Post on 26-Jul-2020
Marine Debris Visual Identification Assessment
By
ZACHARY ANGELINI
Bachelor of Science, Environmental Engineering, University of New Hampshire, 2014
THESIS
Submitted to the University of New Hampshire
in Partial Fulfillment of
the Requirements for the Degree of
Master of Science
In
Civil Engineering
May, 2017
This thesis has been examined and approved by:
Dr. Nancy Kinner, Thesis Director
Professor of Civil Engineering and Environmental at the University of New Hampshire
Dr. Phil Ramsey, Committee Member
Lecturer of Mathematics and Statistics at the University of New Hampshire
Dr. Weiwei Mo, Committee Member
Assistant Professor of Civil and Environmental Engineering at the University of New
Hampshire
Original approval signatures are on file with the University of New Hampshire Graduate
School.
iii
ACKNOWLEDGEMENTS
This research was made possible by the support and funding of the University of
New Hampshire (UNH) Coastal Response Research Center (CRRC) and the
Commonwealth Scientific and Industrial Research Organization (CSIRO) Energy
Flagship. Dr. Nancy Kinner developed a relationship with CSIRO through her oil spill
response research. This relationship enabled me to intern with CSIRO for two, three month
periods. These internships, which included extensive field work for an environmental
baseline study of the Great Australian Bight, led to the research question investigated in
this thesis. The CRRC and CSIRO’s continued support during my thesis research was
crucial for the completion of the final project.
I would like to sincerely thank Dr. Nancy Kinner for her continued support and
expertise. I would also like to thank Dr. Kinner for providing me with the opportunity to
intern with CSIRO in 2014, as well Dr. Andrew Ross of CSIRO for supporting a second
internship in 2015. These were incredible opportunities to experience remote areas of the
world while contributing to research that can help protect and preserve Australia’s amazing
natural places.
I would like to thank all of my team members at CSIRO, including Dr. Andrew
Ross, Christine Trefry, Chris Dyt, Se Gong, Richard Kempton, Stephane Armand, and
Cameron White, as well as the University of Adelaide’s David Mckirdy and Alexander
Corrick. This team of research scientists all significantly assisted in gathering a robust
marine debris dataset during the environmental baseline study. This team, as well as Dr.
Denise Hardesty from CSIRO’s Ocean and Atmosphere Flagship and Dr. Nancy Wallace
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from the National Oceanic and Atmospheric Administration, contributed important thought
leadership that led to the focus of the thesis research.
I would like to thank Dr. Kenneth Fuld, former Dean of the College of Liberal Arts,
for providing his expertise on vision and visual perception. This insight was critical to
ensure a defendable design of experiment and explanation of results.
I would like to thank Justin Thibault, Masters Graduate of UNH’s Department of
Mathematics and Statistics, and Dr. Phil Ramsey, lecturer of Mathematics and Statistics at
UNH, for their immense support in the design of experiment and statistical data analysis
for this research.
I would like to thank my undergraduate research team: Andrew DeMeo, Pia
Marciano, Griffin Parodi, and Ben Sweeney. These students contributed significantly to
the execution of the thesis experiment.
I would also like to thank Dr. Weiwei Mo and Dr. Ramsey for serving on my thesis
committee and providing their feedback on this work.
Finally, I would like to sincerely thank my friends, family, and especially my
parents for their constant support in my academic career.
