Rivers As Sources of Freshwater Ice-Nucleating Particles

RIVERS AS SOURCES OF FRESHWATER ICE-NUCLEATING PARTICLES

Kathryn Knackstedt

A Thesis

Submitted to the Graduate College of Bowling Green State University in partial fulfillment of the requirements for the degree of

MASTER OF SCIENCE

August 2017

Committee:

Robert McKay, Advisor

George Bullerjahn

Paul Morris

iii ABSTRACT

Robert McKay, Advisor

Ice nucleating particles (INP) are a neglected, but integral component of the water cycle.

Preliminary evidence is presented showing that rivers possess high numbers of warm temperature biological INP and that these may become airborne. Whereas recent efforts characterizing marine INPs are beginning to inform circulation models to predict weather patterns, the contribution of freshwater INPs to the water cycle has been largely overlooked. Evidence is presented showing that rivers possess high numbers of warm temperature (≥ -10 °C) INP whose abundance may be several orders of magnitude higher than marine sources. Focusing on the

Maumee River, situated in a predominantly agricultural watershed and which serves as the largest tributary to Lake Erie, a seasonal analysis of surface water INP revealed a strong positive relationship between INP abundance and river discharge with INP abundance varying by three orders of magnitude between low river discharge in summer and early fall and high discharge in winter and spring. Assessing the potential of INP to become aerosolized, INP abundance was consistently higher in air sampled near engineered features promoting turbulence compared to controls located away from direct influence of the river. The analysis also indicates that the vast majority of the INP in river surface waters are subcellular and so not attributable to known INP classes. Apart from seasonally-resolved surveys of the Maumee River, a small focused study was conducted at the University of Michigan Biological Station (UMBS) Stream Research Facility.

This served as a way to engineer different turbulent river features on a smaller scale to determine aerosolization of warm temperature INP. Combined with recent surveys of other major US rivers, there is growing consensus that the presence of abundant warm temperature INP is a common, if not ubiquitous feature of fresh water systems.

iv ACKNOWLEDGMENTS

Firstly, I would like to thank Dr. McKay for his help as my advisor. I have benefitted so much from the opportunity to work with him in his lab and receive advice and guidance to help me to pursue my Master’s degree. I would also like to thank the members of my committee, Dr. George Bullerjahn and Dr. Paul Morris for their support and knowledge that helped me complete this project. Special thanks are given to Dr. Bruce Moffett and Dr. Tom Hill for their contributions providing training in sampling approaches and assay of freshwater INPs. I would also like to thank all the members, most notably Liz Glasgo and Laura Reitz, of the

McKay and Bullerjahn labs for their support throughout my time in Bowling Green. I thank Dr.

Paul Moore and his colleagues at the UMBS Stream Facility for assistance with artificial stream design and construction. I also extend thanks to colleagues at the Leibniz Institute for

Tropospheric Research (Leipzig, Germany) for assistance with sample analysis toward identification of the source of INP from the Maumee River. Funding for this project was provided by the National Science Foundation under grant no. DEB-1354707 to Dr. McKay.

TABLE OF CONTENTS

Page

CHAPTER 1: INTRODUCTION ...... 1

1.1 Specific Aims ...... 1

1.2 Ice Nucleation ...... 2

1.3 Biologic INP ...... 3

1.4 Aqueous INP ...... 4

1.4.1 Marine INP...... 4

1.4.2 Freshwater INP ...... 7

1.4.3 Aerosolization and Cloud Formation ...... 10

CHAPTER 2: METHODS ...... 13

2.1 Field Surveys – Overview ...... 13

2.2 Sample Collection and Analysis ...... 17

2.3 INP Analysis and Characterization ...... 18

2.4 Statistical Analysis ...... 20

CHAPTER 3: Results……………………………………………………………………… 21

3.1 INP Analysis ...... 21

3.1.1 Maumee River ...... 21

3.1.2 Aerosolization of INP ...... 29

3.1.3 University of Michigan Biologic Station (UMBS) ...... 32

CHAPTER 4: DISCUSSION ...... 39

4.1 INP Analysis ...... 39

4.2 Conclusions ...... 44

REFERENCES ...... 46

vii

LIST OF FIGURES

Figure Page

1 Sea surface microlayer … ...... 6

2 INP abundance in seawater vs. freshwater ...... 8

3 Particle residence time in air ...... 12

4 Schematic of UMBS artificial stream ...... 14

5 Air filters at Tahquamenon Falls ...... 16

6 March 2017 INP abundance of Maumee River ...... 22

7 INP vs river km on March 26, 2016 ...... 25

8 Discharge vs INP level in the Maumee River ...... 26

9 Principal component analysis of INP data ...... 27

10 Heat treatment trials and effectiveness of heat treatments...... 28

11 Water levels at Mary Jane Thurston Park ...... 31

12 Tahquamenon Falls and Kathleen Dam compared to Maple River ...... 35

13 Big Riffles air and water samples at UMBS ...... 36

14 Maumee air filters vs Input and Fall UMBS ...... 38

15 INP signature analysis of Maumee River ...... 43

viii

LIST OF TABLES

Table Page

1 Sampling locations by latitude and longitude ...... 17

2 Maumee River INP abundance from monthly sampling ...... 23

3 Size fractionation of INP at Mary Jane Thurston and Miltonville...... 29

4 Weir air filters seasonal INP levels ...... 30

5 INP abundance at UMBS ...... 33

6 Air filter INP abundance at UMBS ...... 33

CHAPTER 1: INTRODUCTION

1.1 Specific Aims

Evidence from recent surveys is presented showing that rivers possess high numbers of warm temperature (≥ -10 °C) biological ice nucleating particles (INPs) whose abundance is several orders of magnitude greater than in oceans.

General circulation models examine variables such as aerosols, clouds and radiative properties of clouds to predict climate (Seinfeld et al. 2016). To better constrain climate predictions, sources, abundance and properties of INPs are needed to appropriately represent aerosol-cloud interactions in these models. While recent efforts characterizing marine INPs

(Wilson et al. 2015; DeMott et al. 2016; McCluskey et al. 2016) are beginning to inform such models, the contribution of freshwater INPs has been overlooked.

The lack of rigorous investigation of fresh waters for INP may reflect bias based on the small footprint of individual systems thus deemed of little influence on weather patterns even regionally. However, preliminary data demonstrate that surface waters from rivers possess several orders of magnitude more INP than do marine systems. Therefore, despite their small surface area (0.2% that of the oceans; Downing et al. 2012), their influence on atmospheric processes is likely significant.

