AN ABSTRACT OF THE THESIS OF

Daniel B. Trebelhorn for the degree of Master of Science in Science presented on

June 1, 2017.

Title: Developing a Standard to Assess Water Repellency and Dimensional Stability of

Preservative Treated Railroad Ties or Other Large Timbers

Abstract approved: ______

Jeffrey J. Morrell

Untreated wood is inherently hygroscopic and moisture content variations can have substantial effects on dimensional stability leading to the development of deep surface checks that further enhance moisture uptake and ultimately encourage deterioration. The horizontal exposure of wood crossties makes water repellency and dimensional stability particularly important in railroad applications. Ties are typically preservative treated to prolong service life. Preservatives primarily protect against insect and fungal attack, but many also alter hygroscopicity and enhance dimensional stability.

Currently, there is no standard method for assessing the water repellency and its interaction with dimensional stability in large wooden members such as railroad ties.

Potential methods for assessing moisture behavior were investigated on four wood species. Untreated or materials treated with , pentachlorophenol or ammoniacal

copper zinc arsenate were assessed. Samples were subjected to eight accelerated wetting and drying cycles. Dimensional stability and water repellency were evaluated after each cycle using digital image analysis techniques to quantify checking and observing water droplet contact angle over time, respectively. Water repellency varied widely with cycles, but creosote and pentachlorophenol treated samples provided the best resistance to moisture uptake. Dimensional stability was more variable and more dependent on treatment in terms of check development. The results of this work will be used to develop a rapid, reproducible and cost-effective method for assessing the water repellency and dimensional stability of preservative treated wood used in railroad and other applications.

©Copyright by Daniel B. Trebelhorn

June 1, 2017

All Rights Reserved

Developing a Standard to Assess Water Repellency and Dimensional Stability of Preservative Treated Railroad Ties or Other Large Timbers

by Daniel B. Trebelhorn

A THESIS

submitted to

Oregon State University

in partial fulfillment of the requirements for the degree of

Master of Science

Presented June 1, 2017 Commencement June 2017

Master of Science thesis of Daniel B. Trebelhorn presented on June 1, 2017.

APPROVED:

Major Professor, representing Wood Science

Head of the Department of Wood Science and Engineering

Dean of the Graduate School

I understand that my thesis will become part of the permanent collection of Oregon State University libraries. My signature below authorizes release of my thesis to any reader upon request.

Daniel B. Trebelhorn, Author

ACKNOWLEDGEMENTS

My genuine appreciation is extended to the following supportive organizations and devoted individuals that played key roles in, and made substantial contributions to, the manifestation and completion of this work. Dr. Jeffrey Morrell has been a key contributor to the development of this work as well as to my individual academic success by providing his unparalleled assistance and guidance. I am also grateful to my committee members, Mike Milota, Scott Leavengood and Brian Fronk, for their service. I would like to acknowledge the previous work completed by H. Greeley Beck (2014) which helped in developing initial project frameworks and referential data. I would like to enthusiastically acknowledge Inc., Pittsburgh, PA for generously funding this project. I would also like to thank The Centre, Corvallis, OR and Northwest

Hardwoods, Eugene, OR for supplying materials. Additionally, I would like to thank JH

Baxter, Eugene, Oregon for kindly treating our samples. I would also like to acknowledge

Daniel Ching, Chad Hammerquist and Ariel Muldoon for their help in obtaining and analyzing the results from this work. Finally, I am thankful of countless students, professors and faculty at Oregon State University, both extended and within the College of , who have supported me throughout my academic career.

TABLE OF CONTENTS

Contents Page

Introduction ...... 1

Literature Review...... 5

History of the Wooden Railroad Tie ...... 5 Early and Railroad Development ...... 5 Early and its Introduction to the Railroad ...... 6 Preservation and Specifications of Wooden Railroad Crossties ...... 10 General Specification for Wood Crossties ...... 10 Crosstie Preservative Treatment ...... 11 Traditional and Modern Preservative Treatments Used in the Current Study ...... 12 Ammoniacal Copper Zinc Asrenate (ACZA) ...... 12 Pentachlorophenol (Penta) ...... 14 Creosote...... 15 Effects of Oil-Borne Preservative Systems on Physical Properties ...... 16 Preparation of Ties for Preservative Treatment ...... 17 Dimensional Stability and Surface Checking in Wood ...... 21 Other Factors Affecting Dimensional Stability ...... 23 Wooden Crosstie Service Life, Failure and Concerns Over Dimensional Stability and Checking in Railroad Ties ...... 25 The Need for a Standard Test Method...... 26 Contributions from Previous Work ...... 27 Research Objective ...... 27

Materials ...... 28

Wood Species and Procurement ...... 28 Preservative Treatment ...... 29

Methods...... 31

Retention Analysis ...... 31 Soxhlet Solvent Extraction for Creosote Treated Samples ...... 31

TABLE OF CONTENTS (Continued) Page X-Ray Fluorescence (XRF) for Penta and ACZA Treated Samples ...... 32 Repeated Moisture Cycling ...... 33 Dimensional Stability - Quantifying Checking ...... 34 Water Repellency – Water Droplet ...... 38 Statistical Analysis ...... 40 Water Repellency ...... 40 Checking ...... 41

Results and Discussion ...... 43

Preservative Treatment Retention Analysis ...... 43 Soxhlet Solvent Extraction for Creosote Treated Samples ...... 43 XRF Analysis of Penta or ACZA Treated Samples ...... 43 Water Repellency ...... 46 Initial Water Droplet Repellency Tests by Treatment (individual 20-minute tests) .. 46 Water Droplet Test Results After Repeated Moisture Cycling ...... 50 Red Water Droplet Test Results ...... 50 Red oak mean droplet ratings for all treatments: initial, final and their comparisons 50 Comparison between water repellency after 6 and 8 cycles for all treatments ...... 53 Comparisons of the maximum and minimum mean rating over all cycles for each treatment ...... 53 Variability in responses for a given treatment ...... 54 Comparisons for Moisture Cycles 0, 5, 6 and 8 ...... 54 White Oak Water Droplet Test Results ...... 58 White oak mean droplet ratings for all treatments: initial, final and their comparisons ...... 58 Comparison of the 6th and 8th cycles, for all treatments ...... 60 Comparisons between maximum and minimum mean rating for all cycles for each treatment ...... 61 Variability in responses for a given treatment ...... 62 Douglas-fir Water Droplet Test Results ...... 65

TABLE OF CONTENTS (Continued) Page Mean droplet ratings for Douglas-fir for all treatments: initial, final and their comparisons ...... 65 Comparison of the 6th and 8th cycles, for all treatments ...... 69 Comparisons of the maximum and minimum mean rating over all cycles, for each treatment ...... 70 Variability in responses for a given treatment ...... 71 Bigleaf Maple Water Droplet Test Results ...... 74 Bigleaf maple mean droplet ratings for all treatments: initial, final and their comparisons ...... 74 Comparison of the 4th and 8th cycles, for all treatments ...... 77 Comparisons of the maximum and minimum mean rating over all cycles, for each treatment ...... 77 Variability in responses for a given treatment ...... 78 Effect of Preservative Treatment on Moisture Uptake ...... 81 Moisture contents achieved by pressure soak contributing to check development ... 81 Moisture contents of untreated controls attained by pressure soak ...... 82 Moisture contents of creosote treated samples attained by pressure soak ...... 82 Moisture contents of penta treated samples attained by pressure soak ...... 83 Moisture contents of ACZA treated samples attained by pressure soak ...... 83 Dimensional Stability Results as Measured by Surface Checking Using Digital Image Analysis ...... 87 Red Oak Checking ...... 89 Estimated median percent checking in red oak by preservative type over cycles 3-8 89 Comparing oilborne creosote or penta treated red oak over cycles 3-8 ...... 94 Comparing ACZA to both creosote or penta treated red oak over cycles 3-8 ...... 95 White Oak Checking Results ...... 97 Estimated median percent checking in white oak by preservative type over cycles 3-8 ...... 97 Comparing oilborne creosote or penta treated white oak over cycles 3-8 ...... 101 Comparing ACZA to both creosote or penta treated white oak over cycles 3-8 ..... 101 Douglas-fir Checking Results ...... 104

TABLE OF CONTENTS (Continued) Page Estimated median percent checking in Douglas-fir by preservative type over cycles 3-8 ...... 104 Comparing oilborne creosote or penta treated Douglas-fir over cycles 3-8 ...... 107 Comparing ACZA to both creosote and penta treated Douglas-fir over cycles 3-8 108 Bigleaf Maple Checking Results ...... 110

Additional Discussion ...... 116

Relationship Between Water Repellency and Dimensional Stability ...... 116 Oven Drying Preservative Treated Wood ...... 120 Effects of Drying on Water Repellency and Dimensional Stability ...... 120 Moisture Movement and Drying Duration When Re-Drying Previously Kiln Dried and Preservative Treated Wood ...... 121 Statistical Analysis Discussion ...... 122 Study Design and Potential Alternatives ...... 125 Design Complexity ...... 125 Alternatives Methods ...... 126

Conclusion ...... 128

Water Repellency ...... 128 Dimensional Stability as Measured by Checking ...... 128 General Conclusions ...... 129

References ...... 130

Appendix ...... 136

Matlab Image Processing Core Script ...... 136 Batch Cropping Raw Images ...... 136 Batch Contrast Equalization and Grayscale Conversion ...... 137 Batch Thresholding (w/ Ordfilt2 noise filter) ...... 138 Supplemental* Matlab Image Processing Code ...... 139 IMNAMESTACK Function ...... 139 IMSTACKLOAD Function ...... 140

TABLE OF CONTENTS (Continued) Page IMSTACKSAVE Function ...... 142 Average Water Droplet Rating by Treatment at Each Moisture Cycle ...... 143 Untreated ...... 143 Creosote Treated ...... 152 Pentachlorophenol Treated...... 161 Ammoniacal Copper Zinc Arsenate (ACZA) Treated ...... 170 Average Percent Checking by Treatment at Each Moisture Cycle ...... 179 Untreated ...... 179 Creosote Treated ...... 179 Pentaclorophenol Treated...... 180 Ammoniacal Copper Zinc Arsenate ...... 180

LIST OF FIGURES

Figure Page

1. Wafers cut from cycled or uncycled creosote treated samples for Soxhlet extraction. 32

2. Wafers (A) cut from cycled or uncycled Penta or ACZA treated samples for XRF analysis. Prepared XRF sample cups, compacted at 250 in-lbs (28 N-m) (B)...... 33

3. Apparatus used to capture sample images for checking analysis...... 34

4. Examples of grayscale (A), unfiltered (B) and filtered (C) binarized mask produced during checking image analysis. Note that sample shown is untreated white oak after two moisture cycles...... 37

5. Water droplet characterization scale for assessing surface-moisture behavior of wood surfaces adopted from Beck, 2014...... 39

6. Initial water repellency test ratings for untreated red oak, white oak, Douglas-fir and bigleaf maple samples...... 48

7. Initial water repellency test ratings for creosote treated red oak, white oak, Douglas-fir and bigleaf maple samples...... 48

8. Initial water repellency test ratings for pentachlorophenol (penta) treated red oak, white oak, Douglas-fir and bigleaf maple samples...... 49

9. Initial water repellency test ratings for ammoniacal copper zinc arsenate (ACZA) treated red oak, white oak, Douglas-fir and bigleaf maple samples...... 49

10. Effect of preservative treatment on water repellency of red oak samples immediately after treatment and when subjected to 8 wet/dry cycles...... 52

11. Effect of eight wet/dry cycles on mean droplet rating for red oak wood samples either untreated or treated with ACZA, creosote or penta...... 56

12. Effect of eight wet/dry cycles on the estimated mean droplet rating for red oak wood samples either untreated or treated with ACZA, creosote or penta...... 56

13. Effect of preservative treatment on water repellency of white oak samples immediately after treatment and when subjected to 8 wet/dry cycles...... 60

14. Effect of eight wet/dry cycles on the mean droplet rating for white oak wood samples either untreated or treated with ACZA, creosote or penta...... 63

15. Effect of eight wet/dry cycles on the estimated mean droplet rating for red oak wood samples either untreated or treated with ACZA, creosote or penta...... 63

LIST OF FIGURES (Continued)

Figure Page

16. Example of migration and surface accumulation of resinous extractives on penta treated Douglas-fir that might have affected water repellency...... 66

17. Effect of preservative treatment on water repellency of Douglas-fir heartwood samples immediately after treatment and when subjected to 8 wet/dry cycles...... 69

18. Effect of eight wet/dry cycles on the mean droplet rating for Douglas-fir samples either untreated or treated with ACZA, creosote or penta...... 72

19. Effect of eight wet/dry cycles on the estimated mean droplet rating for Douglas-fir samples either untreated or treated with ACZA, creosote or penta...... 72

20. Effect of preservative treatment on water repellency of bigleaf maple samples immediately after treatment and when subjected to 8 wet/dry cycles...... 76

21. Effect of eight wet/dry cycles on the mean droplet rating for bigleaf maple wood samples either untreated or treated with ACZA, creosote or penta...... 79

22. Effect of eight wet/dry cycles on the estimated mean droplet rating for bigleaf maple wood samples either untreated or treated with ACZA, creosote or penta...... 79

23. Effect of repeated wet/dry cycles on moisture contents of untreated red oak, white oak, Douglas-fir or bigleaf maple samples achieved at the end of each pressure soak period...... 85

24. Effect of repeated wet/dry cycles on moisture contents of creosote treated red oak, white oak, Douglas-fir or bigleaf maple samples achieved at the end of each pressure soak period...... 85

25. Effect of repeated wet/dry cycles on moisture contents of penta treated red oak, white oak, Douglas-fir or bigleaf maple samples achieved at the end of each pressure soak period...... 86

26. Effect of repeated wet/dry cycles on moisture contents of ACZA treated red oak, white oak, Douglas-fir or bigleaf maple samples achieved at the end of each pressure soak period...... 86

27. Examples of checking in untreated (A), penta (B) or ACZA (C) treated red oak after five moisture cycles. Raw photos illustrate various levels of checking in samples that were either untreated or treated with penta or ACZA...... 90

28. Effect of three to eight wet/dry cycles on mean percent checking for red oak wood samples treated with ACZA, creosote or penta...... 96

LIST OF FIGURES (Continued)

Figure Page

29. Checking in untreated (A), creosote (B), penta (C) or ACZA (D) treated white oak after five moisture cycles...... 99

30. Effect of three to eight wet/dry cycles on mean percent checking for white oak wood samples treated with ACZA, creosote or penta...... 103

31. Checking in untreated (A), ACZA (B) or penta (C) treated Douglas-fir after six moisture cycles. Photo illustrates inconsistent checking patterns in samples with similar anatomical orientations...... 105

32. Effect of three to eight wet/dry cycles on mean percent checking for Douglas-fir wood samples treated with ACZA, creosote or penta...... 109

33. Example of a bigleaf maple section showing the image area (A) analyzed (red boxes) and a deep end split (B) that would not have been measured in the analysis...... 113

34. Effect of eight wet/dry cycles on mean proportion of checking in bigleaf maple either untreated or treated with various preservatives plotted collectively...... 115

35. Effect of eight wet/dry cycles on mean proportion of checking in bigleaf maple either untreated or treated with various preservatives individually plotted...... 115

36. Relationship between water repellency and checking observed for various species treated with ACZA. Illustrates the most expressive relationship...... 117

37. Relationship between water repellency and checking observed for white oak treated with various preservatives. Illustrates a fairly expressive relationship...... 118

38. Relationship between water repellency and checking observed for bigleaf maple treated with various preservatives. Illustrates a less expressive relationship...... 119

LIST OF TABLES

Table Page

1. Calculated net retention by preservative treatment type (kg/m3) ...... 30

2. Process conditions used to treat materials with penta, creosote or ACZA ...... 30

3. Average creosote retentions by species after eight wet/dry cycles, except bigleaf maple (uncycled), determined by soxhlet solvent extraction. Averages based on triplicate species analysis ...... 45

4. Average penta retentions by species after eight wet/dry cycles, except bigleaf maple (uncycled), determined by XRF analysis. Averages based on triplicate species analysis ...... 45

5. Average ACZA component retentions by species after eight wet/dry cycles, except bigleaf maple (uncycled), determined by XRF analysis. Averages based on triplicate species analysis ...... 45

6. Effect of preservative treatment on water repellency of red oak samples prior to exposure to 8 wet/dry cycles ...... 51

7. Effect of preservative treatment on water repellency of red oak samples after 8 wet/dry cycles...... 52

8. Comparison of the effect of preservative treatment on water repellency of red oak samples prior to and after 8 wet/dry cycles. Negative values appear in parentheses. 52

9. Effect of six to eight wet/dry cycles on water repellency of red oak samples treated with selected wood preservatives...... 53

10. Differences in the absolute maximum and minimum mean water repellency ratings after varying wet/dry cycles of red oak treated with selected preservatives...... 54

11. Comparisons between selected preservative treatments and untreated red oak samples on water repellency over 8 wet/dry cycles. Negative mean values are represented by parentheses. Bolded p-values represent significance (α = 0.05)...... 57

12. Effect of preservative treatment on water repellency of white oak samples prior to exposure to 8 wet/dry cycles...... 59

13. Effect of preservative treatment on water repellency of white oak samples after 8 wet/dry cycles...... 59

14. Comparison of the effect of preservative treatment on water repellency of white oak samples prior to and after 8 wet/dry cycles...... 60

LIST OF TABLES (Continued)

Table Page

15. Effect of wet/dry cycles (6 to 8) on water repellency of white oak samples treated with selected wood preservatives. Significant comparisons are represented in bold. 61

16. Differences in water repellency for the maximum and minimum mean repellency occurring at varying wet/dry cycles of white oak treated with selected preservatives...... 62

17. Comparisons between selected preservative treatments and untreated white oak samples on water repellency over 8 wet/dry cycles. Negative mean values are represented by parentheses. Bolded p-values represent significance (α = 0.05)...... 64

18. Effect of preservative treatment on water repellency of Douglas-fir samples prior to exposure to 8 wet/dry cycles...... 68

19. Effect of preservative treatment on water repellency of Douglas-fir samples after 8 wet/dry cycles...... 68

20. Comparison of the effect of preservative treatment on water repellency of Douglas-fir samples prior to and after 8 wet/dry cycles. Negative values appear in parentheses. 68

21. Effect of 6 to 8 wet/dry cycles on water repellency of Douglas-fir samples treated with selected wood preservatives. Significant comparisons are represented in bold. 70

22. Differences in water repellency for the maximum and minimum mean rating occurring at varying wet/dry cycles of Douglas-fir treated with selected preservatives...... 71

23. Comparisons between selected preservative treatments and untreated Douglas-fir samples on water repellency over 8 wet/dry cycles. Negative values are represented by parentheses. Bolded p-values represent significance (α = 0.05)...... 73

24. Effect of preservative treatment on water repellency of bigleaf maple samples prior to exposure to 8 wet/dry cycles...... 75

25. Effect of preservative treatment on water repellency of bigleaf maple samples after 8 wet/dry cycles...... 76

26. Comparison of the effect of preservative treatment on water repellency of bigleaf maple samples prior to and after 8 wet/dry cycles. Percent decreases represented in parentheses...... 76

27. Effect of wet/dry cycles (6 to 8) on water repellency of bigleaf maple samples treated with selected wood preservatives...... 77

LIST OF TABLES (Continued)

Table Page

28. Differences in water repellency for the maximum and minimum mean rating occurring at varying wet/dry cycles of bigleaf maple treated with selected preservatives...... 78

29. Comparisons between selected preservative treatments and untreated bigleaf maple samples on water repellency over 8 wet/dry cycles. Negative values are represented by parentheses. Bolded p-values represent significance (α = 0.05)...... 80

30. Estimated median percent checking of red oak treated with ACZA, creosote or penta and exposed to 3 to 8 wet/dry cycles...... 93

31. Effect of initial preservative treatment on the changes in median percent checking of preservative treated red oak samples subjected to 3 to 8 wet/dry cycles as a ratio between check area at the 8th and 3rd cycle...... 93

32. Differences in median percent checking in red oak samples treated with creosote or penta and exposed to 3 to 8 wet/dry cycles...... 94

33. Comparisons in the degree of checking between ACZA and either oilborne creosote or penta treated red oak over 6 wet/dry cycles...... 95

34. Estimated median percent checking of white oak treated with ACZA, creosote or penta and exposed to 3 to 8 wet/dry cycles...... 100

35. Effect of initial preservative treatment on the changes in median percent checking of preservative treated white oak samples subjected to 3 to 8 wet/dry cycles as a ratio between check area at the 8th and 3rd cycle...... 100

36. Differences in median percent checking in white oak samples treated with creosote or penta and exposed to 3 to 8 wet/dry cycles...... 101

37. Comparisons in the degree of checking between ACZA and either oilborne creosote or penta treated white oak over 6 wet/dry cycles...... 102

38. Estimated median percent checking of Douglas-fir treated with ACZA, creosote or penta and exposed to 3 to 8 wet/dry cycles...... 106

39. Effect of initial preservative treatment on the changes in median percent checking of preservative treated Douglas-fir samples subjected to 3 to 8 wet/dry cycles as a ratio between check area at the 8th and 3rd cycle...... 107

40. Differences in median percent checking in Douglas-fir samples treated with creosote or penta and exposed to 3 to 8 wet/dry cycles...... 107

LIST OF TABLES (Continued)

Table Page

41. Comparisons in the degree of checking between ACZA and either oilborne creosote or penta treated Douglas-fir over 6 wet/dry cycles...... 108

42. Effect of 3 to 8 wet/dry cycles on the percent checking in untreated, ACZA, creosote or penta treated bigleaf maple...... 114

43. Cumulative effect of 3 to 8 wet/dry cycles on the percent checking in untreated, ACZA, creosote or penta treated bigleaf maple...... 114

1

INTRODUCTION

Railroad ties have historically been made from wood. Ties are one of the most vital components in a rail system as they provide the secondary foundation on which the rails are laid and fastened to. Ultimately, they help absorb shock and distribute load as heavily loaded rail cars pass over them. They are also responsible for providing lateral resistance to maintain (i.e. the space between the rails), especially in curved sections to avoid derailment. Wood has been a choice material since the earliest railroads for several reasons. First, wood has historically been relatively inexpensive in comparison to other materials like concrete or . The renewable nature of wood is also a valuable characteristic when considering high volume utilization and competition with other materials. Wood also has several desirable physical properties such as its ability to elastically deform under heavy loading, providing resiliency that is not characteristic of many other materials. Furthermore, wood ties that are removed from a track system can be used as a source of fuel to produce energy or may be repurposed in a number of applications such as residential and industrial landscaping.

Wood is also a cellulosic material and is susceptible to deterioration, especially in long-term outdoor exposure. When ties are compromised by degradation, their overall integrity and service life also diminishes. This can stem from exposure to various abiotic sources such as elements of weathering and repeated shock loading, from biotic sources such as wood inhabiting fungi and/or insects, or combinations of both. Therefore, it is imperative that ties be preservative treated to limit the rate and severity of degradation.

2

Traditional preservatives include formulations like creosote, oilborne pentachlorophenol or copper naphthenate as well as a number of waterborne copper based formulations such as chromated copper arsenate (CCA) or ammoniacal copper zinc arsenate (ACZA).

Preservatives deter wood inhabiting insects and fungi; however, it is also important for preservatives to offer protection from abiotic sources of degradation such as weathering and dimensional stability.

Wood tends to swell when saturated, and shrinks during drying (Conners, 2008).

The severity of moisture cycling is also dependent on the geographic location where the wood is exposed and can be a major concern in regions that experience dramatic seasonal or daily fluctuations in humidity or rainfall followed by dry spells. These alternating conditions lead to cyclical moisture absorption and desorption that produces moisture differentials throughout the wood. These moisture gradients cause differential shrinkage/swelling in the wood and, in turn, produce differential stresses within the wood that lead to the separation of wood fibers (splits or checks). These defects can expose untreated wood to attack by fungi or insects, resulting in earlier decay, and this process can be worsened by repeated shock loading in service. Hence, it is important for wood preservatives to provide both biological and physical protection to limit elevated moisture levels.

There is no standard method for assessing the ability of preservative treatments to repel moisture and impart dimensional stability in wooden railroad ties or other large timbers that are subjected to repeated moisture cycles. Instead, preservatives are usually

3 assessed in terms of their biological efficacy using small-scale laboratory tests like the standard block (AWPA E10-16) or field tests such as field stake, or L-joint tests as prescribed by AWPA E7, E8 or E9-15, respectively. In terms of physical performance, current methods for assessing water repellency include tests such as AWPA E4-15, which only infers water repellency from measured dimensional changes. However, these procedures only consider small samples that are specifically cut to maximize end-grain and accentuate surface to volume ratio. These samples may never develop moisture related physical defects and are not representative of full sized ties in service. Specimen size and geometry are important when considering dimensional change since wood is anisotropic and experiences greater shrinkage and swelling in the tangential and radial planes (Rowell and Banks, 1985). Since larger tangential and radial surfaces are exposed in ties and other large timbers, it follows that test methods investigating dimensional stability should emphasize these surfaces. The current methods also fail to consider physical performance after repeated exposure to abiotic degradative influences altogether.

The goal of this research was to investigate alternative methods to measure the water repellency and dimensional stability of large timbers treated with various preservatives that were subjected to repeated moisture cycles. The test builds on the specimen dimension determined in a previous study (Beck, 2014), that allow for rapid saturation and still produce sufficient dimensional change. This work, in combination with the previous work, would provide preservative manufacturers and or railroads with a

4 rapid method for assessing existing and/or new preservative formulations in terms of these physical properties.

5

LITERATURE REVIEW

History of the Wooden Railroad Tie

Early Track and Railroad Development

The infant concept of the railroad - or “” in Europe - in the mid 1500’s consisted of wooden “rails” on which horse-drawn carriages with wooden wheels were used to freight. The shift to these basic tracks provided much smoother routes of travel than the rarely maintained dirt roads (Bellis, 2016). Wagonways provided drivers with unstable and unreliable paths that often resulted in the failure of carriages and equipment. It was not until the late 1700’s that wooden rails, as well as the traditional wagon wheels, were replaced by iron. The new rail system was quickly adopted and refined. By the early 1800s, advances in the railroad industry led to the development of steam powered locomotives that replaced horse-drawn carriages. By the 1820’s-30’s, the first railroads were being built in America and would have a major influence on industrial growth and westward expansion.

Iron rails had assumed the profile of an overturned T and were secured to stone blocks that elevated the track and provided the rails with a solid foundation. However, stone blocks were inflexible and heavy, making them quite expensive to transport to installation sites. The use of this primitive track foundation would soon reinvent itself due to the tardiness of a shipment of stone blocks in 1832. The late arrival of stone blocks to the Camden and Amboy railroad in New Jersey led to substitution with logs that were spiked perpendicularly below the rails as directed by the president and chief engineer of the project, Robert Stevens (, 1999). This temporary solution was quickly

6 recognized and accepted as a legitimate alternative to the traditional stone block because it offered a more comfortable ride. Wood was also more capable of elastic deformation and impact absorption, readily accepted rail-securing spikes, and was generally cheaper to source and transport. It would not take long for other railroads to catch wind of this accidental discovery and adopt wood ties into the construction of new tracks as well as into the rehabilitation of existing ones.

Early Wood Preservation and its Introduction to the Railroad

Historically, wood was used untreated and was simply replaced on an as-needed basis because it was inexpensive and readily available. Spending extra time and money to preserve wooden ties simply could not be justified. A number of preservatives were developed to increase tie service life and to reduce consumption of forest resources; some with great success. For example, it was noted early on that untreated wood used in salt mines and wood used to construct salt carrying ships often showed greater longevity than untreated wood used in other applications (Oaks, 2006). In terms of purposely developed systems, Johann Glauber, a German chemist, was responsible for the earliest known

“scientific” treating process (Oaks, 2006). His method consisted of using fire to carbonize the wood, followed by a tar coating and finally immersion in a bath of pyroligenous acid.

Early treating methods like these were explored in Europe. Many employed salts and included charring of the wood (Glauber) or the application of various oil coatings. Most of these methods failed to achieve the desired long-term protection.

7

In marine environments, wooden vessels carrying goods and passengers were at serious risk of degradation as they were exposed to a variety of potential degradation agents, including marine borers. This made the shipping industry a major driver for the development of preservative systems. Ships often required massive amounts of timber that placed pressure on forests. Frequent replacement of decaying ship components only amplified the need to prolong wood service life.

Preservation research and development was quite irregular until the 1830’s. From

1830 to 1840, over twenty preservative methods were developed including Kyan’s method (1832), Bethell’s method (1838) and Burnetts method (1838) (Oaks, 2006). Of these three, the Bethell and Burnett methods became the foundation for the railroad industry and are still used with current preservative systems. The two primary chemicals used in these processes were zinc chloride and variations of coal-tar distillate, or creosote. Creosote was a more effective preservative, but it was also more expensive than water-borne zinc chloride.