v
TABLE OF CONTENTS
ACKNOWLEDGEMENTS ............................................................................................ iii
TABLE OF CONTENTS ................................................................................................. v
LIST OF TABLES ........................................................................................................... vi
LIST OF FIGURES ........................................................................................................ vii
LIST OF ACRONYMS ................................................................................................. viii
ABSTRACT ...................................................................................................................... ix
CHAPTER 1 ...................................................................................................................... 1
INTRODUCTION............................................................................................................. 1
CHAPTER 2 ...................................................................................................................... 5
METHODS ........................................................................................................................ 5
2.1 Experimental Set-up.................................................................................................. 5
2.2 Experimental Design ................................................................................................. 9
2.3 Experimental Runs with Human Subjects ................................................................ 9
2.4 Data Analysis .......................................................................................................... 13
CHAPTER 3 .................................................................................................................... 15
RESULTS AND DISCUSSION ..................................................................................... 15
3.1 Effects of Fragment Characteristics ........................................................................ 16
3.2 Effects of Beach Characteristics ............................................................................. 20
3.3 Effects of Observer Characteristics ........................................................................ 22
3.4 Models/Interactions................................................................................................. 23
CHAPTER 4 .................................................................................................................... 26
CONCLUSIONS AND RECOMMENDATIONS ........................................................ 26
4.1 Conclusions ............................................................................................................. 26
4.2 Recommendations for Future Experiments ............................................................ 28
REFERENCES ................................................................................................................ 29
APPENDICES ................................................................................................................. 33
Appendix A: Literature Review .................................................................................... 34
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LIST OF TABLES
Table 1: Munsell Color System Hue Symbol, Chroma, and Value Characterizations ...... 8 Table 2: Breakdown of Accurate Counts Based on Actual Number of Plastic Fragments
Present ............................................................................................................................... 19
Table 3: White Count Accuracy vs. Shell Density ........................................................... 21 Table 4: Clear Count Accuracy vs. Sand Color ................................................................ 22
vii
LIST OF FIGURES
Figure 1: Plan View of Beach Transects ............................................................................. 6
Figure 2: Observer Debris Logging Sheet ........................................................................ 12 Figure 3: Distribution of Under, Over, and Accurate Total Plastic Fragment Counts ..... 15 Figure 4: Distribution of Under, Over, and Accurate Blue Plastic Fragment Counts ...... 16 Figure 5: Distribution of Count Accuracy for Clear Plastic Fragments (a) and White Plastic
Fragments (b) .................................................................................................................... 18
Figure 6: Completion Time vs. Accuracy ......................................................................... 22 Figure 7: Observer Confidence ......................................................................................... 23 Figure 8: White Plastic Model Effect Summary Report ................................................... 24
Figure 9: Clear Plastic Model Effect Summary Report .................................................... 24 Figure 10: Blue Plastic Model Effect Summary Report ................................................... 25
viii
LIST OF ACRONYMS
CRRC: Coastal Response Research Center
CSIRO: Commonwealth Scientific and Industrial Research Organization
GAB: Great Australian Bight
MARPOL: International Convention for the Prevention of Pollution from Ships
NOAA: National Oceanic and Atmospheric Administration
RSP: Regional Seas Program
UNEP: United Nations Environment Program
UNH: University of New Hampshire
UV: UltraViolet
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ABSTRACT
MARINE DEBRIS VISUAL IDENTIFICATION ASSESSMENT
By
Zachary Angelini
University of New Hampshire, May 2017
Estimates of marine debris are often based on beach surveys conducted by
volunteers/citizen scientists. Few studies have documented the veracity of their
observations and the factors that may affect their accuracy. Our laboratory-scale
experiment identified potential sources of error associated with visual identification of
marine debris (1-2 cm long) during shoreline surveys of sand beaches. Characteristics of
the survey site (beach characteristics), observer (personal characteristics), and debris (color
and size) may be important factors to consider when analyzing data from shoreline surveys.
The results of this study show that the ability of individuals to accurately identify plastic
fragments depends on the plastic and sand color, and density of shell fragments, as well as
the time taken on the survey. Most suggestively, the high accuracy of blue plastic counts
(95%) and the under-counting of white (50%) and clear plastic fragments (55%) confirmed
the hypothesis that a significant amount of clear and white plastic fragments may be missed
during shoreline surveys.
1
CHAPTER 1
INTRODUCTION
Marine debris is a burgeoning global issue that threatens marine biota and
ecosystems, as well as human health, safety, and the economy (UNEP 2014). Globally,
plastics are the most common type of marine debris (Coe and Rogers, 1997; Derraik, 2002).
Ubiquitous in the marine environment, plastics have been documented in every part of the
ocean, including Arctic sea ice (UNEP, 2014). Jambeck et al. (2015) estimated that 275
million metric tons of plastic debris were produced by 192 coastal countries in 2010. Their
report suggested that 4.8 - 12.7 million metric tons of debris were input to the world’s
oceans that year. In 2014, Eriksen et al., (2014) estimated that 5.25 trillion plastic particles
were present in the oceans.