With this thesis, I seek to build upon preliminary data providing snapshots of riverine

INP abundance. These objectives are furthered by:

I. Characterizing the spatial- and temporal distribution of freshwater INP along land

use gradients ranging from predominantly forested- to agricultural watersheds

II. Characterizing INP in terms of size, stability and molecular composition 2

III. Determining the potential for INP to become aerosolized

Based in part on preliminary data, I hypothesize that warm temperature (≥ -10 °C) river

INP are of biological origin and that their abundance will vary seasonally and that this variability will reflect changes in river discharge. Further, it is hypothesized that river INP become airborne and that INP abundance in river aerosols will be greater than INP abundance over land.

1.2 Ice Nucleation

Biological ice nucleation is the process by which living organisms promote ice formation on their cell surface using a protein that gives water molecules a place to bind and form an ice crystal. Pure water does not freeze at 0 °C, rather it must be supercooled to temperatures closer to -40 °C before it will freeze (Hobbs 1974, Debenedetti and Stanley 2003). This is because it is thermodynamically unfavorable for it to freeze at 0 °C, as it is unlikely for the water molecules to align and form a crystal lattice structure without a template for binding (Schulli et al. 2010).

The reason water freezes at warmer temperatures is because of impurities in the water that provide a binding surface to promote ice formation. Most of these impurities are abiotic, such as minerals like potassium feldspar (Zolles et al. 2015). Some microbes and organisms are able to create proteins that can form a binding site for water particles to facilitate the formation of ice

(Graether and Jia 2001, Atkinson et al. 2013, Hill et al. 2014). This process is found to contribute to many different phenomena from cloud formation, ice cover, and bacterial pathogenicity.

Abiotic particles including dust, soot and minerals have been long thought to be the backbone of cloud formation (Buseck and Posfai, 1999). This is reflected by current cloud modeling systems such as the Global Model of Aerosol Processes (GLOMAP) that simulates the influence of sulfate, sea salt, black carbon, particulate organic matter (POM) and dust in cloud 3 formation (Spracklen and Heald 2014). More recently, consensus is growing that biological particles can affect ice formation, a process that is more efficient compared to abiotic particles, or even be better candidates for cloud formation at low altitudes due to their ability to promote ice nucleation at relative warm temperatures of ≥-10 °C (Spracklen and Heald 2014). Direct spectrophotometry of high altitude aerosols over Wyoming found that ~50% of the sample was dust and ~33% was biologic material (Pratt et al. 2009). Dust particles are the largest terrestrial source of abiotic ice nucleating particles. These dust particles can also carry biotic particles into the atmosphere such as bacteria and fungal spores. Another potent terrestrial source is biologic nano-INP from ubiquitous fungal genera such as Fusarium that can collect in soil and form a reservoir of particles that can become aerosolized (O’Sullivan et al. 2015).

1.3 Biologic INP

While there are many abiotic compounds that can serve as a nucleating site for water molecules to accumulate, there are also biotic molecules that can accomplish the same result and do so more efficiently and at warmer temperature. Some bacteria do this by coating their cell walls with membrane-bound ice nucleating proteins classified as lipoglycoproteins (Widehem and Cochet 2003). The most commonly studied ice nucleation protein originates from the ubiquitous plant pathogen Pseudomonas syringae which consists of two non-repetitive N- and C- terminal domains and a highly repetitive central domain (Graether and Jia 2001). Water binds to a large repetitive TXT motif (Threonine-X-Threonine) where X is any amino acid (Graether and

Jia 2001). These TXT repeats form a flat disk with the disks stacked upon each other to form the ice nucleating crystal. The flat form of the disk gives water a large binding area for attachment, thus promoting ice formation. 4

Bacteria are not the only biologic INPs. Lichen and some fungi are also strong ice nucleators. Several genera of lichen including Rhizoplaca, Xanthoparmelia, and Xanthoria have been shown to have ice nucleation activity at temperatures > -2.3 °C (Keift and Ahmadjian

1989). The ability to form ice is thought to help lichen acquire water by freezing moisture from the air so that it accumulates on the lichen (Keift and Ahmadjian 1989). Lichen such as

Rhizopluca chrysoleuca have similarly sized nucleation sites compared to P. syringae. At temperatures -3 to -4 °C, nucleation sites were estimated at ~1.0 × 10-6 Da (Keift and Ruscetti

1992). Both species follow the trend that as temperature increases linearly, ice nucleation site size increases logarithmically (Keift and Ruscetti 1992). Some fungi are likewise found to ice nucleate by proteins on mycelia or spores. A ubiquitous soil fungus Mortierella alpina has ice nucleating activity between -5 to -6 °C (Frohlich-Nowoisky et al. 2014). Aside from fungi directly producing INP through their mycelia, fungal spores also have ice nucleation activity and are found in the atmosphere. Mycelia can accumulate in soil and enter into the atmosphere through the same mechanism as dust particles, but spores are even smaller and more easily released into the atmosphere. Rust fungi spores can reach 6 km into the atmosphere and are found in high quantities (2-12 L-1of air) over wheat fields (Haga et al. 2013). At low altitudes, fungi and bacteria are better ice nucleators than abiotic particles, such as soot.

1.4 Aqueous INP

1.4.1 Marine INP

Oceans are the largest source of water on the planet dwarfing liquid freshwater sources.

Sea spray is a global contributor to the reservoir of atmospheric particles and sea spray aerosols

(SSA) contain INP that contribute to cloud formation (DeMott et al. 2016). The sea surface microlayer is the boundary of exchange between the atmosphere and oceans and thus the point 5 from which INP can enter the atmosphere from the oceans. These SSA contain INP that are both abiotic and biotic, but studies have pointed to biologic INP as being a larger contributor to INP that lead to cloud formation (DeMott et al. 2016). Bubbles can scavenge biotic particles and trap them so that they rise through the water column (Fig. 1).

Figure 1: Sea surface microlayer. This layer interactions with the atmosphere (Wilson et al.

2015).

The bubbles then enter the sea surface microlayer which facilitates their aerosolization

(Wilson et al. 2015). Earlier studies showed that sea water from different sampling locations in both the Pacific and Atlantic oceans have roughly the same INP levels and freezing temperatures, except when there was a phytoplankton bloom (Schnell and Vali 1975). The sample associated with a phytoplankton bloom started freezing at -8 °C whereas the other oceanic samples started freezing at -16 °C (Schnell and Vali 1975). In a recent study, a simulated phytoplankton bloom increased the INP numbers up to 50 times when compared with non-bloom conditions when tested at -26 ° to -30 °C (DeMott et al. 2016).

1.4.2 Freshwater INP

Freshwater represents only 2.5% of Earth’s water of which only about 1.2% is accessible liquid surface water (Perlman 2016). At first glance this would seem to suggest that freshwater is an insignificant source for INP contribution to the atmosphere, but recent data show freshwater to possess much higher abundance of INP compared to marine sources which make up for its smaller global foot print (Moffett 2016; Moffett and Hill, unpublished). Indeed, preliminary results from a freshwater to seawater sampling gradient demonstrates that warm temperature (≥-

10 °C) INP are up to 4-fold higher in abundance than seawater (Fig. 2). This makes freshwater potentially an important source to better understand INP and how they contribute to cloud formation.