John Bethell’s method utilized pressure to force chemicals into the wood and is generally associated with the use of creosote, although he claimed that it worked just as effectively with any chemical. This high-retention method begins with a vacuum period to remove air trapped within the wood, followed by the introduction of preservative into the treating retort. Finally, pressure is applied to force preservative into the wood.

8

Sir William Burnett’s method initially consisted of soaking wood in an open vat of zinc chloride but later adopted Bethell’s idea in 1847 of using pressure to force the chemical into the wood (Oaks, 2006). Zinc chloride treatment was inferior to creosote due to its tendency to leach when used in wet conditions. Secondary treatment measures were investigated to enhance the effectiveness of zinc chloride in which -based formulations were used to help precipitate the zinc chloride in the wood cells.

Although Bethell’s method was highly effective, these “full-cell” methods used considerable amounts of solution and produced products that tended to bleed preservative. By the early 1900’s, methods like the “empty-cell” or Rueping

(1902)/Lowry (1906) methods were developed to reduce preservative uptake. The empty- cell processes typically end with different final pressure conditions and/or include additional post-impregnation steps (i.e. steam-flash or final vacuum) to release trapped air and excess preservative from the cell lumens of the wood (Freeman, et. al., 2003).

These methods optimize the quantity of the preservative needed and ultimately produce products with less preservative retention, allow for higher preservative recovery, and result in much cleaner final products that are less likely to bleed or leach preservative into the environment during their service life.

Several methods and chemicals including mercuric chloride and copper sulphate were investigated for use on railroad ties. For some time, mercuric chloride, copper sulphate, zinc chloride and creosote were used. Burt implied that creosote would be the most promising preservative to use industrially, despite its high cost (Oaks, 2006). The

9 superior properties eventually resulted in the dominance of this system. By the mid

1860’s, the Bethell creosoting method had replaced all other methods in England and would soon be embraced by other European countries. Preservation of railroad ties would not catch on in the U.S. or globally until much later, primarily due to the inexpensive

U.S. timber supply. Even in 1940, the Railway Tie Association (2016) noted that nearly

10% of railroad ties were still untreated.

10

Preservation and Specifications of Wooden Railroad Crossties

General Specification for Wood Crossties

Crosstie grading rules are relatively stringent. A committee comprised of The

Railroad Tie Association (RTA) and the American and

Maintenance of Way Association (AREMA) specifies several core requirements for wooden crossties and switch ties that include tie species, physical requirements, design, inspection and even shipping (RTA, 2017). These categories are then broken down into numerous subcategories. In general, standard unseasoned ties are typically 7” by 9”

(17.75 by 22.75 cm) in cross sectional dimension with no more than 1” (2.5 cm) of wane on either the top or bottom face within the rail-bearing area (RBA). The RBA is typically between 20” and 40” (51 and 102 cm) from the center of the tie. Seasoned or preservative treated ties are allowed a tolerance of no more than 0.25” (0.56 cm) less, and no more than 1” (2.5 cm) greater, than the cross-sectional dimension previously stated. While crossties are generally 8.5 feet (2.6 m) in length, ties can also measure 8.0 or 9.0 feet (2.4 or 2.7 m) in length depending on customer requests. Ties must also be primarily pith- centered or “boxed-heart”, although other specifications allow for some degree of variation. Additionally, certain defects such as holes, knots, shakes, splits, slope of grain, bark seams, manufacturing defects and checks are allowable under specific conditions.

Ties that fall outside of these or other specifications may be culled, or rejected, for use in a rail system. Specifications for railroad ties are strict because of the responsibility they carry in safely transporting both passengers and cargo.

11

Crosstie Preservative Treatment

Many wood preservatives have been developed to improve the service life of the variety of wood species used in the demanding and variable railroad environments. Many preservatives are no longer used, either due to their ineffectiveness or over concerns about their potential environmental impacts. On the other hand, many of the modern preservatives have been developed with the user, the environment, and the end-of- life/reuse in mind. Creosote is the most common preservative used for crossties, though ammoniacal copper zinc arsenate (ACZA), copper naphthenate (CuN) and pentachlorophenol (penta) are also used. Many formulations also contain additives that enhance properties such as water repellency, penetration, retention or dimensional stability. Water repellent additives may include waxes or oils that limit moisture uptake to retard decay and stain, decrease dimensional changes or limit extractive bleeding

(Williams and Feist, 1999). Deeper preservative penetration may be achieved by the integration of hydrophilic additives such as borates that can diffuse into otherwise refractory heartwood via affinity to moisture. Although there are many options in the current preservative market, creosote and creosote formulations continue to dominate the wooden crosstie preservative market (RTA, 2016).

12

Traditional and Modern Preservative Treatments Used in the Current Study

Ammoniacal Copper Zinc Asrenate (ACZA)

Ammoniacal Copper Zinc Arsenate (ACZA) – or Chemonite® II – is a water-type formulation that is used in industrial applications including railroad ties, utility poles, building poles, foundation pilings, roller coasters, glue-laminated members and cooling towers. It was standardized by the American Wood Protection Association in 1982 and replaced ammoniacal copper arsenate (ACA) (Lonza/Arch, 2016). In addition to its Class

B fire retardant rating and excellent biological decay resistance, it leaves the wood clean and dry to the touch. A life cycle assessment (LCA) comparing ACZA treated ties with concrete and plastic/composite (P/C) alternatives showed that ACZA treated ties: a) required less energy and resources (fossil fuels and water) to manufacture than concrete and P/C ties, b) imparted lower environmental impacts than concrete and P/C ties (in all six impact categories considered in an LCA), c) accounted for notably less greenhouse gas (GHG) emissions (1.1%) than concrete (6.3%) or P/C (5.5%) ties on the market, and d) offered the ability to offset fossil fuel use and further reduce GHG emissions by their reuse for energy recovery in permitted facilities with the proper emissions controls (Bolin and Smith, 2013).

Furthermore, ACZA treatment is reported to have no negative effects on wood strength. In comparison to its predecessor ACA, ACZA has been reported to cause less corrosion to metal fasteners (Baileys, 2010; Arch Wood Protection, 2015). On the other hand, conflicting reports regarding its causticity are numerous. The use of hot-dipped

13 galvanized fasteners is required for ACZA treated wood as these coatings delay corrosion of the underlying metal (Arch Wood Protection, 2015). Corrosion of metal fasteners is important in railroad applications because it can affect rail-fastening systems and potentially affect track gage width. However, allowing drying or metallic fixation by ammonia evaporation prior to installation can decrease the likelihood of extensive corrosion and is generally recommended for best long-term results (Baileys, 2010).

Properly handled and installed ACZA treated Douglas-fir did not require any re-gaging under “high-tonnage” railway loops until the 200 million gross tons (MGT) mark

(Lonza/Arch, 2016). ACZA is also capable of accepting a number or additives such as borates and approved tie sealants to enhance long-term performance and further limit corrosion.

14

Pentachlorophenol (Penta)

Pentachlorophenol (penta), developed and patented in the 1930’s by W.

Iwanowski and J. Turski, remains widely used for a number of industrial applications.

Penta consists of chlorinated phenols typically dissolved in either a light or heavy oil solvent (Groenier, 2006). This relatively inexpensive oil-borne organic biocide has been used alongside of and even replaced many other first-generation preservatives such as creosote in various industrial applications (Freeman, et. al, 2003). Penta has been used for the treatment of ties, timbers, poles and pilings because of its broad species compatibility and extensive performance history (Lebow, et. al., 2002). Penta offers performance characteristics similar to creosote in that it is known to impart some level of water repellency and does not promote fastener corrosion. Although penta is no longer permitted for use in most European countries and is classified as a restricted-use preservative in the U.S., it is still widely used to treat utility poles and cross-arms and has also been a competitive preservative for railroad ties. Penta was traditionally available to the public as a fungicide, insecticide and herbicide, however, it can now only be purchased and used by licensed pesticide applicators (Washington State Department of

Ecology, 2016). This can be partially attributed to concerns over environmental contamination, particularly in the form of dioxins released from penta treated wood in service. The term “dioxin” refers to an umbrella group of toxic polychlorinated organic chemicals. Penta treated wood used in applications such as animal shelters or feed basins is suggested to be a potential source for dioxins to enter the food chain. Consuming goods containing, or exposed to, dioxins can pose health risks (i.e. immune disorders, cancer,

15 reproductive disturbances, etc.) after prolonged exposure, even at low levels (Piskorska-

Pliszczynska et. al., 2015).

Creosote

Originally patented by Moll in 1836, the development and use of coal-tar creosote formulations would prove to be paramount to the wood preservation industry by the

1930’s (Freeman, et. Al, 2003). Creosote is used to treat 98% of all wood crossties produced in the U.S. and Canada due to its historical efficacy and reasonable cost (RTA,

2016). Creosote is a by-product of the high temperature distillation of coal, through the process of coal coking used in the steel production industry (World Coal Association,

2016). Creosote may vary from one producer to the next depending on how it is distilled, though these mild variations do not typically have a notable effect on performance

(Groenier, 2006). Although alternative formulations may simply imply a difference in distillation methods, it may also refer to the presence of additives. The current restrictions on creosote formulations state that applications should be limited to outdoor commercial uses only, including products such as railroad ties and utility poles. Indoor use of creosote is prohibited, especially where the wood will likely encounter consumables or where human contact is expected, such as handrails. Additionally, some 80% of this preservative is a collection of polycyclic aromatic hydrocarbons (PAHs) (Brooks (2004).

PAHs occur in the natural environment and many organisms can excrete these materials without harm. However, PAH migration and accumulation by leaching of creosote treated wood in-service poses elevated risks to a large spectrum of organisms, including

16 vertebrates. PAHs can be accumulated through the natural food chain, increasing the range of exposure and associated risk. Some PAHs are known to bind with specific components of DNA/RNA when metabolized by an organism and can result in unregulated cell growth and division, or cancer (Brooks, 2004). However, creosote has also had an extensive record of successfully protecting wood against termites, fungi and some marine borers (US EPA, 2016).

Effects of Oil-Borne Preservative Systems on Physical Properties

Oil-borne, or oil-delivered preservative treatments often claim to impart increased dimensional stability to the treated product (Bigelow, 2009). This is a primary concern for structural members in marine or other wet-use applications such as bridge and dock pilings or stringers. However, the stabilizing effect observed with these preservative systems is likely a by-product of an added level of moisture repellency. This effect would limit the rate or amount of moisture absorbed from the surrounding environment, thereby decreasing the tendency to shrink and swell dimensionally. On the other hand, various additives to existing systems may solely improve dimensional stability by means of material bulking or modification of the wood cell structure. Oil-based systems such as creosote and creosote/petroleum are also reported to facilitate lubrication at the spike-tie interface, ultimately reducing the force required to drive new spikes (Morrell et. al.,

2016).

17

Preparation of Ties for Preservative Treatment

Proper drying/seasoning of wooden crossties is one of the most important aspects of preparation prior to preservative treatment (Webb, 2005). Since treatment must move into the wood cells, it is imperative that a large percentage of the free water within the wood be removed. Seasoning prior to treatment can occur by air seasoning, kiln drying,

Boultonizing, or steam conditioning.

From an economic standpoint, air seasoning is the most common and cost- effective method for drying large timbers (150-203 mm cross-section) (Webb, 2005).

One negative aspect of air seasoning is the incurred cost of storing untreated inventory.

Crossties are stacked in a storage yard, with ample space between one another to allow adequate ventilation. Ties are periodically sampled to determine moisture content.

However, from a climatic standpoint, this method can pose challenges in regions with elevated temperatures and humidity. Therefore, accurate monitoring of air seasoned timbers is important for detecting incipient decay – or “stack burn”. The time required to season prior to treatment varies among species and can take as little as four months all the way up to over one year, depending on location. Wood that is dried too quickly can develop defects such as checks and splits that can significantly decrease tie quality.

An alternative to air seasoning is Boultonizing, which consists of putting the wood into a retort that is then filled with enough hot creosote or oil to completely cover the timbers while also leaving enough void space within the vessel to allow for evaporation and condensation of moisture and certain fractions of creosote that inevitably

18 volatilize. The charge is heated and subjected to a vacuum that effectively lowers the boiling point and encourages moisture evaporation from the wood. The condensed vapors are collected and separated. The creosote vapors are recycled to the creosote reservoir tank and the water fraction is collected and measured to determine the rate and amount of moisture removed. This method allows wood to be dried and processed from its green condition much faster than air seasoning. The process generally takes around six to ten hours, which is considerably shorter than air seasoning (Webb, 2005). Boultonizing is only appropriate for oil borne preservatives and is more expensive than air seasoning. It also generates a large quantity of contaminated waste water that must be processed.

Timbers may also be steam conditioned. However, this method is generally species and location dependent for situations where incipient decay is almost inevitable during air seasoning (i.e. southern climates). While this method of seasoning is usually the fastest and easiest to administer using controlled temperatures, it is also only capable of removing some of the total moisture and often requires using much higher temperatures than Boultonizing (Webb, 2005). These high temperatures can have adverse effects on the strength of timbers. Steam conditioning is rarely used to season ties prior to treatment.

Kiln drying is another drying alternative. Although there are many kiln types, the general process typically involves stacking of green, or high moisture content (MC), dimension and placing it in a kiln (essentially a large oven). The individual boards are separated from one another to increase the surface area and encourage

19 consistent, even drying for each board throughout the stack. This is usually accomplished using some type of shim material, like wood, between each board. In most cases, the charge is then dried for a predetermined time and temperature, otherwise known as a kiln

“schedule”. This schedule may alternate between various time/temperature/humidity combinations and is often highly dependent on species, initial moisture content, and volume of material or geographic location. This method is commonly used for drying dimension lumber (< 152-203 mm cross-section) and is used less frequently, if at all, for ties and other large timber products due to the significantly longer drying times and higher costs (Webb, 2005).

Crossties and timbers subject to any one of the above seasoning methods may still experience uneven drying rates that that can adversely affect their physical properties and amplify defects such as surface checks or end splits. Anti-checking devices, known as

“end-plates”, are used extensively on crossties to reduce end splitting. The movement of moisture in wood is greater in the longitudinal direction than in the transverse or radial directions. This can make removing moisture from the center of a timber more difficult than removal near the surface and creates differential drying rates between the inner and outer portions. When this happens, opposing stresses develop and can result in checking.

More uniform drying can be facilitated by incising ties before seasoning (regardless of the method used). Incising is a process that exposes more permeable end grain by creating openings in the longitudinal direction (tracheids/vessels) so that moisture can be evacuated more readily. Incising also reduces the likelihood of extreme checking or

20 splitting, while improving preservative penetration due to the increased end grain exposure (Webb, 2005). Incising results in deeper, more uniform treatment and is often used in refractory species such as Douglas-fir as well as for most . In addition, all notches/cuts for installation should be made prior to treatment since such alterations to a treated tie will damage the preservative envelope and expose untreated wood.

21

Dimensional Stability and Surface Checking in Wood

The separation of wood fibers, or checking, both internally and externally is a major concern during lumber production. These separations, otherwise known as checks, may significantly decrease product value and limit end use applicability due to diminished aesthetics or losses in structural integrity. Checking is a naturally occurring phenomenon that is primarily driven by drying practices, but can also be associated with other steps in manufacturing such as transportation, storage and handling. The driving forces behind checking are the stresses that develop as a result of moisture content differentials from dissimilar drying rates. The environment in which wood is stored or seasoned prior to use, as with crossties, also plays a crucial role in the prevention or onset of check development.

Wood is hygroscopic. Numerous free hydroxyl groups in and readily accept water (Rowell and Banks, 1985). Hydrogen bonding between hydroxyl groups and water molecules occurs as wood sorbs water. The wood fibers tend to change in dimension and swell in volume at rates nearly proportional to the volume of water added, up to the fiber saturation point (FSP) (Stamm 1964). Volumetric, or dimensional, changes have been reported to be generally linear with respect to increases in moisture content (Glass and Zelinka, 2010). The opposite effect is observed during drying – i.e. the wood fibers shrink as these hydrogen bonds are broken and water molecules are evaporated. During drying, outer wood surfaces are the first to expel free water, followed by bound water. The rate of moisture movement slows toward the center

22 of its mass. This creates an increasing moisture gradient within the wood that is responsible for the development of stresses that lead to fiber separation. The drier surface material shrinks and develops tensile stresses, while the core material remains wetter and experiences compressive stresses due to swelling. Stamm (1964) suggested the theoretical swelling pressure of wood during wetting to be ~24,000 lb/in.2, while others have found varying results depending on species. Regardless of the exact compressive and tensile forces exerted by moisture exchanges in wood, the forces are considerable nonetheless and can easily exceed the material properties of the wood, causing splitting or checking.

Species-specific properties can result in differing degrees of shrinkage and swelling that affect checking. However, all wood is anisotropic (i.e. having different properties in different directions). For most species, the greatest changes occur in the tangential [T] (2-15 %) and radial [R] (40-70% that of T) directions depending on density, while dimensional change in the longitudinal direction (i.e. parallel to the direction of trachieds, vessels, or grain) is minimal (0.1-0.2%) (Rowell and Banks, 1985;

Glass and Zelinka, 2010). Differential shrinkage/swelling coefficients, in conjunction with differential moisture contents, add to the stresses developed in the wood. Beck

(2014) suggested that the ratio of these two dominating directions of dimensional change

(i.e. T/R) can be used as an indicator of the expected degree of checking. A higher T/R ratio can generally be associated with a higher degree of checking. For example, red oak should experience the highest degree of checking (T/R = 2.2), while white oak and

23 bigleaf maple should experience a moderate degree of checking (T/R’s =~ 1.9) and

Douglas-fir should experience the lowest degree of checking (T/R = 1.6). However, the species in the current study have an average T/R ratio of 1.9 and the largest deviation was

0.3 in either direction. This may make it difficult to detect any true differences due to species. Wood density can also affect dimensional change with moisture cycling.

Generally, greater shrinkage is associated with a higher density, due to the amount of material within a given volume and available hydroxyl sites for hydrogen bonding (Wood

Handbook, 2010).

Other Factors Affecting Dimensional Stability

Rowell and Banks (2010) stated that the effectiveness of a dimensional stabilizing wood treatment reflects its ability to reduce or prevent the swelling and shrinking of wood resulting from moisture gain. They also noted that dimensional stability is dependent on the extent of moisture gain, rather than the rate of gain – making it important to consider basic wood anatomy when examining swelling. Some 95% of the volume of is occupied by homogeneously distributed longitudinal tracheids whose radial walls are interconnected primarily via bordered pits, while the residual volume is occupied by ray tissue (Rowell and Banks, 1985). Hardwoods, on the other hand, are comprised of vessel elements that are typically connected end to end by perforation plates that provide the primary flow path, along with thick walled fibers and rays. Hardwood rays are often larger and more abundant than in many softwoods and are known to contribute to the development of checks. Differences in hardwood vessel

24 distribution (i.e. ring-, semi ring-, and diffuse porous) can also affect liquid conduction.

Furthermore, cell lumens of either wood type may contain impermeable blockages such as tyloses or resins that limit permeability and subsequent shrinking and swelling.

25

Wooden Crosstie Service Life, Failure and Concerns Over Dimensional Stability and Checking in Railroad Ties

Properly treated railroad ties or timbers can last for decades, especially when appropriately installed and maintained. However, even under these circumstances, wood used in outdoor applications is subjected to a number of biological and physical factors that can lead to degradation and failure. Wood-water relationships are a major concern for wood performance. Wet wood provides favorable conditions for fungal and insect colonization. Even in wood treated with biocides, repeated wetting and drying causes shrinking and swelling that leads to check development. Open checks in the wood surface may extend well beyond the treated area, creating gaps in preservative protection. Checks can also render the wood vulnerable to additional moisture uptake, leading to eventual tie degradation, strength loss and ultimately failure. A report by an extension of the

University of Tennessee suggests that as little as a 2 percent weight loss from fungal decay can cause strength losses of 30 to 50 percent (Taylor et. al., 2016). This is an especially important consideration for ties since their horizontal orientation increases the exposed area. Horizontal exposure also increases the possibility of standing water accumulating on the surface and inside checks. Checking in railroad ties can also affect the ability of the ties to securely hold spikes used to fasten rails to the ties, resulting in variable expansion or contraction of track gage. This can lead to railcar derailment.

Checking can also adversely affect overall strength, flexural capabilities and primary load bearing areas of a tie, particularly in the rail-bearing area (RBA) (Conners, 2008).

Therefore, the ability of a preservative treatment to repel water and limit subsequent

26 checking is of key importance in maintaining the functionality of wood used in railroad and other outdoor applications.

The Need for a Standard Test Method

Several methods are available for assessing water repellency and dimensional stability of wood. American Wood Protection Association (AWPA) Standard E4 submerges thin wood wafers, specifically cut and oriented to accentuate the surface to volume ratio, in deionized water for 30 minutes. Wafer thickness is recorded before and after submersion and these dimensions are then used to calculate the total percent swelling (AWPA, 2015) as an indirect measure of water repellency efficiency (WRE) compared to untreated controls. However, these short exposures are less useful for assessing the effects of repeated moisture cycles responsible for check development that are often experienced by railroad ties or other large timbers. Other methods used to investigate dimensional stability involve fabrication of specialized equipment such as swellometers fitted with extensometers to track dimensional change. However, measuring dimensional change for numerous replicates would require many of these devices and significantly increase equipment cost, material preparation and handling.

Assessing water repellency by water droplet contact angle has been extensively studied (Kalnins and Feist, 1991; Rak, 1975), however, many of the proposed methods again require the use of expensive analytical equipment. There is a continuing need for a simple and practical test for measuring water repellency and wood stability on specimens that are representative of actual exposure conditions.

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Contributions from Previous Work

Previous work by Beck (2014) was used to establish the framework for the current study. Beck’s work showed that smaller samples (25 x 76 x 203 mm long) performed similarly to larger samples. This reduced materials requirements and accelerated wetting and drying. The water droplet method used to assess water repellency in the previous study was also used in this study, although the scale was reversed. Surface checking was used to evaluate dimensional stability in both studies, however, quantification was performed using digital image analysis in the current work. The use of digital analysis increased the rate of data collection decreased tedious manual measurements. The current study built upon the previous work by expanding the body of data for water repellency responses while also providing an opportunity to investigate a potential alternative method for quantifying and understanding dimensional stability of preservative treated timbers.

Research Objective

The objective of this study was to assess the potential for using small scale tests of water repellency and check development on wood subjected to repeated moisture cycles to predict full-scale performance.

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MATERIALS

Wood Species and Procurement

The hardwood species evaluated in this study consisted of red oak (Quercus rubra), white oak (Quercus alba) and bigleaf maple (Acer macrophyllum). These species represent a moderately easy to treat, very difficult to treat species and easy to treat wood species, respectively and have all been historically used in various railroad applications

(RTA, 2016). Douglas-fir (Pseudotsuga menziesii), which has also been used for ties was included to provide a comparator. The species differ in tangential and radial shrinkage values as well as receptivity to preservative penetration. Permeability is also a controlling factor in moisture cycling that can provide a more comprehensive understanding of the effectiveness of a preservative between species.

Red and white oak parent boards (25.4 mm thick) were sourced from The

Hardwood Centre (Corvallis, OR). Bigleaf maple was obtained from Northwest

Hardwoods (NWH) (Eugene, OR) and kiln dried using a dry and wet bulb temperature of

54.5° C and 40.5° C, respectively. Drying was continued for 72 hours and resulted in a final moisture content of between 7% and 12% for each parent board prior to being cut to final dimensions. Standard 2x4 nominal dimension Douglas-fir was purchased locally.

The maple and Douglas-fir were planed to the desired thickness and then cut to their final dimensions (25.4 mm thick x 76 mm wide x 203 mm long). Kiln drying conditions provoked internal stresses that caused significant warping and cupping of the maple and created a challenge in obtaining uniform initial sample dimensions and surface quality.

29

All sample material had final dimensions of 25.4 x 76 x 203 mm long; where the broad face was primarily in the tangential plane.

Beck (2014) identified this dimension as part of a study to determine the minimum sample size to achieve behavior similar to that seen in full-sized ties subjected to repeated moisture cycling. The samples were conditioned (at 20° C and 65% RH) to a stable moisture content before being end-sealed with an elastomeric sealant (GacoFlex

N17) to retard extensive longitudinal preservative penetration and were weighed (nearest

0.1 gram). Finally, all samples were tagged using numbered aluminum tags. Stainless steel tags and fasteners were used on some of the samples due to the caustic nature of one of the preservative treatments (ACZA).

Preservative Treatment

Samples were pressure treated to the AWPA Use Category retention specifications for UC4A for creosote (P1/P13) (CREO), pentachlorophenol (Penta) or ammoniacal copper zinc arsenate (ACZA). The target creosote and pentachlorophenol retentions for the three hardwood species were 112 kg/m3 and 5.6 kg/m3, respectively.

Douglas-fir was treated to a target retention of 128 kg/m3 and 6.4 kg/m3 for creosote and pentachlorophenol, respectively. The target retention for ACZA was 6.4 kg/m3 for all species. Post-treatment net retentions were calculated based on approximated species density, specimen volume and percent weight gain after treatment (Table 1). Pressure treatments were performed in commercial treatment cylinders by JH Baxter & Co.

(Eugene, OR) (Table 2). Pentachlorophenol and creosote were administered using an

30 empty-cell process to limit uptake, while a full-cell process was used for ACZA.

Untreated control specimens were prepared for each species. Each sample was weighed following treatment to determine net solution uptake.

Table 1. Calculated net retention by preservative treatment type (kg/m3). Net Retention by Treatment (kg/m3) Species Penta Creosote ACZA Red oak 9.6 200 9.9 White oak 3.8 82 3.6 Douglas-fir 3.0 184 5.3 Bigleaf maple 11.5 192 11.3 Table 2. Process conditions used to treat materials with penta, creosote or ACZA. Temperature Pressure Vacuum Time Chemical Operation (°C) (kPa) (mmHg) (hrs) Conditioning 77 - 635 5 Air Pressure - 172 - 0.5 Pentachlorophenol Press Period 77 896 - 0.23 (6.94% actual) Expansion Bath 91 - 635 5.5 Final Vacuum - - 686 2

Conditioning 91 - 610 7 Air Pressure - 207 - 0.67 Press Period 84 896 - 2 Creosote Expansion Bath 96 - 635 4.5 (100% actual) Vacuum - - 660 1.5 Steam Cleanup 116 - - 2 Final Vacuum - - 660 2

Steam 116 - - 3 Conditioning Ammoniacal Vacuum - - 660 3.5 Copper Zinc Press Period 54 896 - 4 Arsenate Vacuum 2 - - 660 1 (2.41% actual) Steam Cleanup 93 - - 2.75 Final Vacuum - - 660 4

31

METHODS

Retention Analysis

Soxhlet Solvent Extraction for Creosote Treated Samples

Creosote retentions for all species were determined by Soxhlet extraction with toluene solvent (AWPA A6-09). Wafers for creosote extraction were cut from the center of three moisture cycled samples to determine remaining creosote levels after eight moisture cycles for all species (Figure 1). Non-moisture cycled creosote treated bigleaf maple samples were also examined. The wafers were oven dried at 50°C for 24 hours to obtain unextracted dry weights and avoid potential treatment volatilization. Wafers were then cut into smaller strips, and approximately 5 grams of this material was weighed for extraction. After extractions were concluded (approximately 5 hours), samples were removed and remained in a fume hood overnight for solvent evaporation. Residual moisture collected during extraction was also measured and recorded. Samples were then oven dried again at 103°C for 24 hours and weighed. Creosote retentions were then calculated using the pre- and post-extraction dry weights, accounting for water collected, as follows:

3 (푊1− 푊퐻2푂)− 푊2 Retention (kg/m ) = ∗ 휌푤표표푑 푊2

Where W1 = Pre-extraction OD weight (kg), W2 = Post-extraction OD weight (kg), WH2O = weight of 3 residual water collected during extraction (kg) and ρwood = density of wood species (kg/m ).

32

Figure 1. Wafers cut from cycled or uncycled creosote treated samples for Soxhlet extraction.

X-Ray Fluorescence (XRF) for Penta and ACZA Treated Samples

Penta or ACZA retentions after eight wet/dry cycles were determined by x-ray fluorescence spectroscopy (XRF) on a Spectro Titan benchtop XRF analyzer (AWPA

A9-16). Wafers were cut (Figure 2a) from the center of three moisture cycled samples to determine remaining treatment component levels after eight moisture cycles for all species. Nonexposed Penta or ACZA treated bigleaf maple samples were also examined.

Wafers were oven dried at 50°C overnight and then ground to pass a 20-mesh screen.

Ground wood was then compacted into XRF sample cups (Figure 2b) and analyzed for the appropriate elements following the procedure described in AWPA standard A9.

33

A B Figure 2. Wafers (A) cut from cycled or uncycled Penta or ACZA treated samples for XRF analysis. Prepared XRF sample cups, compacted at 250 in-lbs (28 N-m) (B).

