INPs and primary biological particles during ambient measurements (Prenni et al., 2009b, 2013; Huffman et al., 2013; Tobo et al., 2013). To further investigate the relationship between biological particles and INPs, the size dis-tributions of INPs were compared with the size distributions of total particles and fluorescent bi-oparticles, using samples where all three measurements were available. Shown in Figure 5.6a–d   78 are the average number concentrations of INPs as a function of particle size for droplet freezing temperatures ranging from -15 to -30 °C. The shapes of all four INP size distributions were near-ly identical with a single mode at an aerodynamic diameter of 3.2–5.6 µm. Also shown in Figure 5.6 are the average size distributions of total particles and fluores-cent bioparticles as measured with the WIBS-4A over the size range of 0.5–10 µm. Due to the decrease in WIBS counting efficiency at particle sizes below approximately 0.7 µm (Healy et al., 2012b), the number concentration of particles sized 0.5–1.0 µm should be considered a lower limit. The size distribution of total particles (Figure 5.6e) was found to be unimodal with the mode at 0.5–1.0 µm. Fluorescent bioparticles were bimodally distributed (Figure 5.6f) with one mode at 1.8–3.2 µm and another at 0.5–1.0 µm. Figure 5.6 illustrates that the size distributions of INPs are more closely related to the size distribution of fluorescent bioparticles than total parti-cles, suggesting that biological particles may have had a greater contribution to the INP popula-tion than non-biological particles. In addition to the WIBS-4A, the presence of biological material in sampled air was veri-fied by fluorescence microscopy. Images of a sample collected on August 11, 2013 are shown in Figure 5.7 as an example. The fraction of particles exhibiting fluorescence on this day based on the WIBS-4A was close to the campaign average value; 7.1 % versus an average of 7.8 %. The image here shows a sample containing many biological particles, identified by their blue color which is characteristic of biological fluorophores such as proteins and coenzymes (Pöhlker et al., 2012). Most of these biological particles had a similar morphology with an ellipsoidal shape, ap-proximately 11.9 µm in length × 4.1 µm in width, and multi-nucleation with three septa. Mor-phologically, many of these appear to be fungal macroconidia, consistent with the physical at-  79 tributes of ascospores (Carlile et al., 2001; Maheshwari, 2005; Leslie and Summerell, 2006; Webster and Weber, 2007). Fungal spores can be ice-active at the temperatures used here (Jayaweera and Flanagan, 1982; Pouleur et al., 1992; Tsumuki et al., 1992; Richard et al., 1996; Iannone et al., 2011; Haga et al., 2013, 2014; Fröhlich-Nowoisky et al., 2015), and the size of the bioparticles observed in Figure 5.7 (an estimated aerodynamic diameter of 4.8 µm assuming a prolate spheroid shape and unit density) matches the mode in the INP size distributions of Figure 5.6. Predicting the optical diameter that the WIBS-4A would measure for such a particle is diffi-cult, but it is reasonable that they could be detected as slightly larger or smaller depending on the axis on which the incident light impinges. 5.3.3 Is black carbon a major source of ice nuclei? Sources of BC at the sampling site include local marine ship traffic. Atmospheric size distributions obtained at other locations demonstrate that most BC particles are smaller than 1 µm (Schwarz et al., 2008, 2013; Schroder et al., 2015). As is shown in Figure 5.6, the majority of INPs identified here were larger than 1 µm at all of the temperatures studied. It is therefore likely that BC particles were not the major source of INPs at the sampling site. As correlations between INPs and eBC are moderate at -15 to -25 °C (R = 0.47–0.60, Table 5.1), correlations between INPs and the anthropogenic tracers CO, NOx, and SO2 were also investigated. The correlations between INPs and CO, NOx, and SO2 are not statistically significant (see Table C.2 of Appendix C), further suggesting that BC was not a major INP source. 5.3.4 Are particles from the ocean a major source of ice nuclei? Situated in a region of high oceanic primary productivity (Whitney et al., 2005; Ribalet et al., 2010) with onshore winds, particles of marine origin are a potential source of INPs at the   80 sampling site. Therefore, correlations between INP number concentrations and tracers of marine aerosols and marine biological activity were explored. Since primary marine aerosols are ejected from the ocean by the bursting of entrained bubbles (Blanchard and Woodcock, 1957; Blanchard, 1963, 1989; Andreas, 1998), sodium was used as a tracer of primary particles from the ocean. The strength of correlations between INPs and sodium are given in Table 5.1. Alt-hough the correlations range from weakly-to-moderately negative to strongly positive, the large P values (0.20 or greater) indicate that the results are not statistically significant. Due in part to the long sampling times required for the sodium measurements, only three to six data points were available for the sodium correlation analysis. MSA is often used as a marker for marine biological productivity (Saltzman et al., 1986; Savoie et al., 1994; Sorooshian et al., 2009; Gaston et al., 2010; Becagli et al., 2013) because it is chemically stable and its precursor, dimethylsulfide, is produced by primary biological activity in the ocean (Andreae et al., 1985; Charlson et al., 1987; Keller, 1989; Bates et al., 1992; Kettle et al., 1999). As INP number concentrations are closely correlated to bioparticles at warmer droplet freezing temperatures, one may expect correlations of a similar magnitude between INPs and MSA if the marine environment was indeed acting as an important source of biological INPs. As is shown in Table 5.1, there are no statistically significant correlations as P values are large (0.15-0.50). Finally, correlations between wind speed and INP number concentration were investigat-ed. As the dominant source of bubble entrainment in the oceans is breaking waves (O’Dowd and de Leeuw, 2007), the rate of sea-spray production is dependent in part on wind speed. For this correlation, wind speed was first raised to the power of 3.41 using the power law of Monahan   81 and Muircheartaigh (1980) that relates whitecap coverage to wind speed. The correlation found at -30 °C is statistically significant (P value < 0.05), but the magnitude of the correlation coeffi-cient is only moderate (R = 0.48; see Table 5.1). The average wind speed during INP sampling exceeded the onset speed for whitecap formation, approximately 4 m s-1 (O’Dowd and de Leeuw, 2007), in only 47 % of samples and daily observations at the site noted infrequent wave activity. Furthermore, some of the highest INP concentrations were found when the wind speed was less than 4 m s-1. All correlations between INPs and parameters indicative of marine aerosols and marine biological activity are either moderate at best or not statistically significant. For these reasons, correlations involving sodium, MSA, and wind speed do not provide strong evidence that marine particles were a major contributor to the INP population. Recent measurements have shown the presence of INPs in the sea surface microlayer (Wilson et al., 2015). The measurements present-ed in this chapter do not contradict these findings, since the ocean cannot be ruled out as a source of INPs. One possibility is that biological INPs released by local vegetation were present in suf-ficient numbers at this site to overwhelm the presence of any INPs from the ocean.  5.3.5 What is the major source of ice nuclei active at -30 °C? At warmer droplet freezing temperatures (-15 to -25 °C), the strongest correlations are observed between number concentrations of fluorescent bioparticles and INPs. In contrast, at -30 °C the strength of correlations between INPs and fluorescent bioparticles and INPs and total par-ticles > 0.5 µm in diameter are equal (R = 0.66; Table 5.1). It is therefore likely that both biolog-ical and non-biological particles were important sources of INPs active at -30 °C. Good correla-tions between INPs and total particles > 0.5 µm have also been observed in several other field   82 studies (e.g. DeMott et al., 2010; Chou et al., 2011; Field et al., 2012; Prenni et al., 2013; Tobo et al., 2013; Jiang et al., 2015).  Since the INP size distributions of Figure 5.6 and the correlations of Table 5.1 do not provide strong evidence of BC particles or the ocean being a major source of INPs active at -30 °C, it is possible that mineral dust was a major source of INPs as mineral dust particles are known to efficiently nucleate ice at this temperature (e.g. DeMott et al., 2003b; Cziczo et al., 2004; Field et al., 2006; Möhler et al., 2006; Marcolli et al., 2007; Zimmermann et al., 2008; Klein et al., 2010; Niedermeier et al., 2010; Chou et al., 2011; Atkinson et al., 2013; Yakobi-Hancock et al., 2013a; Wheeler et al., 2015). The size distribution of INPs did not drastically change between -25 and -30 °C (Figure 5.6c and d), and the dominant mode in the surface area distribution of airborne mineral dust (Maring et al., 2003) can occur at approximately the same size range as biological INPs (Després et al., 2012). While in a very different ecosystem and cli-matic region, Prenni et al. (2009b) noted that the relative contribution of mineral dust particles to the total number of INPs in the Amazon region increased as ice nucleation temperature de-creased. Only below -27 °C did the amount of mineral dust significantly influence the number of INPs, while above this temperature most INPs were biological (Prenni et al., 2009b). 5.3.6 Do the major sources of ice nuclei change with air mass classification?  In the preceding sections the data was not differentiated based on air mass classification. Here correlations are presented within each of the four air mass categories introduced in Section 5.2.8 to investigate if the major sources of INPs vary with air mass type. The correlations for each air mass type are given in Table 5.2. Correlations involving sodium and MSA are not includ-  83 ed due to insufficient data, and only statistically significant correlations will be discussed (P < 0.05).   The general trends presented in Table 5.1 for the undifferentiated data are also found in Table 5.2 for the various air mass categories. In coastal NW, Pacific Ocean, and free tropospheric air masses, INP number concentrations are well correlated to those of fluorescent bioparticles at temperatures between -15 and -25 °C with R values ranging from 0.64 to 0.99 (an average of 0.89), and in free tropospheric air masses a very strong correlation is also found at -30 °C (R = 1.00). In most cases, these are the strongest correlations noted at a given temperature. This again suggests that many INPs may have been biological.  In coastal NW and free tropospheric air masses, INPs and total particles are also closely correlated. These correlations are strong in the case of coastal NW air masses at ice activation temperatures of -15 to -25 °C (R = 0.70–0.85) and very strong in air masses from the free tropo-sphere between -20 and -30 °C (R = 0.98–0.99). The correlation coefficients are significantly greater than those found in the undifferentiated data of Table 5.1. With the average fraction of par-ticles that exhibited fluorescence in these air masses being close to the campaign average, the good correlations with total particles suggest that non-biological INPs such as mineral dust may have also contributed to the INP population.  