Figure 2: INP abundance in seawater vs. freshwater. Comparisons between seawater INP abundance (top panels;DeMott et al. 2016) compared to a freshwater to seawater gradient along the River Gwaun (Wales, UK; Moffet 2016). Key to bottom panel: 1– 4, marine; 5, brackish; 6 –

12, fresh water. Freshwater warm temperature (-10 oC) INP abundance is 2-3 orders of magnitude higher than in seawater.

Current climate models such as general circulation models (GCMs) are used to predict changes in cloud formation. They currently examine variables such as aerosol, clouds, and aerosol-cloud radiative properties but do not factor relations between them (Seinfeld et al. 2016).

This means that the interaction between freshwater aerosol and cloud formation is currently under-evaluated in climate predictive models, making it an important study point.

The most important freshwater sources for INP and aerosol study are lakes and rivers.

Freshwater caught in ice caps, glaciers and underground aquifers cannot contribute directly to the water cycle until the water is released, similar to how deep ocean water is sequestered for many years from actively changing in the water cycle. This leads us to look at flowing water that interacts directly with the atmosphere. In addition to lakes and rivers contributing INP into the atmosphere as aerosols, there is also literature to describe a phenomenon for lake ice cover having contributions from ice nucleating organisms. Lakes host a vast community of different microbes that can vary greatly from summer to winter. For lakes and rivers that have ice cover, it is usually a seasonal phenomenon. Ice cover, especially in concert with snow, restricts penetration of light into the water column and is thus detrimental to photosynthetic organisms. It is proposed that diatoms overcome light limitation by physically attaching to the underside of ice

(D’souza et al. 2013).

Lakes can be important sources of aerosols, especially associated with wave action

(Axson et al. 2016). Rivers and streams can be fast flowing bodies of water with expected high levels of aerosolization. Since they are shallower than lakes and flow over large areas they have more surface area for the water to break over the stream bed, causing more bubble bursting and aerosol to be released. Rivers also manage to expose more of their water content to the atmosphere then lakes. 10

1.4.3 Aerosolization and Cloud Formation

Rivers and streams have many different areas where water flow varies and interacts with the atmosphere at different rates. Aerosolization is different depending on the conditions of the stream or river. A higher flow rate with more turbulance increases the amount of bubble bursting. Aerosols directly contribute to cloud formation and weather with current GCM modeling systems able to differentiate different types of aerosol and predict the effect of aerosol on cloud cover (Kirkevag et al. 2008). One way particles can become aerosolized is through bubble bursting, which allows both abiotic particles and microbes access to the atmosphere.

Experiments in the early 1970’s showed that bacteria such as E .coli can be aerosolized by bubble bursting (Baylor et al. 1977). They can also ascend by means of wind lofting and solar heating (Herridge 2013). Cloud formation is mainly a physical process where water molecules accumulate on a particle allowing precipitation to descend from the atmosphere.

Despite it being an extreme environment, microbes are abundant in the Earth’s atmosphere. Bacteria are ubiquitous with cell abundance exceeding 1 × 104 m-3 (Burrows et al.

2009a). With this calculation, the estimated mass of the bacterial component in the atmosphere is

40-1800 Gg (Burrows et al. 2009b). These atmospheric bacteria are also very diverse. Over 300 different bacterial taxa were found 10 km above the earth’s surface over the Gulf of Mexico

(DeLeon-Rodriguez et al. 2013). This significant amount of bacteria shows that biotic particles play a large part in the water cycle. Changes in cloud formation have shown to be of increasing importance to climate change, since a warming atmosphere would feedback into larger thicker clouds expected to have a cascading effect on climate (Storelvmo et al. 2015). To measure how long bacteria or other microbes can be resident in the atmosphere, the aerodynamic diameter is calculated. Aerodynamic diameter is the diameter of an equivalent spherical particle of the same 11 diameter with a density of 1 g cm-3 (Jones and Harrison 2004). Since most bacteria range in size from 0.2 – 10 μm, they have a small aerodynamic diameter which allows them to effectively travel for hours to weeks in the atmosphere (Fig. 3). For some of the smallest microbes and viruses, they can have a virtually infinite residence time in the atmosphere until something causes them to descend. Being higher in the atmosphere exposes them to more UV light which can cause damage, so being able to descend from of the atmosphere can be essential to stop UV damage. Ice nucleation is a way they can effectively exit the atmosphere back to environments more suitable for growth and to escape UV damage.

Figure 3: Particle residence time in air. This is determined by size in still air and turbulent air

(Baron, http://www.cdc.gov/niosh/topics/aerosols/pdfs/Aerosol_101.pdf) 13

CHAPTER 2: METHODS

2.1 Field Surveys – Overview

The spatial- and temporal abundance of INP along land use gradients ranging from predominantly forested to agricultural watersheds was characterized. Seasonal surveys were focused on the Maumee River whose watershed is predominantly agricultural. Sampling was conducted at roughly monthly intervals from shoreline river access points (Table 1) extending from Independence Dam (river km 97) to the Maumee River Yacht Club (river km 14) For a forested watershed, rivers in northern Michigan were surveyed, including the Maple River and the Tahquamenon River and Falls.

For more intensive study of the aerosolization process of INP, an artificial stream created at the University of Michigan Biological Station (UMBS; Pellston, MI) was tested during July-

August 2016. UMBS hosts the Stream Research Facility with water from the Maple River, a third-order, low-nutrient stream (Stelzer and Lamberti 2001) serving as a source for artificial streams. For this study, an artificial stream was created having dimensions 4.2 m long, 0.6 m wide, and 0.2 m high and consisting of five unique sections to promote varying turbulence (cf.

Lahman and Moore 2015) (Fig. 4). The sections consisted of the water inflow which is the head of the stream, big riffles created by using medium to large river rocks, small riffles using pea gravel which also served as the substrate for the stream bottom, a run where the water was unobstructed, and a small waterfall where the water empties from the stream into the drain (Fig.

4).

Figure 4: Schematic of UMBS artificial stream. This artificial stream was fed with water from the Maple River, and that water entered from a pipe labeled inflow, with the water exiting into the drain at the end of the structure. Top right; two test air filters above run location. Bottom right; run air filters with control filters in background on white barrel. 15

This design allowed for five unique areas of varying turbulence to promote production of aerosols along a gradient of slope. Even the undisturbed run should show higher levels than the control based on studies in marine systems that show any bubble over ~43 μm can create a jet of aerosol, before hitting a critical limit (Lee et al. 2011). Other studies have shown that when large bubbles burst, they not only create a jet; they also create a ring of smaller daughter bubbles that can also produce a jet (Brumfiel 2010). Surface water samples to test for INP were collected daily and air filters collected spray originating from the five different sample areas daily. A control filter was placed 6 m away facing in the opposite direction from the stream to ascertain levels of INP in the air and outside the immediate influence of the Stream Research Facility.