By 2025, the amount of plastic being input to the world’s oceans could increase by
an order of magnitude, unless significant steps are taken to improve waste management
infrastructure in coastal countries (Jambeck et al., 2015). In order to minimize marine
debris on a global scale, the problem needs to be recognized, understood, and addressed on
the international and local levels. In 2009, the United Nations Environment Program
(UNEP) developed the Regional Seas Programme (RSP) to respond to the marine plastic
debris issue and to collect and distribute information (Vegter et al., 2014). In total, there
are 18 regions recognized within the RSP. For the majority of these regions, minimal data
exist on the extent of plastic pollution. Furthermore, there are no data standards (i.e.,
standardized survey and reporting protocols, size classification, debris type classification),
and few countries acknowledge marine plastics as a problem (Vegter et al., 2014). Without
2
a scientifically-based global and local understanding of plastic pollution, it is likely that
there will be insufficient governmental and public motivation to make the changes urgently
needed.
One of the most common and well established methods for determining marine
plastic pollution is shoreline or “beach” surveying (Cheshire et al., 2009). Shoreline
surveys provide estimates of plastic loads in coastal waters (Thiel et al., 2013; Ribic et al.,
1992), and are far more economically viable than at-sea (ship-based) surveying (Morishige
et al., 2007). Shoreline surveys are often conducted by volunteers (citizen scientists) and
can be highly effective in increasing social awareness, a key factor in addressing the marine
plastics issue holistically (Vegter et al., 2014). With increased social awareness, the marine
plastics issue can be addressed in a variety of ways, including changes to consumer
behavior.
According to a recently published list of global research priorities to mitigate plastic
pollution impacts on wildlife, one of the greatest limitations to the quantification of marine
plastic debris loadings is the dependence on the human eye (Vegter et al., 2014). This issue
is not specific to citizen scientists. Hardesty et al. (2014) found that citizen scientist-
generated marine debris data are comparable to those collected by trained scientists.
In 2015, marine debris surveys were conducted as part of a collaboration between
the Commonwealth Scientific and Industrial Research Organization (CSIRO) (Perth,
Australia) and the University of New Hampshire (UNH) Coastal Response Research
Center (CRRC) (Durham, NH) along the coastline of the Great Australian Bight (GAB).
For the GAB survey, >64 kilometers of beach were surveyed and the most common debris
item found was small (1-2 cm) plastic fragments (Appendix B). Exposure to ambient
3
ultraviolet (UV) radiation, as well as the ocean’s physical, chemical, and biological
processes, results in plastic fragmentation and size reduction (UNEP, 2014). This leads to
the increased presence of meso-(0.5 - 2.5 cm) and micro-sized (0.1 - 0.5 cm) plastic
particles, which can be difficult to see.
Certain protocols exclude smaller size ranges from visual surveys and only log
debris greater than 2.5 cm, as this is the minimum disposal size allowed under MARPOL
(International Convention for the Prevention of Pollution from Ships) for ground shipment
of waste (Ribic et al., 1992). As awareness of meso- and micro-plastics has risen,
organizations, such as the U.S. National Oceanic and Atmospheric Administration
(NOAA), have developed a sieving method aimed at quantifying these plastic loads on
beaches (Lippiat et al., 2013). The NOAA method involves sediment core sampling,
sieving, and further laboratory analysis to determine meso- and micro-debris densities.
Alternatively, a protocol developed by CSIRO uses visual logging for all size
ranges starting at 0-1 cm, and continuing 1-2 cm, 2-4 cm, 8-16 cm and >16 cm (Hardesty
et al., 2014). This method was designed to allow citizen scientists to rapidly measure debris
in the meso- and micro-size range while simultaneously collecting macro debris data. If
the error associated with visual identification within the lower size ranges could be better
understood, then useful estimates of the uncertainty associated with these CSIRO data
could be generated. Through observations made during the GAB survey, a hypothesis was
developed that plastic debris characteristics (e.g., color); beach characteristics (e.g., sand
color, density of shell fragments); and observer characteristics (e.g., color-blindness,
height, age, sex, eyesight, survey speed) may have impacts on the accuracy of small plastic
fragment identification. Specifically, we hypothesized that under counting of white and
4
clear plastic fragments would occur on beaches with high shell densities, owing to visual
noise and reduced contrast between the plastic debris and its background surface.