Repeated Moisture Cycling

Repeated wetting and drying experienced by railroad ties in service was simulated by subjecting samples to eight repeated wet/dry cycles. Samples were immersed in water for 30 minutes under vacuum (~625-760 mmHg) followed by 30 minutes under pressure

(100-115 psi) to raise the wood moisture content above the fiber saturation point (~28%).

The samples were then oven dried at 100° C. Weights of selected samples were monitored every 24 hours to determine when drying was complete. The samples were considered oven dried when their weight decreased ≤ 0.25 grams/day. The samples were then weighed and allowed to cool with a desiccant in a sealed container to limit moisture uptake. The sealed containers were conditioned at 20° C and 65% RH until the temperature equilibrated. This typically required a minimum of eight (8) hours or longer.

34

Dimensional Stability - Quantifying Checking

As checks open, they create void spaces in the surface of the wood that typically appear darker than the wood surface itself, especially when untreated. Surface checking on the samples was assessed with digital image processing based on pixel intensity using

Matlab, a matrix-based open source product (Mathworks ®). The oven dry samples were placed in a jig that provided a consistent point of reference so that each image could be captured in an identical setting after each wet/dry cycle (Figure 3).

Figure 3. Apparatus used to capture sample images for checking analysis.

All images were captured under the same temperature, relative humidity and lighting conditions. Images were then subjected to several computing processes for check identification, isolation and quantification. Before processing, images were cropped to the same dimensions to capture only the wood surface and eliminate impedances such as

35 tags or labels by specifying a four-element position vector in the Matlab script of the form: xmin, ymin, width and height. A position vector of 575, 650, 1000, 2200 was used which converted to a cropped image measuring about 75 x 175 mm (13,125 mm2) for checking analysis. If necessary, the position vector could be adjusted to capture only the material surface for any extreme cases of sample shrinkage if necessary.

Variability in image color both within and between replicates, species and treatments posed challenges for the processing software. For example, slight variations in the image exposure of replicates, even within the same treatment and species combination, resulted in shade differences (some lighter and some darker) that created issues for the subsequent thresholding processes. Darker photos governed the thresholding process, causing the software to fail to recognize some surface checks in the lighter photos. This problem also occurred with differences in color between wood species and treatments. This issue was addressed by converting the images to an 8-bit grayscale format and then subjecting them to a contrast correction process to equalize/normalize the contrast of images within each batch (10) of photos. Equalizing was done by referencing all photos within the batch to the raw photo from the batch that showed the best quality in terms of color and contrast between the sound material and checks. Normalized photos were then loaded into a secondary open-source, Java-based image processing program (ImageJ - National Institutes of Health) to determine the correct thresholding value for each batch. Thresholding is a relatively simple method for image segmentation (Shapiro et. al. 2001). The grayscale images obtained during the

36 contrast equalization process were then converted to black and white binary images with the black elements corresponding to values of 0, and the white elements to values of 1.

These two distinct values were easily identified and quantified by their abundance.

During this segmentation, surface checks on the samples were identified as white, or 1’s

(Figure 4c).

Photos were subjected to a preliminary thresholding process via ImageJ to determine the value that best isolated only those elements containing a 1, that represented a check or void space. These values were then entered into an algorithm developed in

Matlab that applied this value across all photos within a batch. The thresholding technique required some trial and error to determine the best value. In some cases, the process included areas with no checking such as speckling or “noise” due to dirt or extremely distinct rays (Figure 4b).

A two-dimensional order-statistic filter was applied during the thresholding process to de-noise the photos in a logical manner. The filter considered 15x3 matrices of elements across the entirety of each photo and used the middle (23rd) value of the matrix to make decisions about the surrounding values. The filter identified all elements neighboring the middle element and adjusted them based on the mode of the values 1 and

0. Elements within the 15x3 matrix that were 0’s were converted to 1’s for a matrix in which 1’s were the primary element. As most checking occurs longitudinally, a vertically oriented matrix was chosen to better fit the objects of interest. The final stage of the thresholding process was a summation of the elements with values of 1 as well as both

37 binary elements combined. These two values were then divided by one another and multiplied by 100 to determine the percentage of checking.

The output following raw image acquisition, automated cropping, contrast equalization and final thresholding consisted of both a binarized mask of the thresholded image as well as the percentage of checking found in the binary image (Figure 4c).

Furthermore, the threshold image mask could be used to easily validate the numerical results obtained in addition to verifying that the software executed the commands as desired.

A B C Figure 4. Examples of grayscale (A), unfiltered (B) and filtered (C) binarized mask produced during checking image analysis. Note that sample shown is untreated white oak after two moisture cycles.

38

Water Repellency – Water Droplet

The method used to quantify water repellency was identical to the method developed by Beck (2014) with the scale values reversed. Beck rated high repellency with a 1, while the current work used a 5 to indicate high repellency. Three 20µL droplets of deionized water were pipetted on the tangential (widest) face of each oven dry sample.

Small deviations in droplet size near 25µL are reported to have little to no effect on contact angle (Kalnins & Feist, 1991). Droplets were carefully administered under controlled conditions (23° C and 65% RH) roughly 1 cm above the surface - taking caution to avoid checks, knots or other rough areas that might influence wetting. A total of 30 droplets were applied per species/treatment combination. Droplets were then visually characterized over a 20-minute period based on their apparent contact angle. The measurement was based on the contact angle between the droplet and the wood surface and was given a value between 5 and 1. A droplet with a more globular shape typically corresponded with a contact angle that was greater than 90° and received a value of 5.

Droplets that had a contact angle between: 60°-90° were assigned a value of 4, 30°-60° were assigned a 3, 5°-30° were assigned a 2 and those droplets that were between 0° and

5° or had completely absorbed/wetted were assigned a value of 1 (Figure 5).

39

Figure 5. Water droplet characterization scale for assessing surface-moisture behavior of wood surfaces adopted from Beck, 2014.

40

Statistical Analysis

Water Repellency

Droplet behavior was not assessed between species because inherent differences, such as extractives content, were expected to have a greater effect on moisture responses.

Responses at the longest observation (20-minute) interval after water droplet application were used to investigate trends since this was expected to be the point when the droplet had the lowest contact angle and represented the lowest repellency for a given treatment.

The normality assumption of droplet sample distributions was investigated and reasonably met using histograms and residual plots. Minor violation of the equal variance assumption between treatment groups was detected in standardized residual plots and was corrected using a VarIdent variance structure to allow for heterogeneity of variance between treatment groups. The independence assumption was violated through repeated measures over the eight moisture cycles and addressed using a generalized linear mixed effects model that accounted for random error within replicates or subjects and auto- correlation between moisture cycles. Droplet response data were modeled as continuous over the eight moisture cycles to develop and fit estimated mean relationship curves between treatments within species groups. Droplet response data were then modeled as categorical to make pairwise comparisons or specific contrasts between treatments, moisture cycles or combinations thereof. The adjusted mixed effects models were evaluated with Analysis of Variance (ANOVA) tests to investigate significant interactions between factors. Factor interactions were then further explored using adjusted pairwise comparisons to determine where, and to what extent, these interactions

41 were significant. Pairwise comparisons for all possible treatment, cycle and treatment- cycle differences were used instead of individual contrasts to obtain adjusted p-values, minimizing the potential for Type I error (i.e. incorrectly rejecting H0).

Checking

Differences in anatomy meant that species would behave differently. As a result, checking was analyzed by species individually. Many samples experienced little or no checking in the first three moisture cycles. Although these observations provided a baseline measurement, they were removed since they complicated the analysis and were less useful. Untreated samples experienced little checking over all moisture cycles and were also excluded from the analysis since they did not provide a comparative control.

Sampling distributions were investigated with histograms and residual plots and were found to be non-normal. Log transformations were performed to meet the normality assumption. Investigation of plots of the residuals against the explanatory variable

(treatments) and plots showing auto-correlation revealed heterogeneity of variance between preservative treatments and moisture cycles. Violation of the homogeneity of variance assumption was corrected for using the VarIdent variance structure in R. This allowed for unequal variances for both treatments and cycles. Independence was violated through repeated measures over moisture cycles and was addressed using a generalized linear mixed effects model that accounted for random error within replicates or subjects and auto-correlation. Checking variables were factored and modeled as categorical to generate pairwise comparisons (or contrasts) between treatments, moisture cycles or

42 combinations thereof. The adjusted mixed effects model was evaluated with an ANOVA to investigate significant differences between treatments and moisture cycles and their interaction. The resulting estimates were then back-transformed and explored in detail using adjusted pairwise comparisons. The back-transformed pairwise comparisons for all treatment, cycle and treatment-cycle differences were used in place of individual contrasts to obtain adjusted p-values and minimize the potential for Type I error (i.e. an incorrectly rejected H0). Estimates were reported as medians, while comparisons were reported as ratios of medians as a result of back transformation from the log scale.

43

RESULTS AND DISCUSSION

Preservative Treatment Retention Analysis

Soxhlet Solvent Extraction for Creosote Treated Samples

Retentions determined by soxhlet solvent extraction were lower than expected

(Table 3). On average, creosote treated red oak, white oak, Douglas-fir and bigleaf maple were found to be 54%, 75%, 79% and 63% lower after eight moisture cycles, respectively, than the calculated net retentions. Furthermore, retentions for non-exposed bigleaf maple samples were found to be 77% lower on average than net retentions calculated based on pre- and post-treatment weights. It is still unclear why net retentions were so much lower for the non-exposed bigleaf maple while lower retentions found for exposed materials may be due to removal of solubilized preservative.

XRF Analysis of Penta or ACZA Treated Samples

XRF analysis of penta treated samples revealed levels similar to calculated net retentions, although slightly lower after repeated moisture cycling (Table 4). Species retentions align with their respective permeability and suggested ease of preservative treatment. On average, creosote treated red oak, white oak, Douglas-fir and bigleaf maple were found to be around 36%, 0.26%, 17% and 35% lower after eight moisture cycles, respectively, than the calculated net retentions. Penta retention for the uncycled bigleaf maple were only slightly lower (~10%) than previously calculated.

Retentions determined by XRF analysis for ACZA treated samples were negligibly lower after eight moisture cycles than the previously calculated net retentions

44

(Table 5). Since net retentions were based on post treatment weights immediately after treating, these small losses are probably partially a result of ammonia evaporation.

However, small losses may also suggest that very minor leaching of various ACZA components occurred with cycling. This was evident by the color of effluent water after pressure soaking. Negligible losses may also demonstrate satisfactory fixation within the wood, even after repeated moisture cycling. XRF analysis of ACZA treated uncycled bigleaf maple samples revealed levels equal to those calculated for net retention.

45

Table 3. Average creosote retentions by species after eight wet/dry cycles, except bigleaf maple (uncycled), determined by soxhlet solvent extraction. Averages based on triplicate species analysis. *After eight moisture cycles. Target Retention Actual Retention Species (kg/m3) (kg/m3) Red oak 112 92.2* White oak 112 20.9* Douglas-fir 128 38.3* Bigleaf maple 112 71.7* Bigleaf maple 112 49.4 (uncycled)

Table 4. Average penta retentions by species after eight wet/dry cycles, except bigleaf maple (uncycled), determined by XRF analysis. Averages based on triplicate species analysis. Species Penta (kg/m3) Red oak 6.14 White oak 3.79 Douglas-fir 2.49 Bigleaf maple 7.46 Bigleaf maple (uncycled) 10.39

Table 5. Average ACZA component retentions by species after eight wet/dry cycles, except bigleaf maple (uncycled), determined by XRF analysis. Averages based on triplicate species analysis. Copper Zinc Arsenic Total Species (kg/m3) (kg/m3) (kg/m3) (kg/m3) Red oak 6.33 2.68 1.86 10.88 White oak 2.02 0.85 0.60 3.47 Douglas-fir 4.45 1.91 1.50 7.86 Bigleaf maple 5.75 2.46 1.75 9.95 Bigleaf maple 6.68 2.82 2.09 11.59 (uncycled)

46

Water Repellency

Many water droplet test methods are difficult to use because they assume wood surfaces to be smooth and flat; however, wood has inconsistent topography that can affect droplet behavior (Rowell and Banks, 1985). In addition, only small surface areas are subjected to measurement, therefore many replicates are needed. In the current work, three droplets were administered to each sample since prior tests showed only minor differences in the variability when five droplets were used. Furthermore, it would have been impractical to apply more drops to each sample because of the number of samples involved. Water droplet ratings were frequently confirmed by periodic digital analysis throughout testing.

Initial Water Droplet Repellency Tests by Treatment (individual 20-minute tests)

All untreated species, except for Douglas-fir (DF), exhibited poor water repellency during the initial water droplet test as all droplets were nearly or completely absorbed within 20 minutes of application (Figures 6-9). This is likely due to the smooth surface of the freshly planed DF. Untreated red oak samples showed the largest decrease in contact angle over the 20-minute period but these samples were not planed. Ratings for untreated materials, especially immediately after droplet application, were less than those observed for creosote, penta or ACZA treated materials, reflecting the hygroscopic nature of untreated wood.

Creosote and penta treated materials experienced smaller overall decreases in droplet contact angle over the 20-minute observation period than the untreated material.

47

Again, there was a notable difference in repellency for treated samples compared with the untreated. The trend lines for all creosote or penta treated species were also more tightly grouped than those in the untreated group. The ability of these treatments to maintain consistent hydrophobicity illustrated the repellency offered by oilborne preservatives; rating magnitudes also clearly demonstrated this effect at all time points.

Interestingly, ACZA treated samples also exhibited increased water repellency during the initial test period, although water repellency is not typically associated with waterborne preservatives. While the initial water repellency ratings for ACZA were the greatest of all the treatments, the short-term repellency trends were similar to what was observed for identical species that were untreated.

48

Figure 6. Initial water repellency test ratings for untreated red oak, white oak, Douglas-fir and bigleaf maple samples. Vertical axis showing droplet rating. Horizontal axis showing observation time point in minutes.

Figure 7. Initial water repellency test ratings for creosote treated red oak, white oak, Douglas-fir and bigleaf maple samples. Vertical axis showing droplet rating. Horizontal axis showing observation time point in minutes.

49

Figure 8. Initial water repellency test ratings for pentachlorophenol (penta) treated red oak, white oak, Douglas-fir and bigleaf maple samples. Vertical axis showing droplet rating. Horizontal axis showing observation time point in minutes.

Figure 9. Initial water repellency test ratings for ammoniacal copper zinc arsenate (ACZA) treated red oak, white oak, Douglas-fir and bigleaf maple samples. Vertical axis showing droplet rating. Horizontal axis showing observation time point in minutes.

50

Water Droplet Test Results After Repeated Moisture Cycling

Mean droplet values were compared at the longest (20 minute) observation time after water application when the droplet should have the lowest contact angle and represent the lowest repellency for a given treatment. All water repellency tests showed normal distributions and homogeneity of variance at both the individual testing level and the combined moisture cycle level. Statistical analysis and confirmation of assumptions were performed using R Studio software.

Red Oak Water Droplet Test Results

Red oak mean droplet ratings for all treatments: initial, final and their comparisons

Water droplet results at the beginning and end of moisture cycling were used to compare droplet ratings after repeated moisture cycles for each treatment (Table 8;

Figures 11-12). Comparisons in repellency between treatments after each moisture cycle were also made (Table 11). Mean droplet ratings for untreated red oak control samples at the initial and final test periods were 1.23 and 3.40, respectively, indicating an increase in repellency of 176% for untreated red oak samples over the 8 wet/dry cycles (Tables 6-7).

Creosote and penta treated materials also experienced increased repellency over wet/dry cycles, although to a lesser extent. Creosote treated red oak had an initial mean rating of 2.63, while penta treated materials had an initial mean repellency rating of 2.87.

Creosote and penta treated materials showed mean repellency ratings of 4.70 and 4.10, respectively, at the end of the eight moisture cycles reflecting increases in repellency of

79% for creosote and 43% for penta treated materials.

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Although physical changes in the wood and subsequent preservative loss through repeated wetting and drying were expected to cause decreased repellency, the opposite effect was observed over the eight moisture cycles. Beck (2014) also found increased repellency over repeated moisture cycles. The observed increases may be due to the preservative, and/or hydrophobic extractives migrating and accumulating at the wood surface that could have altered moisture behavior. Beck (2014) also suggested that the absence of ultraviolet (UV) light may also be partially responsible for increases in repellency since there was no possibility of surface photo-oxidation of potential volatiles to accumulate on the wood surface.

In contrast, repellency of ACZA treated materials decreased over all cycles from an initial mean rating of 3.80 to a final mean rating of 1.07. Changes in repellency for

ACZA treated materials were the greatest for all treatments with a decrease of nearly

72%. The ammonia solvent in this preservative is also capable of dissolving materials around pit membranes in softwoods such as Douglas-fir (Lebow, 1993) and could also affect hydrophobic extractives that may have been partially responsible for repellency.

All comparisons were statistically significant (p-value < 0.0001, α = 0.05).

Table 6. Effect of preservative treatment on water repellency of red oak samples prior to exposure to 8 wet/dry cycles Mean Treatment SE DF Lower CL Upper CL Rating UTC 1.23 0.198 39 0.83 1.63 CREO 2.63 0.134 36 2.36 2.91 PENTA 2.87 0.137 36 2.59 3.15 ACZA 3.80 0.177 36 3.44 4.16

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Table 7. Effect of preservative treatment on water repellency of red oak samples after 8 wet/dry cycles Mean Treatment SE DF Lower CL Upper CL Rating UTC 3.40 0.198 39 3.00 3.78 CREO 4.70 0.134 36 4.43 4.97 PENTA 4.10 0.137 36 3.82 4.38 ACZA 1.07 0.177 36 0.71 1.43 Table 8. Comparison of the effect of preservative treatment on water repellency of red oak samples prior to and after 8 wet/dry cycles. Negative values appear in parentheses. Cycle Estimate of the Treatment S.E. DF t.ratio p-value Contrast Difference UTC 0-8 2.17 0.260 288 8.35 <0.0001 CREO 0-8 2.07 0.159 288 12.98 <0.0001 PENTA 0-8 1.23 0.165 288 7.50 <0.0001 ACZA 0-8 (2.73) 0.229 288 -11.95 <0.0001

Figure 10. Effect of preservative treatment on water repellency of red oak samples immediately after treatment and when subjected to 8 wet/dry cycles.

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Comparison between water repellency after 6 and 8 cycles for all treatments

Mean repellency tended to change more slowly after the sixth cycle for all treatments (Table 9). Although decreases were observed for all treatments, changes observed between the 6th and 8th moisture cycle for creosote (Δ 0.300 units) and ACZA

(Δ 0.533 units) treated samples were not statistically significant. However, differences in mean droplet ratings of 0.933 units for the controls and 0.767 units for penta treated samples were statistically significant for the same cycle interval (p-values = 0.0114 and

0.0002, respectively).

Table 9. Effect of six to eight wet/dry cycles on water repellency of red oak samples treated with selected wood preservatives. Cycle Estimate of the Treatment S.E. DF t.ratio p-value Contrast Difference UTC 6-8 0.93 0.260 288 3.59 0.0114 CREO 6-8 0.30 0.159 288 1.88 0.6252 PENTA 6-8 0.77 0.165 288 4.66 0.0002 ACZA 6-8 0.53 0.229 288 2.33 0.3263

Comparisons of the maximum and minimum mean rating over all cycles for each treatment

Several similarities were observed when the maximum and minimum mean repellency ratings were considered over all cycles (Table 10). The two oilborne preservatives had similar overall differences with mean ratings of 2.37 and 2.27, respectively, while both the controls and the ACZA treated samples had absolute mean repellency differences of 3.17 units. All the differences were statistically significant (α =

0.05).

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Table 10. Differences in the absolute maximum and minimum mean water repellency ratings after varying wet/dry cycles of red oak treated with selected preservatives. Cycle Estimate of the Treatment S.E. DF t.ratio p-value Contrast Absolute Difference UTC 0-5 |3.17| 0.260 288 -12.20 <0.0001 CREO 0-6 |2.37| 0.159 288 -14.87 <0.0001 PENTA 1-4 |2.27| 0.165 288 -13.78 <0.0001 ACZA 2-8 |3.17| 0.229 288 13.85 <0.0001

Variability in responses for a given treatment

Standard errors, which are an extension of standard deviation, associated with the oilborne creosote and penta treatments were relatively low and similar (0.1592 and

0.1645, respectively). The standard error of ACZA treated red oak samples was a bit higher (0.2287). Deviation from the mean (variance) in responses is generally greater for experimental controls than in treated groups in controlled experiments, which results in a greater standard error of the estimates. This tendency held true in this study with the greatest standard error (0.2598), though still relatively low, occurring for untreated red oak. This illustrates the inherent variability of untreated wood as well as the improvement in the consistency and uniformity of responses provided by the oilborne treatments. In contrast, water repellency was less consistent in untreated and ACZA treated samples.

Comparisons for Moisture Cycles 0, 5, 6 and 8

Initial repellency tests for all treatments indicated statistically significant differences in the mean droplet ratings for untreated controls indicative of poor resistance of moisture uptake compared to treated samples. The results demonstrate the innate hygroscopicity of untreated wood. Both creosote and penta exhibited a higher repellency

55 than the untreated controls at cycles 5, 6, and 8, though the differences were only statistically significant in the 8th cycle for creosote treated samples (p-value = 0.0016).

The opposite trend was observed for the ACZA treated samples in comparison to the untreated controls. Mean repellency for untreated red oak was greater than those for

ACZA at the 5th, 6th and 8th cycles, illustrating the poor repellency imparted by ACZA.

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Figure 11. Effect of eight wet/dry cycles on mean droplet rating for red oak wood samples either untreated or treated with ACZA, creosote or penta.

Figure 12. Effect of eight wet/dry cycles on the estimated mean droplet rating for red oak wood samples either untreated or treated with ACZA, creosote or penta.

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Table 11. Comparisons between selected preservative treatments and untreated red oak samples on water repellency over 8 wet/dry cycles. Negative mean values are represented by parentheses. Bolded p-values represent significance (α = 0.05). Estimate of the Cycle Contrast S.E. DF t.ratio p-value Difference in Means UTC - CREO (1.40) 0.239 36 -5.88 4.55e-04 0 UTC - PENTA (1.64) 0.241 36 -6.80 <0.0001 UTC - ACZA (2.57) 0.266 36 -9.68 <0.0001

UTC - CREO (0.73) 0.239 36 -3.07 0.452 1 UTC - PENTA 0.97 0.241 36 4.02 0.072 UTC - ACZA 0.53 0.266 36 2.01 0.978

UTC - CREO (0.10) 0.239 36 -0.42 1.000 2 UTC - PENTA (0.43) 0.241 36 -1.80 0.995 UTC - ACZA (0.73) 0.266 36 -2.76 0.660

UTC - CREO (2.10) 0.239 36 -8.79 <0.0001 3 UTC - PENTA (2.53) 0.241 36 -10.53 <0.0001 UTC - ACZA (1.00) 0.266 36 -3.77 0.127

UTC - CREO (0.67) 0.239 36 -2.79 0.640 4 UTC - PENTA (0.80) 0.241 36 -3.33 0.302 UTC - ACZA 1.40 0.266 36 5.27 0.003

UTC - CREO (0.30) 0.239 36 -1.26 0.999 5 UTC - PENTA (0.23) 0.241 36 -0.97 1.000 UTC - ACZA 2.90 0.266 36 10.92 <0.0001

UTC - CREO (0.67) 0.239 36 -2.79 0.639 6 UTC - PENTA (0.53) 0.241 36 -2.22 0.936 UTC - ACZA 2.73 0.266 36 10.29 <0.0001

UTC - CREO (1.10) 0.239 36 -4.61 0.016 7 UTC - PENTA (0.87) 0.241 36 -3.60 0.179 UTC - ACZA 2.63 0.266 36 9.92 <0.0001

UTC - CREO (1.30) 0.239 36 -5.44 0.002 8 UTC - PENTA (0.70) 0.241 36 -2.91 0.560 UTC - ACZA 2.33 0.266 36 8.79 <0.0001

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White Oak Water Droplet Test Results

White oak mean droplet ratings for all treatments: initial, final and their comparisons

Mean droplet ratings for untreated white oak control samples at the initial and final wet/dry cycles were 1.22 and 4.87, respectively (Tables 12-13). Like the red oak materials, water repellency increased for untreated white oak samples over the 8 wet/dry cycles. Untreated white oak experienced the largest increase in repellency over all moisture cycles possibly due to extractive migration to the surface with moisture movement.

Similar to the untreated controls, both the creosote and penta treated materials also experienced increased repellency over wet/dry cycles (Table 14). Creosote treated white oak had an initial mean rating of 2.88, while penta treated materials had an initial mean repellency rating of 3.03. By the end of the eight moisture cycles, the creosote and penta treated materials showed mean repellency ratings of 4.23 and 4.37, respectively, and both followed similar trends over all moisture cycles.

ACZA treated materials also experienced increased water repellency over all cycles with an initial mean rating of 2.67 and a final mean rating of 3.20. The trend for

ACZA treated white oak was the opposite of what was observed for the ACZA treated red oak. Increases in water repellency as measured by the water droplet method were the opposite of what was expected after repeated moisture cycling. These trends may be due to the presence of extractives at the wood surface. Hse and Kuo (1988) noted that even though extractives represented only a small percentage of wood (~5-10%), they

59 influenced several wood properties such as odor, color, light stability, flammability, hygroscopicity, density, strength, decay and insect resistance, and permeability.

White oak has both water soluble and insoluble extractives that may migrate through the wood and accumulate on the surface through moisture cycling. These extractives remain on the wood surface where they might affect moisture behavior. Hse and Kuo (1988) found that this surface accumulation negatively affected gluing because of poor wetting (i.e. liquid repellency). Several other studies have found similar results for white oak reporting poor wetting and surface contamination due to heavy extractive deposits on the wood surface (Craft, 1970; Roffael and Rauch, 1974; Kuo et. al., 1984). It is unclear whether ACZA, which has a known ability to dissolve extractives in Douglas- fir, has similar capabilities in white oak. Ammonia solubilization of extractives could exaggerate the increased repellency observed with moisture cycling.

Table 12. Effect of preservative treatment on water repellency of white oak samples prior to exposure to 8 wet/dry cycles.

Treatment Mean Rating SE DF Lower CL Upper CL UTC 1.22 0.198 39 0.82 1.62 CREO 2.88 0.138 36 2.60 3.16 PENTA 3.03 0.141 36 2.75 3.32 ACZA 2.67 0.154 36 2.36 2.98 Table 13. Effect of preservative treatment on water repellency of white oak samples after 8 wet/dry cycles. Treatment Mean Rating SE DF Lower CL Upper CL UTC 4.87 0.198 39 4.47 5.27 CREO 4.23 0.138 36 3.95 4.51 PENTA 4.37 0.141 36 4.08 4.65 ACZA 3.20 0.154 36 2.89 3.51

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Table 14. Comparison of the effect of preservative treatment on water repellency of white oak samples prior to and after 8 wet/dry cycles. Cycle Estimate of the p- Treatment S.E. DF t.ratio Contrast Difference value UTC 0-8 3.65 0.263 288 13.88 <.0001 CREO 0-8 1.35 0.170 288 7.97 <.0001 PENTA 0-8 1.33 0.174 288 7.68 <.0001 ACZA 0-8 0.53 0.194 288 2.75 0.1370

Figure 13. Effect of preservative treatment on water repellency of white oak samples immediately after treatment and when subjected to 8 wet/dry cycles.

Comparison of the 6th and 8th cycles, for all treatments

As with red oak, mean repellency tended to decrease after the fifth or sixth cycle drying period for white oak, with the exception of the penta treatment group which experienced repellency decreases near the fourth moisture cycle. However, a notable inflection was also observed for the penta group at the sixth cycle. Therefore, water

61 repellency comparisons were again made between the sixth and eighth cycles to investigate the effects of these cycles on water repellency (Table 17). Although decreased water repellency was observed for all treatments except penta, changes observed between the 6th and 8th moisture cycle were statistically significant for creosote (Δ 0.733 units) and

ACZA (Δ 1.33 units) treated samples. This was the opposite from what was seen for red oak (p-values = 0.0099 and 2.59e-08, respectively). Instead, differences in mean droplet rating for the untreated controls and penta treated samples (though in the positive direction, i.e. more repellent), were not statistically significant for the same cycle interval. The overall trends would indicate that the two oaks were similar in the rate of response to moisture cycling, though there were differences between treatments.