While at this facility, two additional areas were also surveyed, Lake Kathleen Dam and

Tahquamenon Falls (Fig. 5). For Lake Kathleen Dam, the overflow where the air sample was taken was about a 3 m drop by 4.5 m wide creating a large area of spray. Tahquamenon Falls, a prominent feature in Michigan’s Upper Peninsula, are more than 60 m across and drop 14 m.

Both the dam and the falls are large scale turbulence features that can be compared to the small, although controlled site at the UMBS Stream Research Facility.

Figure 5: Air filters at Tahquamenon Falls. Similar to the artificial stream two test air filter are set run, in the bottom left corner along the grass, orange arrow points to filter. Close up of test filters at the edge of land above Tahquamenon falls, with orange arrow pointing to filters. 17

Table 1: Sampling locations by latitude and longitude.

Site Lat No Long Wo Mary Jane Thurston 41.4122 -83.8758 Bridge at Waterville - MB4 41.5002 -83.7139 Miltonville Fishing Access 41.4894 -83.7149 Maumee River Yacht Club 41.6125 -83.5834 Independence Dam 41.2915 -84.2704 Tahquamenon Falls 46.5751 -84.7508 Maple River 45.5648 -84.7512 Stream Lab 45.5639 -84.7512

Lake Kathleen Dam 45.5293 -84.7752

2.2 Sample Collection and Analysis

Prior to sampling at each site, we measured physico-chemical parameters of surface waters using a YSI Model 600QS Multiparameter Water Quality Sonde (YSI, Yellow Springs,

OH). Where possible, samples were collected from surface waters by placing an open bottle such that the opening was just submerged. In this way, there is a sampling bias toward collecting the surface microlayer. Samples were processed within 4 h for analysis of chl a biomass and both particulate- and dissolved (< 0.2 μm) nutrients. In addition, seston for DNA extraction was collected using Sterivex cartridge filters (0.22 μm; EMD Millipore, Billerica, MA). DNA extraction followed the manufacturer’s protocol for the Powerwater® Sterivex™ DNA isolation kit (MO BIO Laboratories, Inc., Carlsbad, CA). 18

Additional data were acquired from U.S. Geological Survey (USGS) gauging station

04193500 (41° 30′ 00″ N, 83° 42′ 46″ W) near Waterville, OH (river km 33). The station provides a record of river discharge and gauge height extending to 1939

(https://waterdata.usgs.gov/nwis). Additional nutrient data were collected at the City of Bowling

Green water intake (river km 37) as part of the Tributary Loading Program maintained by the

National Center for Water Quality Research (NCWQR) at Heidelberg University. This site is located 3.2 km upstream of USGS gauging station from which discharge data were obtained.

The potential for INP to become aerosolized was measured from air samples collected using sterile analytical filter units (150 mL capacity; Thermo Fisher Scientific, Waltham, MA) fitted with H2O2-cleaned 0.2 μm polycarbonate membranes. Air was drawn through the filters over 4-12 h at 4 L min-1 with a Gilian BDXII air sampling pump (Sensidyne, LP, St. Petersburg,

FL). At each sampling location, two pumps were used with one pump positioned in proximity to a turbulence feature (e.g. weir, waterfall) and a control unit located away from direct influence of the water source. At the UMBS Stream Research Facility, the number of pumps was doubled with two controls and two tests for each sampling effort.

2.3 INP Analysis and Characterization

To assay INP abundance and activity, we used an MJ Research (Waltham, MA) PTC-200 thermal cycler modified for cooling to ~20 °C below ambient (Moffett 2016). Thus, if used in a cold room or refrigerator, temperatures between -15 to -20 °C could be achieved. A 96 well plate was filled with sample water as well as a control of 0.02 μm-filtered milliQ water and progressively cooled, stopping for two min at each temperature between -2 to -20 °C. At each time point, the lid of the machine was lifted and the number of frozen wells counted. After the initial run, the plate was heated in the thermal cycler at 95 oC for 20 min which served to 19 denature proteins including those involved in biological ice nucleation. The plates were then re- run to assess the fraction of INP attributed to biotic components.

For air filtered samples, the filters were removed from the filtration unit, inserted into a

50 mL Falcon tube and resuspended using 2 mL of control water with gentle shaking for 20 min.

Resuspended particles were then treated like conventional water samples and distributed into the

96 well plates. If all sample replicates froze before reaching -20 oC, then the sample was diluted and re-analyzed. Control wells that froze were subtracted from the sample in the calculation using the formula of Vali (1971).

Number of INP = [lnNo – lnN(T)]

No = total number of wells

N(T) = the number of wells unfrozen at temperature T

V = the volume of each well

The same calculation was performed on the heated (i.e. denatured samples) wells and the two compared to assess the fraction of INP that were biological in origin.

Size-selected filtration (0.22 μm) was used to distinguish between cell-associated and subcellular INPs. Subcellular macromolecules were further characterized by use of centrifugal filter units (3 kDa – 100 kDa). Amicon® Ultra-15 centrifugal filter devices (EMD Millipore

Corp., Billerica. MA) with different pore sizes were used to size fractionate the particles in the water samples. Units with molecular size thresholds of 3 kDa, 30 kDa, and 100 kDa were used.

The water samples collected from these sampling locations were pre-filtered using Sterivex 20 cartridges (0.22 μm) prior to loading the centrifugal filter units. This allowed the smaller pore filters to remain unclogged by large particles in the original water sample. Filter units were centrifuged for 1 h at 5,000 g using a JA-20 fixed angle rotor and the filtrate was collected and used to conduct freezing assays as described earlier. The assays were run in the same way as other water samples then compared with the unfiltered original sample water to determine the size difference contribution to INP amounts.

2.4 Statistical Analysis

In order to quantify the changes in INP across seasons in the Maumee River, a principal component analysis (PCA) was performed. PCA uses orthogonal transformation to convert observations or possibly correlated values into linear values called principal components. To do this analysis, the data were put through a summary of statistics that looked for: the number of observations, any missing observations, minimum, maximum, mean, and standard deviation.

Then a correlation matrix was performed, in this case a Pearson (n) correlation matrix.

Eigenvalues were then assigned for each observation, with only eigenvalues above 1 being significant contributors (Jolliffe 2014). Eigenvalues were then converted to eigenvectors that were plotted alongside the factor loading data to create a component loading plot (Smith 2002).

The observations with the two highest eigenvalues are plotted on an x and y axis with eigenvectors of the observations overlaid. 21

CHAPTER 3: RESULTS

3.1 INP Analysis

Using the calculation of Vali (1971), profiles of INP abundance related to freezing temperature were generated (Fig. 6). Profiles represent results obtained following several dilutions of the sample since samples in undiluted water usually freeze at the warmer temperatures tested.