Correlations of INPs with eBC are strong (R = 0.71–0.84) at -25 °C and above in coastal NW air masses and very strong (R = 0.99) at -15 °C in air masses from the free troposphere. Cor-relations of INPs with CO and SO2 in these air masses are also moderate to very strong in some cases (see Table C.3). However, more than 84 and 100 % of INPs active at these temperatures were larger than 1 µm in size in air masses from the coastal NW and the free troposphere, re-  84 spectively. Given the dominance of supermicron INPs in these two air mass types, it is unlikely that BC was an important source of INPs.  5.3.7 Can existing parameterizations accurately predict measured INP concentrations? Empirical parameterizations have been developed to predict ice nucleation in atmospheric models. Here I investigate whether or not a number of these parameterizations are consistent with the current measurements. In total, six different parameterizations were tested: those of Fletcher (1962), hereafter F62; Cooper (1986), hereafter C86; Meyers et al. (1992), hereafter M92; DeMott et al. (2010), hereafter D10; and two from Tobo et al. (2013), hereafter T13total and T13fluorescent. Details on these parameterizations are given in Appendix D.  In Figure 5.8 I compare measured INP number concentrations with predicted INP number concentrations based on the parameterizations discussed above. The parameterizations of D10, T13total and T13fluorescent require knowledge of either total particle or fluorescent bioparticle num-ber concentrations with sizes > 0.5 µm. Data from the WIBS-4A is used over over its full size range (0.5–23.7 µm) to better match the sampling conditions used in D10 and T13. Note the pa-rameterization of T13fluorescent based on fluorescent bioparticle number concentrations was formu-lated using measurements from an ultraviolet aerodynamic particle sizer (UV-APS), whereas this study uses a WIBS-4A. As noted in Healy et al. (2014), there may be discrepancies between the number concentrations of fluorescent bioparticles detected by the UV-APS and WIBS-4A. With more fluorescent channels and more sensitive electronics, the WIBS-4A may probe different fluorophores than the UV-APS, thus detecting greater concentrations of fluorescent bioparticles and in turn leading to greater predicted INP number concentrations. Also, the INP number con-centrations measured by the MOUDI-DFT are for particle sizes of 0.18–10 µm, whereas the INP   85 measurements used to formulate the parameterizations of M92, D10, and T13 were for particles ≤ 3, ≤ 1.6, and ≤ 2.4 µm, respectively. As a result, when reporting measured INP number con-centrations in Figure 5.8 MOUDI-DFT data is limited to particle sizes that overlap with those used to formulate the parameterizations (see Appendix D for details). It is evident in Figure 5.8 that none of the parameterizations are able to consistently pre-dict the measured INP number concentrations within a factor or 5 over the entire temperature range investigated. The most accurate parameterization is that of C86 (Figure 5.8b), predicting 25 % and 57 % of the INP number concentrations within a factor of 2 and 5, respectively, of the solid 1:1 line. While the C86 parameterization works reasonably well at temperatures of -15 to -25 °C, at lower temperatures it becomes increasingly inaccurate, possibly due to it being applied outside the temperature range over which it was developed (-5 to -25 °C).  The parameterizations of D10, T13total and T13fluorescent incorporate measurements of total particles or fluorescent bioparticles, but are found to be poor predictors of the values measured in this study as on average only 41% of INP number concentrations are predicted within a factor of 5 (Figure 5.8d–f). A number of datasets from diverse locations were used in the development of the D10 parameterization, but those with a strong marine influence were not included because sea salt is not known to be an efficient ice nucleus under the conditions investigated. Given the proximity of the sampling site to the Pacific Ocean (Figure 5.1) and the back trajectories of the sampled air masses (Figure 5.2), a marine influence in the samples may contribute to the some-what poor performance of the D10 parameterization and the over-estimation of INPs shown in Figure 5.8d. The T13total and T13fluorescent parameterizations were developed using data from a Colorado forest. Differences in the composition, concentration, and ice nucleating ability of both   86 biological and non-biological particles between the continental forest of T13 and the coastal site of this study may have contributed to the inaccuracy of the T13total and T13fluorescent parameteriza-tions (Figure 5.8e–f). 5.4 Summary and conclusions  The number concentrations of 0.18–10 µm INPs active in the immersion mode were de-termined at a coastal site in Western Canada during the summer of 2013 as part of the NETCARE project. I investigated the strength of linear correlations between these INP values and measurements of total particles, fluorescent bioparticles, eBC, sodium, MSA, and wind speed and also compared their size distributions where these measurements were available. I found that (1) biological particles, possibly from local vegetation, were likely the major source of ice nuclei at freezing temperatures between -15 and -25 °C; (2) non-biological particles such as mineral dust may also have had an important contribution to the population of INPs active at -30 °C; (3) the prevalence of supermicron INPs makes BC particles an unlikely source of ice nuclei; and (4) there was no evidence of marine particles being a significant source of ice nuclei, alt-hough the ocean as a source of INPs cannot be ruled out. One possibility is that biological INPs released by local vegetation were present in sufficient numbers at this site to overwhelm the presence of any INPs from the ocean.     Six empirical parameterizations of ice nucleation for use in atmospheric models were tested to determine the accuracy with which they predict INP number concentrations at this coastal site. Overall, none of the parameterizations were found to be suitable, predicting only 1 to 57 % of INPs within a factor of 5 of the measured value. This highlights the need for the devel-opment of INP parameterizations that are appropriate for this complex environment.   87 5.5 Chapter 5 tables and figures Table 5.1: Correlation coefficients (R) for linear regression analyses of INPs versus fluorescent bioparticles, total aerosol particles, eBC, sodium, MSA, and wind speeda. Correlations with sta-tistical significance (P < 0.05) are shown in bold.  Relation to the INP number concentration  -15 °C  -20 °C  -25 °C  -30 °C Measurement R Pb nc  R P n  R P n  R P n Fluorescent bioparticles       [0.5–10 µm] 0.74 < 0.01 28  0.77 < 0.01 28  0.83 < 0.01 28  0.66 < 0.01 23 Total particles   [0.5–10 µm] 0.33 0.04 28  0.36 0.03 28  0.49 < 0.01 28  0.66 < 0.01 23 eBC 0.47 < 0.01 34  0.59 < 0.01 34  0.60 < 0.01 34  0.25 0.11 27 Sodium -0.35 0.25 6  0.13 0.40 6  0.32 0.27 6  0.82 0.20 3 MSA 0.17 0.38 6  0.51 0.15 6  0.27 0.30 6  0.00 0.50 3 (Wind speed)3.41 0.05 0.39 34  0.01 0.48 34  0.15 0.19 34  0.48 < 0.01 27 aUsing the power law dependence of whitecap coverage on wind speed found by Monahan and Muircheartaigh (1980), wind speed was raised to the power of 3.41. bThe P value is a conditional probability that is the probability of obtaining an R value equal to or greater than the given R value if there is no correlation between INPs and the given parameter. cn represents the number of data points used in determining the correlation.           88 Table 5.2: Correlation coefficients (R) for linear regression analyses of INPs versus fluorescent bioparticles, total aerosol particles, eBC, and wind speeda within each category of air mass. Cor-relations with statistical significance (P < 0.05) are shown in bold.   Relation to the INP number concentration   -15 °C  -20 °C  -25 °C  -30 °C Air Mass Measurement R Pb nc  R P n  R P n  R P n Coastal NW Fluorescent      bioparticles          [0.5–10 µm] 0.94 <0.01 9  0.94 <0.01 9  0.96 <0.01 9  0.65 0.08 6  Total particles      [0.5–10 µm] 0.85 <0.01 9  0.70 0.02 9  0.71 0.02 9  0.67 0.07 6  eBC 0.71 <0.01 11  0.80 <0.01 11  0.84 <0.01 11  0.53 0.11 7  (Wind speed)3.41 -0.38 0.12 11  -0.39 0.12 11  -0.22 0.26 11  0.26 0.29 7 Coastal SE Fluorescent      bioparticles          [0.5–10 µm] -0.07 0.48 3  -0.53 0.32 3  -0.85 0.17 3  NAd    Total particles      [0.5–10 µm] -0.17 0.45 3  -0.61 0.29 3  -0.90 0.14 3  NA    eBC 0.07 0.46 5  0.28 0.32 5  0.67 0.11 5  0.96 0.09 3  (Wind speed)3.41 -0.34 0.29 5  -0.27 0.33 5  -0.14 0.41 5  0.21 0.43 3 Pacific Ocean Fluorescent       bioparticles          [0.5–10 µm] 0.80 <0.01 12  0.74 <0.01 12  0.64 0.01 12  0.23 0.24 12  Total particles      [0.5–10 µm] 0.13 0.34 12  0.30 0.17 12  0.21 0.25 12  0.25 0.22 12  eBC 0.24 0.21 14  0.37 0.10 14  0.26 0.19 14  0.06 0.42 13  (Wind speed)3.41 -0.10 0.37 14  -0.26 0.18 14  -0.26 0.19 14  -0.21 0.25 13 Free tropo-sphere Fluorescent      bioparticles          [0.5–10 µm] 0.97 0.02 4  0.99 <0.01 4  0.99 <0.01 4  1.00 <0.01 4  Total particles      [0.5–10 µm] 0.86 0.07 4  0.98 0.01 4  0.99 <0.01 4  0.98 0.01 4  eBC 0.99 <0.01 4  0.89 0.05 4  0.88 0.06 4  0.89 0.06 4  (Wind speed)3.41 -0.89 0.05 4  -0.70 0.15 4  -0.67 0.17 4  -0.68 0.16 4 aUsing the power law dependence of whitecap coverage on wind speed found by Monahan and Muircheartaigh (1980), wind speed was raised to the power of 3.41. bThe P value is a conditional probability that is the probability of obtaining an R value equal to or greater than the given R value if there is no correlation between INPs and the given parameter. cn represents the number of data points used in determining the correlation. dNA = not available due to insufficient data.     89  Figure 5.1: A satellite image of the sampling site: (1) location of the MOUDIs and the WIBS-4A; (2) location of the MAAP; and (3) Amphitrite Lighthouse where most meteorological data was collected; and (4) a station of the Canadian Coast Guard with supporting infrastructure. The image was modified from Bing Maps, 2014 (http://bing.com/maps/). Inset: the location of the sampling site in British Columbia, Canada.          90  Figure 5.2: Seventy-two hour HYSPLIT4 back trajectories of the air masses analyzed at the coastal site (black star) during INP sampling periods. Each back trajectory was initiated from a height of 5.5 m agl and at the midpoint of the sampling period.          91  Figure 5.3: INP number concentrations as a function of date determined at ice-activation temper-atures of (a) -15 °C, (b) -20 °C, (c) -25 °C, and (d) -30 °C. Error bars represent the upper and lower bounds to the INP number concentration as defined by the uncertainty in the MOUDI-DFT. The symbols are color coded by air mass category (see Section 5.2.8 for details). Fewer data points are available at -30 °C as INP number concentrations can only be determined to the temperature where all droplets are frozen.         92  Figure 5.4: Mean INP number concentrations found in each of the four categories of air masses sampled at (a) -15 °C, (b) -20 °C, (c) -25 °C, and (d) -30 °C. The scheme for air mass classifica-tion is given in Section 5.2.8. Uncertainties are given as the standard error of the mean.        93  Figure 5.5: Number concentrations of INPs active at -25 °C plotted against concentrations of (a) fluorescent bioparticles 0.5–10 µm, (b) total particles 0.5–10 µm, (c) eBC, (d) sodium, (e) MSA, and (f) (wind speed)3.41 based on the power law function of Monahan and Muircheartaigh (1980) where wind speed was in units of m s-1. Linear fits are shown with corresponding correlation co-efficients (R) and probability values (P).       94  Figure 5.6: Mean number concentrations as a function of size for INPs active at (a) -15 °C, (b) -20 °C, (c) -25 °C, and (d) -30 °C, total particles (e) and fluorescent bioparticles (f) using only samples where both the MOUDI-DFT and WIBS-4A were operating. Uncertainties are given as the standard error of the mean. As INP number concentrations can only be determined at temper-atures less than the temperature where all droplets are frozen and Equation (4.1) becomes unde-fined, fewer samples are represented at -30 °C. Number concentrations below 0.5 µm were not measured by the WIBS-4A for panels (e) and (f) but plot axes are consistent for easier compari-son of the size distributions.   95  Figure 5.7: Fluorescence microscopy images of an aerosol sample collected on August 11, 2013: (a) bright-field image; (b) an overlay of red, green, and blue fluorescence channels. A blue color-ation is characteristic of biological material (Pöhlker et al., 2012).            