Table 15. Effect of wet/dry cycles (6 to 8) on water repellency of white oak samples treated with selected wood preservatives. Significant comparisons are represented in bold. Cycle Estimate of the Treatment S.E. DF t.ratio p-value Contrast Difference UTC 6-8 0.13 0.263 288 0.51 1.0000 CREO 6-8 0.73 0.170 288 4.33 0.0099 PENTA 6-8 -0.07 0.174 288 -0.38 1.0000 ACZA 6-8 1.33 0.194 288 6.86 <.0001

Comparisons between maximum and minimum mean rating for all cycles for each treatment

Examining maximum and minimum mean repellency ratings over all cycles revealed some distinct patterns (Table 16). Creosote and penta no longer produced significantly similar overall differences in mean repellency ratings on white oak and red oak samples. However, differences in ratings were still less than 1 repellency unit. As

62 with the red oak samples, the oilborne treated white oak still experienced the smallest changes in repellency for all treatment groups illustrating the ability of oilborne preservatives to repel water. The untreated controls and the ACZA treated materials experienced the largest changes in repellency. All the differences were statistically significant at α = 0.05.

Table 16. Differences in water repellency for the maximum and minimum mean repellency occurring at varying wet/dry cycles of white oak treated with selected preservatives. Cycle Estimate of the Treatment S.E. DF t.ratio p-value Contrast Absolute Difference UTC 0-6 |3.78| 0.263 288 -14.39 <.0001 CREO 0-6 |2.08| 0.170 288 -12.29 <.0001 PENTA 0-4 |1.27| 0.174 288 -7.30 <.0001 ACZA 0-3 |2.27| 0.194 288 -11.67 <.0001

Variability in responses for a given treatment

Similar to red oak, the standard error associated with estimates of the differences for the oilborne creosote and penta treated white oak were relatively low and similar, with values of 0.1695 and 0.1735, respectively. The standard error of ACZA treated white oak samples was negligibly higher with a value of 0.1943. As mentioned previously, there is generally a greater deviation from the mean (variance) in responses observed in experimental controls than in treated groups, which results in a greater standard error of the estimates. Once again, this premise held true with the greatest standard error, though again still relatively low, of 0.2628 found for untreated white oak.

This suggests a more consistent water repellent effect for the oilborne preservatives.

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Figure 14. Effect of eight wet/dry cycles on the mean droplet rating for white oak wood samples either untreated or treated with ACZA, creosote or penta.

Figure 15. Effect of eight wet/dry cycles on the estimated mean droplet rating for red oak wood samples either untreated or treated with ACZA, creosote or penta.

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Table 17. Comparisons between selected preservative treatments and untreated white oak samples on water repellency over 8 wet/dry cycles. Negative mean values are represented by parentheses. Bolded p-values represent significance (α = 0.05). Estimate of the Cycle Contrast S.E. DF t.ratio p-value Difference in Means UTC - CREO (1.66) 0.242 36 -6.88 <.0001 0 UTC - PENTA (1.81) 0.243 36 -7.46 <.0001 UTC - ACZA (1.45) 0.251 36 -5.77 <.0001

UTC - CREO (1.07) 0.242 36 -4.41 0.027 1 UTC - PENTA (0.57) 0.243 36 -2.33 0.898 UTC - ACZA (0.47) 0.251 36 -1.86 0.992

UTC - CREO (0.40) 0.242 36 -1.66 0.999 2 UTC - PENTA (0.73) 0.243 36 -3.02 0.488 UTC - ACZA (1.40) 0.251 36 -5.58 0.001

UTC - CREO (1.10) 0.242 36 -4.55 0.019 3 UTC - PENTA (1.90) 0.243 36 -7.81 <.0001 UTC - ACZA (1.97) 0.251 36 -7.84 <.0001

UTC - CREO 0.07 0.242 36 0.28 1.000 4 UTC - PENTA (0.10) 0.243 36 -0.41 1.000 UTC - ACZA 0.00 0.251 36 0.00 1.000

UTC - CREO 0.27 0.242 36 1.10 0.999 5 UTC - PENTA 0.43 0.243 36 1.78 0.996 UTC - ACZA 0.47 0.251 36 1.86 0.992

UTC - CREO 0.03 0.242 36 0.14 1.000 6 UTC - PENTA 0.70 0.243 36 2.88 0.580 UTC - ACZA 0.47 0.251 36 1.86 0.992

UTC - CREO 0.10 0.242 36 0.41 1.000 7 UTC - PENTA 0.50 0.243 36 2.06 0.971 UTC - ACZA 0.70 0.251 36 2.79 0.641

UTC - CREO 0.63 0.242 36 2.62 0.751 8 UTC - PENTA 0.50 0.243 36 2.06 0.971 UTC - ACZA 1.67 0.251 36 6.64 <.0001

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Douglas-fir Water Droplet Test Results

Mean droplet ratings for Douglas-fir for all treatments: initial, final and their comparisons

Water droplet test results are presented in Table 23 and droplet behavior over all cycles is illustrated in Figures 18 and 19. Mean droplet ratings for untreated Douglas-fir control samples at the initial and final test periods were 2.74 and 4.33, respectively, indicating a substantial increase in repellency of 58% over the 8 wet/dry cycles. This trend was consistent with observations for untreated samples of the other species in this study and indicated that water repellency improved with repeated moisture cycling.

Creosote and penta treated materials also exhibited increased repellency over wet- dry cycles. ACZA treated materials experienced a slight initial increase in water repellency in the first three moisture cycles and then a decrease over the remaining cycles. Creosote treated Douglas-fir had an initial mean water repellency rating of 3.00, while penta treated samples had an initial mean repellency rating of 2.93. Creosote and penta treated materials showed mean repellency ratings of 4.40 and 3.83, respectively, at the end of the eight moisture cycles, reflecting increased repellency of 47% for creosote and 31% for penta treated materials.

Physical changes and preservative loss in the wood through repeated wetting and drying were expected to result in decreases in water repellency; however, the opposite effect was observed. Repellency increases for Douglas-fir may, once again, be due to migration of the preservative or resinous extractives to the wood surface. Where they altered moisture behavior. Douglas-fir heartwood contains resin as well as extractive

66 compounds that can remain mobile after harvest. Wetting can solubilize some of these compounds. As the wood dries, these compounds can migrate to the surface where they accumulate and remain, unless physically removed or degraded by an external influence like ultraviolet exposure (Beck, 2014). Migration was evident in the penta treated

Douglas-fir, as these hardened deposits were clearly visible on the surface of many samples (Figure 16).

Figure 16. Example of migration and surface accumulation of resinous extractives on penta treated Douglas-fir that might have affected water repellency.

Migration of extractives may also be affected by the treatments involved.

Migration in the untreated controls was solely dependent on the solubilization of extractives during soaking, followed by their migration to the surface as the wood dried.

In addition to the inherent water repellency of the creosote and penta treatments, there is a potential for the solvents to solubilize resins and wood extractives. Furthermore,

67 extractives may have been initially mobilized during the treating process, bringing them closer to the surface. A combination of factors may help to explain the trends observed for ACZA treated Douglas-fir. The ammonia in the ACZA solution may have solubilized resin or extractives that could migrate to the surface; however, once the ammonia evaporated, these samples may tend to behave more like untreated samples in terms of water repellency. The slower changes in water repellency in samples treated with the two oilborne preservatives may reflect the slower removal of these surface compounds over the eight moisture cycles (Figure 17). The absence of ultraviolet exposure and other non- moisture related environmental effects may also affect the rate of removal of surface contaminants that might occur by physical degradation. The limited changes in repellency likely reflect losses due to material handling during moisture cycling.

Although repellency initially improved for ACZA treated materials, it decreased over all moisture cycles from an initial mean rating of 4.40 to a final mean rating of 3.33, representing an overall decrease of 24%. The ammonia solvent used to deliver this preservative into the wood is capable of dissolving materials around pit membranes in softwoods such as Douglas-fir (Lebow, 1993). This may have altered the distribution of hydrophobic resinous extractives or degraded , and may have been partially responsible for repellency. Table 20 illustrates the differences observed for water repellency for each treatment and the direction of the change. All comparisons were statistically significant (p-value < 0.005) at the α = 0.05 level.

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Table 18. Effect of preservative treatment on water repellency of Douglas-fir samples prior to exposure to 8 wet/dry cycles. Treatment Mean Rating SE DF Lower CL Upper CL UTC 2.74 0.209 39 2.32 3.16 CREO 3.00 0.123 36 2.75 3.25 PENTA 2.93 0.127 36 2.68 3.19 ACZA 4.40 0.178 36 4.04 4.76

Table 19. Effect of preservative treatment on water repellency of Douglas-fir samples after 8 wet/dry cycles. Treatment Mean Rating SE DF Lower CL Upper CL UTC 4.33 0.209 39 3.91 4.76 CREO 4.40 0.123 36 4.15 4.65 PENTA 3.83 0.127 36 3.58 4.09 ACZA 3.33 0.178 36 2.97 3.69

Table 20. Comparison of the effect of preservative treatment on water repellency of Douglas-fir samples prior to and after 8 wet/dry cycles. Negative values appear in parentheses. Cycle Estimate of the Treatment S.E. DF t.ratio p-value Contrast Difference in Means UTC 0-8 1.59 0.288 288 5.53 <.0001 CREO 0-8 1.40 0.161 288 8.68 <.0001 PENTA 0-8 0.90 0.168 288 5.35 <.0001 ACZA 0-8 (1.07) 0.244 288 -4.38 0.0006

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Figure 17. Effect of preservative treatment on water repellency of Douglas-fir heartwood samples immediately after treatment and when subjected to 8 wet/dry cycles.

Comparison of the 6th and 8th cycles, for all treatments

Estimates of mean repellency ratings for all treatment groups converged near a mean repellency rating of 4.5 after the 5th moisture cycle. Mean repellency tended to decrease with additional moisture cycles (Table 21). Although decreased repellency was observed for all treatments between the 6th and 8th moisture cycles, the decreases for untreated (Δ 0.433 units), creosote (Δ 0.267 units) and penta (Δ 0.467 units) treated samples were not statistically significant. However, mean repellency for ACZA treated samples decreased by about 30% between the 6th and 8th moisture cycle and this decrease was significant (p-value = 0.0228, α = 0.05).

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Table 21. Effect of 6 to 8 wet/dry cycles on water repellency of Douglas-fir samples treated with selected wood preservatives. Significant comparisons are represented in bold. Cycle Estimate of the Treatment S.E. DF t.ratio p-value Contrast Difference in Means UTC 6-8 0.43 0.288 288 1.51 0.9999 CREO 6-8 0.27 0.161 288 1.65 0.9997 PENTA 6-8 0.47 0.168 288 2.78 0.6565 ACZA 6-8 1.00 0.244 288 4.11 0.0228

Comparisons of the maximum and minimum mean rating over all cycles, for each treatment

Examining the maximum and minimum mean water repellency over all moisture cycles again showed some notable similarities regarding absolute differences. Creosote and penta, had similar overall differences in mean repellency ratings of 1.67 and 1.87, respectively (Table 22). These results were similar to the observations on red oak samples with the same treatments. Oilborne treatments experienced the smallest changes in repellency which is consistent with the hydrophobic nature of these systems. As with the red oak and white oak, untreated controls and ACZA treated materials both experienced the largest changes in repellency of 2.03 and 2.17, respectively. All the differences were statistically significant (p-value < 0.0001, α = 0.05).

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Table 22. Differences in water repellency for the maximum and minimum mean rating occurring at varying wet/dry cycles of Douglas-fir treated with selected preservatives. Cycle Estimate of the Absolute Treatment S.E. DF t.ratio p-value Contrast Difference in Means UTC 0-6 |2.03| 0.288 288 -7.04 <.0001 CREO 0-6 |1.67| 0.161 288 -10.33 <.0001 PENTA 1-4 |1.87| 0.168 288 -11.10 <.0001 ACZA 2-7 |2.17| 0.244 288 8.89 <.0001

Variability in responses for a given treatment

The standard error associated with the oilborne creosote and penta treatments were relatively low and similar, with values of 0.1614 and 0.1681, respectively. The standard error of ACZA treated Douglas-fir samples was a bit higher with a value of

0.2436. The greatest standard error, though still relatively low, of 0.2880 was found for untreated Douglas-fir. The hierarchy of the treatment group standard errors was consistent between the red oak, white oak and Douglas-fir samples.

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Figure 18. Effect of eight wet/dry cycles on the mean droplet rating for Douglas-fir samples either untreated or treated with ACZA, creosote or penta.

Figure 19. Effect of eight wet/dry cycles on the estimated mean droplet rating for Douglas-fir samples either untreated or treated with ACZA, creosote or penta.

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Table 23. Comparisons between selected preservative treatments and untreated Douglas- fir samples on water repellency over 8 wet/dry cycles. Negative values are represented by parentheses. Bolded p-values represent significance (α = 0.05). Estimate of the Cycle Contrast S.E. DF t.ratio p-value Difference in Means UTC - CREO (0.26) 0.242 36 -1.08 0.9999 0 UTC - PENTA (0.19) 0.244 36 -0.79 1.0000 UTC - ACZA (1.66) 0.274 36 -6.06 0.0003

UTC - CREO 0.20 0.242 36 0.83 1.0000 1 UTC - PENTA 1.50 0.244 36 6.15 0.0002 UTC - ACZA (0.53) 0.274 36 -1.95 0.9854

UTC - CREO 0.30 0.242 36 1.24 0.9999 2 UTC - PENTA 0.23 0.244 36 0.96 1.0000 UTC - ACZA (1.00) 0.274 36 -3.65 0.1626

UTC - CREO (0.97) 0.242 36 -4.00 0.0753 3 UTC - PENTA (1.30) 0.244 36 -5.33 0.0022 UTC - ACZA (2.03) 0.274 36 -7.42 <.0001

UTC - CREO (0.10) 0.242 36 -0.41 1.0000 4 UTC - PENTA (0.30) 0.244 36 -1.23 0.9999 UTC - ACZA (0.53) 0.274 36 -1.95 0.9854

UTC - CREO 0.33 0.242 36 1.38 0.9999 5 UTC - PENTA 0.33 0.244 36 1.37 0.9999 UTC - ACZA 0.27 0.274 36 0.97 1.0000

UTC - CREO 0.10 0.242 36 0.41 1.0000 6 UTC - PENTA 0.47 0.244 36 1.91 0.9884 UTC - ACZA 0.43 0.274 36 1.58 0.9994

UTC - CREO 0.23 0.242 36 0.97 1.0000 7 UTC - PENTA 0.63 0.244 36 2.60 0.7663 UTC - ACZA 1.93 0.274 36 7.05 <.0001

UTC - CREO (0.07) 0.242 36 -0.28 1.0000 8 UTC - PENTA 0.50 0.244 36 2.05 0.9727 UTC - ACZA 1.00 0.274 36 3.65 0.1626

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Bigleaf Maple Water Droplet Test Results

Bigleaf maple mean droplet ratings for all treatments: initial, final and their comparisons

Water droplet results for bigleaf maple (BL) are presented in Table 29 and droplet comparisons over all cycles are illustrated in Figures 21 and 22. All comparisons were statistically significant (p-value < 0.0001, α = 0.05). Mean droplet ratings for untreated control BL samples at the initial and final test periods were 1.43 and 3.13, respectively, indicating an increase in repellency of 119% over the 8 wet/dry cycles. Untreated wood subjected to UV exposure typically experiences lignin degradation, which alters hydrophobic characteristics. The lack of UV exposure as suggested by Beck (2014) may be, in part, responsible for these increases in repellency. Furthermore, the effects of long term oven drying may have affected normally hygroscopic in untreated wood. Stamm (1964) found that that thermal degradation, even at lower drying temperatures, resulted in formation of less hygroscopic furfural polymers from these degraded hexose and pentose sugars. These effects may help explain these initial increases in hydrophobicity.

Creosote and penta treated materials also experienced increased repellency over wet/dry cycles. Creosote treated BL had an initial mean rating of 3.33, while penta treated materials had an initial mean repellency rating of 2.70. Creosote and penta treated materials showed mean repellency ratings of 4.70 and 4.50, respectively, at the end of the eight moisture cycles reflecting repellency increases of 41% for creosote and 67% for penta treated materials. Evidence of both sapwood and heartwood were present in the BL

75 materials, so the potential for solubilization of extractives and their migration to the wood surface may have altered surface moisture behavior. Again, the potential for migration and accumulation of these hydrophobic oilborne preservatives may also help to explain their greater water repellency.

Water repellency of ACZA treated materials, on the other hand, decreased over moisture cycles from an initial mean rating of 3.70 to a final mean rating of 1.27. The change in repellency for these materials resulted in a decrease of 66% over the eight moisture cycles. Waterborne preservatives like ACZA are not known to impart water repellency and have not typically been used to treat hardwoods for several reasons.

Physical changes in the wood and subsequent preservative loss through repeated wetting and drying were expected to cause decreased repellency, although again, the opposite effect was observed over the eight moisture cycles. These results align with those observed by Beck (2014).

Table 24. Effect of preservative treatment on water repellency of bigleaf maple samples prior to exposure to 8 wet/dry cycles. Mean Treatment S.E. DF Lower CL Upper CL Rating UTC 1.43 0.215 39 1.00 1.87 CREO 3.33 0.150 36 3.03 3.64 PENTA 2.70 0.160 36 2.38 3.03 ACZA 3.70 0.220 36 3.25 4.15

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Table 25. Effect of preservative treatment on water repellency of bigleaf maple samples after 8 wet/dry cycles. Mean Treatment S.E. DF Lower CL Upper CL Rating UTC 3.13 0.215 39 2.70 3.57 CREO 4.70 0.150 36 4.40 5.01 PENTA 4.50 0.160 36 4.18 4.83 ACZA 1.27 0.220 36 0.82 1.71 Table 26. Comparison of the effect of preservative treatment on water repellency of bigleaf maple samples prior to and after 8 wet/dry cycles. Percent decreases represented in parentheses. Cycle Estimate of the Treatment S.E. DF t.ratio p-value Contrast Difference (%) UTC 0-8 1.70 0.265 288 -6.43 <.0001 CREO 0-8 1.37 0.152 288 -9.01 <.0001 PENTA 0-8 1.80 0.171 288 -10.55 <.0001 ACZA 0-8 (2.43) 0.273 288 8.91 <.0001

Figure 20. Effect of preservative treatment on water repellency of bigleaf maple samples immediately after treatment and when subjected to 8 wet/dry cycles.

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Comparison of the 4th and 8th cycles, for all treatments

Mean repellency generally decreased after the fourth cycle drying period for all treatments (Table 27). While decreases in repellency between the 6th and 8th moisture cycles were significant for untreated and penta treated red oak, creosote and ACZA treated white oak and only ACZA treated samples for Douglas-fir, no significant differences were found between these same cycles for any of the four treatments of BL.

Table 27. Effect of wet/dry cycles (6 to 8) on water repellency of bigleaf maple samples treated with selected wood preservatives. Cycle Estimate of the Treatment S.E. DF t.ratio p-value Contrast Difference UTC 6-8 0.53 0.265 288 2.01 0.5365 CREO 6-8 0.20 0.152 288 1.32 0.9251 PENTA 6-8 0.17 0.171 288 0.98 0.9877 ACZA 6-8 0.57 0.273 288 2.07 0.4930

Comparisons of the maximum and minimum mean rating over all cycles, for each treatment

Maximum and minimum mean repellency ratings over all cycles showed some notable similarities in absolute differences and these differences align with the other species in the study. Creosote and penta had the smallest overall differences in mean repellency ratings of 1.63 and 2.37, respectively (Table 28). In terms of their order from smallest to largest change, these results were similar to observations made for the other three species. Oilborne treatments experienced the smallest changes in repellency which is consistent with the hydrophobic nature of oilborne preservatives and reflects their ability to maintain this performance parameter after repeated moisture cycling. As with the other species, untreated controls and ACZA treated BL both experienced the largest

78 changes in repellency of 2.64 and 3.57, respectively. All the differences were statistically significant (p-value < 0.0001, α = 0.05).

Table 28. Differences in water repellency for the maximum and minimum mean rating occurring at varying wet/dry cycles of bigleaf maple treated with selected preservatives. Cycle Estimate of the Absolute Treatment S.E. DF t.ratio p-value Contrast Difference UTC 0-4 |2.64| 0.265 288 -9.95 <.0001 CREO 0-4 |1.63| 0.152 288 -10.77 <.0001 PENTA 1-4 |2.37| 0.171 288 -13.87 <.0001 ACZA 2-8 |3.57| 0.273 288 13.06 <.0001

Variability in responses for a given treatment

Standard errors, an extension of the standard deviation, associated with the oilborne creosote and penta treatments were relatively low and similar, with values of

0.1516 and 0.1706, respectively. These also closely aligned with the standard error associated with these same treatments of the other species in the study. However, standard error of ACZA treated BL samples was a bit higher (0.2732) than for the untreated BL (0.2651). This was opposite of what was observed for the other three species in the study. The results indicate that the ability of untreated and ACZA treated

BL samples to repel moisture was less consistent than for the oilborne treatments and suggests greater consistency and uniformity of repellency responses in the creosote and penta treated materials.

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Figure 21. Effect of eight wet/dry cycles on the mean droplet rating for bigleaf maple wood samples either untreated or treated with ACZA, creosote or penta.

Figure 22. Effect of eight wet/dry cycles on the estimated mean droplet rating for bigleaf maple wood samples either untreated or treated with ACZA, creosote or penta.

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Table 29. Comparisons between selected preservative treatments and untreated bigleaf maple samples on water repellency over 8 wet/dry cycles. Negative values are represented by parentheses. Bolded p-values represent significance (α = 0.05). Estimate of the Cycle Contrast S.E. DF t.ratio p-value Difference in Means UTC - CREO (1.90) 0.262 36 -7.26 <.0001 0 UTC - PENTA (1.27) 0.268 36 -4.74 0.0115 UTC - ACZA (2.27) 0.308 36 -7.38 <.0001

UTC - CREO (1.53) 0.262 36 -5.85 0.0005 1 UTC - PENTA 0.10 0.268 36 0.37 1.0000 UTC - ACZA (2.13) 0.308 36 -6.94 <.0001

UTC - CREO (0.77) 0.262 36 -2.92 0.5500 2 UTC - PENTA (0.30) 0.268 36 -1.12 0.9999 UTC - ACZA (1.53) 0.308 36 -4.99 0.0059

UTC - CREO (1.33) 0.262 36 -5.08 0.0044 3 UTC - PENTA (1.27) 0.268 36 -4.73 0.0119 UTC - ACZA (1.57) 0.308 36 -5.09 0.0043

UTC - CREO (0.90) 0.262 36 -3.43 0.2488 4 UTC - PENTA (0.63) 0.268 36 -2.36 0.8849 UTC - ACZA 0.80 0.308 36 2.60 0.7626

UTC - CREO (0.77) 0.262 36 -2.92 0.5500 5 UTC - PENTA (0.53) 0.268 36 -1.99 0.9807 UTC - ACZA 1.70 0.308 36 5.53 0.0013

UTC - CREO (1.23) 0.262 36 -4.70 0.0127 6 UTC - PENTA (1.00) 0.268 36 -3.73 0.1368 UTC - ACZA 1.83 0.308 36 5.96 0.0004

UTC - CREO (1.43) 0.262 36 -5.47 0.0015 7 UTC - PENTA (1.13) 0.268 36 -4.23 0.0432 UTC - ACZA 1.80 0.308 36 5.85 0.0005

UTC - CREO (1.57) 0.262 36 -5.97 0.0003 8 UTC - PENTA (1.37) 0.268 36 -5.10 0.0043 UTC - ACZA 1.87 0.308 36 6.07 0.0003

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Effect of Preservative Treatment on Moisture Uptake

Moisture contents achieved by pressure soak contributing to check development

The use of a pressure vessel allowed for rapid sample saturation up to, or above the fiber saturation point (~28-30% moisture content). This ensured that the wood had swelled as much as possible. This should have encouraged high stresses during drying that produced higher degrees of checking. The fiber saturation point was achieved for all species in each of the treatments, except for the first three moisture cycles for creosote treated Douglas-fir and a few instances of untreated, creosote treated or ACZA treated white oak. However, both of these species are refractory. White oak vessels are often blocked by tyloses and potentially hydrophobic extractives that limit longitudinal flow.

Douglas-fir heartwood is generally considered to be refractory due to its aspirated pits, but it also contains hydrophobic extractives, resins and low molecular weight fatty acids that can limit moisture uptake; especially at the surface after drying. This would decrease wettability due to wood surface inactivation (Christiansen, 1990; Gray, 1962). Moisture contents at the end of pressure soaking for untreated, penta and ACZA treated samples tended to increase over the eight moisture cycles. Moisture contents over all the moisture cycles tended to be lower for creosote treated samples than those for the other treatments.

Creosote is reported to impart water repellency to the wood, and our results support this premise.

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Moisture contents of untreated controls attained by pressure soak

Untreated Douglas-fir controls had the highest moisture contents (> 160%) of all the species followed by the maple samples (Figure 23). These species performed similarly for all treatments except for ACZA where red oak and maple moisture contents were higher than those achieved for Douglas-fir. Vessels in untreated red oak samples are open and more receptive to water ingress. Untreated white oak, which is typically less permeable due to potentially obstructed vessels, failed to reach the fiber saturation during the first three moisture cycles and had the lowest moisture content (<20%) of all species after the second moisture cycle.

Moisture contents of creosote treated samples attained by pressure soak

Wetting of creosote treated samples to the fiber saturation point was more difficult than for other treatments; likely because hydrophobic creosote filled the tracheid or vessel lumens (Figure 24). Creosote treated maple samples tended to reach fiber saturation for all moisture cycles, although moisture levels were much lower than those for untreated maple. Creosote treated red and white oak were both at the fiber saturation point after the first moisture cycle. Douglas-fir failed to reach fiber saturation during the first three moisture cycles, but was above this level for all other cycles. While creosoted samples reached the fiber saturation point for most species after most wetting cycles, it is important to note that the moisture contents achieved were lower than those for the other preservative treatments. This illustrates the ability of creosote to limit moisture uptake, even when wood is pressure soaked. The lowest moisture content achieved in creosote

83 treated samples was less than 20% for Douglas-fir, while the highest was greater than

65% with maple.

Moisture contents of penta treated samples attained by pressure soak

Moisture uptakes were also lower with oilborne penta in many species compared to the untreated samples, although all penta treated samples reached moisture contents above the fiber saturation point (Figure 25). Moisture contents at the end of the pressure soak tended to increase with cycles, especially for Douglas-fir (Figure 25). These increases may be due to increases in checking or splitting after oven drying that created avenues for moisture ingress. Moisture contents above the fiber saturation point were easily achieved for Douglas-fir. All species appeared to behave similarly in the first three moisture cycles. Similar trends for all species were also observed for the remaining moisture cycles, except for Douglas-fir, which had higher moisture contents than the other species. The trends observed for penta treated materials were very similar to those observed for the untreated controls, except that moisture uptake was better in the white oak samples. Overall, Douglas-fir had the highest (120%) and lowest (40%) moisture contents for all species treated with penta.

Moisture contents of ACZA treated samples attained by pressure soak

ACZA is not known for its ability to repel moisture and most of the samples treated with this system reached moisture contents well above fiber saturation, including

Douglas-fir, although slightly lower than untreated materials (Figure 26). Once again, moisture uptake in white oak samples was limited and failed to reach the fiber saturation

84 point during the second and third wetting cycles. Moisture uptakes in all species, except

Douglas-fir, closely resembled those seen for the untreated controls. Decreased moisture contents in the ACZA treated Douglas-fir compared to the untreated group suggested that

ACZA may alter the moisture relationship in Douglas-fir. As noted earlier, solubilization of wood extractives by ammonia could have altered wood/moisture interactions, resulting in lower MCs compared with the untreated samples (Lebow, 1989). The lowest moisture content achieved for ACZA treated materials was less than 20% for white oak while the greatest was over 120% for maple.

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Figure 23. Effect of repeated wet/dry cycles on moisture contents of untreated red oak, white oak, Douglas-fir or bigleaf maple samples achieved at the end of each pressure soak period.

Figure 24. Effect of repeated wet/dry cycles on moisture contents of creosote treated red oak, white oak, Douglas-fir or bigleaf maple samples achieved at the end of each pressure soak period.

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Figure 25. Effect of repeated wet/dry cycles on moisture contents of penta treated red oak, white oak, Douglas-fir or bigleaf maple samples achieved at the end of each pressure soak period.

Figure 26. Effect of repeated wet/dry cycles on moisture contents of ACZA treated red oak, white oak, Douglas-fir or bigleaf maple samples achieved at the end of each pressure soak period.