3.1.1 Maumee River

Maumee River sites were sampled initially in October 2015 and then on a monthly basis from March 2016 through April 2017 (Table 2). Except for two sampling dates and locations, warm temperature (-10 oC) INP were identified (Table 2). On average, warm temperature INP represented 42% of total INP present at Mary Jane Thurston. For Miltonville the average warm temperature INP represented 23% of total INP at that location. The Maumee Bridge had an average of 32% compared to total INP for that sampling spot. Amongst total warm temperature

INP, heat resistant INP averaged <0.01%,

Figure 6: March 2017 INP abundance of Maumee River. INP level vs freezing temperature at the weir adjacent to Mary Jane Thurston State Park (river km 55), Miltonville Fishing Access site

(river km 35.5) and SR 64 bridge (MB4; river km 32).

Table 2: Maumee River INP abundance from monthly sampling. The maximum warm temperature (10 oC) INP and maximum INP. Heat resistant INP (% Heat Resistant) is also displayed after the INP amount.

Sample Date INP -10oC % Heat Resistant -10oC INP Max % Heat Resistant Max Mary Jane Thurston 4/13/2016 1673.98 0.0043 5753.64 0.0505 Miltonville 4/13/2016 0.00 0 11631.51 0.0060 Maumee Bridge 4/13/2016 2100.00 0.0025 2800.00 0.0119 Mary Jane Thurston 5/13/2016 0.00 0 13862.94 0.0104 Miltonville 5/13/2016 645.39 0.0250 20794.42 0.0099 Maumee Bridge 5/13/2016 2772.59 0 11631.51 0.0149 Mary Jane Thurston 6/8/2016 2772.59 0.0012 13353.14 0.0702 Miltonville 6/8/2016 575.36 0.0028 20763.94 0.0250 Maumee Bridge 6/8/2016 980.83 0.0016 20763.94 0.0250 Mary Jane Thurston 7/20/2016 207.64 0.0078 4700.04 0.0737 Miltonville 7/20/2016 64.54 0 5753.64 0.0359 Maumee Bridge 7/20/2016 133.53 0 5753.64 0.0505 Mary Jane Thurston 9/1/2016 207.64 0 9808.29 0.0147 Miltonville 9/1/2016 64.54 0 13862.94 0.0104 Maumee Bridge 9/1/2016 64.54 0 13862.94 0.0104 Mary Jane Thurston 10/14/2016 693.15 0 20794.42 0.0140 Miltonville 10/14/2016 575.36 0 27725.89 0.0151 Maumee Bridge 10/14/2016 575.36 0.0028 27725.89 0.0151 Mary Jane Thurston 11/18/2016 287.68 0 8266.79 0.0063 Miltonville 11/18/2016 470.00 0.0071 8266.79 0.0113 Maumee Bridge 11/18/2016 470.00 0.0034 6931.47 0.0104 Mary Jane Thurston 12/14/2016 2076.39 0 3746.93 0.0192 Miltonville 12/14/2016 645.39 0.0250 4700.04 0.0153 Maumee Bridge 12/14/2016 4700.04 0 8266.79 0.0113 Mary Jane Thurston 1/9/2017 167397.64 0 207944.15 0.0083 Miltonville 1/9/2017 57536.41 0 98082.93 0.0096 Maumee Bridge 1/9/2017 82667.86 0 167397.64 0.0043 Mary Jane Thurston 2/15/2017 20763.94 0 82667.86 0.0210 Miltonville 2/15/2017 20763.94 0 138629.44 0.0068 Maumee Bridge 2/15/2017 47000.36 0 138629.44 0.0104 Mary Jane Thurston 3/21/2017 28768.21 0 57536.41 0.0090 Miltonville 3/21/2017 20763.94 0 116315.08 0.0029 Maumee Bridge 3/21/2017 13353.14 0 57536.41 0.0163 Mary Jane Thurston 4/10/2017 13353.14 0 138629.44 0.0068 Miltonville 4/10/2017 37469.34 0 138629.44 0.0125 Maumee Bridge 4/10/2017 28768.21 0 116315.08 0.0124

Whereas spatial abundance of INP varied only modestly along the main stem of the

Maumee River (Fig. 7), seasonal abundance of warm temperature (-10 °C) INP varied dramatically. Warm temperature INP were most abundant during winter and early spring and least abundant in summer and fall (Table 2). This pattern showed tight coupling to seasonal changes in river discharge (Fig. 8), a relationship confirmed by subjecting INP abundance data to principal component analysis (PCA) (Fig. 9). PCA confirmed that discharge was the most relevant parameter to predicting INP abundance. Other parameters tested included temperature, conductivity and nutrients.

Heat-denaturing trials were also performed in order to test whether INP activity of filtered samples could be inactivated by boiling. This treatment indicated that the samples were inactivated, suggesting they were composed of biologic INP. Each sample tested in this experiment was heated at 95 oC as determined to be the optimum denaturing temperature (Fig.

10). For a typical sampling month, relatively few warm temperature INP were abiotic (Fig. 10).

Figure 7: INP vs river km on March 26, 2016. Warm temperature ( -10 °C) INP levels were plotted against Maumee River km. Regardless of location along the main stem of the river, INP were of similar abundance.

Figure 8: Discharge vs INP level in the Maumee River. Variability of warm temperature ( -10 °C)

INP abundance is related to seasonal cycles in Maumee River discharge. INP abundance is conveyed as the mean (± S.D.) of 5 samples collected between river kms 34 (SR 64 bridge) – 51

(Mary Jane Thurston Park) whereas discharge data comes from USGS gauging station 04193500 located at river km 34.

Figure 9: Principal component analysis of INP data. The observations of the analysis were

o -1 discharge (cfs), temperature ( C), conductivity (μS cm ), Cl, SO4, NO3, Si, soluble reactive phosphorus, total phosphorus, and total nitrogen (all mg L-1). INP data taken from Mary Jane

Thurston Park are represented for the sampling dates.

Figure 10: Heat treatment trials and effectiveness of heat treatments. Top panel: Effect of heating at different temperatures on INP freezing profile and abundance. Bottom panel: Effect of heating at 95 °C on INP freezing profile and abundance at 3 sampling sites on the Maumee

River. 29

Characterization of Maumee River INP continued with size fractionation analysis to determine if whole cells or subcellular macromolecules were primary contributors to INP abundance. Size fractionation analysis showed that most biologic INP passed through a Sterivex cartridge filter (0.22 μm). For example, a mid-January sample from Mary Jane Thurston Park demonstrated a decline in warm temperature INP abundance of only 17% after the sample had been passed through the cartridge filter thus indicating that the majority of INP found in this study may be macromolecules and not whole cells (Table 3). Further, processing samples with different molecular weight cut-off filter units showed that INP abundance declined dramatically with only a handful of INP present in the filtrate of the 100 kDa centrifugal filter device (Table

3). Even fewer INP were evident in the filtrates of the 30 kDa and 3 kDa units (Table 3).