96  Figure 5.8: Predicted versus measured INP number concentrations based on the parameteriza-tions of (a) Fletcher (1962); (b) Cooper (1986); (c) Meyers et al. (1992); (d) DeMott et al. (2010); and (e–f) Tobo et al. (2013). Details on these parameterizations are given in Appendix D. Data color represents the ice nucleation temperatures, and the dashed and dotted lines are within a factor of 2 and 5, respectively, of the solid 1:1 line. This figure uses the format of Figure 9 in Tobo et al. (2013).   97 Chapter 6. Size-resolved measurements of ice nucleating particles at six locations in North America and one in Europe 6.1 Introduction  Ice nucleating particles (INPs) are a unique class of aerosol particles that catalyze ice formation under atmospheric conditions. A variety of particle types have been identified as INPs, including mineral dust, black carbon, volcanic ash, glassy aerosols, and primary biological parti-cles such as bacteria, fungal spores, and pollen (see reviews by Szyrmer and Zawadzki, 1997; Möhler et al., 2007; Ariya et al., 2009; Després et al., 2012; Hoose and Möhler, 2012; Murray et al., 2012; Yakobi-Hancock et al., 2013b). Although only a small fraction of aerosol particles nu-cleate ice (e.g. Rogers et al., 1998), INPs are important since they can lead to changes in the properties and lifetimes of mixed-phase and ice clouds, ultimately affecting climate and precipi-tation (Baker, 1997; Lohmann and Feichter, 2005; Baker and Peter, 2008; DeMott et al., 2010; Creamean et al., 2013).  Due to the importance of INPs for climate and precipitation, there has been a renewed interest in measuring the concentrations of INPs in the atmosphere (DeMott et al., 2011). While much of this work has focused on measurements of the total number concentration of INPs, there has been less emphasis on determining their size distributions in the atmosphere. Information on airborne INP size distributions may be particularly helpful in identifying the predominant INP sources. For example, information on the size distribution of INPs may help rule out or support the role of fungal spores in atmospheric ice nucleation since they are often in the supermicron range (Graham et al., 2003; Elbert et al., 2007; Sesartic and Dallafior, 2011; Després et al., 2012; Huffman et al., 2012). A similar approach can be used with black carbon particles, since they are   98 mainly in the submicron (Clarke et al., 2004; Schwarz et al., 2008, 2013).  Previous modeling studies have shown that the transport and distribution of INPs, and aerosol particles in general, are sensitive to the size of the particles assumed in the models (Burrows et al., 2009; Wilkinson et al., 2011). Information on the size distributions of INPs are thus needed for accurate modeling of their transport and impact on climate and precipitation, as well as for the aerial dispersal of fungi and bacteria (Morris et al., 2004; Hoose et al., 2010a, 2010b; Sesartic et al., 2013; Haga et al., 2014; Spracklen and Heald, 2014).  Information on the size distribution of INPs is also needed to determine if techniques used to measure atmospheric INP concentrations capture the entire INP population. For example, the continuous flow diffusion chamber (Rogers et al., 2001b) is often used for measuring INPs (e.g. DeMott et al., 1998; Rogers et al., 2001a; Richardson et al., 2007; Pratt et al., 2009; Prenni et al., 2009b; Eidhammer et al., 2010; Chou et al., 2011; Friedman et al., 2011; Hoyle et al., 2011; Corbin et al., 2012; Garcia et al., 2012; Tobo et al., 2013; McCluskey et al., 2014), but the aerodynamic diameter of particles measured with it is limited, from ≤ 2.4 µm in some studies (e.g. Garcia et al., 2012) to ≤ 0.75 µm in others (e.g. DeMott et al., 2003a). Such techniques miss supermicron or coarse mode particles, the latter defined here as particles larger than 2.5 µm. The exact proportion missed may depend on temperature as well due to the potential dependence of ice-active size on temperature. Such online instruments have typically focused on measurements below approximately -20 °C as sample volume considerations limit effective sampling of lower INP number concentrations at warmer temperatures.  Previous field studies of INPs as a function of size have been carried out using samples from Western Canada (Vali, 1966), the Pacific Ocean (Rosinski et al., 1986), the Gulf of Mexico   99 (Rosinski et al., 1988), Eastern Europe (Berezinski et al., 1988), Italy (Santachiara et al., 2010), and the Central United States (Huffman et al., 2013). These studies are further discussed in Sec-tion 6.3.2. In this chapter we add to the existing body of size-resolved INP measurements by re-porting ground-level INP size distributions from six locations in North America and one in Eu-rope, investigating immersion freezing at -15, -20, and -25 °C. We found that both supermicron and coarse mode aerosol particles were a significant component of the INP population at all sur-face-based sampling locations. These results indicate that, when averaged over all locations and temperatures, the total INP number concentrations measured in similar ecosystems may be un-derestimated by a factor of 4.4 and 2.1 if supermicron or coarse mode particles, respectively, are not analyzed. 6.2 Methods 6.2.1 Sampling sites The seven locations used in this study are detailed in Table 6.1 and shown in Figure 6.1. All dates reported are based on local time. Measurements using the sampling instrumentation described in the next section were made at five locations in Canada: Alert, Nunavut; the Labra-dor Sea near Newfoundland and Labrador; and Whistler Mountain, the University of British Co-lumbia (UBC) campus, and Amphitrite Point in British Columbia. Measurements in Canada were conducted as part of the larger NETwork on Climate and Aerosols: addressing key uncertainties in Remote Canadian Environments project (NETCARE; http://netcare-project.ca/). Measure-ments were also made at Saclay, France and Colby, Kansas, USA.     100 6.2.1.1 Alert, Nunavut Arctic sampling was conducted at the Dr. Neil Trivett Global Atmosphere Watch Obser-vatory in Alert, Nunavut, Canada (labeled 1 in Figure 6.1; Cobbett et al., 2007) between March 29 and July 23, 2014. This Arctic research station is part of a global network for measuring chemical and physical perturbations of the atmosphere. Aerosol particles were collected through a total suspended particulate (TSP) inlet (Mesa Labs Inc., Butler, NJ, USA) and 0.9 m mast lo-cated on the upper level of an outdoor platform free of surrounding obstructions, and were stored in the dark at -15 or 4 °C for a period of 10–112 days prior to analysis. 6.2.1.2 The Labrador Sea The Canadian Coast Guard Service vessel CCGS Amundsen serves as both an icebreaker for shipping lanes and an Arctic research vessel. One set of aerosol particle samples was collect-ed from the top of the bridge of this vessel on July 11, 2014 while in the Labrador Sea off the coast of Newfoundland and Labrador, Canada (labeled 2 in Figure 6.1). While sampling was within the marine boundary layer in the presence of sea spray aerosols, back trajectories (not in-cluded) calculated using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT4) model of the National Oceanographic and Atmospheric Administration (Draxler and Rolph, 2014) indicate that the sampled air mass spent the majority of the previous 72-hour period over land. Air was passed through a louvered TSP inlet and 1.5 m mast during sampling, and collected aerosol particles were stored in the dark at 4 °C for a period of 45–46 days prior to analysis.    101 6.2.1.3 Whistler Mountain The Whistler Peak High Elevation Site is located at the summit of Whistler Mountain in Whistler, British Columbia, Canada (labeled 3 in Figure 6.1) and operated by Environment Can-ada (Gallagher et al., 2011; Macdonald et al., 2011). Aerosol particle collection at this alpine site occurred between March 30 and April 23, 2014. The louvered TSP inlet was located approxi-mately 10 m from a chairlift operating station. Although there are no continuous combustion sources at the site, sampled air may have been influenced by engine exhaust for short periods of time due to nearby snowmobile operation. Samples were stored in the dark at 4 °C for a period of 1–4 days prior to analysis. 6.2.1.4 UBC campus Four sets of aerosol particle samples were collected from a weather station on the roof of the five-story Earth Sciences Building on the UBC campus in British Columbia, Canada (labeled 4 in Figure 6.1). The UBC campus is located on a peninsula and is surrounded by forest on three sides and ocean on the fourth. The site has been classified as suburban since it is less than 10 km from downtown Vancouver. Samples were collected through a TSP inlet and 0.5 m mast between May 12 and May 16, 2014. The aerosol particles were stored in the dark at 4 °C for a period of 21–23 days prior to analysis. 6.2.1.5 Amphitrite Point The coastal site at Amphitrite Point on Vancouver Island, British Columbia, Canada (la-beled 5 in Figure 6.1) is operated by Environment Canada, the BC Ministry of Environment, and Metro Vancouver for the continuous monitoring of aerosols and trace gases influenced by marine trajectories (McKendry et al., 2014; Yakobi-Hancock et al., 2014; Mason et al., 2015). The mo-  102 bile laboratory used during sampling was located approximately 100 m from the high tide line of the Pacific Ocean along a rocky shoreline, separated from the ocean by a narrow row of trees and shrubs approximately 2–10 m in height. Sampling took place from August 6 to August 27, 2013 using a louvered TSP inlet and 3 m mast. Aerosol particles were stored at room temperature and analyzed within 1 day of collection. 6.2.1.6 Colby, Kansas Aerosol particles were collected at the soybean and sorghum fields of the Kansas State University Northwest Research Center in Colby, KS, USA (labeled 6 in Figure 6.1). One sample was collected at each location during combine harvesting from a distance approximately 3–10 m downwind of the field. A third sample was also collected at the sorghum field the night follow-ing harvest. Sampling took place on October 14 and 15, 2014 and samples were stored in the dark at 4 °C for a period of 41–46 days prior to analysis. 6.2.1.7 Saclay, France  Aerosol particle samples were collected at the Commissariat à l’Energie Atomique (CEA) Atmospheric Supersite (AS), CEA l’Orme des Merisiers. The CEA-AS Observatory is a suburban area located 30 km southeast of Paris in Saclay, France (labeled 7 in Figure 6.1). The CEA-AS Observatory is surrounded by different sources of bioaerosols such as forest and agri-cultural fields, and is often influenced by marine or urban air masses (Baisnée et al., 2014). Measurements were made as part of the BIODETECT 2014 intensive campaign, an intercompar-ison of bioaerosol detection methods (Sarda-Estève et al., 2014). During this study period, the site was heavily influenced by urban outflow. A large set of ancillary measurements was done to constrain all the particulate matter sources. Aerosol particles were sampled through a TSP inlet   103 and 10 m mast between July 15 and August 4, 2014, and were stored in the dark at 4 °C for a pe-riod of 55–217 days prior to analysis. 6.2.2 Size-resolved INP number concentrations INP number concentrations were measured in the immersion mode using the micro-orifice uniform deposit impactor-droplet freezing technique (MOUDI-DFT). Substrate holders were used in the collection of all samples and the data were analyzed as described in Chapter 4. MOUDI stages 2–9 were used at most locations, corresponding to particle size bins of 10–5.6, 5.6–3.2, 3.2–1.8, 1.8–1.0, 1.0–0.56, 0.56–0.32, 0.32–0.18, and 0.18–0.10 µm (50 % cutoff aero-dynamic diameter; Marple et al., 1991). The range in particle size collected at each location is given in Table 6.1. Reported INP number concentrations at each location are averaged over all samples and have been adjusted to standard temperature and pressure. As was done previously, ice nucleation is considered to be a singular process (i.e. strictly temperature-dependent), but note that the stochastic (i.e. time-dependent) component to immer-sion freezing (Vali, 2014) may alter the median freezing temperature of a droplet by 0.5–2 °C per decade change in cooling rate (Murray et al., 2011; Welti et al., 2012; Wright and Petters, 2013; Wright et al., 2013; Wheeler et al., 2015). 