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Dimensional Stability Results as Measured by Surface Checking Using Digital Image Analysis

Checking is a variable phenomenon that is dependent on a host of factors including, but not limited to wood species, dimension or geometry, orientation, and slope of grain (Conners, 2008). Surface checking may also be influenced by preservative treatments. For example, reductions in surface checking of treated wood may be achieved by using oilborne type preservative systems that provide an increased level of water repellency and limit moisture sorption (Beck, 2014). Although ultraviolet (UV) exposure was not a factor in this study, it is important to note that UV light absorbed by wood can degrade the surface. Rapid photo -oxidation and -degradation of the lignin that binds cellulose and hemicellulose is primarily responsible for the weakening and separation of fibers at the wood surface (Chang et Al., 1982; Schauwecker, 2011). Loss of this structural component may compromise wood surface integrity, so that even minor stresses lead to more check formation. This degradation can also result in increased moisture sorption via exposed hygroscopic cellulose and hemicellulose. However, the addition of radical scavengers to preservative systems can effectively absorb many of the reactive free radicals produced during photooxidation, lessening component degradation and reducing checking as a result (Schauwecker, 2011). In the current study, surface checking was highly variable and no checking was detected in some samples. In order to discuss possible effects of moisture cycling and treatment relationships and to simplify data analysis, checking observations were only considered when responses greater than zero were detected on the sampled surface (1001x2201 pixels). Image analysis was a

88 relatively efficient way of capturing the extent of surface checking in specimens subjected to repeated wet/dry cycles. However, differences in sample color, surface impedances (i.e. dirt or ID tagging) and checking outside of the photo view created difficulties. These issues, among others, were also encountered by Christy et al. (2005).

The potential for these unpredictable issues to influence responses is a drawback of relying on pixel intensity to measure checking. On the other hand, the method is reasonably reliable if corrections are made and the resulting binarized images are frequently reviewed.

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Red Oak Checking

Estimated median percent checking in red oak by preservative type over cycles 3-8

Median checking percentages tended to increase between three to eight moisture cycles regardless of treatment, although there were substantial differences in the magnitude of checking with treatment (Tables 30-31; Figure 28). Untreated red oak samples were included as controls; however, they developed few checks. As a result, they were not used in the analysis.

Creosote and penta treated red oak samples behaved similarly, experiencing relatively low percentages of checking over the six moisture cycles (Table 31). Creosote treated samples experienced median checking of 0.068 percent of the surface area after the third moisture cycle while penta treated samples had median checking of 0.112 percent. Checking observed in the creosote and penta treated samples increased to 0.305 and 0.267 percent of the surface area, respectively, after eight moisture cycles. These changes represented 4.49 and 2.38-fold increases in median checking between the third and eighth cycles for the creosote and penta treatments, respectively. ACZA treated samples experienced median checking of 1.56 percent of the surface area after the third moisture cycle, which increased to a median of 3.33 percent after eight moisture cycles.

This represented a 2.14-fold increase in median checking between the third and eighth cycles.

Almost no checking was observed in many of the untreated materials while minor, more sporadic checking was observed for samples treated with creosote or penta.

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Red oak samples treated with ACZA experienced a much greater amount of checking that usually consisted of many short separations. These separations tended to occur in the rays in the ACZA treated samples, while checking was more random in the penta treated samples (Figure 27).

A B C Figure 27. Examples of checking in untreated (A), penta (B) or ACZA (C) treated red oak after five moisture cycles. Raw photos illustrate various levels of checking in samples that were either untreated or treated with penta or ACZA.

Checking was sporadic for the ACZA treated materials at each moisture cycle, but there was a trend of increased checking with increased moisture cycling. High initial median percent checking and increased checking with moisture cycles suggested that

ACZA did not offer post treatment protection against moisture, and in turn, did not

91 improve dimensional stability. The severe initial checking experienced by the ACZA treated samples reflects the absence of any water repellency in the treatment and resulted in a much more dramatic moisture ingress that exacerbated checking.

In contrast, checking was less severe, more consistent and similar for the two oilborne treatments. This suggests that the amount and rate at which liquid water could move into and out of the wood was restricted due to the hydrophobic nature of these systems. Limited initial water uptake reduced internal stress development and slowed subsequent check formation. The migration and accumulation of preservative components at the surface, followed by their eventual removal by material handling and/or volatilization by oven drying may also help explain the gradual checking increases observed over moisture cycles.

Comparisons between treatment types were difficult due to the differing degrees of checking. For example, the ACZA treated samples experienced greater overall checking and showed the largest numerical increase in checking between the third and eighth moisture cycles. However, the changes experienced by the creosote and penta treatments were proportionally greater as a percentage of original checking. In other words, seemingly small changes in samples with limited initial checking suggested a much greater effect than would an identical change to samples that already had high levels of checking. Checking can be compared using the overall median checking between treatment groups, or by using the overall changes in checking between moisture cycles.

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Overall median checking may be used to estimate the ability of a preservative to limit total checking, while examining the overall changes in checking may be used to estimate the ability of a preservative to limit changes in existing checks. Overall checking is more important for railroad ties and other large timbers.

Although the proportional increases in checking for creosote and penta treated materials were larger than those for ACZA, this resulted because checking was extremely low at the starting points. As a result, small increases in checking produced higher rates of increase. It was clear that both the creosote and penta treated samples were far more stable than the ACZA treated materials and that many additional cycles would be required to reach the same degree of checking. Additionally, increased checking aligned with decreased water repellency during the last three moisture cycles.

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Table 30. Estimated median percent checking of red oak treated with ACZA, creosote or penta and exposed to 3 to 8 wet/dry cycles. Estimated Median Lower Upper Treatment Cycle S.E. DF Percent Checking CI CI 3 1.56 0.204 29 1.16 2.08 4 3.26 0.427 29 2.45 4.34 5 1.95 0.255 29 1.46 2.59 ACZA 6 2.78 0.365 29 2.09 3.70 7 1.36 0.179 29 1.02 1.81 8 3.33 0.436 29 2.50 4.44 3 0.07 0.012 27 0.05 0.10 4 0.05 0.008 27 0.03 0.07 5 0.04 0.008 27 0.03 0.06 CREO 6 0.04 0.007 27 0.03 0.07 7 0.09 0.016 27 0.07 0.13 8 0.31 0.054 27 0.20 0.46 3 0.11 0.021 27 0.07 0.18 4 0.05 0.009 27 0.03 0.07 5 0.16 0.031 27 0.12 0.22 PENTA 6 0.05 0.010 27 0.03 0.09 7 0.11 0.020 27 0.08 0.15 8 0.27 0.051 27 0.19 0.37

Table 31. Effect of initial preservative treatment on the changes in median percent checking of preservative treated red oak samples subjected to 3 to 8 wet/dry cycles as a ratio between check area at the 8th and 3rd cycle. Ratio of the Estimated Treatment Cycle S.E. DF t.ratio p-value Median Percent Checking ACZA 8v3 2.14 0.013 135 -27.70 <0.0001 Creosote 8v3 4.49 0.038 135 -8.81 <0.0001 Penta 8v3 2.38 0.083 135 -4.42 <0.0001

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Comparing oilborne creosote or penta treated red oak over cycles 3-8

There were no significant differences in checking of creosote and penta treated red oak samples over the six moisture cycles except at the fifth moisture cycle (Table 32).

It is unclear why this cycle was affected, but check development is a variable property that can be influenced by a variety of factors. Checks can open and close over moisture cycles, making it difficult to detect minor differences. In general, the oilborne preservatives performed similarly over multiple moisture cycles and checking was limited when compared with the ACZA treated samples. The inherent water repellency of creosote and penta likely limited the development of drying induced stress that could exacerbate checking.

Table 32. Differences in median percent checking in red oak samples treated with creosote or penta and exposed to 3 to 8 wet/dry cycles. Treatment Ratio of the Estimated Cycle S.E. DF t.ratio p-value Comparison Median Percent Checking 3 0.60 0.157 27 -1.94 0.8711 4 1.04 0.270 27 0.14 1.0000 CREO v 5 0.26 0.069 27 -5.13 0.0022 PENTA 6 0.77 0.199 27 -1.02 0.9998 7 0.87 0.225 27 -0.56 1.0000 8 1.14 0.296 27 0.50 1.0000

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Comparing ACZA to both creosote or penta treated red oak over cycles 3-8

The degree of checking of creosote and penta treated red oak samples was compared to levels in samples treated with ACZA over the last six moisture cycles.

Significant differences (by orders of magnitude) were clearly evident in the raw data between the two preservative types (Table 33). Maximum checking responses for all three treatments occurred after the eighth moisture cycle. Median checking for ACZA treated red oak was nearly 11 and 12.5 times that observed for creosote and penta treated samples, respectively, after the eighth moisture cycle.

Table 33. Comparisons in the degree of checking between ACZA and either oilborne creosote or penta treated red oak over 6 wet/dry cycles. Treatment Ratio of the Estimated Cycle S.E. DF t.ratio p-value Comparison Median Percent Checking 3 22.95 5.06 27 14.23 <0.001 4 69.34 15.27 27 19.25 <0.001 ACZA v 5 45.15 9.95 27 17.30 <0.001 CREO 6 66.63 14.68 27 19.07 <0.001 7 14.66 3.23 27 12.19 <0.001 8 10.92 2.41 27 10.85 <0.001

3 13.86 3.20 27 11.39 <0.001 4 71.98 16.61 27 18.53 <0.001 ACZA v 5 11.92 2.75 27 10.74 <0.001 PENTA 6 51.12 11.80 27 17.05 <0.001 7 12.69 2.93 27 11.01 <0.001 8 12.45 2.87 27 10.93 <0.001

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Figure 28. Effect of three to eight wet/dry cycles on mean percent checking for red oak wood samples treated with ACZA, creosote or penta.

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White Oak Checking Results

Estimated median percent checking in white oak by preservative type over cycles 3-8

Median checking percentages for white oak are presented in Tables 34 and 35 for each preservative treatment and their behavior over cycles three to eight is illustrated in

Figure 30. Untreated white oak samples were included in testing to provide a controlled comparator. However, untreated samples experienced little checking over all moisture cycles and were not used as a treatment comparator.

Creosote and penta treated white oak samples behaved similarly with relatively low percentages of checking observed over the six moisture cycles. Creosote treated samples began with an estimated median of 0.60 percent checking after the third moisture cycle while penta treated samples began with a median of 0.35 percent. The relatively low initial checking in the white oak samples was similar to the red oak samples, although the values for white oak were slightly greater and reflect the greater shrinkage and swelling values associated with this species (USDA, 2010). By the end of the eighth moisture cycle, checking observed in the creosote and penta treated samples increased to

1.94 and 0.84 percent, respectively. This suggests that the median checking observed in the eighth moisture cycle was 3.22 and 2.45 times that of the median checking observed in the third moisture cycle for the creosote and penta treatments, respectively.

Waterborne ACZA treated white oak materials experienced a much greater percentage of checking over the same cycle interval, similar to ACZA treated red oak.

Therefore, differences in magnitudes must again be taken into consideration. ACZA

98 treated white oak began with 5.79 median percent checking after the third moisture cycle and ended with 5.74 median percent checking by the end of the eighth moisture cycle.

This suggests that median checking for ACZA treated white oak in the eighth cycle was

1% less than the median checking percentage in the third moisture cycle, or that the median checking in the eighth cycle was slightly lower than the median checking percentage in the third moisture cycle for ACZA. This is likely attributed to the irregular nature of checking.

Untreated white oak materials experienced greater checking than untreated red oak, although checking was still minimal. Samples treated with creosote or penta also experienced greater checking; however, the checks were often shorter and narrower. This reflected the moisture repellency of these preservatives. In contrast, ACZA treated white oak experienced severe checks that were usually 25-50mm long (Figure 29).

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A B C D

Figure 29. Checking in untreated (A), creosote (B), penta (C) or ACZA (D) treated white oak after five moisture cycles.

Generally, the trends and hierarchy of checking responses for the different treatments for white oak were similar to those seen in the red oak. Specifically, ACZA treatment was associated with greater surface checking than creosote and penta, which behaved similarly. Additionally, the greater shrinkage and swelling associated with white oak might explain the slight differences in the degree of checking observed for each treatment between the two oak species. The results for white oak continue to support the initial interpretations made for behavior seen for red oak in terms of treatment effects, response variability, and the relationship of water repellency versus dimensional stability, based on treatment type (i.e. waterborne versus oilborne).

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Table 34. Estimated median percent checking of white oak treated with ACZA, creosote or penta and exposed to 3 to 8 wet/dry cycles. Estimated Median Lower Upper Treatment Cycle S.E. DF Percent Checking CI CI 3 5.79 0.365 29 5.19 6.46 4 5.99 0.378 29 5.38 6.67 5 6.83 0.430 29 6.13 7.60 ACZA 6 6.44 0.406 29 5.79 7.18 7 5.61 0.354 29 5.04 6.24 8 5.74 0.362 29 5.16 6.40 3 0.60 0.042 27 0.52 0.70 4 1.07 0.075 27 0.95 1.21 5 1.40 0.098 27 1.25 1.58 CREO 6 1.02 0.071 27 0.89 1.17 7 1.37 0.095 27 1.23 1.53 8 1.94 0.135 27 1.72 2.18 3 0.35 0.037 27 0.25 0.48 4 0.44 0.047 27 0.34 0.56 5 0.18 0.019 27 0.14 0.23 PENTA 6 0.30 0.033 27 0.26 0.36 7 0.89 0.095 27 0.80 0.99 8 0.84 0.090 27 0.72 0.98

Table 35. Effect of initial preservative treatment on the changes in median percent checking of preservative treated white oak samples subjected to 3 to 8 wet/dry cycles as a ratio between check area at the 8th and 3rd cycle. Ratio of the Treatment Cycle Estimated Median S.E. DF t.ratio p-value Percent Checking ACZA 8v3 0.99 0.010 135 0.84 0.9999 Creosote 8v3 3.22 0.013 135 -27.42 <0.0001 Penta 8v3 2.45 0.050 135 -7.29 <0.0001

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Comparing oilborne creosote or penta treated white oak over cycles 3-8

Comparisons for creosote and penta treatment for white oak samples compared over the six moisture cycles showed significant differences between the two preservatives except at cycle 7. It should be noted that these differences only appear significant due to their orders of magnitude (Table 36). The creosote treated checking responses were typically greater than 1%, while observations for penta treated samples typically stayed below 1% over all cycles. Thus, comparisons appear to be statistically significant.

However, while the magnitudes differed for this species, the trends over moisture cycling were similar and suggest some level of moisture repellency that limited the development of the extreme moisture gradients such as those observed for ACZA.

Table 36. Differences in median percent checking in white oak samples treated with creosote or penta and exposed to 3 to 8 wet/dry cycles. Treatment Ratio of the Estimated Cycle S.E. DF t.ratio p-value Comparison Median Percent Checking 3 1.75 0.223 27 4.36 0.0149 4 2.47 0.315 27 7.07 <0.0001 CREO v 5 7.87 1.005 27 16.15 <0.0001 PENTA 6 3.36 0.429 27 9.48 <0.0001 7 1.55 0.197 27 3.41 0.1241 8 2.30 0.293 27 6.50 <0.0001

Comparing ACZA to both creosote or penta treated white oak over cycles 3-8

Creosote and penta treated white oak samples were compared to those treated with ACZA over six moisture cycles to illustrate the differences in checking responses by the two preservative types. Again, differences were clearly evident in the raw data and all cycle comparisons suggest significant differences between the two preservative types

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(Table 37). The ability of oilborne preservatives to repel moisture likely explains most these differences, although, the large differences in magnitude also contribute to these large differences. Median checking after the eighth moisture cycle for ACZA treated white oak was 2.97 and 6.81 times that of creosote and penta treated samples, respectively.

Table 37. Comparisons in the degree of checking between ACZA and either oilborne creosote or penta treated white oak over 6 wet/dry cycles. Treatment Ratio of the Estimated Cycle S.E. DF t.ratio p-value Comparison Median Percent Checking 3 9.63 0.904 27 24.12 <0.0001 4 5.59 0.524 27 18.33 <0.0001 ACZA v 5 4.87 0.457 27 16.85 <0.0001 CREO 6 6.32 0.593 27 19.64 <0.0001 7 4.09 0.384 27 15.01 <0.0001 8 2.97 0.279 27 11.58 <0.0001

3 16.80 2.088 27 22.7 <0.0001 4 13.78 1.713 27 21.11 <0.0001 ACZA v 5 38.27 4.757 27 29.32 <0.0001 PENTA 6 21.22 2.637 27 24.58 <0.0001 7 6.33 0.786 27 14.84 <0.0001 8 6.81 0.846 27 15.43 <0.0001

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Figure 30. Effect of three to eight wet/dry cycles on mean percent checking for white oak wood samples treated with ACZA, creosote or penta.

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Douglas-fir Checking Results

Estimated median percent checking in Douglas-fir by preservative type over cycles 3-8

Median checking percentages tended to increase over three to eight moisture cycles regardless of treatment, although there were substantial differences in the magnitude of checking with treatment (Figure 32; Tables 38 and 39). Untreated Douglas- fir samples were included in testing to provide controls; however, the samples had few checks. As a result, they were not used as a treatment comparator.

Creosote and penta treated Douglas-fir samples behaved relatively similarly to one another in comparison with ACZA and had moderately low percentages of checking over the six moisture cycles (Table 39). Creosote treated samples experienced median checking of 0.03 percent of the surface area after the third moisture cycle, while penta treated samples had a median of 0.43 percent. Checking observed in the creosote and penta treated samples increased to 0.27 and 1.14 percent of the surface area, respectively, after eight moisture cycles. These changes represented 9.00 and 2.65-fold increases in median checking between the third and eighth cycles for the creosote and penta treatments, respectively. ACZA treated samples experienced median checking of 0.52 percent of the surface area after the third moisture cycle. Checking increased to a median of 4.27 percent after eight moisture cycles, representing an 8.21-fold increase in median checking between the third and eighth cycles.

Almost no checking was observed for the untreated materials while minor and sporadic checking was observed for samples treated with creosote. Douglas-fir samples

105 treated with penta or ACZA experienced a much greater amount of checking that usually consisted of a few longitudinal separations that were wide and extended along the length of the samples. These separations in the ACZA treated samples tended to occur at multiple growth ring boundaries in samples that were flatsawn from areas closer to the pith. On the other hand, checks in penta treated samples with similar anatomical orientation tended to occur at either a single growth ring boundary, or between them, and were more irregular (Figure 31). In contrast, checks in the oak species tended to be thinner and much shorter in length, following the rays.

A B C

Figure 31. Checking in untreated (A), ACZA (B) or penta (C) treated Douglas-fir after six moisture cycles. Photo illustrates inconsistent checking patterns in samples with similar anatomical orientations.

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Again, the trends and hierarchy of checking responses for the different treatments for Douglas-fir were similar to those seen in the red and white oak. In general, ACZA treated materials experienced greater surface checking than the oilborne preservatives, which behaved similarly. The lower shrinkage and swelling associated with Douglas-fir might also explain the differences in the degree of checking for a given treatment when compared with the oaks. These results continue to support the initial interpretations made for behavior observed in the oak in terms of the effect of preservative treatment on response variability and the repellency versus stability relationship, based on preservative treatment type (i.e. waterborne versus oilborne).

Table 38. Estimated median percent checking of Douglas-fir treated with ACZA, creosote or penta and exposed to 3 to 8 wet/dry cycles. Estimated Median Lower Upper Treatment Cycle S.E. DF Percent Checking CI CI 3 0.52 0.0310 29 0.45 0.60 4 0.99 0.0590 29 0.86 1.14 5 1.81 0.1078 29 1.59 2.06 ACZA 6 2.56 0.1528 29 2.23 2.95 7 2.51 0.1492 29 2.16 2.90 8 4.27 0.2546 29 3.63 5.03 3 0.03 0.0066 27 0.02 0.07 4 0.08 0.0161 27 0.05 0.13 5 0.30 0.0599 27 0.23 0.39 CREO 6 0.81 0.1607 27 0.68 0.97 7 0.48 0.0962 27 0.42 0.56 8 0.27 0.0528 27 0.22 0.32 3 0.43 0.0268 27 0.37 0.50 4 0.99 0.0613 27 0.86 1.14 5 0.71 0.0440 27 0.62 0.82 PENTA 6 0.62 0.0384 27 0.53 0.72 7 1.09 0.0678 27 0.94 1.27 8 1.14 0.0710 27 1.00 1.31

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Table 39. Effect of initial preservative treatment on the changes in median percent checking of preservative treated Douglas-fir samples subjected to 3 to 8 wet/dry cycles as a ratio between check area at the 8th and 3rd cycle. Ratio of the Estimated Treatment Cycle S.E. DF t.ratio p-value Median Percent Checking ACZA 3v8 8.21 0.0047 135 -54.85 <0.0001 Creosote 3v8 7.98 0.0339 135 -7.68 <0.0001 Penta 3v8 2.65 0.0173 135 -21.20 <0.0001

Comparing oilborne creosote or penta treated Douglas-fir over cycles 3-8

Comparisons for creosote and penta treated Douglas-fir samples compared over the six moisture cycles showed significant differences between the two preservatives except at the 6th moisture cycle (Table 40). Again, it is unclear why this cycle differed, but check development is inconsistent in nature. Checks can open and close over moisture cycles, making it difficult to detect minor differences. Overall, the oilborne preservatives performed similarly over moisture cycles and checking was significantly limited when compared with the ACZA treated samples. The inherent water repellency of these systems likely influenced the development of extreme moisture gradients that could exacerbate checking. Regardless, these preservative treatments appear to limit checking in the same order as observed for the oak species.

Table 40. Differences in median percent checking in Douglas-fir samples treated with creosote or penta and exposed to 3 to 8 wet/dry cycles. Treatment Ratio of the Estimated Cycle S.E. DF t.ratio p-value Comparison Median Percent Checking 3 0.08 0.0161 27 -12.31 <0.0001 4 0.08 0.0171 27 -12.02 <0.0001 5 0.43 0.0886 27 -4.10 0.02754 CREO v PENTA 6 1.31 0.2728 27 1.30 0.99581 7 0.44 0.0924 27 -3.90 0.04336 8 0.23 0.0485 27 -7.00 <0.0001

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Comparing ACZA to both creosote and penta treated Douglas-fir over cycles 3-8

The degree of checking of creosote and penta treated Douglas-fir samples was compared to levels in samples treated with ACZA over the six moisture cycles. Although differences were clearly evident in the raw data, there were significant differences between the two preservative types (Table 41). While the primary cause of the differences reflected preservative capabilities, the major differences in magnitude also explain these significant differences. Maximum checking responses occurred after the eighth moisture cycle for the ACZA and penta treatments and after the sixth moisture cycle for creosote. Median checking for ACZA treated Douglas-fir was close to 16 and

3.75 times that observed for creosote and penta treated samples, respectively, after the eighth moisture cycle.

Table 41. Comparisons in the degree of checking between ACZA and either oilborne creosote or penta treated Douglas-fir over 6 wet/dry cycles. Treatment Ratio of the Estimated Cycle S.E. DF t.ratio p-value Comparison Median Percent Checking 3 15.61 3.2352 27 13.26 <0.0001 4 12.24 2.5374 27 12.09 <0.0001 ACZA v 5 6.00 1.2425 27 8.64 <0.0001 CREO 6 3.17 0.6564 27 5.56 <0.0001 7 5.17 1.0714 27 7.93 <0.0001 8 16.05 3.3271 27 13.39 <0.0001

3 1.21 0.1039 27 2.18 0.7511 4 1.00 0.0865 27 0.05 1.0000 ACZA v 5 2.56 0.2200 27 10.89 <0.0001 PENTA 6 4.15 0.3577 27 16.54 <0.0001 7 2.30 0.1976 27 9.65 <0.0001 8 3.74 0.3223 27 15.33 <0.0001

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Figure 32. Effect of three to eight wet/dry cycles on mean percent checking for Douglas- fir wood samples treated with ACZA, creosote or penta.

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Bigleaf Maple Checking Results

Information on preservative treatment and use of bigleaf maple in railroad tie applications is sparse. This is likely because this species is seldom used in outdoor applications, especially in ground contact. Bigleaf maple naturally grows in a small region confined to middle-low elevations in the coastal range (< 186 miles inland) of the

Pacific Northwest, extending from southern California up into parts of British Columbia

(Niemiec et. al., 1995). The large-scale commercialization of bigleaf maple has been historically negligible and its use as railroad crosstie material limited.

Soft maple tends to be used in specialty type products such as furniture, veneer

(hardwood ), musical instruments, etc. (Niemiec, et. al., 1995). Additionally, its uniform anatomy and inherent dimensional stability (T/R = 1.92) after proper seasoning or drying makes it an easily workable wood for crafting high value products. However, this otherwise favorable behavior created difficulties in measuring defects such as surface checking in the current study. On the other hand, red maple (Acer rubrum) is another soft maple species that is anatomically comparable with bigleaf maple and has been extensively studied, preservative treated, and used in railroad applications with reasonable success.

Net preservative retentions for bigleaf maple samples were relatively high for all preservative treatments. The calculated net retentions for samples treated with creosote

(192 kg/m3) were second to red oak (200 kg/m3) which is generally considered an easy to treat hardwood due to its large and unobstructed vessel elements that provide easy

111 longitudinal penetration. Net retentions for bigleaf maple treated with penta and ACZA were the highest compared with the other species in this study with the same treatments.

This result aligns with those published by Smith et. al. (1996), showing exceptional penetration and retention in red maple, especially when treated with various waterborne preservatives. Red maple also showed greater penetrability for both waterborne and oilborne preservative types in sapwood than in heartwood (Smith et. al., 1996). This treatment effect was also evident in this study for several bigleaf maple samples containing both sapwood and heartwood, although only by visual appearance since samples could not afford to be destructively assessed for penetration. On the other hand,

Smith’s (1996) results contradict those found by Lebow et. al. (2005) when red maple was treated with similar waterborne preservatives, even after materials had been incised.

Minor, and highly variable, checking was observed in the bigleaf maple samples that created difficulties for statistical analysis. Many of the measurements captured the tips of cracks that stemmed from end checks or splits, likely originating from stresses occurring in the weaker ray tissue. These splits tended to extend completely through the samples in most cases (Figure 33). This occurrence aligns with reports of this fine- grained, relatively stable, diffuse porous species having a greater tendency to develop end splits and experience cell collapse during drying over other defects such as surface checking (Niemiec, et. al., 1995). However, Lebow and Halverson (2015) found that red maple decking specimens treated with various copper based preservatives experienced greater cupping, twisting and checking when compared with the other species in the

112 study, including southern pine (Pinus taeda). However, previous studies suggest that BL has a limited tendency to develop surface checks along with the results and observations in this study reflect the relatively low shrinkage values associated with this species (T =

7.1%, R 3.7%) (Wood Handbook, 2010). These were the smallest values of the four species explored in this study.

The areas outlined in red in Figure 33 represent an example of the standard region typically captured for analysis since it excluded sample ID tagging that could have interfered with check detection. One shortcoming of using image analysis for check assessment was that not all surface checking was detectable because of the need to eliminate the area occupied by the identification tags (Figure 33). However, this limitation occurred for all samples in the study. This constraint did eliminate more severe end checks from the measurement. It is also important to note that moisture related defects detected at the ends of the samples may not have been true surface checks. As a result, descriptive statistics and plots of the raw data for bigleaf maple over cycles three through eight are provided, but the data were not subject to statistical modeling.

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A B Figure 33. Example of a bigleaf maple section showing the image area (A) analyzed (red boxes) and a deep end split (B) that would not have been measured in the analysis. Note that outlined areas are not to scale.

The descriptive statistics (Tables 42 and 43) and plots of the raw data (Figures 34 and 35) illustrate the difficulties in working with checking responses for the bigleaf maple material. Mean checking responses over all, and after each moisture cycles for this species were very low, especially for the two oilborne preservative treatments. Mean checking in ACZA treated materials was significantly higher, but was also far more variable in comparison with the oilbornes. This aligns with the other species in the study.

Checking in bigleaf maple increased after the third moisture cycle similar to the other three species. In the case of bigleaf maple, variability may be the result of end splits being mistaken as checks that propagated into the measured area. This also caused large peaks in the plots of checking versus moisture cycles. Surface checks were of more importance and additional moisture cycling would have only resulted in further check development that formed from end splits.

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Table 42. Effect of 3 to 8 wet/dry cycles on the percent checking in untreated, ACZA, creosote or penta treated bigleaf maple. Standard Treatment Cycle Median (%) Mean (COV %) 95% CI Deviation 3 0.18 0.21 (45.43) 0.094 0.067 4 0.00 0.00 (0.00) 0.000 0.000 5 0.04 0.03 (37.20) 0.013 0.009 ACZA 6 0.01 0.01 (66.00) 0.008 0.006 7 0.40 0.44 (33.60) 0.149 0.106 8 0.40 0.45 (31.50) 0.143 0.102 3 0.00 0.00 (220.00) 0.007 0.005 4 0.02 0.02 (81.99) 0.015 0.011 5 0.01 0.01 (145.01) 0.016 0.011 CREO 6 0.01 0.01 (114.96) 0.015 0.011 7 0.02 0.02 (96.24) 0.022 0.016 8 0.03 0.03 (57.91) 0.015 0.011 3 0.03 0.03 (63.25) 0.016 0.011 4 0.06 0.05 (61.96) 0.030 0.021 5 0.10 0.08 (50.82) 0.042 0.030 PENTA 6 0.12 0.10 (41.71) 0.043 0.031 7 0.08 0.08 (52.62) 0.041 0.029 8 0.08 0.07 (49.30) 0.032 0.023 3 0.00 0.00 (0.00) 0.000 0.000 4 0.01 0.01 (86.00) 0.005 0.004 5 0.02 0.02 (40.00) 0.007 0.005 UTC 6 0.05 0.06 (68.94) 0.041 0.030 7 0.02 0.02 (47.00) 0.009 0.007 8 0.12 0.12 (37.59) 0.044 0.032 Table 43. Cumulative effect of 3 to 8 wet/dry cycles on the percent checking in untreated, ACZA, creosote or penta treated bigleaf maple. Treatment Statistic ACZA CREO PENTA UTC Median 0.055 0.010 0.060 0.020 Mean (COV %) 0.192 (112.2) 0.016 (107.3) 0.067 (63.0) 0.037 (1.29) St. Deviation 0.215 0.017 0.042 0.048 95% CI 0.056 0.004 0.011 0.012

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Figure 34. Effect of eight wet/dry cycles on mean proportion of checking in bigleaf maple either untreated or treated with various preservatives plotted collectively.