Table 3: Size fractionation of INP at Mary Jane Thurston and Miltonville. Maximum warm temperature INP at 10 oC and maximum INP overall are displayed. Likewise, heat-resistant (95

°C) INP associated with each fraction are shown.

Sample Fraction Date INP/mL -10oC % Heat Resistant -10oC INP/mL Max % Heat Resistant Max Mary Jane Thurston 100kDa 12/14/2016 3.75 0 6.93 0.0075 Mary Jane Thurston 30kDa 12/14/2016 0 0.0071 0 0 Mary Jane Thurston 3kDa 12/14/2016 0 0 3.75 0.0250 Miltonville 100kDa 12/14/2016 0 0 4.70 0.0199 Miltonville 30kDa 12/14/2016 0 0 0.00 0 Miltonville 3kDa 12/14/2016 0 0.0121 1.34 0.0702 Mary Jane Thurston Sterivex 0.22 µm 1/19/2017 138629.40 0 207944.15 0.0045 Mary Jane Thurston 100kDa 1/19/2017 4.70 0 20.79 0.0045 Mary Jane Thurston 30kDa 1/19/2017 0.65 0.0043 4.70 0.0199 Mary Jane Thurston 3kDa 1/19/2017 0 0 3.75 0.0089 Miltonville Sterivex 0.22 µm 1/19/2017 57536.41 0 98082.93 0.0096 Miltonville 100kDa 1/19/2017 1.34 0 13.86 0.0052 Miltonville 30kDa 1/19/2017 0 0 6.93 0.0135 Miltonville 3kDa 1/19/2017 0 0 2.88 0.0116

3.1.2 Aerosolization of INP

To test for aerosolization of INP, an air pump was positioned adjacent to the weir at Mary

Jane Thurston Park (Grand Rapids, OH) with a control pump positioned inland in the forest 30 surrounding the park. This control was to ascertain abundance of INP in the air without direct contribution from turbulence attributed to the weir. At no time did INP abundance from control pumps exceed those placed adjacent to the weir (Table. 4). The abundance of INP collected on test filters increased depending on discharge, consistent with changes in turbulence over the weir

(Fig. 11). Differences in warm temperature INP and maximum INP were seen for both air samples and water samples. In water samples, the difference between warm temperature INP and maximum INP was somewhat muted, the differences were smaller. The average for all air test samples during the year showed 212% more INP in test filters than in control filters. For test air filter samples, the difference between warm temperature INP and maximum INP was much larger with an average of 1097% more maximum INP.

Table 4: Weir air filters seasonal INP levels. Both warm temperature INP and maximum INP values are relative to percent of abiotic INP (% Heat Resistant). Months with no data are indicated by nd (not determined) in the table.

Weir Test Filter Weir Control Filter Date INP -10oC % Heat Resistant INP Max % Heat Resistant INP -10oC % Heat Resistant INP Max % Heat Resistant 10/25/2015 13.00 nd nd nd nd nd nd nd 3/26/2016 0.16 nd nd nd 0.05 nd nd nd 5/13/2016 0.43 1.00 28.77 7.10 0 0 47.00 16.34 7/21/2016 0 0 6.45 1.00 0.43 0 0 0 9/1/2016 nd nd nd nd 0.00 0 16.88 8.13 10/14/2016 3.75 5.81 82.67 0 1.34 1.00 20.76 0 11/18/2016 2.08 1.55 98.08 7.35 0 0 116.32 4.20 12/14/2016 1.34 0 5.75 1.22 0 0 4.70 1.25 1/19/2017 0 0 8.27 2.87 0 0 6.93 2.41 2/15/2017 0.65 0 8.27 2.21 0 0 5.75 2.77 3/21/2017 0.65 0 9.81 1.70 0 0 4.70 1.63 4/10/2017 4.70 3.52 13.86 3.70 2.08 1.55 4.70 2.26 31

Figure 11: Water levels at Mary Jane Thurston Park. Pictures shown are from fall (October

2015), winter January 2017), and spring (May 2017). All three pictures represent typical seasonal discharge and turbulence at this location. 32

3.1.3 University of Michigan Biological Station (UMBS)

Surveys conducted at UMBS confirmed that engineered features can affect INP abundance in air samples. Minor variation was evident amongst warm temperature INP abundance in water samples collected from the five custom stream locations (Table 5). This is to be expected given the short length of the artificial stream, being less than 5 m long. Another reason they are nearly identical is because the artificial stream was fed by a single source. In contrast, differences based on sampling location were evident for air samples (Table 6). The average warm temperature INP for the Maple was 87 INP mL-1 compared to 209 INP mL-1 for the artificial stream, 875 INP mL-1 at Tahquamenon Falls, and 226 INP mL-1 at Lake Kathleen

Dam. These values were comparable to Maumee River samples taken from the dates 7/20/16 and

9/1/16 which were close to the time of the UMBS trip. The Maumee River average (All Maumee

River sites were used for the average including Mary Jane Thurston, Miltonville, and Maumee

Bridge) for warm temperature INP on those dates was 124 INP mL-1. This value was very similar to the Maple River and artificial stream since they average to 169 INP mL-1. For air filters the

INP amounts showed larger differences between sampling locations. The artificial stream showed virtually no differences in INP amount at different sampling locations because it is one small body of water with no differences in input or substrate. The inflow location had the highest amount of warm temperature INP on average with 3.1 INP L-1, while all other locations had an average INP L-1 of<1. For warm temperature air filters, the inflow amount was roughly half

(47%) of all total warm temperature INP from UMBS, with an average of 6.5 INP L-1 for all sites. The fall sampling location had the next most INP L-1 which was 14% of the average of all

UMBS warm temperature INP. 33

Table 5: INP abundance at UMBS. Sampling locations and warm temperature INP abundance at

UMBS and other locations in northern Michigan.

Sample Date INP/mL -10oC % Heat Resistant Maple River 7/28/2016 87.01 0 Maple River 8/7/2016 87.01 0 Artificial Stream 7/31/106 329.01 0.002 Artificial Stream 8/3/2016 108.85 0.005 Artificial Stream 8/4/2016 248.66 0 Artificial Stream 8/7/2016 151.57 0.005 Tahquamenon Falls 8/5/2016 875.50 0

Lake Kathleen Dam 8/2/2016 226.20 0.007

Table 6: Air filter INP abundance at UMBS. Air samples show both maximum warm temperature

INP (INP mL-1 -10 oC) as well as maximum warm temperature INP overall (INP mL-1 Max).