6.3 Results and discussion 6.3.1 INP number concentrations  The total number concentration of INPs active at -15, -20, and -25 °C are shown for each site in Figure 6.2. Freezing events were rare at temperatures warmer than -15 °C and are there-fore not reported. Some of the DFT experiments proceeded such that all droplets froze at temper-atures slightly below -25 °C. As this scenario prohibits calculation of INP number concentra-  104 tions, -25 °C is the lowest temperature reported. As expected, INP number concentrations were found to increase with decreasing freezing temperature with the average concentration at -25 °C (3.77 L-1) being more than an order of magnitude larger than at -15 °C (0.25 L-1).  INP number concentrations were relatively low at the Alert, NU and Whistler Mountain sites with values of 0.05 and 0.10 L-1 at -15 °C, 0.22 and 0.16 L-1 at -20 °C, and 0.99 and 1.06 L-1 at -25 °C, respectively. These findings are consistent with previous measurements at similar lo-cations. For example, Arctic measurements of Bigg (1996) and Fountain and Ohtake (1985) found mean INP concentrations of 0.01 L-1 at -15 °C and 0.13 L-1 at -20 °C, respectively, using a diffusion chamber, and Prenni et al. (2007) measured an average INP number concentration of approximately 0.33 L-1 between -8 and -28 °C. At high elevation sites, Bowers et al. (2009) at Mt. Werner in Colorado and Conen et al. (2012) at the research station Jungfraujoch in Switzer-land measured mean INP number concentrations of approximately 0.02 L-1  at -10 and -12 ºC, respectively, using freezing assays. High elevation sites can receive large quantities of dust, which can be good ice nuclei at lower temperatures (Chou et al., 2011), but this was unlikely dur-ing the measurement period based on the low INP number concentrations. INP number concentrations at Amphitrite Point were 0.23, 0.94, and 2.15 L-1 at droplet freezing temperatures of -15, -20, and -25 °C, respectively. Despite the predominance of marine air masses being sampled, the major source of INPs at Amphitrite Point during the study period was likely biological particles from local vegetation (Mason et al., 2015). Similar values were measured in the Labrador Sea, where INP number concentrations at -15, -20, and -25 °C were 0.38, 1.32, and 2.79 L-1, respectively. These concentrations are consistent with previous meas-urements within the marine boundary layer in regions influenced by air flow off of the nearby   105 eastern coasts of continents, for instance those of Schnell (1977) off the coast of Nova Scotia, roughly 1100–1500 km southwest of the sampling site in the Labrador Sea, and Rosinski et al. (1995) over the East China Sea. However, INP number concentrations found during marine stud-ies can vary by several orders of magnitude with location as summarized by Burrows et al. (2013).  The highest concentrations of INPs at a freezing temperature of -25 °C were found at the Colby, KS sites, where the average number concentration was 8.89 L-1. Aerosol sampling was conducted adjacent to soya and sorghum fields during and following periods of combine opera-tion. This high concentration of INPs is consistent with previous work of Garcia et al. (2012) that showed elevated concentrations of INPs downwind of corn fields during combine harvesting, and Bowers et al. (2011) who found greater INP concentrations in air above cropland than above suburban or forest sites. The suburban sites of Saclay, France and the UBC campus also showed high INP concen-trations, being 4.39 and 6.12 L-1, respectively, at a freezing temperature of -25 °C. Both sites were likely influenced by multiple sources of INPs. For example, both are in close proximity to major metropolitan centers and forest vegetation, which are potential sources of anthropogenic INPs (e.g. Hobbs and Locatelli, 1970; Al-Naimi and Saunders, 1985; Knopf et al., 2010, 2014; Ebert et al., 2011; Corbin et al., 2012; Cziczo et al., 2013; Brooks et al., 2014) and biological INPs (e.g. Vali et al., 1976; Kieft and Ruscetti, 1990; Richard et al., 1996; Hirano and Upper, 2000; Diehl et al., 2002; Prenni et al., 2009b; Iannone et al., 2011; Pummer et al., 2012; Huffman et al., 2013; Tobo et al., 2013; Haga et al., 2014; Wright et al., 2014), respectively. The sampling site at Saclay, France was also within 1 km of agricultural fields, an additional source of biologi-  106 cal aerosols that may act as INPs (e.g. Lindow et al., 1982; Hirano et al., 1985; Georgakopoulos and Sands, 1992; Möhler et al., 2008b; Bowers et al., 2011; Garcia et al., 2012; Haga et al., 2013; Morris et al., 2013; Hiranuma et al., 2015b).  6.3.2 INP size distributions Figure 6.3a shows the relative contribution of supermicron aerosol particles to the total measured INP population. Note that the same particle size range was not investigated at all loca-tions with particles in the range of 0.10–0.18 µm not being measured at Whistler Mountain or Amphitrite Point. Averaging over all sampling locations, 91, 79, and 63 % of INPs had an aero-dynamic diameter > 1 µm at ice activation temperatures of -15, -20, and -25 °C, respectively. At -15 °C, the percentage of supermicron INPs ranged from 78 % at Whistler Mountain up to 100 % at the Labrador Sea and Colby, KS sites. At lower temperatures, there was more variation be-tween samples: at -20 ºC the percentage of supermicron INPs ranged from 52 % at Whistler Mountain to 100 % over the Labrador Sea, and at -25 ºC the percentage of supermicron INPs ranged from 39 % at Whistler Mountain to 95 % over the Labrador Sea.  Figure 6.3b shows the fraction of INPs that are in the coarse mode, calculated by assum-ing that half of the INPs found in the 1.8–3.2 µm MOUDI size cut were larger than 2.5 µm (i.e. INPs are uniformly distributed over this size range). Averaging over all sampling locations, we estimate that approximately 62, 55, and 42 % of INPs were in the coarse mode at ice activation temperatures of -15, -20, and -25 °C, respectively. The percentage of INPs in the coarse mode was found to range from 38 % at Saclay, France to 91 % in Colby, KS at -15 °C, from 26 % at Whistler Mountain to 73 % in Colby, KS at -20 °C, and from 20 % at Alert, NU to 64 % at the Labrador Sea at -25 °C. Despite great diversity in the studied locations, each had a significant   107 contribution from coarse mode particles to the measured INP population.  The median sizes of INPs at ice activation temperatures of -15, -20, and -25 °C are shown in Figure 6.4 with the 25th and 75th percentile values. At -15 °C, the median INP size is relatively large at all locations, varying from 2.1 µm at Saclay, France to 4.7 µm at Colby, KS with an av-erage of 3.2 µm. As droplet freezing temperature decreased, the median INP size also decreased, with the exception of samples from the Labrador Sea and the UBC campus. At -25 °C, the medi-an size of INPs varied from 0.83 µm at Alert, NU to 3.1 µm at the Labrador Sea site with an av-erage of 1.9 µm. The median size of the INPs was > 1 µm in all cases with the exception of the Alert, NU and Whistler Mountain sites at a freezing temperature of -25 °C. Alert, NU, in the polar tundra at high latitude, and Whistler Mountain, at high elevation and periodically in the free troposphere, are unique sites in that they are remote with fewer local sources of aerosols. At times, long-range transport may strongly influence atmospheric composition at these two locations (Barrie, 1986; Worthy et al., 1994; Liang et al., 2004; McKendry et al., 2008; Shindell et al., 2008; Macdonald et al., 2011; Takahama et al., 2011; Fan, 2013). If INP sources are not local to these sites, re-moval of large particles via wet and dry deposition processes during long-range transport may account for the greater contribution from smaller INPs. Similar shifts in size distributions during long-range transport have been observed at other locations for total particles (Mori et al., 2003) and fungal spores (Fröhlich-Nowoisky et al., 2012). Transport losses may have also contributed to the small overall INP number concentrations at the Alert, NU and Whistler Mountain sites as shown in Figure 6.2.  In Figure 6.4, the difference between the 75th and 25th percentile sizes is relatively small   108 at all locations at -15 °C. A narrow INP size distribution at -15 °C is consistent with a single type or class of particles dominating freezing at this temperature. With decreasing temperature, the interquartile range significantly increased: the 75th and 25th percentile INP sizes decreased by an average factor of 1.2 and 3.7, respectively, between -15 and -25 °C, corresponding to a 63 % in-crease in the average interquartile range. INP size distributions are further explored in Figure 6.5, where the fraction of the meas-ured INP number concentration found in each MOUDI size bin is shown. Colors on the red end of the scale illustrate that a large fraction of the INPs measured at a particular location belong to that particle size bin. Histograms of the INP size distributions are also available in Figures E.1–E.7 of Appendix E.  Figure 6.5a shows that most INPs active at -15 °C were 1–10 µm in size. In particular, when averaged over all locations, 72 % of the INPs active at -15 °C were between 1.8 and 5.6 µm. Furthermore, the major mode (i.e. the global maximum in a size distribution) was always larger than 1.8 µm. At lower freezing temperatures the INP size distributions broadened with in-creased contributions from smaller aerosol particles, evident by the more uniform intensity of Figure 6.5b and c. By -25 °C, six of the seven locations had a submicron INP mode, and at Alert, NU and Whistler Mountain this was the major mode. A general broadening of the INP size dis-tribution with decreasing temperature would be expected if there were an increase in the number of particle types exhibiting ice activity with decreasing activation temperature.  Several previous studies have conducted size-resolved INP measurements. In most of these studies, the fraction of INPs larger than a given particle size was not reported, and in some cases the temperatures studied were different than in the current study. To better compare the   109 MOUDI-DFT data with these previous studies, I have used the literature data to calculate the fraction of INPs larger than either 1, 1.2, or 2.5 µm at temperatures as close as possible to the freezing temperatures used here. Details of the calculations are presented in Appendix G, and the results of the calculations are summarized in Table 6.2.  Table 6.2 shows that in six out of the seven previous studies, a large fraction (16–100 %) of the INPs was found to be supermicron in size, consistent with the current study. Here the work of Rosinski et al. (1986) is considered as two separate studies given the change in the investigat-ed mode of ice nucleation. The only measurements that didn’t observe a large fraction of INPs in the supermicron size range were the condensation freezing measurements by Rosinski et al. (1986), where approximately 1 % of the INPs were supermicron. However, for the same envi-ronment their data suggests that 100 % of INPs active in the immersion freezing mode were su-permicron (when calculated for the size range > 0.5 µm). Taken together, measurements by Rosinski et al. (1986) suggest that the fraction of supermicron INPs may have a dependence on the mode of ice nucleation.   