Most current survey methodologies include quality assurance procedures
(Cheshire, 2009; Lippiatt et al., 2013; Hardesty et al., 2014), but they rely on sample re-
surveying by visual means, which does not address the fundamental errors in human visual
performance.
To study these issues, a laboratory experiment was conducted to directly quantify
the potential error associated with visual identification of plastic fragments (1-2 cm long)
during mock shoreline surveys. By quantifying the accuracy of counts associated with
different plastic fragment colors, beach and observer characteristics, and interactions
between these characteristics, visual estimates in the meso- and micro-size range plastics
can be better understood and the uncertainty associated with them further defined. This
understanding can be used to develop methods that allow for more reliable and rapid
measurements of meso- and micro-debris on sandy beaches. It can also be used to estimate
the uncertainty bounds associated with visual estimates made by citizen scientists, based
on known levels of accuracy associated with debris, beach, and/or surveyor characteristics.
5
CHAPTER 2
METHODS
Artificial beach transects were constructed containing known concentrations of
plastic fragments in a UNH environmental engineering laboratory. Volunteers were asked
to log plastic debris along the transects and the results were analyzed to determine the
impacts that debris characteristics, beach characteristics, and observer characteristics
have on the accuracy of visual identification.
2.1 Experimental Set-up
Four beach “transects” were constructed in the laboratory, each comprised of six 0.25
m2 black plastic trays of sand aligned in a row on the floor (Figure 1). Each transect was
located in its own section of the laboratory. Within each transect, plastic fragment and
beach characteristics varied.
6
Figure 1: Plan View of Beach Transects
The plastic fragments varied in chromaticity (appearing blue, white, clear
(translucent colorless)) and density (#/m2) in each tray. The colors were chosen based on
the preliminary GAB survey which found white and blue plastic fragments are the most
commonly occurring colors. Observations made during that survey led to our hypothesis
that blue fragments may be highly distinguishable and logged with greater accuracy, while
white and clear may be less distinguishable colors and logged with greater error. The 1-
2cm plastic fragments used in this study were provided by Poly Recovery (Portsmouth,
NH), a company that collects recycled plastics and processes them for post-consumer use.
They fell within the size range of 1-2 cm.
Each color fragment was characterized following the Munsell Color System
(Munsell, 1929) by hue symbol, chroma, and value (Table 1). Each hue is assigned a
number and letter. The letter stands for Red, Yellow, Green, Blue, Purple, or a combination
7
thereof. Numbers indicate the level of hue. Each hue and hue combination are divided into
four increments measured in differences of 2.5. Value represents a 0 to 10 scale of lightness
(0 = total blackness, 10 = total whiteness). Chroma, equivalent to saturation, is represented
from 0 to 12. These characteristics provide a standardized way to compare colors,
determine the perceptual distance between them, as well as measure color for repeatability
of the experiment.
The total plastic densities varied between 0 fragments/0.25 m2 to 18 fragments/0.25
m2. The latter resulted from a maximum of six fragments of each of the three colors in each
0.25 m2 tray. These densities represented the range encountered during the GAB survey.
The beach characteristics considered were sand color and shell density.
White/cream, gray, and brown sands were chosen as they are common hues. Hue symbol,
chroma, and value are shown in Table 1.
Three different shell densities were used to represent the common range found on
sand beaches: no shell, moderate shell (20 mL of shell fragments randomly spread over
0.25 m2), and high shell (80 mL randomly spread over 0.25 m2) densities. The naturally
weathered shells were collected from Wallis Sands Beach (Rye, NH). They were an
average of 1.1 cm (Range of 0.4 – 3.5 cm) by their longest length. Their average hue
symbol, chroma, and value are shown in Table 1.
8
Plastic Fragment Categories Hue Symbol Chroma Value
Blue 5B /6 6/
White 2.5Y /0 9/
Clear (on white/cream sand) 10Y /1 7/
Clear (on gray sand) 2.5Y /2 8/
Clear (on brown sand) 5Y /2 9/
Sand Categories Hue Symbol Chroma Value
White/Cream 10Y /1 9/
Gray 2.5Y /2 7/
Brown 5Y /4 9/
Shell Categories Hue Symbol Chroma Value
Average 5R /1 9/
Table 1: Munsell Color System Hue Symbol, Chroma, and Value Characterizations
Several parameters were controlled. All outside light was blocked from entering
the laboratory and the experimental area was continually illuminated with 6500K daylight-
balanced bulbs, in order to replicate the temperature of sunlight. The luminance reflected
off the trays of sand was an average of 206 cd/m2 (Range = 120 - 320 cd/m2). This value
is below the luminance measured in real daylight conditions (e.g., luminance on a cloudy
day is 1000 to 5000 cd/m2)(Beck, 1999), however, as long as sufficient time has been
provided for luminance adaptation, the human visual system calibrates itself to lower
luminance levels (Graham, 1965), eliminating the need to replicate the exact luminance of
daylight.