Due to laboratory constraints, UMBS samples reached a maximum sampling temperature of just

-14 oC compared with -20 oC for all other samples.

Test Filter Control Filter Sample Date INP -10oC % Heat Resistant INP Max % Heat Resistant INP -10oC % Heat Resistant INP Max % Heat Resistant inflow 7/28/2016 0 0 5.754 0.013 0 0 2.076 0.045 inflow 7/28/2016 0.645 0 9.808 0.010 0 0 1.335 0.012 inflow 8/1/2016 4.700 0 27.726 0.019 0 0 0.645 0 inflow 8/1/2016 6.931 0 27.726 0.012 0 0 0 0 big riffles 7/29/2016 0 0 4.700 0.007 0 0 0.645 0.025 big riffles 7/29/2016 0 0 1.335 0.054 0 0 0 0 big riffles 8/3/2016 1.335 0.012 5.754 0 0 0 0.645 0 big riffles 8/3/2016 0.645 0 2.877 0 0 0 0.645 0 small riffles 7/30/2016 0.000 0 2.076 0.016 0 0 0.645 0.025 small riffles 7/30/2016 0.645 0 0.645 0 0 0 0.645 0 small riffles (N) 8/3/2016 0 0 1.335 0.054 0.645 0 0.645 0.111 small riffles (N) 8/6/2016 0.645 0.025 1.335 0.012 0 0 2.076 0 run 7/31/2016 0 0 2.076 0.008 0 0 0.645 0 run 7/31/2016 0 0 0.645 0 0.645 0 0.645 0 run (N) 8/3/2016 2.076 0 20.794 0.014 0.645 0 0.645 0.111 run (N) 8/6/2016 0.645 0 5.754 0.013 0 0 2.076 0 fall 7/31/2016 0 0 3.747 0.014 0 0 2.076 0.016 fall 7/31/2016 0 0 1.335 0.012 0 0 1.335 0.012 fall (N) 8/3/2016 3.747 0 20.794 0.020 0.645 0 0.645 0.111 fall (N) 8/6/2016 0.000 0 8.267 0.011 0 0 2.076 0 Lake Kathleen Dam 8/2/2016 1.335 0 11.632 0.006 0 0 2.076 0.045 Lake Kathleen Dam 8/2/2016 0.645 0 9.808 0.010 0 0 1.335 0.012 Tahquamenon Falls 8/5/2016 0 0 3.747 0.014 0 0 1.335 0.025 Tahquamenon Falls 8/5/2016 0 0 2.076 0.016 0 0 1.335 0.012 34

Sampling trips to Kathleen Dam and Tahquamenon Falls had comparable results to each other and to the Maple River (Fig. 12). Both locations are areas with large waterfalls, with

Tahquamenon being the third most voluminous vertical waterfall east of the Mississippi River.

The INP concentrations on UMBS air filters compared to Maumee weir air filters were similar

(Fig. 13). A technical constraint on the results compiled at UMBS and elsewhere in northern

Michigan, compared to the Maumee River, is the fact that these samples had a maximum freezing of -14 oC whereas all other samples had a maximum freezing of -20 oC (Fig. 14). This is because the ambient temperature can prevent the PCR machine from reaching its maximum low temperatures. During the UMBS sampling trip the PCR machine was kept in a poorly-chilled refrigerator instead of a 4 oC cold room and the temperature difference reflects the end results.

Maple river, Kathleen Dam, and Tahquamenon falls Air Filters and Water Samples

10000 maple river 7/28/16 maple river 8/7/16 Kathleen air filter 1 8/2/16 1000 Kathleen air filter 2 8/2/16

air)

-1 Kathrleen water 8/2//16 Tahquamenon air filter 1 8/5/16 Tahquamenon air filter 2 8/5/16

w ater/ mL 100 Tahquamenon water 8/5/16

-1 -1

Ice Nucleating Particles (mL Particles Nucleating Ice 1

0.1 -16 -14 -12 -10 -8 -6 -4 -2

Temperature (C)

Figure 12: Tahquamenon Falls and Kathleen Dam compared to Maple River. Air filters for these trials were run for four hours, with duplicate test filters.

Big Riffles Air flow and Water

10000 air filter 1 7/29/16 air) air filter 2 7/29/16

-1 air filter 2 8/3/16 1000 water 7/31/16 water 8/3/16

water/ L water/ water 8/4/16

-1 water 8/7/16 100

Ice Nucleating ParticlesIce Nucleating (mL 0.1 -16 -14 -12 -10 -8 -6 -4 -2

Temperature (C)

Figure 13: Big Riffles air and water samples at UMBS. UMBS air filter data compared to water data from the same site across different days. Water samples had higher abundance of INP compared to air filters. Four air samplers were deployed each sampling day, two for the test site and two for controls creating the duplicate filter dates as seen above. Water samples for the

UMBS artificial stream were collected on the same days independent of the air samples but corresponding to the same locations.

Air samples from the UMBS artificial stream facility were compared to air samples taken at the Grand Rapids (Maumee River) weir. Weir air filters were taken monthly but for this comparison the two samples taken closest to the UMBS sampling trip were used for the comparison (Fig. 14). UMBS air samples do not have the same temperature range as samples from the Maumee River, which makes comparison difficult, but overall the artificial stream inflow samples of 28 July seem to mimic the Maumee River samples most closely. The artificial stream inflow was more turbulent than the stream ′fall′ sampling location. The two ′fall′ night samples are more similar to the Maumee River air samples than the day samples.

Maumee air filters vs Input and Fall UMBS

1000 Weir 7/21/16 Weir 10/14/16 inflow 1 7/28/16 inflow 2 7/28/16 inflow 1 8/1/16 100 inflow 2 8/1/16

air) fall 1 7/31/16 -1 fall 2 7/31/16 fall night 8/3/16 fall night 8/6/16 10

Ice Particles Nucleating (L 1

0.1 -22 -20 -18 -16 -14 -12 -10 -8 -6 -4

Temperature (C)

Figure 14: Maumee air filters vs Input and Fall UMBS. UMBS artificial stream air sampling compared to Maumee River air sampling. Two dates closest to the UMBS sampling trip from the

Maumee River air filters were used. UMBS samples were only able to reach -14 oC compared to

-20 oC for Maumee samples. UMBS stream inflow and ′fall′ locations were used as they were the most turbulent features of the stream.

CHAPTER 4: DISCUSSION

4.1 INP Analysis

Water samples analyzed for warm temperature ( 10 °C) INP demonstrated seasonal variation in their abundance driven largely by changes in river discharge. Identifying INP in a freshwater environment follows investigation decades earlier of INP derived from seawater

(Bigg 1973, Schnell and Vali 1975). More recently, sea spray aerosols have been analyzed for a variety of INP and calculations of aerosol surface area, with individual INP signatures such as soil and mineral dust, supporting the presence of INP in oceans (Wilson et al. 2015, DeMott et al. 2016). By comparison, analysis of freshwater INP has received less attention. In lakes, diatoms (and their associated bacterial epiphytes) are thought to be ice nucleators as demonstrated by rafting to maintain a favorable position in the photic zone (D’souza et al. 2013)

Morris et al. (2010) found Pseudomonas syringae to be ice active in 11 rivers worldwide.