Kumai (1961) and Rucklidge (1965) also made direct measurements of INP size by measuring aerosol particle residuals found in collected ice crystals using electron microscopy. When multiple particles were present in their samples, the INP was assumed to be the largest particle. Kumai (1961) determined that all INPs were between 0.2 and 8 µm in crystals formed at temperatures of approximately -4 to -21 °C, with the size of maximum frequency ranging from 1–3.5 µm depending on the ice-crystal form. Rucklidge (1965), measuring INPs activated be-tween -12 and -25 °C, found the mode of the INP size distribution to be 0.4–0.7 µm and approx-imately 86 % of INPs were smaller than 1 µm. The results of Kumai (1961) and Rucklidge   110 (1965) are not included in Table 6.2 as INPs could not be conclusively identified in all samples given that multiple particles were occasionally present.  In addition to the previous studies that measured INPs as a function of size, several stud-ies have investigated correlations between the concentrations of INPs and aerosol particles above a certain size (DeMott et al., 2010; Chou et al., 2011; Field et al., 2012; Huffman et al., 2013; Prenni et al., 2013; Tobo et al., 2013; Ardon-Dryer and Levin, 2014; Jiang et al., 2014, 2015). For example, using data from a variety of field measurements DeMott et al. (2010) observed a correlation between INP concentrations and the concentration of aerosol particles > 0.5 µm, and Ardon-Dryer and Levin (2014) found that INP concentrations in Israel were better correlated to the concentration of aerosol particles 2.5–10 µm in size than those < 2.5 µm. These results are also consistent with INPs being relatively large in size.    6.4 Summary and conclusions INP number concentrations in the immersion mode as a function of size and droplet freezing temperature were determined at six locations across North America and one in Europe. INP number concentrations varied by as much as an order of magnitude between locations, and were generally found to be lowest at the remote sites of Alert, NU and Whistler Mountain and highest at the agricultural sites of Colby, KS and the suburban sites of Saclay, France and the UBC campus, consistent with previous studies. Several key findings indicate the potential im-portance of large particles as a source of ground-level ice nuclei: (1) 91 and 62 % of INPs meas-ured at -15 °C across all locations are supermicron or in the coarse mode, respectively; (2) at the lowest temperature analyzed, -25 °C, 63 and 42 % of INPs across all locations remained in the supermicron regime or coarse mode, respectively; (3) at -15 °C, the median INP size was rela-  111 tively large at all locations, varying from 2.1 µm at Saclay, France to 4.7 µm at Colby, KS with an average of 3.2 µm; and (4) at -25 °C, the median size INP varied from 0.83 µm at Alert, NU to 3.1 µm above the Labrador Sea with an average of 1.9 µm. These measurements indicate that, when averaged over all studied locations and tempera-tures, 78 and 53 % of immersion-mode INPs may be missed if either supermicron particles or coarse mode particles are not sampled. As noted in Sect. 1, some instrumentation for measuring ambient INP number concentrations restricts the upper range of sampled aerosol particles. The data presented may be useful for estimating the fraction of INPs not measured with these instru-ments at ground sites and in different environments. One caveat to this study is that MOUDI-DFT measurements were confined to aerosol particle sizes greater than either 0.10 or 0.18 µm. If there were a significant contribution from INPs of smaller sizes (Vali, 1966; Schnell and Vali, 1973; Pummer et al., 2012; Augustin et al., 2013; Fröhlich-Nowoisky et al., 2015; O’Sullivan et al., 2015; Tong et al., 2015; Wilson et al., 2015), then the values presented would represent upper limits to the contribution of supermicron and coarse mode particles to the total INP population. Additional studies exploring the relative atmospheric abundance of INPs < 0.10 µm are therefore necessary (Hader et al., 2014).          112 6.5 Chapter 6 tables and figures Table 6.1: The seven locations used in this study and conditions during sampling. Location Environment Coordinates Elevation (m) Sampling period (local time) Number of    Samples Average sampling time (h) Average temperature (°C) Average RH (%) Particle size range (µm)a Alert, NU, Canada Arctic 82.45° N 62.51° W 12 agl 200 asl Mar. 29 - Jul. 23, 2014 9 17.6 -17.4 77 0.10–10 Labrador Sea,    Canada Marine 54.50° N 55.37° W 2 agl 15 asl Jul. 11, 2014 1 6.2 10.9 75 0.10–10 Whistler, BC, Canada Alpine 50.06° N 122.96° W 2 agl 2182 asl Mar. 30 - Apr. 23, 2014 4 6.7 0.8 83 0.18–10 University of British Columbia, BC, Canada Suburban Canada 49.26° N 123.25° W 24 agl 120 asl May 12–16, 2014 4 6.3 15.2 70 0.10–10 Amphitrite Point, BC, Canada Coastal 48.92° N 125.54° W 5.5 agl 25 asl Aug. 6–27, 2013 34 7.8 13.8 97 0.18–10 Colby, KS, USA Agricultural 39.39° N 101.06° W and    39.39° N 101.08° W 2 agl 968 asl Oct. 14–15, 2014 3 4.5 17.0 48 0.10–10 Saclay, France Suburban France 48.71° N 2.21° E 10 agl 168 asl Jul. 15 -  Aug. 4, 2014 15 7.2 20.6 69 0.10–10 aAerodynamic diameter based on the 50 % cutoff of the MOUDI (Marple et al., 1991).           113 Table 6.2: Previous size-resolved INP measurements. Study Location Geographical Description Altitude Particle sizes investigated (µm) Mode of ice nucleation Ice nucleation temperature (°C )a Result Vali (1966) Alberta, Canada Western Canada Hail melt water TSPb Immersion -12.8 16 % of INPs   > 1.2 µmc Rosinski    et al. (1986) Central and western South Pacific Ocean Marine Near sea level 0.5 to > 8 Immersion -10.8 100 % of INPs > 1 µm < 0.5 to > 8 Condensation -5 to -6 1 % of INPs     > 1 µm Rosinski    et al. (1988) Gulf of   Mexico Marine Near sea level 0.1 to > 4.5 Condensation -15 to -16 45 % of INPs   > 1 µm Berezinski et al. (1988) European territory of the former Soviet Union Eastern   Europe 100–500 m agl 0.1 to > 100 Condensation -15 to -20 37 % of INPs   > 1 µm Santachiara et al. (2010) S. Pietro Capofiume, Italy Rural Italy 3 m agl TSP Condensation -17 to -19 47 and 30 %    of INPs > 1   and 2.5 µm, respectively Huffman   et al. (2013) Manitou Experimental Forest, CO, USA Forest    during/after rainfall 4 m agl 0.32 to >18 Immersion and deposition -15 to -20 89 % of INPs   > 1 µm Forest during dry periods 4 m agl 0.32 to >18 Immersion and deposition -15 to -20 46 % of INPs   > 1 µm aData at the temperatures of this study (-15, -20, and -25 °C) were used when available, otherwise the next closest temperature was used. bTSP = total suspended particulate. cParticle size reported for Vali (1966) is based on filter pore size. In all other studies, particle size is given as the aerodynamic diameter.           114  Figure 6.1: Sampling locations used in this study: (1) Alert, Nunavut, Canada; (2) the Labrador Sea, Canada; (3) Whistler Mountain, British Columbia, Canada; (4) the University of British Co-lumbia campus, British Columbia, Canada; (5) Amphitrite Point, British Columbia, Canada; (6) Colby, Kansas, USA; and (7) Saclay, France. Site coordinates are given in Table 6.1 with details in Section 6.2.1. The image was modified from Bing Maps, 2014 (http://bing.com/maps).         115  Figure 6.2: Mean INP number concentrations at droplet freezing temperatures of -15 °C (dark grey), -20 °C (intermediate grey), and -25 °C (light grey). Uncertainty is given as the standard error of the mean with the exception of the Labrador Sea sample, where the uncertainty is that of the measurement technique.             116  Figure 6.3: The mean fraction of INPs larger than (a) 1 µm and (b) 2.5 µm. Uncertainty is the standard error of the mean. Shading in the histogram corresponds to INP activation temperature: -15 °C is dark grey, -20 °C is an intermediate grey, and -25 °C is light grey. As 2.5 µm does not align with the size cut of a MOUDI stage, the fraction of INPs larger than 2.5 µm was found by assuming that number concentration of INPs 1.8–3.2 µm in size was uniformly distributed over that size range. As only one sample was available from the Labrador Sea, no uncertainty is re-ported.     117  Figure 6.4: The median size of INPs at ice-activation temperatures of -15 °C (green), -20 °C (blue), and -25 °C (red) when averaged over all analyzed samples. Upper and lower uncertainties are the 75th and 25th percentiles, respectively.             118  Figure 6.5: Fractional INP concentrations as a function of aerosol particle size, location, and ac-tivation temperature: (a) -15 °C; (b) -20 °C; and (c) -25 °C. The color bar indicates the fraction of INPs measured in each particle size bin. Aerosol particle sizes correspond to the 50 % cutoff aerodynamic diameters of the MOUDI stages (Marple et al., 1991). Missing sizes for the Whis-tler Mountain and Amphitrite Point sites are uncolored.     119 Chapter 7. Conclusions and future work 7.1 Development of the micro-orifice uniform deposit impactor-droplet freezing       technique (MOUDI-DFT)  Chapters 2 and 4 described in detail the micro-orifice uniform deposit impactor-droplet freezing technique (MOUDI-DFT), a method that combines inertial impaction for particle collec-tion with a droplet freezing apparatus to measure the aerodynamic size of atmospheric INPs. This technique offers a high size resolution and a broader temperature range than available in previous techniques, extending to temperatures approaching that of homogeneous ice nucleation.  Chapter 4 largely focused on the issue of experimental uncertainty in the MOUDI-DFT arising from aerosol deposit non-uniformity. Given that only a small subsection of the sample is analyzed in this technique with the results extrapolated to the entire deposit, variations in the concentration of aerosol particles across the sample must be taken into account. To this end, sample deposit non-uniformity was quantified at spatial resolutions of 1, 0.25, and 0.10 mm. These results were then used to make substrate holders for MOUDI sample collection and deter-mine non-uniformity correction factors to be applied during INP measurements.  Also presented in Chapter 4 was an initial intercomparison of the MOUDI-DFT with the continuous flow diffusion chamber (CFDC) of Colorado State University. The CFDC is an estab-lished instrument for measuring total atmospheric INP concentrations that has been involved in several previous intercomparison studies, thus making it an ideal technique against which to compare my own. Using two ambient aerosol samples collected in consecutive days, it was found that in the first sample, CSU-1, the average value of the INP concentration obtained by the CFDC was a factor of approximately 3.8 larger than the median value determined with the   120 MOUDI-DFT at a temperature of -21.7 °C, and during in the second sample, CSU-2, the median INP concentration of the MOUDI-DFT was a factor of approximately 1.1 larger than the average value from the CFDC at a temperature of -26.6 °C. These techniques were not in disagreement if the uncertainties in the measurements were considered and the results are commensurate with those in other studies involving the CFDC. 7.2 Field measurements of INPs: determining size and source   The remaining research chapters of this dissertation involved field measurements of aero-sols using the MOUDI-DFT in conjunction with a number of additional offline and online tech-niques. The earliest of these field campaigns is described in Chapter 3 and took place in Manitou Experimental Forest, Colorado. These measurements identified a strong dependence of atmos-pheric biological particles and INPs on rainfall, where the concentrations were enhanced during and following rainfall when compared to dry periods (at ice-active temperatures of -15 and -20 °C). Furthermore, biological particles were likely the primary source of INPs during rainfall pe-riods given the much stronger linear correlation between INP and fluorescent bioparticle concen-trations for wet vs. dry samples (correlation coefficients, R, of 0.94 and -0.06, respectively) and a better match in the size distribution of INPs and fluorescent bioparticles in rain-associated sam-ples, where particles 1.8–5.6 µm were dominant. These particle sizes are typical of bacteria and fungal spores.  