Upon visual inspection, the plastic fragments varied by specular reflectance as well
as color. In order to remove specular reflectance as a variable, the plastics were artificially
weathered for 30 minutes in a tumbler filled with sand. This resulted in fragments with
uniform visually reduced specular reflectance for all plastic fragments.
9
2.2 Experimental Design
The statistical software JMP Pro (Version 12; Cary, NC) was used to create a
custom design that allowed estimates of two-way interactions. This generated a design with
a default of 24 trays observed (6 trays in each of 4 transects) and no aliasing between any
of the main effects and the two-way interactions. No aliasing allows determination of
influence from all main effects and two-way interactions. The design automatically
randomized the values of each factor in each tray, as well the order of the trays. Each of
the 103 human subjects (observers) examined all of the trays in a random order.
2.3 Experimental Runs with Human Subjects
Observers were recruited from within the UNH community and included first
through fourth year undergraduate students, graduate students, faculty and staff. Upon
entering the laboratory, observers were asked to sign a letter of consent, following
protocols mandated by the UNH Institutional Review Board for the Protection of Human
Subjects in Research. Observers were given a written experimental protocol and were
asked to read it prior to receiving verbal instructions. They were asked to provide their age,
academic major, height, gender, and vision (near, far or normal sighted), use of corrective
lenses, and previous participation in marine debris surveys or beach clean-ups. Observers
conducted an online version of the Ishihara color blindness test (www.color-
blindness.com/ishihara-38-plates-cvd-test). The Ishihara test is usually conducted by an
eye care professional and the online version is problematic due to variations in computer
monitors (Flück, 2013). While the monitor we used was color calibrated and the test was
performed under the appropriate lighting, the color-blindness classification was still not
fully accurate. This test was conducted only as a preliminary test for color-blindness. After
10
completing the Ishihara test, observers recorded their score along a sliding scale with
categorical values of “None”, “Weak”, and “Moderate/Strong”. [N.B., Observers with
color blindness were not excluded from the study because observers used in real-world
marine debris projects are not screened for this trait.]
Each participant was verbally informed of the debris logging protocol. Observers
were shown NOAA’s debris photo manual (Lippiatt et al., 2013) for reference and were
told to specifically look for hard plastic fragments, greater than 1 cm in size, and with the
colors listed on their debris logging sheets (Figure 2) (Color categories replicated those
used by CSIRO). Observers were told to stand upright and walk beside the transects while
staying on a designated path located approximately 8 cm from the transects. Observers
were asked to log the color and number of plastics that they saw in each of the six trays
within each of the four transects. Observers were asked to be as accurate as possible,
logging only the plastics they saw. They were told to be careful not to log shell fragments
or any other natural debris items. After determining that observers understood the protocol,
their start time was recorded and they began logging plastic debris. (On average, observers
spent approximately 10 minutes completing the survey preparation procedures, allowing
sufficient time for visual calibration to the ~206 cd/m2 luminance before beginning the
survey) (Hecht, Haig, and Chase, 1937). After logging debris along each of the four
transects, total observer survey time was recorded and observers were asked to judge their
confidence in the accuracy of their results on a scale from 1-10 (10 = fully confident).
11
12
Figure 2: Observer Debris Logging Sheet
13
2.4 Data Analysis
Data distributions were generated for each of the variables (e.g., fragment color,
sand color). Each data point was representative of one tray, viewed by one observer. There
were 2472 counts (103 observers recording 24 trays each). From these distributions, initial
trends in the data were determined.
Factor and covariant significance on plastic counts were observed through
distributions, analysis of variance and simple modelling.