Figure 35. Effect of eight wet/dry cycles on mean proportion of checking in bigleaf maple either untreated or treated with various preservatives individually plotted.

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ADDITIONAL DISCUSSION

Relationship Between Water Repellency and Dimensional Stability

The data suggest a potential relationship between the two response variables, water repellency and dimensional stability, over the eight moisture cycles. Decreases in water repellency tended to be followed by increases in the amount of checking observed, suggesting that moisture may be influencing stability. The suggestive relationship was easier to identify in the data collected for the ACZA treated materials since they experienced substantial decreases in water repellency and excessive checking over all moisture cycles (Figure 36). A similar, although less obvious, relationship was observed for the other treatments. The relationship was less apparent over all moisture cycles for the other three treatments since the levels of checking were less severe; a potential result of increased repellency as previously discussed. The relationship was especially apparent in white oak (Figure 37) which typically has a greater potential to experience dimensional change due to its relatively high density and large, multiseriate rays that contribute to the development of severe checks. On the other hand, this relationship was less apparent in other species such as bigleaf maple where decreases in repellency over moisture cycles and the overall degree of checking observed were minimal (Figure 38). The results indicate that a minimum of three wet/dry cycles is necessary to delineate differences in checking and water repellency, although more cycles would improve the ability to distinguish treatment effects and may have amplified the relationship profile.

Additionally, the use of a single species appeared to be acceptable for differentiating treatments, provided it was susceptible to checking.

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Figure 36: Relationship between water repellency and checking observed for various species treated with ACZA. Illustrates the most expressive relationship.

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Figure 37: Relationship between water repellency and checking observed for white oak treated with various preservatives. Illustrates a fairly expressive relationship.

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Figure 38: Relationship between water repellency and checking observed for bigleaf maple treated with various preservatives. Illustrates a less expressive relationship.

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Oven Drying Preservative Treated Wood

Effects of Drying on Water Repellency and Dimensional Stability

In the current work, the untreated samples were dried at 100°C to encourage extreme moisture gradients and result in large internal stresses that would lead to wood fiber failure, or checks. However, these samples experienced very little or no checking and tended to increase in water repellency over the eight moisture cycles. Several studies have suggested that oven drying wood can affect subsequent moisture behavior and related changes in dimensional stability, even at lower temperatures (25-88°C)

(Kininmonth and Williams, 1972). A review by Kininmonth (1976) found reductions in equilibrium moisture content (EMC), hygroscopicity and dimensional change when various species were subjected to a wide range of drying temperatures (70-200°C). Oven drying conditions may help to explain the increases in repellency and the lack of checking observed over moisture cycles in the current work in the untreated samples.

Stamm (1964) suggested that increased water repellency may be a result of thermal degradation of normally hygroscopic hemicellulose to less hygroscopic furfural polymers formed from degraded hexose and pentose sugars. This hypothesis aligns with the high repellency observed in the oak species since hardwoods contain higher levels of hemicelluloses than softwoods (Fengel et. al., 1983).

Furthermore, changes in dimensional stability and moisture sorption due to oven drying were found to be both temperature and time dependent (Kininmonth, 1976).

Drying in the current study was concluded when materials weight losses were less than or

121 equal to 0.25 grams per 24 hours. This typically resulted in considerably long drying periods, even at 100°C. This was especially true for the more difficult to dry species like white oak as well as the preservative treated materials. Changes in wood color over the eight moisture cycles in this study also suggest the beginning stages of thermal modification. Although species differ between this and Kininmonth’s study, wood composition (i.e. cellulose, hemicellulose, lignin and extractives) is similar and allows for some level of comparison between the two studies.

Moisture Movement and Drying Duration When Re-Drying Previously Kiln Dried and Preservative Treated Wood

The previous section suggests decreases in moisture sorption and increases in dimensional stability after drying untreated wood, however, this effect may be emphasized when drying preservative treated wood for a few reasons. First, initial kiln drying of untreated lumber from a green state can cause physical changes in the wood that can affect the ability of moisture to move as freely in the oven dry state (Mackay,

1957). The removal of water during initial drying can cause large capillary forces in the wood that are responsible for pit aspiration which creates blockages in bordered pits in softwoods, for example, and cannot be reversed. This makes the wood less permeable than it was previously. Subsequent wetting and drying rates may become slower as a result because pathways for moisture movement are obstructed. However, not all pits become aspirated during initial drying and still permit sufficient pressure treatment penetration. Second, the preservative treatment of wood will undoubtedly increase the moisture content of the wood and will require re-drying. The extent of this may depend

122 on the treatment type used. In waterborne preservative treated wood, drying may still take longer than initial untreated kiln drying. However, with lower target moisture contents for the treated wood, steeper moisture gradients may also develop and lead to more severe surface checking than in untreated kiln dried lumber with higher target moisture contents.

The combination of physical changes in the wood from initial drying and the use of oilborne preservative treatments may produce the opposite effect during re-drying. In other words, moisture content gradients and surface checking may be lessened because removal of moisture within the wood may essentially be restricted by the preservative treatment shell. Intuitively, this would decrease the rate of moisture removal and increase drying time. These increased drying times could ultimately result in the increased dimensional stability and water repellency discussed in the previous section, especially at

100°C.

Statistical Analysis Discussion

Since samples were subjected to repeated moisture cycling, the study design was categorized as longitudinal. This required that water droplet and checking responses be analyzed under generalized linear mixed effect models to account for potential dependence caused by the repeated measurement of the same samples. While the analysis of water droplet responses was more straightforward, checking responses caused several difficulties for analysis.

Published differences in the dimensional change characteristics (i.e. shrinkage and swelling values) between wood species due to anatomy and classification are well known

123 and were expected to influence responses after repeated moisture cycling. Comparing checking responses between species would also add another unnecessary variable and convolute the statistical analyses and interpretation. Additionally, the sporadic nature of checking created difficulties in consistent measurement and analysis.

Checking observed between preservative treatment groups of similar species were still highly variable and often contained many zeros, especially at early moisture cycles

(i.e. cycles 0-2). These data were less useful and produced heavily right-skewed, non- normal, bodies of data. Min and Agresti (2002) term data sets containing probability masses at or near zero as “semicontinuous” and suggest that they often cause analytical problems under the more common normal or gamma type distributions. Furthermore,

Poisson or overdispersed count distributions fitted with generalized linear models

(GLMs) on data containing many zeros (i.e. “zero-inflated”) can result in a lack of fit for the model (Min and Agresti, 2002). To avoid these complications, the checking data sets were truncated to optimize and simplify the mixed models that were used.

Normality of checking responses was still a concern in the reduced data sets and was violated in many cases due to large differences of magnitude between the waterborne and oilborne preservative groups. Typical log transformations were performed to normalize treatment response distributions for analysis and were followed by back- transformations prior to interpreting the results. After truncating the data sets, two

Douglas-fir samples from the third moisture cycle experienced no checking and conflicted with the transformation operation because the natural log function is only

124 defined for numbers greater than zero. This required an additional operation to be performed on the Douglas-fir group that consisted of identifying the minimum response value, dividing it by two and adding the quotient to every response in the data set (y+c).

This essentially shifted the data set by a common factor. Small areas of each image were analyzed for checking in the image analysis method for consistency. However, it is possible that a response of zero resulting from the image analysis technique is not a

“true” zero value. Undetected checking in a sample was possible if it fell outside of the area covered in the photo. Therefore, adding half of the smallest response to every response in the data set was a reasonable means of shifting the data to allow for a transformation and achieve normality, especially when only two data points were being assigned to the arbitrary value.

Violation of the homogeneity of variance assumption for these mixed models in checking responses was not only due to differences in preservative and species groups, but was also due to the erratic nature of checking. Heterogeneity of variance was generally due to responses from the ACZA treatment group since changes in moisture and subsequent checking for this group were greater than for either of the oilborne preservatives. However, useful tools such as log transformations and the varIdent variance structure within the nlme package in R allowed for compliance with these assumptions.

Although adaptations of models and datasets allowed for some generalized statistical analysis, the level of understanding necessary to use them appropriately would

125 be too advanced for a standardized test, particularly over an array of different species and treatments. However, if the standard assumptions were met, the use of typical t-tests and/or ANOVAs may still be valid when comparing responses at the end of moisture cycling to investigate final differences in preservative effectiveness or between species treated with the same preservative provided the test assumptions are reasonably met. In general, abnormal distributions, zero probability masses in the early moisture cycles, auto-correlation, high variability and lack of independence in sampling meant that many traditional statistical techniques would be incompatible, especially across cycles or species and would have violated many assumptions. Specifically modified generalized mixed effects models and data sets were used instead.

Study Design and Potential Alternatives

Design Complexity

Due to the number of species and preservative treatments in the current study, responses were highly variable from one another and often differed by significant degrees of magnitude. This made treatment comparisons difficult. In order to manage the level of differentiation, replication also had to be reduced for practicality. Since wood is inherently highly variable, even within the same test specimen, relatively low replication and the constraint of repeated measurements could have influenced responses. In future studies investigating potential test methods, it is recommended that one or two species be used to investigate one preservative treatment and be compared with one set of untreated controls. This would not only decrease confounding variables such as treatment types and

126 differences inherent to species, but would also allow for increased replication. This would also strengthen the power of any statistical inferences drawn from the results.

Alternatives Methods

While the low-cost water droplet method provided a reasonably accurate assessment of surface moisture behavior, there were concerns over reproducibility and subjectivity of using a visual rating technique. These were tradeoffs involved with not using more advanced equipment to measure contact angle. Alternatively, several other methods for measuring surface water repellency of treated wood may be worth investigating. For example, rilem tubes are water filled instruments that are adhered to a substrate to measure the amount and rate of moisture sorption of a material. The tubes can easily be installed and removed between moisture cycles to measure moisture uptake since they are adhered with a putty. However, two potential limitations exist with this method as well. First, it may be difficult to achieve adequate tube adhesion on an oily preservative treated surface. Second, the measured moisture uptake would only represent a small area of the entire treated surface and may not reflect true repellency as a result.

Another potentially useful method for assessing water repellency could be the use of diffusion cups. This would consist of preparing thin wafers treated with a selected preservative that would substitute the “lid” of a jar. The adapted wafer-lid jars would be filled with a prescribed amount of deionized water and placed in controlled chambers where moisture could be driven from within the jar, through the wafer lid, into the surrounding air by diffusion due to various levels of humidity. However, this method

127 may be difficult to scale up to represent the repellency of full sized ties or timbers.

Dimensional stability, on the other hand, could be investigated using some non- destructive ultrasonic evaluation technique that would detect checks or other internal voids by measuring the speed of a sound wave to travel through a sample. This would not only allow for the measurement and visualization of surface checks, but also internal voids such as honeycombing that result from stresses due to moisture content gradients.

Still, this type of method would be accompanied by its own set of caveats as well such as increased costs. Any of these potential alternatives would still need to be evaluated to determine if increases in accuracy would warrant added costs.

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CONCLUSION

Water Repellency

Water repellency increased through five to six moisture cycles and usually required a minimum of three to four moisture cycles before definitive trends were apparent. Extractives and/or preservative fractions migrating to the sample surface, long drying times at 100˚C, and a lack of exposure to ultraviolet (UV) light may have contributed to this result. However, the water droplet method employed resulted in overall behaviors that were consistent with the expected performance of the preservative systems. The results provided a good indication of how standing water might behave on treated wood surfaces after repeated moisture cycling experienced in service. The results also agree with those found by Beck (2014).

Dimensional Stability as Measured by Checking

The inherent variability of surface checking made it difficult to distinguish trends between individual cycles. Checking on wood surfaces increased with moisture cycles.

The smaller amount of checking observed for untreated controls was counterintuitive but may have been influenced by initial end-sealing that was not removed or degraded by a preservative treatment process. On the other hand, checks in untreated samples were often wider than those present in oilborne treated samples, suggesting that treatments limited the severity of check development. ACZA treated samples experienced the greatest degree of surface checking. Digital image analysis provided reasonable representations of surface checking and produced masked images that enhanced the accuracy of the analysis.

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General Conclusions

The methods provided rapid and cost-effective techniques for assessing the water repellency and dimensional stability of preservative treated railroad ties and other large timbers subjected to repeated moisture cycling and illustrated clear treatment system effects. The methods could be used to predict the effects of preservatives on physical performance attributes of treated wood. Further studies are recommended to better understand the nature of increased surface water repellency observed with repeated moisture cycling.

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APPENDIX

Matlab Image Processing Core Script

Batch Cropping Raw Images

Code crops raw images from specified folder and saves them into another specified folder. Cropping size may be specified by a four-dimensional vector.

OPEN = ('ENTER_FILE_PATH_TO_OPEN_HERE'); SAVE = ('ENTER_FILE_PATH_TO SAVE_HERE');

% Load the names of the files namelist = imnamestack(OPEN, 10);

%Crop loop for i = 1:numel(namelist) sample = namelist{i};

I = imread(sample); I2 = imcrop(I,[575 650 1000 2200]);

% **OPTIONAL** Removing “%”’s from four lines below will show the original image, apply a title to original image, show cropped image and apply title to cropped image for comparison; %imshow(I); %title('Original Image'); %imshow(I2); %title('Cropped Image'); imwrite(I2, [SAVE '\' 'crpd_' sample], 'jpg') %%%%%%%%%%%%imwrite(I2, strcat(SAVE, sprintf('%d.jpg', i)), 'jpg') end

%h = fopen(OUTPUT_TEST,'w'); %fprintf(h,'%s',out_string); %fclose(h);

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Batch Contrast Equalization and Grayscale Conversion

Code equalizes photo contrast histograms based on a single selected photo that has the ideal exposure necessary for thresholding. Photo location is pulled from specified folder and saved into another specified folder.

OPEN = ('ENTER_FILE_PATH_TO_OPEN_HERE'); %specify file input path, name and type SAVE = ('ENTER_FILE_PATH_TO_SAVE_HERE'); %specify file output path, name and type refN = 3; % selected reference photo from batch

% Load the names of the files namelist = imnamestack(OPEN, 10);

% Convert reference image to grayscale reference = imread([OPEN '/' namelist{refN}]); reference = rgb2gray(reference);

%loop for i = 1:numel(namelist) sample = namelist{i};

% Read images and convert to grayscale image = imread([OPEN '/' sample]); grayimage = rgb2gray(image);

% Adjusts brightness/contrast for all images to a reference image newimage = imhistmatch(grayimage,reference); imwrite(newimage, [SAVE '\' 'contrast_' sample]) % imwrite(newimage, [SAVE '\' 'contrast_' num2str(i),'.jpg']) end

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Batch Thresholding (w/ Ordfilt2 noise filter)

Code selects pixels in images that have a value less than or equal to the specified threshold value. Image is then binarized to a series of ones and zeros. Pixels that meet the the threshold criteria are summed and divided by the total number of pixels in the image.

Output results in a text file as a proportion of identified pixels as well as a "mask" of the binarized image. Filter identifies neighboring pixels and corrects them based on their abundance. Input and output locations/paths can be specified.

OUTPUT_TEST = 'ENTER_FILE_PATH_TO_SAVE_HERE_AND_SPECIFY_.txt'; %specify file output path, name and type MASKS = 'ENTER_FILE_PATH_TO_SAVE_MASK_HERE'; %specify file output path, name and type THRESHOLD = 68; %Use ImageJ to determine threshold value DIRECTORY = 'ENTER_FILE_PATH_TO_OPEN_HERE'; %specify input path; cannot call single files only % Load the names of the files namelist = imnamestack(DIRECTORY, 10); out_string = ''; percentages = nan(numel(namelist),1); for i = 1:numel(namelist) sample = namelist{i}; image = imread([DIRECTORY '/' sample]);

%Filters image by center of a 15x3 matrix filtered_image = ordfilt2(image,23,ones(15,3));

% Threshold the image mask = (filtered_image <= THRESHOLD); %imshow(mask); %remove “%” to activate line – shows live mask image imwrite(mask, [MASKS '\' 'MASK_of_' sample]) % Writes new MASK identifier name

% calculate the percentage under the threshold percent = sum(mask(:))/numel(mask); percentages(i) = percent;

% put the filename into the output string out_string = (out_string sample ',' sprintf(' %0.4f\n',percent)); end h = fopen(OUTPUT_TEST,'w'); fprintf(h,'%s',out_string); fclose(h);

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Supplemental* Matlab Image Processing Code

* Required for running “Core Code” and must be saved in the same location.

IMNAMESTACK Function

Code returns the names all images in the directory and loads the actual file data. Does not sort directory before loading image names.

kEXTENSION = {'.jpeg', '.jpg', '.tiff'}; % The filetype to be loaded. % Notch is the index of the last image in the stack; Depth is the number of names before “notch” to be loaded notch = depth; if exist(directory,'dir') % Get the details of all the files in DIRECTORY. fcontents = dir(directory); fprintf('Loading filenames from %s ... ', directory); % Skip the first two entries (./ and ../) and determine if DIRECTORY is empty. start = 1; while fcontents(start).isdir == 1 start = start + 1; if start > length(fcontents), error('There are no files there!'); end end % Preallocate a cell for the names. namestack = cell(depth,1); % Loads all the names into the cell. count = 0; stop = length(fcontents); start = max(start, start + (notch - 15 - depth)); for i = start:stop % Check that the entry is not a directory. if(~fcontents(i).isdir) [~,~,ext] = fileparts(fcontents(i).name); % Check for the desired filetype. if strcmp(ext, kEXTENSION{1})||strcmp(ext, kEXTENSION{2})||strcmp(ext, kEXTENSION{3}) count = count + 1; namestack{count} = fcontents(i).name; end end end fprintf('LOADED %i NAMES\n', count); if(count ~= depth) warning('Matrix size does not match desired depth.'); namestack = namestack(1:count); end else

140

error('%s does not exist!', directory); end

IMSTACKLOAD Function

Loads the names of all the files in the directory. Returns a 3D images stack of all images in the directory. Only works for stacks of images whose largest images is listed first. Does not sort directory before loading images.

% version = 1.1.0 % INPUTS: % directory: the folder of images from which the stack will be constructed. type (string): OPTIONAL determines the type of data returned e.g. uint8, double, uint16, etc. % fraction (optional): Some number in the range (0,1]. The returned stack will be a randomly selected fraction of the images in directory. % ------if nargin < 3, fraction = 1; end kEXTENSION = {'.tif', '.png', '.tiff'};

% Load the names of all the files in the directory fcontents = dir(directory); addpath( genpath(directory) ); fprintf('Loading files from %s ... ', directory); image_count = 0; namestack = cell(numel(fcontents),1); for i = 1:numel(fcontents) % Check that the entry is not a directory. if(~fcontents(i).isdir) [~,~,ext] = fileparts(fcontents(i).name); % Check for the desired filetype. if strcmp(ext, kEXTENSION{1}) || strcmp(ext, kEXTENSION{2}) || strcmp(ext, kEXTENSION{3}) image_count = image_count + 1; namestack{image_count} = fcontents(i).name; end end end if image_count == 0 error('No images found!'); end clear fcontents;

% Load the actual file data if fraction < 1

141

numsamples = ceil(fraction*image_count); %warning('NUM SAMPLED SLICES IS %i \n', numsamples); namestack = namestack(random('unid', image_count, [1,numsamples])); image_count = numsamples; end if nargin < 2 % Automagically check the bitdepth of the first loaded image and % allocate the appropriate array. info = imfinfo(namestack{1}); switch(info.BitDepth) case 8 type = 'uint8'; case 16 type = 'uint16'; case 32 type = 'single'; otherwise type = 'double'; end end

% Determines the size of the images that will be loaded [m,n,o] = size(imread(namestack{1})); if o == 1 % the images are not color % preallocate the array for the images stack = zeros([m,n,image_count], type);

%loads all the files into the matrix for i = 1:image_count stack(:,:,i) = imread(namestack{i}); end

fprintf('LOADED %i FILES\n', image_count); else % the images are color and need a cell stack = cell(image_count,1);

%loads all the files into the cell for i = 1:image_count; stack{i} = imread(namestack{i}); end

fprintf('LOADED %i FILES\n', image_count); warning('Color images detected. Returning a cell.'); end

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IMSTACKSAVE Function

Saves an image stack to outdir/samplename_0000.png. Images are slice in dimension 3. Stack (cell or matrix) color images have to be a cell because they are MxNx3 whereas grey images are MxN.

if samplename(1) ~= '/', samplename = ['/' samplename]; end mkdir(outdir); fprintf('Saving images from stack to %s\n', outdir); addpath(outdir); if iscell(stack) z = length(stack); parfor k = 1:z filename = [outdir sprintf('%s_%04i.png', samplename, k )]; %disp(filename); imwrite( stack{k}, filename, 'png' ); end else [~,~,z] = size(stack); parfor k = 1:z filename = [ outdir sprintf('%s_%04i.png', samplename, k )]; imwrite( stack(:,:,k), filename, 'png' ); end end

Average Water Droplet Rating by Treatment at Each Moisture Cycle

Untreated

Cycle 0 Time (min.) Replicate --> 1 2 3 4 5 6 7 8 9 10 Species Averages RO 2.7 2.7 2.3 3.3 3.0 3.3 3.0 2.7 3.0 2.7 2.9 WO 2.3 1.3 2.0 2.3 2.0 2.0 2.0 1.7 3.0 3.0 2.2 1 min DF 2.3 4.3 4.3 4.7 5.0 2.0 2.0 2.0 2.0 4.3 3.3 Maple 2.0 2.0 3.0 2.7 2.0 2.0 2.0 3.0 2.3 2.7 2.4

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 2.3 2.7 2.3 3.0 2.7 2.7 2.0 2.3 2.0 2.0 2.4 WO 1.3 1.0 1.7 2.0 2.0 1.3 1.3 1.0 2.7 1.3 1.6 5 min DF 2.3 4.7 4.0 5.0 4.3 2.0 1.7 2.0 2.0 3.7 3.2 Maple 2.0 2.0 2.0 2.0 2.0 2.0 2.0 3.0 2.0 2.7 2.2

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 2.3 2.3 1.7 2.7 2.0 2.0 1.7 1.7 1.3 1.0 1.9 WO 1.3 1.0 1.3 1.7 2.0 1.3 1.0 1.0 2.3 1.3 1.4 10 min DF 2.0 4.7 4.0 4.7 4.0 2.0 1.7 2.0 2.0 3.0 3.0 Maple 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.3 2.3 2.0 2.1

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 1.7 1.3 1.0 1.7 1.0 1.3 1.0 1.3 1.0 1.0 1.2 WO 1.0 1.0 1.0 1.3 1.3 1.3 1.0 1.0 2.3 1.0 1.2 20 min DF 1.7 4.0 4.0 4.3 3.0 2.0 1.7 2.0 2.0 2.7 2.7 Maple 1.3 1.7 1.0 2.0 1.0 1.0 1.3 2.3 1.0 1.7 1.4

143

Cycle 1 Replicate --> 1 2 3 4 5 6 7 8 9 10 Species Averages RO 5.0 4.3 5.0 5.0 4.3 5.0 5.0 4.7 4.7 4.0 4.7 WO 5.0 2.7 4.7 4.7 4.3 3.0 4.0 2.7 5.0 5.0 4.1 1 min DF 5.0 4.7 5.0 4.7 5.0 5.0 5.0 5.0 5.0 5.0 4.9 Maple 3.0 3.3 3.7 2.7 2.3 2.7 2.0 3.0 3.0 5.0 3.1

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 4.3 3.3 4.7 4.7 3.7 5.0 5.0 4.7 3.7 3.0 4.2 WO 5.0 2.0 4.7 4.7 4.0 2.3 3.0 2.0 5.0 4.3 3.7 5 min DF 5.0 4.3 4.7 4.7 4.7 4.3 4.7 5.0 4.7 4.3 4.6 Maple 2.3 3.0 2.7 2.3 2.3 2.3 2.0 2.7 3.3 4.7 2.8

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 4.0 3.3 4.7 4.3 3.7 4.3 5.0 4.0 3.3 3.0 4.0 WO 4.7 2.0 4.7 4.7 4.0 2.0 12.7 1.7 4.3 4.3 4.5 10 min DF 5.0 4.0 4.3 4.7 4.7 4.3 4.7 5.0 4.0 4.3 4.5 Maple 2.0 2.3 2.3 2.0 2.0 2.0 2.0 2.7 2.7 4.7 2.5

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 4.0 3.0 4.3 3.3 3.3 4.0 5.0 3.7 3.0 2.7 3.6 WO 4.3 2.0 4.3 3.7 4.0 2.0 2.7 1.7 4.3 4.3 3.3 20 min DF 5.0 3.7 3.7 4.3 4.7 4.3 4.7 5.0 3.7 4.0 4.3 2.0 2.3 2.3 2.0 2.0 2.0 2.0 2.7 2.3 4.7 Maple 2.4

144

Cycle 2 Replicate --> 1 2 3 4 5 6 7 8 9 10 Species Averages RO 4.0 4.3 4.3 3.3 4.3 4.0 4.0 4.0 4.3 3.3 4.0 WO 4.7 3.0 4.7 5.0 5.0 2.3 4.0 3.0 5.0 4.3 4.1 1 min DF 5.0 4.3 4.0 4.7 4.7 4.7 4.3 4.3 4.3 4.0 4.4 Maple 3.0 4.3 3.7 5.0 3.3 3.3 3.0 4.0 4.0 5.0 3.9

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 4.0 4.3 4.0 3.3 4.3 4.0 3.7 4.0 4.3 3.0 3.9 WO 4.7 2.3 4.3 5.0 4.7 2.3 4.0 2.7 4.7 4.0 3.9 5 min DF 4.7 4.3 3.7 4.7 4.0 4.0 4.0 4.3 4.3 3.7 4.2 Maple 2.0 4.3 3.3 4.3 3.0 3.3 3.0 4.0 4.0 5.0 3.6

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 4.0 4.3 3.7 3.0 4.3 4.0 3.7 4.0 4.0 2.0 3.7 WO 4.3 2.3 4.3 5.0 4.7 2.3 3.0 2.3 4.7 4.0 3.7 10 min DF 4.7 4.0 3.3 4.7 4.0 4.0 4.0 4.3 4.0 3.3 4.0 Maple 2.0 3.3 3.0 4.3 3.0 3.0 3.0 3.7 3.3 4.7 3.3

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 3.3 3.7 3.7 3.0 4.3 4.0 3.7 3.7 3.7 2.0 3.5 WO 4.0 2.0 4.3 4.7 4.3 2.0 3.0 2.0 4.0 4.0 3.4 20 min DF 4.3 4.0 3.3 4.7 4.0 3.7 4.0 4.3 4.0 3.3 4.0 Maple 2.0 4.0 3.0 4.0 2.7 3.0 3.0 3.7 3.0 4.7 3.3

145

Cycle 3 Replicate --> 1 2 3 4 5 6 7 8 9 10 Species Averages RO 3.0 3.0 3.7 2.3 4.3 2.3 4.7 3.3 4.7 1.7 3.3 WO 3.7 2.0 4.0 4.7 4.3 2.0 2.7 2.7 4.0 4.3 3.4 1 min DF 4.7 3.3 5.0 3.0 4.3 4.7 2.3 3.7 4.0 4.0 3.9 Maple 2.0 3.7 3.0 2.7 3.3 3.7 3.7 4.0 3.7 4.0 3.4

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 3.0 2.7 3.0 2.0 3.7 2.0 4.7 3.0 4.3 1.3 3.0 WO 3.0 2.0 4.0 4.0 4.0 2.0 2.7 2.0 4.0 4.0 3.2 5 min DF 4.3 3.0 4.7 2.3 4.0 4.7 2.0 3.3 3.3 3.0 3.5 Maple 2.0 3.3 2.7 2.3 2.3 3.0 3.7 4.0 3.7 4.0 3.1