In Chapter 5, measurements were conducted at the coastal site of Amphitrite Point. The strongest correlations with INPs were found with fluorescent bioparticles with R values of 0.74, 0.77, and 0.83 at temperatures of -15, -20, and -25 °C, respectively. As no evidence was found of marine particles being a significant source of ice nuclei, black carbon to be an unlikely compo-  121 nent of the INP population given its small size, and no obvious trend between INP concentrations and the type of air mass sampled, it is likely that biological particles released by local vegetation were an important source of INPs at this site. At -30 °C, correlations suggested that non-biological particles such as mineral dust may have played a larger role in ice nucleation. I also evaluated the performance of existing empirical parameterizations in predicting the INP concen-trations at this coastal site. None of the six tested parameterizations were found to be adequate over the entire temperature range studied.  Chapter 6 further took advantage of the size-resolving capability of the MOUDI-DFT in presenting size-resolved INP measurements from seven field studies covering a diverse array of environments. INP concentrations were found to be, on average between -15 and -25 °C, lowest at the remote sites of Alert, NU and Whistler Mountain (0.43 L-1) and highest at the agricultural sites of Colby, KS and the suburban sites of Saclay, France and the UBC campus (2.70 L-1). When averaged over all locations and temperatures, 78 and 53 % of immersion-mode INPs may be missed if supermicron particles or coarse mode particles, respectively, are not sampled. The predominance of large INPs should therefore be considered when conducting ground-level measurements with instrumentation that excludes supermicron or coarse mode particles and when modeling the atmospheric transport of INPs. 7.3 Directions of future research  As the MOUDI-DFT is a new approach to measuring atmospheric INP concentrations, further characterization and intercomparison studies are warranted. For example, it would be use-ful to quantify particle bounce under various sampling conditions. Bounce within impactors such as the MOUDI can occur during aerosol sampling, where particles impact the collection substrate   122 but are not retained. This rebounding of particles from the surface could possibly alter the INP concentrations and size distributions being measured. Studies have shown that having a sample RH of 70 % or greater can be effective in reducing particle bounce (e.g. Winkler, 1974; Fang et al., 1991; Stein et al., 1994; Vasiliou et al., 1999; Chen et al., 2011; Bateman et al., 2014), alt-hough its efficacy is dependent on particle type (Winkler, 1974; Lawson, 1980; Saukko et al., 2012). MOUDI sampling for this dissertation often occurred under conditions of high RH, as shown in Table 6.1, and the two intercomparison studies completed thus far, one using ambient continental aerosols (Chapter 4) and the other using laboratory-generated sea spray aerosols (not shown), indicate a reasonable agreement between the MOUDI-DFT and other INP instrumenta-tion. Based on the high RH used in most of the previous field studies and the reasonable agree-ment observed in the two intercomparison studies, it is unlikely that bounce significantly affected these results, but experiments measuring particle bounce as a function of RH and sample compo-sition are still needed to better quantify the effect of bounce on the INP measurements.  A strong coupling between bioparticles, INPs, and rainfall was identified in Chapter 3. It would be useful to replicate these measurements in different locations to determine if these ob-servations are unique to the studied forest or are characteristic of many ecosystems. Regions of interested may include agricultural land, a source of epiphytic (Lindow et al., 1978a, 1978b; Hirano et al., 1996) and airborne INPs (Bowers et al., 2011; Garcia et al., 2012), and marine sys-tems given that some marine particles nucleate ice (e.g. Schnell, 1977; Parker et al., 1985; Knopf et al., 2011) and rainfall contributes to the bubble-bursting mechanism of aerosol production (Blanchard and Woodcock, 1957; Prosperetti et al., 1989; Marks, 1990).   123  In an effort to determine the types of aerosol particles exhibiting ice-nucleating activity, it is a common practice to use correlations involving INPs with other available measurements and from this infer INP composition. For example, in Chapter 5 I proposed that INPs at the coastal site were likely biological based in part on the magnitude of linear correlations. While useful, such analyses cannot be conclusive. A modification to the DFT that allows characterization of collected aerosol particles would help resolve this issue. If an experimental approach similar to that of Knopf et al. (2014) was adopted, droplet residuals could be examined following water evaporation. While Knopf et al. (2014) utilize scanning electron microscopy and energy-dispersive X-ray spectroscopy to determine particle morphology and elemental composition, this could be extended to include fluorescence spectroscopy to complement available online meas-urements. This would allow ice-nucleating activity to be identified on a droplet-by-droplet or, with the proper aerosol deposit concentrations, a particle-by-particle basis.  All measurements presented were conducted at ground level and, apart from those at Whistler Mountain, were also close to sea level. 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The change in ener-gy associated with the formation of a cluster in pure liquid water is given by the following equa-tion: ∆𝐺!"# = − 4𝜋𝑟!!3 𝑁!𝑘𝑇ln𝑆 + 4𝜋𝑟!!𝜎!,!  , (A.1) where ΔGhom is the Gibbs free energy of homogeneous nucleation, ri is the radius of the newly formed cluster, Ni is the concentration of water molecules in the cluster, k is the Boltzmann con-stant, T is the temperature in Kelvin, S is the supersaturation, and σi,w is the interfacial energy at the ice-water boundary. Here, S is defined as: 𝑆 = 𝑒!,!𝑒!,!   ,   (A.2) where es,w and es,i are the saturation vapor pressures over water and ice, respectively, which can be calculated using available parameterizations (Murphy and Koop, 2005).   155  The first term in Equation (A.1) describes the energy associated with the formation of the new phase. This term is dependent on cluster volume and is favorable at temperatures below 0 °C because S > 1. The second term in Equation (A.1) describes the energy associated with form-ing a new interface and is dependent on cluster surface area. The dependence of ΔGhom on the size of the cluster is shown in Figure A.1 as the blue trace.  At smaller cluster sizes the surface area term (the green trace in Figure A.1) dominates, making growth by the addition of water molecules unfavorable given that ΔGhom of the system increases. These small clusters will therefore disintegrate, giving rise to their transient nature. If a cluster of the critical size is formed, the addition of water molecules is favorable since ΔGhom of the system now decreases as a result of an increasingly negative volume term (the red trace in Figure A.1). The incorporation of available water into this supercritical cluster is a spontaneous process. The size of the critical cluster needed to overcome the energy barrier to ice nucleation (ri*) can be calculated from the point at which ΔGhom reaches its maximum value: 𝑟!∗ = 2𝜎!,!𝑁!𝑘𝑇ln𝑆  .   (A.3) For example, the radius of the critical cluster is approximately 0.8, 1.8, and 7.0 nm at tempera-tures of -40, -20, and -5 °C, respectively, corresponding to roughly 70, 650, and 45000 molecules of water, respectively (Vali, 1995). As formation of the critical cluster in a liquid water droplet is kinetically hindered at temperatures above -37 °C, ice nucleation in the supercooled state is an exceedingly slow pro-cess (slower than atmospheric time scales) unless an INP is present to lower the energy barrier to ice cluster formation. When considering heterogeneous nucleation of ice from liquid water via   156 the immersion mode, it is necessary to consider the interfacial energies of the ice-substrate (σi,n) and water-substrate (σw,n) boundaries in addition to that of the ice-water boundary. Here the sim-plified case of a cluster with the shape of a spherical cap on an INP with a flat surface is used. Within this system there is an equilibrium condition with balance of interfacial energies that can be represented mathematically by Young’s equation using the contact angle, 𝜃. The contact angle is then used to define 𝑚: 𝑚 = cos𝜃 = 𝜎!,! − 𝜎!,!𝜎!,!   . (A.4) The relationship given in Equation (A.4) can be used, along with geometric relationships between 𝑚 and the surface area and volume of the spherical cap, to determine the change in the Gibbs free energy corresponding to the formation of a cluster by a heterogeneous process: ∆𝐺!"# = 𝑓 𝑚 4𝜋𝑟!!3 𝐶𝜖! − 𝑁!𝑘𝑇ln𝑆 + 4𝜋𝑟!!𝜎!,!   , (A.5) where f(𝑚) is the geometric factor given below, C is the elastic strain coefficient, and 𝜖 is the elastic strain of the lattice, a product of the crystallographic mismatch that may exist between the ice cluster and the surface of the INP. Analogous to the homogeneous case, Equation (A.5) can be used to find the size of the critical cluster at which ice nucleation occurs spontaneously for a heterogeneous process. The geometric factor is given by the following equation: 𝑓 𝑚 = (2+𝑚)(1−𝑚)!4   , (A.6)   157 where values of f(𝑚)  range from 0 when the 𝜃 = 0°, corresponding to no energy barrier to ice nucleation and a “perfect” INP, to 1 when 𝜃 = 180°, meaning the substrate does not contribute to ice nucleation which thus can only proceed homogeneously. As the critical cluster in a real sys-tem and may not truly adopt the shape of a spherical cap on a flat surface, the contact angle is best considered as a helpful conceptual framework with 𝜃 being a convenient parameter for de-scribing the ability of a particle to effectively nucleate ice.    Figure A.1: The Gibbs free energy change (blue trace) associated with the formation and growth of a new phase as a function of cluster size (ri). The green and red traces are calculated from the surface area and volume terms, respectively, of Equation (A.1). The size of the critical cluster needed to overcome the energy barrier to ice nucleation is noted as ri*.     158 Appendix B  The impact of MOUDI deposit non-uniformity on forest INP concentrations   Based on the work of Maenhaut et al. (1993) which found that particle concentrations varied by at most 50 % across a MOUDI deposit, I did not account for aerosol deposit non-uniformity when calculating the offline INP concentrations at the Colorado forest site as present-ed in Chapter 3 and again in Figure B.1 below for reference. However, Chapter 4 shows that non-uniformity can be very large depending on the impactor stage of interest and should there-fore be considered during calculations. Since templates were not used in MOUDI aerosol collec-tion for the forest samples and the exact location of the hydrophobic glass cover slips on the im-paction plates during sampling is unknown, INP concentrations cannot be precisely corrected for non-uniformity. Instead, the effect of non-uniformity on previous MOUDI-DFT data can be con-sidered in three extreme cases.   The first possibility is that the cover slips were positioned such that the DFT was always investigating the region of highest aerosol particle concentrations. If true, the offline INP con-centrations found during both dry and wet weather conditions would be overestimates. Using the non-uniformity data presented in Figures 4.3 and 4.4 of Chapter 4, the size-resolved INP concen-trations were corrected and are presented in Figure B.2. As non-uniformity was not quantified for the inlet or stage 1, these two stages will not be discussed. INP concentrations are smaller by a factor of 1.1–1.6 but the trend with size is unchanged, where particles 1.8–5.6 µm are the prima-ry source of INPs at both -15 and -20 °C and many more INPs were present during and following rain events than under dry conditions. The conclusions given in Chapter 3 are therefore unaltered in this situation.  