Influencing the work reported here was the study by Moffett (2016) who showed a gradient of declining INP abundance through a freshwater to seawater transect in western Wales (Moffett

2016).

The Maumee River runs 220 km draining 1.68 × 106 hectares of predominantly agricultural watershed across three Midwestern U.S. states en route to Lake Erie’s western basin.

In the present study, each sampling date showed measurable INP in the Maumee River, the amount varying through the year, closely correlated to discharge. Compared to the earlier study of the River Gwaun (Moffett 2016), the average abundance of warm temperature INP (-10 oC) in the Maumee River in January 2017 was 1.02 × 105 INP mL-1, an abundance greater by two orders of magnitude. This difference may be related to differences in river discharge between the 40 two systems. Alternatively, INP abundance may be related to differences in land use; our survey of rivers in forested watersheds of upper Michigan returned similar INP abundance as reported by Moffett (2016).

Overall for the Maumee River, stream discharge appeared to be the factor most directly correlating to INP abundance. Peak INP levels for the water samples were in April 2016 and

February-April 2017, being two orders of magnitude higher than summer samples and three orders of magnitude higher than November 2016, the month with fewest warm temperature INP enumerated. This coincides with discharge being highest at around 10,000 cfs for April 2016 and

February-April 2017, which was an order of magnitude higher than summer months. This confirms that discharge plays a significant role in INP levels in both water and air samples, but is not the only factor. Seasonality plays a role in composition of the microbial environment for providing different biologic INPs.

Detailed information on the size of INP may be useful in source identification, modeling their transport in the atmosphere to improve climate predictions, and determining how effectively instrumentation used for quantifying INPs in the atmosphere captures the full INP population. For biological INP, size-selected filtration (0.22 μm) was used to distinguish between cell-associated and macromolecular INPs. Characterization of the INPs revealed that most were part of the soluble phase, passing through a 0.22 μm cartridge filter. While the INP were characterized as soluble, relatively few passed through a 100 kDa centrifugal filter unit thus constraining a size range for further characterization. That INP can exist as macromolecules render them particularly important for atmospheric ice nucleation as they might accumulate and become airborne again while attached to other (including abiotic) particles. 41

Weir air filters showed that sampling in proximity to an engineered feature promoting turbulence resulted in an order of magnitude higher INP amount than controls located away from direct influence of the river. This confirmed the belief that the spray from the weir froth will promote higher levels of INP in the air. Air filters are especially affected by discharge and the amount of spray resulting from the water flowing over the weir. With greater discharge, more spray can be captured by the air filters. Another factor in the efficiency of the air filters is the wind direction; if the wind is to the back of the filter opening then they will have fewer INP attached. Months with higher discharge show more warm temperature INP captured on the air filters at the weir. Warm temperature INP were most abundant in April consistent with high river discharge. One caveat with the air filtering was the pore size of the polycarbonate filter. The pore size was 0.2 µm, which would be large enough that whole cells would be captured but sub cellular particles could pass through. Recognizing that the majority of warm temperature INP were subcellular, it is likely that the air sampling was underrepresenting the total INP. A smaller filter size would be difficult to use in this study as it would inhibit the air flow of the pump.

Through collaboration with colleagues at the Leibniz Institute for Tropospheric Research

(TROPOS; Leipzig, Germany), we were afforded the opportunity to identify INP in samples from the Maumee River based on comparison to a database of macromolecules for which the ice nucleation behavior has been characterized (Pummer et al. 2012; Augustin et al. 2013; Hartmann et al. 2013). This collaboration offered ice nucleation measurements using complementary methods including the Leipzig Aerosol Cloud Interaction Simulator (LACIS; Hartmann et al.

2011), a cold stage, and a freezing array similar to the approach used at BGSU. Utilizing these techniques, the TROPOS group is able to cover wide temperature and INP concentration ranges and the approach is particularly valuable at the concentration levels encountered in natural 42 samples where bulk chemical analyses are generally not suitable. The analysis showed that the

INP sampled from the Maumee river most closely resemble the ice signature of a fungus, M. alpina (Fig. 15), thus suggesting that the largest contributor to INP in the Maumee River was fungal, either water borne or possibly derived from the watershed. M. alpina is a soil saprotroph that is ubiquitously found in terrestrial environments, even in Antarctica (Goncalves 2012).

Figure 15: INP signature analysis of Maumee River. Preliminary results from the Department of

Experimental Aerosol and Cloud Microphysics, Leibniz Institute for Tropospheric Research

(TROPOS). This shows that the signature of the INP found in the Maumee River water sample most closely resembles a fungal INP signature. The designations LINA and LACIS relate to the two different methods used to test INP used by TROPOS.

The Maumee River is the largest drainage basin in the Great Lakes watershed (Maumee

River Management 2016), with most of the main stem and tributaries bordering farm fields which leads to high nutrient run off. Fungi can easily drain into the river from fields during rain events or wash off from plants bordering the river (Hajek and Delalibera 2009). A study in Lake

Michigan found around thirty species of fungi were distributed in the water column at different depths and near and far from shore during the unstratified periods of April and October

(Kiziewicz and Nalepa 2008). Transient fungi will also increase as temperatures rise and terrestrial organic matter grows and can be consumed. These fungi should then be washed off during rain events and enter the watershed (Sridhar and Kaveriappa 1984). Temperature is a large contributor to mycorrhizal fungal growth, literature shows that mycorrhizal growth can happen at low temperatures of 5 °C in bluebell plants (Daft et al. 1980), but in soybeans, fungal growth was repressed at 15 °C (Zhang et al. 1995).

4.2 Conclusions

This survey showed that there are high numbers of biologic INPs found in the Maumee

River with INP abundance being especially high in the spring when river discharge elevated.

When comparing freshwater and seawater, INP abundance in freshwater is at least one order of magnitude higher than in seawater. This study also facilitated investigation into the effect of natural- and engineered river features on the aerosolization of INPs into the atmosphere. Air sampling at the Maumee River weir showed that engineered features produce an order of magnitude higher INP compared to an inland control. A small controlled study at UMBS showed that even small areas that produce spray and bubble bursting have a measurable effect on the abundance of INP found in air. Even undisturbed runs produce measurable INP albeit in smaller 45 levels than areas with spray. This study also showed that warm temperature INP (<-10oC) may be fungal in origin, although more work is needed to draw further conclusions on INP identity.

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