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 2.0 1.7 2.7 2.0 3.7 2.0 4.7 2.7 4.3 1.3 2.7 WO 2.3 1.7 4.0 4.0 4.0 2.0 2.7 2.0 4.0 4.0 3.1 10 min DF 3.3 3.0 4.7 1.7 4.0 4.7 2.0 3.0 3.3 3.0 3.3 Maple 2.0 3.3 2.7 2.3 2.3 3.0 3.7 3.3 3.0 4.0 3.0

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 2.0 1.7 2.3 1.3 3.3 2.0 4.0 2.3 3.7 1.0 2.4 WO 2.3 1.3 4.0 4.0 4.0 2.0 2.7 2.0 3.7 3.7 3.0 20 min DF 3.0 2.7 4.7 1.0 4.0 4.7 1.0 3.0 2.7 2.7 2.9 Maple 1.3 3.3 2.7 2.0 2.3 2.7 3.7 3.0 3.0 4.0 2.8

14

6

Cycle 4 Replicate --> 1 2 3 4 5 6 7 8 9 10 Species Averages RO 4.7 4.3 4.3 4.7 5.0 5.0 5.0 5.0 4.7 4.0 4.7 WO 4.7 5.0 4.7 5.0 5.0 5.0 5.0 4.7 5.0 5.0 4.9 1 min DF 5.0 5.0 5.0 4.7 4.7 5.0 5.0 5.0 5.0 5.0 4.9 Maple 3.7 4.7 4.0 4.3 4.3 4.3 3.3 5.0 4.7 5.0 4.3

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 4.3 3.7 4.3 4.7 5.0 5.0 5.0 4.7 4.7 4.0 4.5 WO 4.7 5.0 5.0 5.0 5.0 5.0 5.0 4.7 5.0 5.0 4.9 5 min DF 4.0 4.7 4.7 4.3 4.7 5.0 5.0 5.0 5.0 5.0 4.7 Maple 2.7 4.7 4.0 4.0 4.3 4.3 3.0 5.0 4.7 5.0 4.2

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 4.3 3.7 4.3 4.7 5.0 5.0 4.7 4.7 4.0 4.0 4.4 WO 4.7 5.0 5.0 5.0 5.0 5.0 5.0 4.7 5.0 5.0 4.9 10 min DF 3.7 4.7 4.7 4.3 4.7 5.0 4.7 5.0 5.0 4.7 4.6 Maple 2.7 4.7 4.0 4.0 4.3 4.3 3.0 4.7 4.3 5.0 4.1

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 3.7 3.0 4.3 4.3 5.0 4.7 4.7 4.7 3.7 3.3 4.1 WO 4.7 5.0 4.3 5.0 5.0 4.7 5.0 4.7 5.0 5.0 4.8 20 min DF 3.7 4.7 4.7 3.7 4.0 4.7 4.7 4.7 4.7 4.3 4.4 Maple 2.3 4.7 4.0 4.0 4.3 4.3 3.0 4.7 4.3 5.0 4.1

147

Cycle 5 Replicate --> 1 2 3 4 5 6 7 8 9 10 Species Averages RO 4.3 4.3 5.0 4.7 5.0 5.0 5.0 5.0 5.0 4.0 4.7 WO 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 1 min DF 5.0 4.7 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 Maple 4.0 4.7 4.3 4.0 4.7 4.7 4.0 4.0 4.7 5.0 4.4

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 3.7 4.3 4.7 4.7 5.0 5.0 5.0 5.0 5.0 3.7 4.6 WO 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5 min DF 5.0 4.7 5.0 4.7 5.0 5.0 5.0 5.0 5.0 5.0 4.9 Maple 3.7 4.7 4.3 4.3 4.3 4.3 4.0 3.3 4.3 5.0 4.2

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 3.3 4.0 4.7 4.7 5.0 4.7 4.7 5.0 5.0 3.7 4.5 WO 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 10 min DF 5.0 4.7 5.0 4.7 4.7 4.7 5.0 5.0 5.0 5.0 4.9 Maple 3.7 4.7 4.0 4.0 4.3 4.3 3.7 3.3 4.3 5.0 4.1

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 3.0 4.0 4.7 4.7 5.0 4.7 4.7 5.0 5.0 3.3 4.4 WO 4.7 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 20 min DF 4.7 4.7 4.7 4.7 4.3 4.3 5.0 4.7 4.7 4.7 4.6 Maple 3.0 4.7 4.0 4.0 4.0 4.0 3.7 3.3 4.0 5.0 4.0

148

Cycle 6 Replicate --> 1 2 3 4 5 6 7 8 9 10 Species Averages RO 3.7 4.3 4.7 4.7 5.0 5.0 5.0 4.7 5.0 5.0 4.7 WO 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 1 min DF 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 Maple 4.0 5.0 4.3 4.0 5.0 4.0 3.7 4.7 4.7 5.0 4.4

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 3.7 4.0 4.7 4.7 5.0 4.3 5.0 4.7 5.0 4.0 4.5 WO 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5 min DF 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 Maple 3.7 4.7 4.3 3.7 5.0 3.7 3.0 4.3 4.7 5.0 4.2

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 3.7 3.7 4.7 4.7 5.0 4.3 5.0 4.7 5.0 4.0 4.5 WO 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 10 min DF 5.0 5.0 5.0 5.0 5.0 5.0 4.7 5.0 4.7 4.7 4.9 Maple 3.7 4.7 4.3 2.7 4.3 3.3 2.0 4.3 4.7 5.0 3.9

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 3.0 3.0 4.7 4.7 5.0 4.3 5.0 4.7 5.0 4.0 4.3 WO 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 20 min DF 5.0 4.7 5.0 4.7 5.0 5.0 4.7 4.7 4.7 4.3 4.8 Maple 2.7 4.7 4.0 2.0 4.3 3.3 2.0 4.3 4.3 5.0 3.7

149

Cycle 7 Replicate --> 1 2 3 4 5 6 7 8 9 10 Species Averages RO 4.3 4.3 4.0 4.3 4.7 4.3 4.7 4.0 5.0 4.0 4.4 WO 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 1 min DF 4.7 4.7 5.0 4.7 4.7 5.0 5.0 5.0 5.0 5.0 4.9 Maple 4.0 4.7 3.7 4.0 3.7 4.3 3.7 4.0 4.7 5.0 4.2

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 3.7 4.3 4.0 4.0 4.7 4.3 4.7 4.0 5.0 4.0 4.3 WO 4.7 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5 min DF 4.3 4.7 5.0 4.7 4.7 5.0 5.0 5.0 4.7 4.7 4.8 Maple 3.0 4.0 3.3 4.0 3.3 4.3 3.7 4.0 4.3 5.0 3.9

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 3.7 3.7 4.0 4.0 4.7 4.0 4.7 4.0 5.0 4.0 4.2 WO 4.7 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 10 min DF 4.3 4.7 5.0 4.7 4.7 5.0 5.0 5.0 4.7 4.7 4.8 Maple 3.0 3.7 3.3 3.7 3.3 4.3 3.3 4.0 4.3 4.3 3.7

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 3.3 3.7 3.3 4.0 4.7 3.3 4.7 3.7 4.7 3.7 3.9 WO 4.7 5.0 5.0 4.7 5.0 5.0 5.0 5.0 4.7 4.7 4.9 20 min DF 4.0 4.7 5.0 4.7 4.7 5.0 5.0 5.0 4.7 4.7 4.7 Maple 2.3 3.7 2.3 3.7 2.7 4.0 3.0 3.7 4.3 4.3 3.4

150

Cycle 8 Replicate --> 1 2 3 4 5 6 7 8 9 10 Species Averages RO 3.7 4.3 4.7 4.0 4.3 3.0 4.7 3.0 5.0 3.7 4.0 WO 5.0 5.0 4.7 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 1 min DF 5.0 4.0 5.0 5.0 5.0 5.0 4.7 5.0 4.7 5.0 4.8 Maple 3.0 4.7 3.3 4.3 4.3 3.3 4.0 5.0 3.3 5.0 4.0

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 3.7 4.0 4.3 3.7 4.3 3.0 4.7 3.0 5.0 3.3 3.9 WO 5.0 5.0 4.7 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5 min DF 4.7 4.0 5.0 5.0 5.0 5.0 4.7 5.0 4.3 5.0 4.8 Maple 3.0 4.3 2.3 4.0 4.7 3.0 4.0 4.7 3.0 5.0 3.8

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 3.7 4.0 4.3 3.3 3.7 3.0 4.7 2.3 4.7 3.3 3.7 WO 4.7 5.0 4.7 5.0 5.0 5.0 5.0 5.0 5.0 4.7 4.9 10 min DF 4.7 4.0 5.0 5.0 5.0 5.0 4.0 5.0 4.3 5.0 4.7 Maple 2.0 4.0 2.3 3.7 4.0 2.7 4.0 4.7 2.7 4.7 3.5

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 3.3 3.3 3.7 3.3 3.3 2.7 4.7 2.3 4.3 3.0 3.4 WO 4.7 5.0 4.7 5.0 5.0 5.0 5.0 5.0 5.0 4.3 4.9 20 min DF 4.3 3.3 4.7 4.0 5.0 4.7 4.0 4.7 4.0 4.7 4.3 Maple 2.0 3.7 1.7 3.3 3.7 2.0 3.3 4.3 2.7 4.7 3.1

151

Creosote Treated

Cycle 0 Time (min.) Replicate --> 1 2 3 4 5 6 7 8 9 10 Species Averages RO 4.7 4.0 4.0 3.3 3.7 4.0 3.7 4.0 3.7 3.0 3.8 WO 4.7 4.0 3.7 3.0 3.3 3.7 4.3 4.0 4.0 3.3 3.8 1 min DF 4.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.1 Maple 4.7 3.7 4.0 3.3 2.7 4.7 3.3 4.0 4.0 3.3 3.8

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 4.0 3.3 3.0 3.0 2.7 4.0 3.3 3.0 2.7 2.7 3.2 WO 4.7 3.7 3.0 2.7 2.7 3.3 4.0 3.3 3.0 2.3 3.3 5 min DF 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 Maple 4.7 3.3 3.3 3.3 3.0 4.3 3.3 3.7 4.0 3.3 3.6

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 4.0 3.0 2.0 2.3 2.3 3.7 3.3 3.0 2.7 2.3 2.9 WO 4.0 3.0 3.0 2.7 2.3 3.3 3.7 3.0 2.7 2.3 3.0 10 min DF 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 Maple 3.7 3.0 3.0 3.3 3.0 3.7 3.3 3.7 3.3 3.3 3.3

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 4.0 2.3 2.0 2.0 2.3 3.3 3.0 2.7 2.3 2.3 2.6 WO 3.0 2.7 2.7 2.7 2.3 3.3 4.0 3.0 2.7 2.5 2.9 20 min DF 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 Maple 4.0 3.0 3.0 3.3 3.0 3.3 3.3 3.7 3.3 3.3 3.3

152

Cycle 1 Replicate --> 1 2 3 4 5 6 7 8 9 10 Species Averages RO 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 WO 5.0 4.7 4.3 5.0 5.0 5.0 4.7 5.0 5.0 4.7 4.8 1 min DF 5.0 4.0 4.0 3.7 4.7 5.0 5.0 5.0 4.7 4.7 4.6 Maple 4.0 4.3 3.3 4.0 5.0 5.0 5.0 5.0 5.0 3.3 4.4

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 4.7 4.7 4.9 WO 4.7 4.0 4.3 5.0 5.0 4.7 4.7 5.0 5.0 5.0 4.7 5 min DF 5.0 4.3 4.0 4.0 3.7 4.7 4.7 4.7 4.0 4.3 4.3 Maple 4.0 4.3 3.3 4.0 5.0 4.7 4.7 5.0 4.7 3.3 4.3

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 4.7 4.7 4.9 WO 4.7 4.0 4.3 5.0 5.0 4.3 4.7 5.0 4.7 5.0 4.7 10 min DF 5.0 4.3 3.7 3.7 3.3 4.7 4.7 4.7 4.0 4.3 4.2 Maple 4.0 4.3 3.3 3.7 5.0 4.3 4.7 4.7 4.7 3.3 4.2

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 5.0 4.3 4.0 4.7 4.3 4.0 4.3 5.0 4.3 3.7 4.4 WO 4.3 4.0 4.3 5.0 4.7 4.3 4.0 5.0 4.3 4.0 4.4 20 min DF 5.0 4.3 3.7 3.0 3.3 4.7 4.7 4.3 4.0 4.0 4.1 Maple 4.0 4.0 3.3 3.7 4.3 4.3 4.3 4.0 4.7 3.0 4.0

153

Cycle 2 Replicate --> 1 2 3 4 5 6 7 8 9 10 Species Averages RO 3.7 3.7 4.0 4.3 4.7 4.0 3.7 4.3 4.0 4.3 4.1 WO 4.0 4.0 4.0 4.3 4.3 5.0 4.0 4.7 4.0 4.3 4.3 1 min DF 3.7 3.3 3.3 3.7 4.3 3.7 4.0 3.7 4.0 4.0 3.8 Maple 4.0 5.0 3.3 4.0 4.3 4.0 4.0 4.3 4.0 4.0 4.1

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 3.3 3.7 3.7 4.3 4.7 3.7 3.3 4.3 4.0 4.3 3.9 WO 4.0 4.0 3.7 4.0 4.0 5.0 3.7 4.7 3.7 4.0 4.1 5 min DF 3.3 3.3 3.3 3.7 4.3 3.7 3.7 3.7 4.0 4.0 3.7 Maple 4.0 5.0 3.3 4.0 4.3 4.0 4.0 4.3 4.0 4.0 4.1

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 3.3 3.7 4.0 4.0 4.0 3.7 3.3 4.3 4.0 4.0 3.8 WO 4.0 4.0 3.7 3.7 4.0 5.0 3.3 4.7 3.7 3.7 4.0 10 min DF 3.3 3.3 3.3 3.3 4.3 3.7 3.7 3.7 4.0 4.0 3.7 Maple 4.0 5.0 3.3 4.0 4.0 4.0 4.0 4.3 4.0 4.0 4.1

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 3.0 3.3 3.7 3.7 3.7 3.7 3.3 4.0 3.7 4.0 3.6 WO 4.0 3.7 3.3 3.7 3.7 4.7 3.3 4.7 3.7 3.7 3.8 20 min DF 3.3 3.3 3.3 3.3 4.3 3.7 3.7 3.7 4.0 4.0 3.7 Maple 4.0 5.0 3.3 4.0 4.0 4.0 4.0 4.3 4.0 4.0 4.1

154

Cycle 3 Replicate --> 1 2 3 4 5 6 7 8 9 10 Species Averages RO 5.0 5.0 4.7 5.0 5.0 5.0 5.0 4.7 5.0 4.7 4.9 WO 4.7 4.3 4.3 5.0 4.7 4.7 4.7 5.0 5.0 5.0 4.7 1 min DF 4.0 3.7 4.0 3.7 4.0 4.7 4.7 4.3 4.3 4.3 4.2 Maple 5.0 5.0 4.7 4.3 5.0 5.0 4.7 5.0 5.0 5.0 4.9

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 5.0 5.0 4.7 5.0 5.0 5.0 5.0 4.7 5.0 4.7 4.9 WO 4.7 4.3 4.3 4.7 3.7 4.7 4.7 5.0 4.7 5.0 4.6 5 min DF 4.0 3.7 4.0 3.7 4.0 4.7 4.0 4.0 4.0 4.0 4.0 Maple 5.0 5.0 4.7 4.0 5.0 5.0 4.7 5.0 5.0 4.7 4.8

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 5.0 5.0 3.3 4.3 4.3 4.7 4.7 4.7 5.0 4.3 4.5 WO 4.7 4.0 3.7 4.0 3.7 4.0 4.3 4.7 4.7 4.3 4.2 10 min DF 4.0 3.7 4.0 3.7 4.0 4.7 4.0 4.0 3.7 4.0 4.0 Maple 4.7 5.0 5.0 3.7 4.7 4.7 3.7 5.0 4.0 4.0 4.4

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 5.0 5.0 3.3 4.3 4.3 4.7 4.3 4.3 5.0 4.3 4.5 WO 4.7 4.0 3.7 3.3 3.7 3.7 4.3 4.7 4.3 4.3 4.1 20 min DF 4.0 3.7 3.7 3.7 4.0 4.7 3.7 4.0 3.7 4.0 3.9 Maple 4.0 5.0 4.0 4.0 4.7 4.0 3.7 4.7 4.0 3.3 4.1

155

Cycle 4 Replicate --> 1 2 3 4 5 6 7 8 9 10 Species Averages RO 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 WO 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 1 min DF 5.0 5.0 5.0 5.0 4.7 5.0 4.7 5.0 4.7 5.0 4.9 Maple 4.7 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 5.0 5.0 4.7 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 WO 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5 min DF 4.3 4.7 4.7 4.7 4.7 5.0 4.7 5.0 4.7 4.7 4.7 Maple 4.7 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 5.0 5.0 4.7 4.7 5.0 5.0 4.7 4.7 4.7 5.0 4.8 WO 5.0 5.0 5.0 5.0 5.0 5.0 4.3 5.0 5.0 5.0 4.9 10 min DF 4.3 4.7 4.7 4.7 4.3 5.0 4.7 5.0 4.0 4.3 4.6 Maple 4.7 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 5.0 5.0 4.3 4.7 5.0 5.0 4.7 4.7 4.7 5.0 4.8 WO 4.7 4.7 4.7 4.7 4.7 5.0 4.7 5.0 4.7 5.0 4.8 20 min DF 4.3 4.3 4.7 4.3 4.3 5.0 4.7 5.0 4.0 4.0 4.5 Maple 4.7 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0

156

Cycle 5 Replicate --> 1 2 3 4 5 6 7 8 9 10 Species Averages RO 5.0 5.0 5.0 5.0 4.7 5.0 5.0 5.0 5.0 5.0 5.0 WO 4.7 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 1 min DF 4.7 4.0 5.0 5.0 4.7 5.0 5.0 5.0 5.0 5.0 4.8 Maple 4.7 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 4.7 5.0 5.0 5.0 4.7 5.0 5.0 5.0 5.0 5.0 4.9 WO 4.3 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 4.9 5 min DF 4.3 4.0 4.7 4.7 4.3 5.0 5.0 5.0 5.0 5.0 4.7 Maple 4.7 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 4.7 5.0 4.7 5.0 4.7 5.0 5.0 5.0 5.0 5.0 4.9 WO 4.3 5.0 5.0 5.0 5.0 5.0 4.7 5.0 5.0 5.0 4.9 10 min DF 4.3 4.0 4.7 4.3 4.0 5.0 5.0 5.0 5.0 4.7 4.6 Maple 4.7 5.0 5.0 5.0 5.0 5.0 5.0 4.7 5.0 4.3 4.9

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 4.7 4.7 4.3 4.3 4.3 5.0 4.7 5.0 5.0 5.0 4.7 WO 3.7 5.0 4.3 5.0 4.7 5.0 4.7 5.0 4.7 5.0 4.7 20 min DF 4.0 3.7 4.3 4.3 3.7 4.7 4.7 4.7 4.3 4.7 4.3 Maple 4.0 4.7 5.0 5.0 5.0 5.0 5.0 4.7 5.0 4.0 4.7

157

Cycle 6 Replicate --> 1 2 3 4 5 6 7 8 9 10 Species Averages RO 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 WO 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 1 min DF 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 Maple 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 WO 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5 min DF 4.7 5.0 5.0 4.7 5.0 5.0 5.0 5.0 4.7 5.0 4.9 Maple 5.0 5.0 5.0 5.0 5.0 5.0 4.7 5.0 5.0 5.0 5.0

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 WO 5.0 5.0 4.7 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 10 min DF 4.7 5.0 5.0 4.7 5.0 5.0 5.0 5.0 4.7 5.0 4.9 Maple 5.0 5.0 5.0 5.0 5.0 5.0 4.7 5.0 5.0 5.0 5.0

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 WO 5.0 5.0 4.7 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 20 min DF 4.3 4.7 4.3 4.7 5.0 5.0 4.3 4.7 4.7 5.0 4.7 Maple 4.7 5.0 5.0 4.7 5.0 5.0 4.7 5.0 5.0 5.0 4.9

158

Cycle 7 Replicate --> 1 2 3 4 5 6 7 8 9 10 Species Averages RO 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 WO 4.7 4.7 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 4.9 1 min DF 5.0 4.7 4.7 5.0 4.7 5.0 5.0 4.7 5.0 5.0 4.9 Maple 5.0 5.0 5.0 5.0 5.0 5.0 4.7 5.0 5.0 5.0 5.0

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 WO 4.7 4.3 5.0 5.0 4.7 5.0 5.0 5.0 5.0 5.0 4.9 5 min DF 5.0 4.7 4.0 5.0 4.7 4.7 4.7 4.7 5.0 4.7 4.7 Maple 5.0 5.0 5.0 5.0 5.0 5.0 4.7 5.0 5.0 5.0 5.0

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 WO 4.3 4.0 4.7 5.0 4.7 5.0 5.0 5.0 5.0 5.0 4.8 10 min DF 5.0 4.7 4.0 5.0 4.7 4.7 4.3 4.7 5.0 4.3 4.6 Maple 4.7 4.7 5.0 5.0 5.0 5.0 4.7 5.0 5.0 5.0 4.9

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 WO 4.3 4.0 4.7 5.0 4.7 5.0 5.0 5.0 5.0 5.0 4.8 20 min DF 5.0 4.7 4.0 4.7 4.7 4.3 4.0 4.7 4.7 4.3 4.5 Maple 4.7 4.7 4.3 5.0 5.0 5.0 4.7 5.0 5.0 5.0 4.8

159

Cycle 8 Replicate --> 1 2 3 4 5 6 7 8 9 10 Species Averages RO 5.0 5.0 5.0 4.7 5.0 5.0 5.0 4.7 5.0 5.0 4.9 WO 4.7 3.7 5.0 5.0 4.3 5.0 5.0 4.3 5.0 5.0 4.7 1 min DF 5.0 5.0 5.0 4.7 5.0 4.7 5.0 5.0 5.0 4.7 4.9 Maple 5.0 4.7 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 5.0 5.0 5.0 4.7 5.0 5.0 5.0 4.0 5.0 5.0 4.9 WO 5.0 3.7 5.0 5.0 4.3 5.0 5.0 4.3 5.0 5.0 4.7 5 min DF 5.0 5.0 5.0 4.7 4.7 4.3 5.0 5.0 5.0 4.7 4.8 Maple 5.0 4.7 5.0 4.7 5.0 4.7 5.0 5.0 5.0 5.0 4.9

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 5.0 5.0 5.0 4.7 5.0 5.0 5.0 4.0 5.0 5.0 4.9 WO 4.3 3.3 4.7 5.0 4.3 5.0 5.0 4.3 5.0 5.0 4.6 10 min DF 5.0 5.0 4.7 4.3 4.3 4.3 5.0 5.0 5.0 4.7 4.7 Maple 5.0 4.3 5.0 4.3 5.0 4.7 5.0 5.0 5.0 5.0 4.8

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 5.0 4.7 4.3 4.3 5.0 5.0 5.0 3.7 5.0 5.0 4.7 WO 4.3 3.0 4.0 4.7 3.7 4.7 4.7 4.0 4.3 5.0 4.2 20 min DF 4.7 4.3 4.3 4.7 4.7 3.7 4.7 4.3 4.7 4.0 4.4 Maple 4.7 4.3 5.0 4.0 5.0 4.7 5.0 5.0 5.0 4.3 4.7

160

Pentachlorophenol Treated

Cycle 0 Time (min.) Replicate --> 1 2 3 4 5 6 7 8 9 10 Species Averages RO 3.0 3.3 3.0 3.0 2.7 3.0 2.7 3.7 3.0 3.0 3.0 WO 3.3 3.7 3.7 4.0 3.7 3.0 3.0 3.7 4.0 3.3 3.5 1 min DF 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 Maple 3.0 3.0 2.7 3.0 3.7 3.0 3.7 3.7 3.0 3.0 3.2

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 3.0 3.3 3.3 3.3 3.0 3.3 3.0 3.0 2.7 2.7 3.1 WO 3.0 3.3 3.0 3.3 3.7 3.0 3.0 3.7 3.7 3.7 3.3 5 min DF 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 Maple 3.0 3.3 2.7 3.3 3.0 3.3 3.7 3.0 3.0 3.0 3.1

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 3.0 3.3 3.3 3.3 3.0 3.3 3.0 3.0 2.7 3.0 3.1 WO 3.3 3.3 3.3 4.0 3.7 3.0 3.0 3.7 3.3 3.0 3.4 10 min DF 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 Maple 2.7 2.3 2.7 2.7 3.0 3.0 3.3 3.0 2.7 3.0 2.8

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 3.0 3.0 3.0 3.0 2.7 3.3 3.0 2.7 2.3 2.7 2.9 WO 3.3 3.0 2.7 3.0 3.0 3.0 3.0 3.7 3.0 2.7 3.0 20 min DF 3.0 3.0 2.7 3.0 3.0 3.0 3.0 3.0 2.7 3.0 2.9 Maple 2.0 2.3 2.3 2.7 3.0 3.0 3.3 3.0 2.7 2.7 2.7

161

Cycle 1 Replicate --> 1 2 3 4 5 6 7 8 9 10 Species Averages RO 2.0 4.0 4.3 4.0 4.0 3.7 3.7 4.3 4.3 4.7 3.9 WO 4.0 5.0 3.7 4.7 4.3 4.0 5.0 3.7 4.3 5.0 4.4 1 min DF 3.0 3.0 3.3 3.0 3.0 3.3 3.0 4.0 4.0 3.3 3.3 Maple 3.0 3.7 3.0 3.0 2.7 3.0 3.0 3.0 2.7 4.3 3.1

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 2.0 3.3 3.3 3.3 4.0 3.3 3.0 4.0 4.0 3.7 3.4 WO 4.0 4.3 3.7 4.3 4.3 4.0 4.3 3.7 4.3 4.0 4.1 5 min DF 3.0 3.0 3.3 3.0 3.0 3.0 3.0 4.0 3.7 3.0 3.2 Maple 3.0 3.0 3.0 3.0 2.3 2.7 3.0 2.7 2.7 3.3 2.9

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 1.7 3.0 3.3 3.3 4.0 2.7 3.0 3.7 3.3 3.7 3.2 WO 3.7 4.3 3.7 4.0 4.0 4.0 4.0 3.7 4.0 4.0 3.9 10 min DF 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.3 3.3 3.0 3.1 Maple 3.0 3.0 2.7 3.0 2.3 2.7 3.0 2.7 2.7 3.3 2.8

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 1.3 3.0 3.3 3.0 3.0 2.3 2.3 3.3 2.3 2.7 2.7 WO 3.7 4.3 3.7 4.0 4.0 4.0 4.0 3.7 4.0 3.7 3.9 20 min DF 2.0 3.0 3.0 2.3 2.7 3.0 2.7 3.3 3.0 3.0 2.8 Maple 2.7 2.7 2.0 2.3 1.7 2.0 3.0 1.7 2.7 2.7 2.3

162

Cycle 2 Replicate --> 1 2 3 4 5 6 7 8 9 10 Species Averages RO 4.0 4.7 5.0 5.0 5.0 5.0 5.0 4.7 5.0 5.0 4.8 WO 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 1 min DF 3.7 4.0 4.7 4.7 4.7 5.0 4.0 5.0 4.7 5.0 4.5 Maple 3.0 4.3 4.7 4.0 4.7 4.3 4.7 4.7 3.3 4.7 4.2

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 3.7 4.3 4.0 4.7 4.7 4.3 4.7 4.0 4.7 5.0 4.4 WO 4.3 4.7 4.0 5.0 4.3 5.0 5.0 5.0 5.0 5.0 4.7 5 min DF 3.3 4.0 4.0 4.3 4.3 4.7 4.0 4.0 4.0 4.3 4.1 Maple 3.0 4.3 4.7 3.7 4.7 4.3 4.7 4.7 3.0 4.7 4.2

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 3.7 4.0 4.0 4.0 4.7 4.0 4.3 3.7 4.0 5.0 4.1 WO 4.0 5.0 4.0 4.0 4.0 5.0 5.0 4.3 4.7 5.0 4.5 10 min DF 3.0 3.3 4.0 4.0 4.0 4.3 3.7 4.0 4.0 4.0 3.8 Maple 3.0 3.7 4.0 3.7 4.0 3.7 4.0 4.7 3.0 4.7 3.8

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 3.3 3.7 3.7 4.0 4.3 4.0 4.3 3.3 4.0 4.7 3.9 WO 3.7 4.0 4.0 4.0 4.0 4.0 5.0 4.0 4.7 4.3 4.2 20 min DF 3.0 3.3 3.7 4.0 4.0 4.3 3.3 4.0 4.0 3.7 3.7 Maple 2.7 3.7 4.0 3.7 4.0 3.7 4.0 4.0 2.7 3.7 3.6

163

Cycle 3 Replicate --> 1 2 3 4 5 6 7 8 9 10 Species Averages RO 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 WO 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 1 min DF 4.0 5.0 4.0 5.0 4.7 5.0 4.3 4.3 5.0 5.0 4.6 Maple 4.3 4.7 5.0 5.0 5.0 5.0 5.0 5.0 4.7 5.0 4.9