A second possibility is that the cover slips were positioned such that the DFT was always   159 investigating the region of lowest aerosol particle concentrations. Given that some stages have regions that are largely devoid of particles, the INP concentrations found under the assumption of a uniform aerosol deposit may be significant underestimates. Non-uniformity data presented in Figures 4.3 and 4.4 of Chapter 4 were used to correct the INP concentrations for an underes-timate scenario, and results are presented in Figure B.3. As stages 4–6 have the greatest degree of non-uniformity, the INP concentrations in these stages are factor of 40–50 larger in Figure B.3 than Figure B.1. Changes in other MOUDI stages are less significant, being on average a factor of 2 larger. The overall results are: (1) the total INP concentrations increase by a factor of ~17 at -15 °C and ~28 at -20 °C; (2) as opposed to the uncorrected data, aerosol particles 3.2–5.6 µm in size are only a minor contributor to the INP population; (3) the major mode in the INP size dis-tribution, 1.8–3.2 µm, is still in agreement with the major mode of the fluorescent bioparticle dis-tribution; and (4) INP concentrations are still greater under wet conditions than dry conditions. Therefore, other than a smaller contribution from 3.2–5.6 µm INPs, the same conclusions from Chapter 3 would be reached.  The third possibility is that the cover slips were positioned such that the DFT was inves-tigating the region of highest aerosol particle concentrations during wet periods (i.e. overestimat-ing) and the region of lowest aerosol particle concentrations during dry periods (i.e. underesti-mating). This scenario is shown in Figure B.4, where the INP size distribution under wet condi-tions is still a better match to the bioparticle size distribution than during dry conditions, alt-hough the difference is less pronounced. This maintains the possibility that INPs under dry con-ditions were more likely non-biological particles such as mineral dust whereas rainfall initiated the release of ice-active biological particles as discussed in Chapter 3. A key difference between Figures B.1 and B.4 is that in the latter, INP concentrations during dry periods are comparable to   160 concentrations during wet periods. This would mean that INP concentrations were not enhanced by rainfall as strongly as indicated in Chapter 3. However, the online CFDC measurements of Chapter 3 found large enhancements in INP concentrations with rainfall, with the average INP concentration being nearly a factor of 8 larger during and following rainfall as compared to dry conditions and the maximum a factor of almost 16 larger. It is therefore unlikely that all INP concentrations from wet sampling conditions were overestimated and those from dry sampling conditions were underestimated to the extent shown in Figure B.4, making this last scenario highly unlikely and the conclusions of Chapter 3 still valid.            161  Figure B.1: Average size distributions of INP active at droplet freezing temperatures of (a) -15 °C and (b) -20 °C for samples collected during rain events and dry periods under the assumption of uniform particle deposits. (c) The size distribution of bioparticles measured during the same periods.   162  Figure B.2: Average size distributions of INP active at droplet freezing temperatures of (a) -15 °C and (b) -20 °C for samples collected during rain events and dry periods under the assumption that the DFT analyzed the region of the aerosol deposit with the greatest particle concentrations. Note that INP concentrations could not be calculated for particles > 10 µm. (c) The size distribu-tion of bioparticles measured during the same periods.    163  Figure B.3: Average size distributions of INP active at droplet freezing temperatures of (a) -15 °C and (b) -20 °C for samples collected during rain events and dry periods under the assumption that the DFT analyzed the region of the aerosol deposit with the lowest particle concentrations. Note that INP concentrations could not be calculated for particles > 10 µm. (c) Size distribution of bioparticles measured during the same periods.   164  Figure B.4: Average size distributions of INP active at droplet freezing temperatures of (a) -15 °C and (b) -20 °C for samples collected during rain events and dry periods under the assumption that the DFT analyzed the region of the aerosol deposit with the greatest particle concentrations in samples collected during and following rainfall while the DFT analyzed the region of the aer-osol deposit with the lowest particle concentrations in samples collected during dry periods. Note that INP concentrations could not be calculated for particles > 10 µm. (c) Size distribution of bi-oparticles measured during the same periods.   165 Appendix C  Aerosol measurements at the coastal marine boundary layer site C.1 INP sampling periods and conditions Table C.1: Details of the INP sampling periods. The meteorological parameters given have been averaged over the stated sampling duration and are described in Section 5.2 of Chapter 5. Times are reported as Pacific Daylight Time (PDT). Sample ID Start date and time (PDT) End date and time (PDT) Sampling time (min) Temp. (°C) Relative   Humidity (%) Air mass category N3 08/06 23:17:48 08/07 09:27:14 609 11.5 100 Coastal NW D5* 08/08 11:43:23 08/08 22:36:01 645 13.7 100 Pacific Ocean N5 08/08 23:09:19 08/09 09:46:40 637 12.6 100 Coastal NW D6 08/09 10:52:52 08/09 19:41:50 529 12.9 99 Coastal NW N6 08/09 23:37:17 08/10 06:06:32 389 12.1 100 Coastal NW D7 08/10 10:16:37 08/10 18:16:00 479 12.8 100 Coastal NW D8 08/11 10:50:02 08/11 18:50:03 480 13.3 99 Coastal NW N8 08/11 22:15:02 08/12 06:15:02 480 12.7 100 Coastal NW D9 08/12 11:30:02 08/12 19:30:02 480 14.2 97 Coastal NW N9 08/12 22:00:02 08/13 06:00:02 480 13.9 98 Pacific Ocean D10 08/13 10:52:02 08/13 18:52:02 480 13.8 97 Pacific Ocean N10 08/13 22:00:03 08/14 06:00:03 480 15.0 91 Pacific Ocean D11A 08/14 11:30:02 08/14 16:55:02 325 14.9 91 Coastal SE D11B 08/14 17:16:02 08/14 22:05:30 289 14.3 98 Coastal SE N11 08/14 22:32:02 08/15 06:32:02 480 14.0 100 Free troposphere D12 08/15 11:05:06 08/15 19:05:07 480 14.9 97 Pacific Ocean N13 08/16 23:01:33 08/17 07:00:33 479 14.4 100 Pacific Ocean D16 08/19 10:30:02 08/19 18:30:02 480 15.1 94 Pacific Ocean N16 08/19 22:00:02 08/20 06:00:03 480 13.4 96 Coastal NW D17* 08/20 11:10:02 08/20 19:09:22 443 14.7 91 Coastal NW N17 08/21 00:17:37 08/21 06:04:36 347 13.2 96 Coastal NW D18* 08/21 10:50:02 08/21 18:49:54 445 15.7 89 Free troposphere N18 08/21 22:02:02 08/22 06:02:02 480 13.4 100 Free troposphere D19* 08/22 10:42:02 08/22 18:41:52 438 15.0 97 Free troposphere N19 08/22 22:00:02 08/23 06:00:02 480 13.9 100 Pacific Ocean D20* 08/23 10:15:02 08/23 18:14:25 462 13.9 100 Pacific Ocean N20 08/23 22:00:02 08/24 06:00:02 480 13.5 100 Pacific Ocean D21 08/24 11:40:02 08/24 19:40:02 480 13.8 99 Pacific Ocean N21 08/24 22:00:02 08/25 06:00:03 480 13.2 100 Pacific Ocean D22B 08/25 14:26:03 08/25 20:26:03 360 15.2 88 Pacific Ocean N22 08/25 22:00:03 08/26 06:00:03 480 13.7 92 Pacific Ocean D23 08/26 10:35:03 08/26 18:35:03 480 14.2 90 Coastal SE N23 08/26 22:00:02 08/27 06:00:02 480 13.3 100 Coastal SE D24* 08/27 11:22:02 08/27 17:57:43 367 14.2 99 Coastal SE *Sampling was not continuous    166 C.2 Correlations involving INPs, aerosol composition, and wind speed  Figure C.1: INP number concentrations from -15 to -30 °C (columns I–IV) plotted against total particle number concentrations (panels a–d), fluorescent bioparticle number concentrations (pan-els e–h), and eBC mass concentrations (panels i–l). Linear fits are given as solid lines with corre-sponding correlation coefficients (R) and probability values (P).     167  Figure C.2: INP number concentrations from -15 to -30 °C (columns I–IV) plotted against sodi-um mass concentrations (panels a–d), MSA mass concentrations (panels e–h), and (wind speed)3.41 based on the power law dependence of whitecap coverage on wind speed by Monahan and Muircheartaigh (1980) with wind speed in m s-1 (panels i–l). Linear fits are given as solid lines with corresponding correlation coefficients (R) and probability values (P).      168 C.3 Measurements of CO, NOx, and SO2  Table C.2: Correlation coefficients (R) for linear regression analyses of INPs versus CO, NOx, and SO2. No correlations had statistical significance (P < 0.05).  Relation to the INP number concentration  -15 °C  -20 °C  -25 °C  -30 °C Measurement R Pa nb  R P n  R P n  R P n CO 0.14 0.22 34  0.24 0.08 34  0.28 0.06 34  0.32 0.05 27 NOx -0.11 0.27 34  -0.25 0.08 34  -0.27 0.06 34  -0.06 0.38 27 SO2 0.07 0.34 34  0.05 0.39 34  0.07 0.34 34  0.13 0.27 27 aThe P value is a conditional probability that is the probability of obtaining an R value equal to or greater than the given R value if there is no correlation between INPs and the given parameter. bn represents the number of data points used in determining the correlation.  Table C.3: Correlation coefficients (R) for linear regression analyses of INPs versus CO, NOx, and SO2 within category of air mass, which are described in Section 5.2.8 of Chapter 5. Correla-tions with statistical significance (P < 0.05) are shown in bold.     Relation to the INP number concentration   -15 °C  -20 °C  -25 °C  -30 °C Air Mass Measurement R Pb nc  R P n  R P n  R P n Coastal NW CO 0.55 0.04 11  0.52 0.05 11  0.55 0.04 11  0.42 0.17 7  NOx 0.01 0.49 11  -0.32 0.17 11  -0.29 0.19 11  -0.04 0.46 7  SO2 0.62 0.02 11  0.26 0.22 11  0.26 0.22 11  0.76 0.02 7 Coastal SE CO -0.59 0.15 5  -0.38 0.26 5  0.14 0.41 5  0.76 0.23 3  NOx 0.15 0.40 5  0.35 0.28 5  0.70 0.10 5  -0.05 0.48 3  SO2 -0.36 0.28 5  -0.26 0.34 5  0.12 0.42 5  -0.38 0.38 3 Pacific Ocean CO -0.03 0.46 14  0.07 0.41 14  0.04 0.45 14  0.50 0.04 13  NOx -0.19 0.26 14  -0.23 0.22 14  -0.28 0.16 14  0.18 0.28 13  SO2 -0.21 0.24 14  -0.19 0.26 14  -0.32 0.13 14  0.05 0.44 13 Free      troposphere CO 0.74 0.13 4  0.92 0.04 4  0.93 0.03 4  0.92 0.04 4  NOx -0.73 0.13 4  -0.74 0.13 4  -0.70 0.15 4  -0.67 0.17 4  SO2 0.08 0.46 4  0.24 0.38 4  0.30 0.35 4  0.33 0.34 4 aThe P value is a conditional probability that is the probability of obtaining an R value equal to or greater than the given R value if there is no correlation between INPs and the given parameter. bn represents the number of data points used in determining the correlation.   169 Appendix D  Empirical parameterizations of ice nucleation D.1 Literature parameterizations  The accuracy of six INP parameterizations from the literature was determined using data from this study. The parameterizations tested were those of Fletcher (1962), hereafter F62; Cooper (1986), hereafter C86; Meyers et al. (1992), hereafter M92; DeMott et al. (2010), hereaf-ter D10; and two from Tobo et al. (2013), hereafter T13total and T13fluorescent. The parameterization of F62 used the following equation: [INPs(𝑇)] = 𝐴exp(−𝐵𝑇)  ,  (D.1) where [INPs(T)] is the number concentration of INPs in L-1 at temperature T in °C, and A and B are fitted constants with values of A = 0.00001 and B = 0.6. Another single-variable function was proposed by C86 with the following form: INPs 𝑇 = 10 !!! !!   ,  (D.2) where D and E are fitted constants with values of D = -2.35 and E = 0.135. This parameterization was developed over a temperature range of -5 to -25 °C using measurements of ice crystal con-centrations. The final single-variable function that was evaluated was that of M92: INPs 𝑆! = exp 𝐹 + 𝐺 100 𝑆! − 1   , (D.3) where F and G are fitted constants with values of F = -0.639 and G = 0.1296, and Si is the ice supersaturation. This parameterization was developed using measurements between -7 to -20 °C. D10 and T13 have recently proposed INP parameterizations to predict number concentra-  170 tions of INPs as a function of both temperature and aerosol concentrations. Following work that showed a correlation between INP number concentration and aerosol particles larger than 0.5 µm (DeMott et al., 2006), D10 developed the following parameterization for mixed-phase cloud conditions: INPs 𝑇,AP!!.! = 𝑎 −𝑇 ! AP!!.! !!"!!   ,  (D.4) where [AP>0.5] is the number concentration of aerosol particles with diameters larger than 0.5 µm in cm-3, and a, b, c, and d are fitted constants with values of a = 0.0000594, b = 3.33, c = 0.0264, and d = 0.0033. This parameterization was found to be more accurate than the earlier parameteri-zations of F62, C86, and M92. T13 applied the same principle as D10 and used INP measurements from a forested site in Colorado to develop the following parameterization, T13total: INPs 𝑇,AP!!.! = AP!!.! !∝!!! exp −𝛾𝑇 + 𝛿   , (D.5) where α, β, γ, and δ are fitted constants with values of α = -0.074, β = 3.8, γ = 0.414, and δ  = -9.671. As T13 found that INP number concentrations were strongly correlated to number con-centrations of fluorescent bioparticles, they also proposed a second parameterization, T13fluorescent: INPs 𝑇, FB!!.! = FB!!.! !∝!!!!! exp −𝛾!𝑇 + 𝛿!   , (D.6) where [FB>0.5] is the number concentration of fluorescent bioparticles with diameters larger than 0.5 µm in cm-3, and αʹ, βʹ,  γʹ, and δʹ are fitted constants with values of αʹ = -0.108, βʹ = 3.8, γʹ = 0, δʹ = 4.605.   171 D.2 Reducing the MOUDI-DFT size range to match parameterization conditions The MOUDI-DFT used at the coastal site measured INP number concentrations for parti-cles between 0.18–10 µm in size (50% cutoff aerodynamic diameter). The parameterizations of M92, D10, and T13 were formulated using INP measurements at particles sizes ≤ 3, ≤ 1.6, and ≤ 2.4 µm, respectively. To better match the size range of INPs measured in this study with those used to formulate the parameterizations of M92, D10, and T13, here I limit the measured INP data to sizes ≤ 3 µm when testing the parameterization of M92, ≤ 1.6 µm when testing the pa-rameterization of D10, and ≤ 2.4 µm when testing the T13total and T13fluorescent parameterizations. As these sizes do not correspond to the size cut of any MOUDI stage, INP number concentra-tions smaller than a given particle size were calculated by the following method: 1) Identify the MOUDI stage that overlaps with the CFDC size range above. For example, the size range of ≤ 1.6 µm from D10 overlaps with the size range of MOUDI stage 5 (1.0–1.8 µm). 2) Multiply the INP number concentration found in this MOUDI stage by the fraction of the par-ticle size range that overlaps with that of the CFDC. For example, using ≤ 1.6 µm from D10 this multiplication factor would be (1.6-1.0)/(1.8-1.0) = 0.75. 3) Add the INP number concentration found above to the total INP number concentration found on all lower MOUDI stages.  As an example using the above procedure, the INP number concentrations used for test-ing the parameterization of D10 were calculated using the following equation: INPs 𝑇 = 0.75 INPs 𝑇 !.!!!.!  !! + INPs 𝑇 !.!"!!.!  !!  , (D.7) where [INPs(T)]1.0–1.8µm is the number concentration of INPs 1.0–1.8 µm in size and   172 [INPs(T)]0.18–1.0µm is the number concentration of INPs 0.18–1.0 µm in size.                     173 Appendix E  INP size distributions at sites in North America and Europe  Figure E.1: Mean INP size distributions at the Arctic site of Alert, NU, Canada at (a) -15 °C, (b) -20 °C, and (c) -25 °C. Here the fraction of INPs in each MOUDI size bin is reported as the mean of all samples with uncertainty as the standard error of the mean.   174  Figure E.2: Mean INP size distributions at the alpine site of Whistler, BC, Canada at (a) -15 °C, (b) -20 °C, and (c) -25 °C. Here the fraction of INPs in each MOUDI size bin is reported as the mean of all samples with uncertainty as the standard error of the mean. Number concentrations below 0.18 µm were not measured but plot axes are consistent with the other figures for easier comparison of the size distributions.   175  Figure E.3: Mean INP size distributions at the coastal site of Amphitrite Point, BC, Canada at (a)  -15 °C, (b) -20 °C, and (c) -25 °C. Here the fraction of INPs in each MOUDI size bin is reported as the mean of all samples with uncertainty as the standard error of the mean. Number concentra-tions below 0.18 µm were not measured but plot axes are consistent with the other figures for easier comparison of the size distributions.   176  Figure E.4: Mean INP size distributions at the marine site in the Labrador Sea at (a) -15 °C, (b) -20 °C, and (c) -25 °C. Here the fraction of INPs in each MOUDI size bin is reported as the mean of all samples with uncertainty as the standard error of the mean. As only one sample was col-lected at this location, no experimental uncertainty is reported.    177  Figure E.5: Mean INP size distributions at the suburban France site of Saclay, France at (a) -15 °C, (b) -20 °C, and (c) -25 °C. Here the fraction of INPs in each MOUDI size bin is reported as the mean of all samples with uncertainty as the standard error of the mean.    178  Figure E.6: Mean INP size distributions at the suburban Canada site on the UBC campus, BC, Canada at (a) -15 °C, (b) -20 °C, and (c) -25 °C. Here the fraction of INPs in each MOUDI size bin is reported as the mean of all samples with uncertainty as the standard error of the mean.    179  Figure E.7: Mean INP size distributions at the agricultural sites in Colby, Kansas, USA at (a) -15 °C, (b) -20 °C, and (c) -25 °C. Here the fraction of INPs in each MOUDI size bin is reported as the mean of all samples with uncertainty as the standard error of the mean.    180 Appendix F  Calculating the percentile size of INPs using binned data  The median, 25th percentile, and 75th percentile size of INPs at each location were calcu-lated from the binned MOUDI data. The INP sized distribution was first used to find the cumula-tive INP concentration in each bin from the smallest to the largest aerosol particle size. The fol-lowing equation was then used to calculate the percentile size of INPs: percentile  size  of  INPs = 𝑙 + 𝑝𝑡 − 𝑞𝑓 𝑤  , (F.1) where l is the lower size limit of the bin containing the percentile of interest, p is the desired per-centile (p = 0.25, 0.50, and 0.75 for the 25th percentile, median, and 75th percentile, respectively), t is the total INP concentration at a given temperature, q is the cumulative concentration of the preceding bin, and f and w are the INP concentration and width, respectively, of the bin contain-ing the percentile of interest.           181 Appendix G  Calculating the fractions of INPs larger than 1, 1.2, or 2.5 µm from previous studies  G.1 Vali (1966)  The size distribution of INPs found in hail melt water was reported by Vali (1966). Hail from Alberta, Canada was melted and some of this water was passed through filters of either 0.01 or 1.2 µm pore size. Samples were then analyzed using a drop freezing method. The con-centration of INPs in the immersion mode as a function of temperature was given in Figure 3 of that study for three size ranges: unfiltered, 1.2 µm filtered, and 0.01 µm filtered hail melt water. To calculate the fraction of INPs > 1.2 µm at -12.8 °C (the lowest temperature available and therefore the closest to -15 °C), the concentration of INPs in the 1.2 µm-filtered sample was first divided by the concentration of INPs in the unfiltered sample at this temperature. This fraction was then subtracted from unity. G.2 Rosinski et al. (1986)  The size distributions of INPs active in the immersion and condensation nucleation modes over the central and western South Pacific Ocean were determined by Rosinski et al. (1986). Aerosol particle samples were size-selected by an Anderson cascade impactor (similar in principle to the MOUDI) where the stage size cuts were 8, 6, 5, 4, 1, and 0.5 µm. There were al-so two after filters connected to the impactor in parallel to collect particles smaller than 0.5 µm. Samples were analyzed using either the drop freezing method (immersion mode) or a dynamic developing chamber (condensation mode).  Immersion mode freezing data for twelve samples was reported in Tables 1–12 of Rosinski et al. (1986) with each table corresponding to a different sampling period. As filter   182 measurements were not reported for all samples and it is unclear whether differences in the size of deposits between the impactor and filter samples was accounted for during immersion freez-ing measurements, here I focus on the impactor samples for the immersion freezing data. I also assume that the freezing of a drop was caused by the presence of a single INP. The fraction of INPs > 1 µm was calculated for each sample in 0.1 °C intervals, and these values were then av-eraged over all samples. The average fraction of INPs > 1 µm is reported at -10.8 °C. Values are not reported at lower temperatures because of sample saturation.  Condensation mode freezing data was reported in Table 13 of Rosinski et al. (1986). Samples V–VII and IX–XI were used here as these report INP concentrations for all impactor stages, one after filter, and for particles > 1 µm. Although not reported in Table 13, the INP con-centrations on the second after filter are assumed to equal those found on the first after filter as instructed in the text. INP concentrations missing from Table 13 were calculated by linear inter-polation where possible. The fraction of INPs > 1 µm was first determined for each sample in 1 °C intervals, and then averaged over all samples. The average fraction of INPs > 1 µm is report-ed at -5 to -6 °C as this is the lowest temperature where data is available for all particle sizes in all samples. G.3 Rosinski et al. (1988)  Rosinski et al. (1988) measured the INP size distribution over the Gulf of Mexico by first size selecting aerosol particles with an Andersen cascade impactor with after filters and then ana-lyzing these samples with a dynamic developing chamber. Five size cuts were used for size se-lection: > 4.5, 3.1, 1.0, 0.4, and 0.1 µm. Figures 2 and 5–7 of that study presented INP concentra-tions for the condensation freezing mode in twenty samples.    183  The fraction of INPs > 1 µm was determined in 1 °C intervals within each sample, and then averaged over all samples. In this analysis, sample 1 from August 6, 1986 was excluded as data was missing for particle sizes > 3.1 µm. The average fraction of INPs > 1 µm is reported over -15 to -16 °C. Values were not calculated for lower temperatures due to sample saturation. G.4 Berezinski et al. (1988)  The size distribution of INPs active in the condensation nucleation mode over Eastern Europe was determined by Berezinski et al. (1988). Aerosol particle samples were first collect by a cascade impactor with size cuts of 100, 30, 10, 1.0, and 0.1 µm and then analyzed using a thermal diffusion chamber and microscope. Data is presented in Table 1 of that study at freezing temperatures of -8, -10, -12, -15, and -20 °C. Data was used directly from Table 1 to determine the average fraction of INPs > 1 µm. To match the conditions used in this study, the average fraction of INPs > 1 µm is reported for temperatures of -15 and -20 °C. G.5 Santachiara et al. (2010)  Santachiara et al. (2010) collected size-resolved aerosol samples on filters by passing ambient air through various sampling heads with size cut-points of either 1, 2.5, or 10 µm. The total suspended particulate was also collected. Aerosol particle samples were then analyzed in a dynamic developing chamber to determine the concentration of INPs active in the condensation mode of freezing. Table 3 of that study presented the fractions of INPs < 1 and < 2.5 µm, which were subtracted from unity here. Averaged values between -17 and -19 °C are reported. G.6 Huffman et al. (2013)   The size distribution of INPs at a forest site in Colorado was measured by Huffman et al. (2013) using an early iteration of the MOUDI-DFT used in this study. Figure 4 of that study pre-  184 sented INP concentrations as a function of size, which I used to calculate the average fraction of INPs > 1 µm. As was done in Huffman et al. (2013), INP values are reported separately for sam-ples collected during rainfall and samples collected during dry weather. I report the average frac-tion of INPs > 1 µm at -15 to -20 °C for both sampling conditions. G.7 Other studies  Two additional studies reporting INP sizes have not been included here; Bigg and Hopwood (1963) because INP size was calculated based on several assumptions that were not confirmed, and Rosinski et al. (1987) because only the onset freezing temperature was given for each experiment. 

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