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 WO 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5 min DF 4.0 5.0 4.0 5.0 4.7 5.0 4.3 4.3 5.0 5.0 4.6 Maple 4.0 4.0 5.0 4.0 4.7 4.7 5.0 4.7 4.7 5.0 4.6

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 4.7 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 WO 5.0 4.7 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 10 min DF 4.0 4.7 4.0 5.0 4.0 4.7 4.3 4.0 4.3 5.0 4.4 Maple 4.0 4.0 4.7 3.7 4.7 5.0 5.0 5.0 4.7 5.0 4.6

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 4.3 5.0 5.0 5.0 5.0 5.0 5.0 4.7 5.0 5.0 4.9 WO 4.7 4.7 5.0 5.0 4.7 5.0 5.0 5.0 5.0 4.7 4.9 20 min DF 3.7 4.7 3.7 4.7 4.0 4.7 4.0 3.7 4.3 5.0 4.2 Maple 3.7 3.7 4.3 3.7 4.3 4.3 4.7 4.3 3.7 4.0 4.1

164

Cycle 4 Replicate --> 1 2 3 4 5 6 7 8 9 10 Species Averages RO 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 WO 4.7 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 1 min DF 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 Maple 5.0 5.0 5.0 5.0 5.0 4.7 5.0 5.0 5.0 5.0 5.0 3 Replicate --> 3 2 3 4 5 6 7 8 9 10 RO 5.0 5.0 5.0 5.0 4.7 5.0 5.0 5.0 5.0 5.0 5.0 WO 4.7 5.0 5.0 5.0 5.0 5.0 5.0 5.0 4.7 5.0 4.9 5 min DF 4.7 4.3 5.0 5.0 5.0 4.7 5.0 5.0 4.7 5.0 4.8 Maple 4.7 5.0 5.0 5.0 5.0 4.7 5.0 5.0 5.0 4.3 4.9

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 4.7 5.0 5.0 5.0 4.7 5.0 5.0 5.0 5.0 5.0 4.9 WO 4.7 5.0 5.0 5.0 5.0 5.0 5.0 5.0 4.7 5.0 4.9 10 min DF 4.7 4.3 4.3 5.0 5.0 4.7 4.7 5.0 4.7 5.0 4.7 Maple 4.3 5.0 5.0 5.0 5.0 4.7 5.0 5.0 5.0 4.3 4.8

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 4.7 5.0 5.0 5.0 4.7 5.0 5.0 5.0 5.0 5.0 4.9 WO 4.7 5.0 5.0 5.0 5.0 5.0 5.0 5.0 4.7 5.0 4.9 20 min DF 4.7 4.3 4.3 4.3 5.0 4.7 4.7 5.0 4.7 5.0 4.7 Maple 4.0 4.7 5.0 4.7 5.0 4.7 4.7 5.0 5.0 4.3 4.7

165

Cycle 5 Replicate --> 1 2 3 4 5 6 7 8 9 10 Species Averages RO 5.0 4.7 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 WO 5.0 4.7 5.0 5.0 4.7 5.0 5.0 4.7 4.7 5.0 4.9 1 min DF 4.3 5.0 4.3 5.0 4.7 5.0 4.7 4.7 4.7 5.0 4.7 Maple 4.3 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 4.9

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 4.3 4.7 5.0 5.0 5.0 5.0 5.0 4.7 5.0 5.0 4.9 WO 4.7 4.7 4.7 4.7 4.3 5.0 5.0 4.3 4.3 5.0 4.7 5 min DF 4.3 5.0 4.0 5.0 4.7 5.0 4.7 4.0 4.0 5.0 4.6 Maple 4.3 5.0 5.0 4.7 5.0 5.0 5.0 5.0 5.0 5.0 4.9

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 3.7 4.7 5.0 5.0 4.7 5.0 5.0 4.7 5.0 5.0 4.8 WO 4.7 4.7 4.7 4.7 4.3 5.0 5.0 4.3 4.3 5.0 4.7 10 min DF 4.3 5.0 4.0 4.7 4.7 4.7 4.0 4.0 4.0 4.3 4.4 Maple 3.7 5.0 4.7 4.7 5.0 5.0 5.0 4.7 5.0 5.0 4.8

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 3.7 4.7 5.0 4.7 4.7 4.7 4.7 4.3 5.0 5.0 4.6 WO 4.3 4.7 4.7 4.7 4.3 4.7 5.0 4.0 4.0 5.0 4.5 20 min DF 4.3 5.0 4.0 4.7 4.3 4.7 4.0 4.0 4.0 4.0 4.3 Maple 3.7 4.0 4.7 4.7 4.3 4.7 4.7 4.3 5.0 5.0 4.5

166

Cycle 6 Replicate --> 1 2 3 4 5 6 7 8 9 10 Species Averages RO 4.7 5.0 5.0 5.0 5.0 5.0 5.0 5.0 4.7 5.0 4.9 WO 4.0 4.7 4.7 5.0 5.0 5.0 4.7 4.7 5.0 4.3 4.7 1 min DF 5.0 5.0 4.7 5.0 4.7 5.0 4.3 5.0 4.7 5.0 4.8 Maple 5.0 5.0 5.0 5.0 5.0 5.0 5.0 4.0 5.0 5.0 4.9

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 4.7 5.0 5.0 5.0 5.0 5.0 5.0 4.7 4.7 5.0 4.9 WO 3.7 4.7 4.7 5.0 5.0 5.0 4.3 4.3 5.0 4.3 4.6 5 min DF 4.7 5.0 4.0 5.0 4.3 5.0 4.0 4.7 4.3 5.0 4.6 Maple 5.0 5.0 5.0 4.7 5.0 5.0 5.0 4.0 5.0 5.0 4.9

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 4.7 5.0 5.0 5.0 5.0 5.0 5.0 4.7 4.7 5.0 4.9 WO 3.7 4.3 4.7 5.0 4.7 5.0 4.0 4.3 5.0 4.0 4.5 10 min DF 4.7 5.0 3.7 5.0 4.3 4.3 3.7 4.7 4.3 4.7 4.4 Maple 5.0 5.0 5.0 4.7 5.0 5.0 5.0 4.0 5.0 5.0 4.9

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 4.3 5.0 5.0 5.0 5.0 5.0 5.0 4.7 4.7 5.0 4.9 WO 3.3 4.0 4.3 5.0 4.7 5.0 4.0 4.0 4.7 4.0 4.3 20 min DF 4.7 5.0 4.0 5.0 4.0 4.3 3.3 4.3 4.0 4.3 4.3 Maple 4.7 5.0 5.0 4.7 4.7 4.7 5.0 3.7 4.7 4.7 4.7

167

Cycle 7 Replicate --> 1 2 3 4 5 6 7 8 9 10 Species Averages RO 4.7 5.0 5.0 4.7 5.0 5.0 5.0 4.7 5.0 5.0 4.9 WO 5.0 4.7 5.0 5.0 4.7 5.0 4.7 5.0 5.0 5.0 4.9 1 min DF 5.0 5.0 5.0 4.7 5.0 5.0 4.0 4.7 4.3 5.0 4.8 Maple 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 4.7 5.0 5.0 4.7 4.7 5.0 5.0 4.7 5.0 5.0 4.9 WO 5.0 4.7 5.0 5.0 4.7 5.0 4.3 4.7 5.0 5.0 4.8 5 min DF 4.7 4.3 4.3 4.0 5.0 5.0 4.0 4.7 3.7 4.7 4.4 Maple 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 4.7 4.7 4.9

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 4.7 5.0 5.0 4.3 4.7 5.0 5.0 4.7 5.0 5.0 4.8 WO 5.0 4.7 5.0 5.0 4.7 5.0 4.3 4.0 5.0 5.0 4.8 10 min DF 4.7 4.3 4.3 3.7 4.7 5.0 3.7 4.7 3.7 4.3 4.3 Maple 5.0 5.0 5.0 5.0 5.0 5.0 4.7 5.0 4.3 4.7 4.9

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 4.3 5.0 5.0 4.3 4.7 5.0 5.0 4.3 5.0 5.0 4.8 WO 4.3 4.3 4.7 4.3 4.3 5.0 3.3 3.7 4.7 5.0 4.4 20 min DF 4.7 4.0 4.3 3.7 4.3 5.0 3.7 4.0 3.3 4.0 4.1 Maple 3.7 4.3 4.7 5.0 5.0 4.7 5.0 4.7 4.3 4.0 4.5

168

Cycle 8 Replicate --> 1 2 3 4 5 6 7 8 9 10 Species Averages RO 3.3 4.0 5.0 4.3 4.7 5.0 4.0 4.0 5.0 5.0 4.4 WO 3.7 4.7 5.0 5.0 4.7 4.7 5.0 4.7 5.0 5.0 4.7 1 min DF 4.0 4.7 4.3 4.3 5.0 5.0 4.0 4.7 4.7 4.7 4.5 Maple 4.7 5.0 5.0 5.0 5.0 5.0 5.0 4.3 5.0 5.0 4.9

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 2.7 4.0 5.0 4.3 4.7 5.0 4.0 4.0 5.0 5.0 4.4 WO 3.0 4.7 5.0 5.0 4.7 3.7 5.0 4.3 4.3 5.0 4.5 5 min DF 4.0 4.7 4.0 4.3 5.0 5.0 4.0 4.7 4.3 4.3 4.4 Maple 4.3 5.0 5.0 4.7 5.0 5.0 5.0 4.3 5.0 5.0 4.8

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 2.0 4.0 5.0 4.3 4.3 5.0 4.0 4.3 5.0 4.7 4.3 WO 3.0 4.7 4.7 5.0 4.7 3.7 5.0 4.0 4.3 5.0 4.4 10 min DF 4.0 4.7 3.7 3.7 4.7 4.7 4.0 4.7 4.3 4.3 4.3 Maple 4.0 5.0 5.0 4.7 5.0 4.7 4.7 4.0 4.7 5.0 4.7

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 2.0 3.7 5.0 4.3 4.0 4.7 4.0 3.7 5.0 4.7 4.1 WO 3.0 4.7 4.7 5.0 4.3 3.7 5.0 4.0 4.3 5.0 4.4 20 min DF 3.7 4.0 3.3 3.3 4.3 4.3 3.3 4.3 3.7 4.0 3.8 Maple 3.7 5.0 4.3 4.7 5.0 4.7 4.7 3.7 4.3 5.0 4.5

169

Ammoniacal Copper Zinc Arsenate (ACZA) Treated

Cycle 0 Time (min.) Replicate --> 1 2 3 4 5 6 7 8 9 10 Species Averages RO 3.7 3.7 4.0 4.3 5.0 4.7 4.7 3.7 5.0 5.0 4.4 WO 4.7 3.0 4.0 3.0 3.7 4.0 2.7 2.7 3.3 4.0 3.5 1 min DF 4.7 4.3 5.0 5.0 5.0 3.0 5.0 5.0 5.0 5.0 4.7 Maple 4.0 4.7 4.7 4.3 4.0 5.0 4.7 3.7 4.0 4.7 4.4

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 3.0 3.7 3.7 4.0 5.0 4.0 4.3 3.3 5.0 4.3 4.0 WO 4.3 3.0 3.7 3.0 3.0 4.0 2.7 2.7 3.0 3.7 3.3 5 min DF 5.0 4.3 5.0 5.0 5.0 3.0 5.0 4.3 4.3 5.0 4.6 Maple 4.0 4.0 4.3 4.0 4.0 5.0 4.3 3.3 3.7 4.7 4.1

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 3.0 3.3 3.3 4.0 5.0 4.3 4.3 3.0 4.7 4.3 3.9 WO 4.0 3.3 3.3 2.7 3.0 3.3 2.7 2.0 2.7 3.7 3.1 10 min DF 5.0 4.0 5.0 5.0 5.0 3.0 5.0 4.0 4.7 5.0 4.6 Maple 3.3 4.0 4.3 3.7 3.0 4.7 4.0 3.0 3.7 4.7 3.8

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 3.0 3.3 3.3 3.7 5.0 3.7 4.3 3.0 4.7 4.0 3.8 WO 4.0 2.3 2.7 2.3 2.7 3.0 2.0 1.7 2.3 3.7 2.7 20 min DF 4.7 4.0 4.3 4.3 5.0 3.0 5.0 4.0 4.7 5.0 4.4 Maple 3.3 4.0 4.0 3.7 3.0 4.0 4.0 2.7 3.7 4.7 3.7

170

Cycle 1 Replicate --> 1 2 3 4 5 6 7 8 9 10 Species Averages RO 4.0 4.3 4.0 5.0 4.7 4.7 4.7 4.7 4.0 4.3 4.4 WO 5.0 4.7 5.0 4.3 5.0 5.0 4.7 4.7 4.3 5.0 4.8 1 min DF 4.7 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 Maple 5.0 4.7 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 4.0 4.3 4.0 4.7 4.0 4.3 4.0 4.7 4.0 4.3 4.2 WO 5.0 4.0 4.3 3.7 4.7 5.0 4.3 4.7 4.0 4.7 4.4 5 min DF 4.7 5.0 5.0 5.0 5.0 4.7 5.0 5.0 5.0 5.0 4.9 Maple 5.0 4.0 4.3 5.0 5.0 5.0 5.0 5.0 4.7 5.0 4.8

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 3.0 3.0 3.0 3.7 3.0 4.0 3.7 4.3 3.0 4.3 3.5 WO 5.0 4.0 4.3 3.3 4.7 4.7 4.0 3.7 3.7 4.3 4.2 10 min DF 4.7 5.0 5.0 5.0 5.0 4.3 5.0 5.0 5.0 5.0 4.9 Maple 4.7 3.3 4.0 5.0 5.0 5.0 5.0 5.0 4.7 5.0 4.7

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 3.0 3.0 3.0 2.7 3.0 3.3 2.7 4.0 3.0 3.3 3.1 WO 5.0 3.7 3.3 3.3 4.3 4.3 3.7 3.7 3.0 3.7 3.8 20 min DF 4.3 4.7 5.0 5.0 5.0 4.3 5.0 5.0 5.0 5.0 4.8 Maple 4.0 3.3 3.7 5.0 5.0 5.0 5.0 5.0 4.7 5.0 4.6

171

Cycle 2 Replicate --> 1 2 3 4 5 6 7 8 9 10 Species Averages RO 5.0 4.7 4.7 4.3 5.0 5.0 5.0 4.7 4.3 4.7 4.7 WO 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 1 min DF 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 Maple 4.7 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 5.0 4.7 4.0 4.3 5.0 4.7 5.0 4.3 4.3 4.7 4.6 WO 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5 min DF 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 Maple 4.7 4.7 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 4.9

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 5.0 4.7 4.0 4.3 5.0 4.3 5.0 4.3 4.0 4.3 4.5 WO 5.0 4.7 5.0 4.7 5.0 5.0 5.0 5.0 4.3 5.0 4.9 10 min DF 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 Maple 4.7 4.7 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 4.9

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 4.3 4.0 4.0 4.3 5.0 4.3 5.0 4.0 3.3 4.0 4.2 WO 5.0 4.7 5.0 4.7 5.0 5.0 5.0 5.0 4.0 5.0 4.8 20 min DF 4.7 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 Maple 4.7 4.7 4.3 5.0 5.0 5.0 5.0 5.0 4.7 5.0 4.8

172

Cycle 3 Replicate --> 1 2 3 4 5 6 7 8 9 10 Species Averages RO 3.7 3.0 3.7 3.7 4.7 4.7 4.3 4.7 5.0 4.7 4.2 WO 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 1 min DF 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 Maple 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 3.0 3.0 3.7 3.3 4.3 4.3 4.3 4.3 5.0 4.7 4.0 WO 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5 min DF 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 Maple 4.7 4.7 4.7 5.0 5.0 4.7 5.0 4.7 5.0 4.3 4.8

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 3.0 3.0 3.7 3.0 3.7 4.0 3.7 4.0 4.3 4.7 3.7 WO 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 10 min DF 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 Maple 4.7 4.3 4.3 4.7 5.0 4.7 5.0 4.7 5.0 3.7 4.6

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 2.7 3.0 3.0 2.3 3.0 4.0 3.7 3.3 4.3 4.3 3.4 WO 5.0 5.0 5.0 4.7 5.0 5.0 5.0 5.0 4.7 5.0 4.9 20 min DF 4.7 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 Maple 4.7 4.0 4.3 4.7 4.7 4.0 4.7 4.0 5.0 3.7 4.4

173

Cycle 4 Replicate --> 1 2 3 4 5 6 7 8 9 10 Species Averages RO 4.0 4.0 4.3 4.3 4.0 3.3 4.0 4.0 4.3 3.7 4.0 WO 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 1 min DF 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 Maple 4.3 3.7 4.7 4.3 4.0 4.7 4.3 5.0 5.0 5.0 4.5

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 3.7 3.7 4.3 4.0 3.7 2.7 3.3 3.3 4.0 2.7 3.5 WO 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5 min DF 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 Maple 3.7 3.3 4.0 3.7 4.0 4.3 4.0 4.7 4.3 2.7 3.9

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 3.3 3.3 4.3 4.0 3.0 2.7 3.0 2.7 3.3 2.3 3.2 WO 5.0 5.0 5.0 4.7 5.0 5.0 5.0 5.0 4.7 5.0 4.9 10 min DF 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 Maple 3.7 3.0 3.7 3.3 3.3 4.0 3.3 4.3 5.0 2.3 3.6

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 2.3 3.3 3.7 3.0 2.7 2.7 2.7 2.3 3.0 1.7 2.7 WO 5.0 4.3 5.0 4.7 5.0 5.0 4.7 5.0 4.7 5.0 4.8 20 min DF 4.3 4.7 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 4.9 Maple 3.0 2.0 3.3 3.0 3.3 4.0 3.0 4.0 5.0 2.0 3.3

174

Cycle 5 Replicate --> 1 2 3 4 5 6 7 8 9 10 Species Averages RO 3.3 3.0 3.3 2.0 3.0 4.0 2.3 2.0 2.3 2.7 2.8 WO 5.0 4.7 4.7 4.7 5.0 5.0 5.0 5.0 4.0 5.0 4.8 1 min DF 4.7 4.0 4.7 4.7 5.0 5.0 5.0 4.7 5.0 4.7 4.7 Maple 2.3 1.7 2.3 3.3 4.7 4.3 4.3 3.0 5.0 2.0 3.3

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 2.0 2.0 3.0 1.0 2.0 3.3 2.3 1.7 1.3 2.3 2.1 WO 5.0 4.3 4.3 4.3 5.0 5.0 5.0 4.7 4.0 5.0 4.7 5 min DF 4.0 4.0 4.7 4.7 5.0 5.0 5.0 4.3 5.0 4.7 4.6 Maple 2.0 1.0 1.3 3.0 4.0 4.0 3.7 2.0 5.0 2.0 2.8

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 1.7 1.7 3.0 1.0 1.3 3.3 2.0 1.7 1.0 1.3 1.8 WO 5.0 3.7 4.3 4.3 5.0 5.0 4.7 4.7 4.0 5.0 4.6 10 min DF 4.0 4.0 4.3 4.7 4.7 5.0 5.0 4.3 5.0 4.7 4.6 Maple 1.7 1.0 1.3 2.3 4.0 3.3 3.7 2.0 5.0 1.3 2.6

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 1.7 1.3 2.0 1.0 1.0 2.3 2.0 1.7 1.0 1.0 1.5 WO 5.0 3.7 4.3 4.0 5.0 5.0 4.7 4.7 3.7 5.0 4.5 20 min DF 4.0 3.3 4.3 4.0 4.3 5.0 5.0 4.3 4.7 4.7 4.4 Maple 1.7 1.0 1.0 1.3 3.7 2.3 3.7 2.0 4.7 1.3 2.3

175

Cycle 6 Replicate --> 1 2 3 4 5 6 7 8 9 10 Species Averages RO 3.3 3.0 3.3 2.7 3.0 3.0 3.3 3.0 2.0 2.3 2.9 WO 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 1 min DF 4.3 4.0 5.0 5.0 5.0 5.0 5.0 4.7 5.0 5.0 4.8 Maple 2.3 2.0 2.3 4.7 1.3 3.7 4.0 3.0 4.7 4.0 3.2

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 3.0 3.0 3.0 1.7 2.3 2.7 2.7 2.0 1.0 1.3 2.3 WO 5.0 4.3 4.7 5.0 4.7 5.0 4.7 5.0 5.0 5.0 4.8 5 min DF 4.0 4.0 4.7 4.3 5.0 5.0 5.0 4.7 5.0 5.0 4.7 Maple 2.3 1.0 1.3 4.0 1.3 3.0 3.3 2.3 4.3 3.0 2.6

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 2.3 2.7 2.0 1.0 1.7 2.7 2.7 1.0 1.0 1.3 1.8 WO 4.7 4.0 4.3 4.3 4.7 5.0 4.7 5.0 5.0 5.0 4.7 10 min DF 3.3 3.0 4.7 4.3 5.0 5.0 5.0 4.3 4.3 5.0 4.4 Maple 1.3 1.0 1.3 3.7 1.3 2.0 3.0 2.3 4.3 2.7 2.3

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 2.0 2.0 2.0 1.0 1.3 2.7 2.0 1.0 1.0 1.0 1.6 WO 4.7 4.0 4.3 4.0 4.7 5.0 4.3 5.0 4.7 4.7 4.5 20 min DF 3.0 3.0 4.7 4.3 4.7 5.0 5.0 4.3 4.3 5.0 4.3 Maple 1.3 1.0 1.0 2.7 1.3 1.7 2.3 1.3 3.7 2.0 1.8

176

Cycle 7 Replicate --> 1 2 3 4 5 6 7 8 9 10 Species Averages RO 3.3 3.0 3.0 2.7 2.7 3.3 2.7 2.3 1.7 2.7 2.7 WO 5.0 4.7 5.0 4.3 5.0 5.0 4.0 5.0 4.0 5.0 4.7 1 min DF 3.0 3.0 4.3 3.3 4.0 4.0 5.0 5.0 3.0 3.7 3.8 Maple 2.3 1.7 1.0 3.3 3.7 2.0 2.7 2.3 3.3 2.7 2.5

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 2.7 2.0 2.3 2.0 1.7 2.0 2.0 1.7 1.3 1.7 1.9 WO 5.0 4.3 5.0 4.3 5.0 5.0 4.0 5.0 4.0 4.3 4.6 5 min DF 3.0 3.0 3.7 2.7 4.0 3.7 5.0 4.3 3.0 2.7 3.5 Maple 2.3 1.7 1.0 2.3 3.3 1.7 2.0 1.7 2.7 2.7 2.1

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 2.3 1.0 2.0 1.3 1.3 1.7 1.3 1.3 1.3 1.7 1.5 WO 5.0 4.0 4.7 4.0 5.0 5.0 4.0 5.0 4.0 4.0 4.5 10 min DF 2.0 2.3 3.7 2.7 3.3 3.0 4.3 4.3 3.0 2.7 3.1 Maple 2.3 1.7 1.0 2.0 3.0 1.7 1.7 1.7 2.3 2.7 2.0

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 2.0 1.0 1.3 1.3 1.0 1.3 1.3 1.0 1.0 1.3 1.3 WO 4.3 3.7 4.3 4.0 5.0 5.0 3.7 5.0 3.0 3.7 4.2 20 min DF 1.7 2.0 3.0 2.3 3.3 3.0 4.3 4.3 2.0 2.0 2.8 Maple 1.7 1.7 1.0 2.0 1.7 1.3 1.7 1.0 2.3 1.7 1.6

177

Cycle 8 Replicate --> 1 2 3 4 5 6 7 8 9 10 Species Averages RO 2.7 1.0 1.3 2.0 2.0 2.3 2.3 1.3 1.3 2.0 1.8 WO 4.7 3.7 4.7 4.0 4.7 5.0 3.7 5.0 4.3 3.7 4.3 1 min DF 2.0 3.7 4.7 4.7 5.0 4.7 3.7 4.7 4.3 4.7 4.2 Maple 2.3 1.7 2.3 1.7 3.0 2.3 2.0 2.3 3.3 2.7 2.4

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 2.0 1.0 1.0 1.3 1.7 2.0 1.7 1.3 1.0 1.3 1.4 WO 4.7 3.7 4.0 4.0 4.3 5.0 3.7 4.0 4.0 3.0 4.0 5 min DF 1.7 3.7 3.3 4.7 4.7 4.7 3.3 4.3 4.3 4.0 3.9 Maple 1.3 1.3 1.3 1.3 2.3 1.7 1.3 1.7 3.0 1.7 1.7

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 1.7 1.0 1.0 1.3 1.7 1.7 1.3 1.3 1.0 1.3 1.3 WO 4.0 3.3 3.7 4.0 4.0 5.0 3.0 3.7 3.7 3.0 3.7 10 min DF 1.7 3.3 3.3 4.0 4.7 4.3 3.3 4.3 4.3 3.7 3.7 Maple 1.3 1.3 1.3 1.0 2.3 1.7 1.0 1.3 2.7 1.7 1.6

Replicate --> 1 2 3 4 5 6 7 8 9 10 RO 1.3 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.3 1.1 WO 3.7 3.0 3.0 3.0 4.0 4.3 2.0 3.7 3.3 2.0 3.2 20 min DF 1.3 3.0 3.3 4.0 4.3 4.0 2.3 4.0 3.7 3.3 3.3 Maple 1.0 1.0 1.0 1.0 1.7 1.3 1.0 1.0 2.3 1.3 1.3

178

Average Percent Checking by Treatment at Each Moisture Cycle

Untreated

Cycle 0 Cycle 1 Cycle 2 Cycle 3 Cycle 4 UTC % UTC % UTC % UTC % UTC % RO 0 RO 0 RO 0.001 RO 0 RO 0.001 WO 0.003 WO 0.408 WO 1.081 WO 0.891 WO 0.989 DF 0 DF 0.002 DF 0.038 DF 0.006 DF 0.019 M 0 M 0 M 0 M 0 M 0.006 Cycle 5 Cycle 6 Cycle 7 Cycle 8 UTC % UTC % UTC % UTC % RO 0.026 RO 0.003 RO 0.019 RO 0.039 WO 0.682 WO 1.172 WO 0.96 WO 1.454 DF 0.006 DF 0.011 DF 0.001 DF 0.019 M 0.017 M 0.06 M 0.02 M 0.117 Creosote Treated

Cycle 0 Cycle 1 Cycle 2 Cycle 3 Cycle 4 CREO % CREO % CREO % CREO % CREO % RO 0 RO 0.006 RO 0.044 RO 0.086 RO 0.064 WO 0.022 WO 1.109 WO 0.38 WO 0.635 WO 1.11 DF 0.003 DF 0.001 DF 0.003 DF 0.053 DF 0.102 M 0 M 0 M 0 M 0.003 M 0.018 Cycle 5 Cycle 6 Cycle 7 Cycle 8 CREO % CREO % CREO % CREO % RO 0.055 RO 0.065 RO 0.114 RO 0.316 WO 1.423 WO 1.069 WO 1.401 WO 1.959 DF 0.322 DF 0.816 DF 0.499 DF 0.277 M 0.011 M 0.013 M 0.023 M 0.026 179

Pentaclorophenol Treated

Cycle 0 Cycle 1 Cycle 2 Cycle 3 Cycle 4 PENTA % PENTA % PENTA % PENTA % PENTA % RO 0 RO 0 RO 0.016 RO 0.122 RO 0.052 WO 0 WO 0 WO 0.201 WO 0.388 WO 0.475 DF 0 DF 0 DF 0.151 DF 0.434 DF 0.987 M 0 M 0 M 0.062 M 0.025 M 0.048 Cycle 5 Cycle 6 Cycle 7 Cycle 8 PENTA % PENTA % PENTA % PENTA % RO 0.173 RO 0.074 RO 0.124 RO 0.286 WO 0.194 WO 0.313 WO 0.897 WO 0.853 DF 0.715 DF 0.628 DF 1.095 DF 1.148 M 0.083 M 0.103 M 0.078 M 0.065 Ammoniacal Copper Zinc Arsenate

Cycle 0 Cycle 1 Cycle 2 Cycle 3 Cycle 4 ACZA % ACZA % ACZA % ACZA % ACZA % RO 0.799 RO 0.269 RO 2.298 RO 1.572 RO 3.261 WO 0.666 WO 1.801 WO 4.662 WO 5.82 WO 6.013 DF 0 DF 0 DF 0.068 DF 0.527 DF 1.004 M 0 M 0 M 0 M 0.206 M 0 Cycle 5 Cycle 6 Cycle 7 Cycle 8 ACZA % ACZA % ACZA % ACZA % RO 1.957 RO 2.791 RO 1.369 RO 3.331 WO 6.84 WO 6.457 WO 5.619 WO 5.764 DF 1.832 DF 2.586 DF 2.511 DF 4.275 M 0.034 M 0.012 M 0.443 M 0.454

180