First, several covariates were removed due to a lack of information. “Color
blindness” was removed as a factor since only one of the observers, exhibited the condition
through the test provided. The covariant “age” was also removed, as only 17% of the
observers were between the ages of 30 and 62, whereas, 79% were between 18 and 24.
Similarly, “experience” and “amount of experience” were removed since there were so few
individuals who had participated in marine debris surveying or beach clean-ups. “Major”
(area of study) was also removed because the vast majority of observers were in the fields
of civil or environmental engineering.
Several factors were removed because they were not observed to have measurable
effects. These factors had a sufficient amount of data, however, did not indicate
significance (P > 0.3), nor did the interactions between them. The factors about the observer
removed included “height”, “class” (year or status at university), “corrected vision status”,
and “near, far, or normal sightedness”.
The factors for analysis were “plastic debris color”, “plastic debris density”, “sand
color”, and “shell density”, and their interactions, and “completion time”. These factors
14
were determined to have a potentially significant impact on the counts of plastic debris and
the observers’ accuracy for those counts and were selected for further analysis.
15
CHAPTER 3
RESULTS AND DISCUSSION
73% of the total plastic fragment counts were under-counts, 8% were over-counts,
and 19% were accurate (Figure 3). All over-counts were grouped into a single category due
to the occurrence of infinite numbers (i.e., recording any amount of plastic fragments when
none were present).
Figure 3: Distribution of Under, Over, and Accurate Total Plastic Fragment Counts
16
Data peaks at “Accurate”, “50%”, and “0%” are representative of observers
recording exactly the number of plastic fragments present, exactly half of the plastic
fragments present, and none of the plastic fragments present, respectively, in each tray. The
median of the observers’ counts was a 50% under-count.
3.1 Effects of Fragment Characteristics
Consistent with our hypothesis, blue plastic fragments were logged with high
accuracy (Figure 4), and therefore, were not a major contributor to the inaccuracy of the
total plastic fragment counts.
Figure 4: Distribution of Under, Over, and Accurate Blue Plastic Fragment Counts
Approximately 3.2% of blue plastic fragment counts were under-counts, 93.9%
were accurate counts, and 2.9% were over-counts.
Also consistent with our hypothesis from the GAB survey, the accuracy of clear
(Figure 5a) and white (Figure 5b) plastic counts were significantly different than that of
17
blue. Using t-tests, the differences between the accuracy of blue and white plastic counts
and blue and clear counts were highly significant (P-values < 0.0001). There was no
significant difference between the number of accurate white plastic counts and accurate
clear plastic counts (P-value =0.6018). There was also no significant difference between
the number of white plastic under-counts and clear plastic under-counts (P-value = 0.1617).
However, there was a significant difference between the number of white plastic over-
counts and clear plastic over-counts (P-value <0.001).
18
Figure 5: Distribution of Count Accuracy for Clear Plastic Fragments (a) and White
Plastic Fragments (b)
(a
)
(b
)
19
Approximately 55.2% of clear plastic fragment counts were under-counts, 42.4%
were accurate counts, and 2.4% were over-counts. A high percentage of under-counting
was expected due to the lack of hue contrast and resulting minimal perceptual distance
between the clear plastic fragments and sand (as determined by the Munsell Color System).
A low percentage of over counting was also expected due to the greater perceptual distance
between clear plastic fragments and shell fragments, minimizing the probability of
mistaken identity.
Approximately 50.5% of white plastic counts were under-counts, 42.2% were
accurate, and 7.3% were over-counts. Compared to clear plastic counts, a greater
percentage of white plastic counts were over-counts. It is probable that mistaken identity
between white plastic and white shell caused this. Consistent with our hypothesis from the
GAB survey, it is probable that a large amount of the under-counts for white plastic
occurred due to mistaken identity between plastic fragments and shell as well. While the
perceptual distance between white plastic and sand is relatively large (as determined by the
Munsell Color System), the perceptual distance between the white plastic fragments and
white shell is less.
The accurate counts for clear and white plastic fragments can be further explained
by the actual amounts of plastic fragments present (Table 2).
Plastic Color Number of Plastic Fragments Present
0 2 4 6
Clear 86% 5% 1% 8%
White 87% 1% 5% 7%
Table 2: Breakdown of Accurate Counts Based on Actual Number of Plastic Fragments
Present
20
The majority of accurate white and clear plastic counts (86% and 87%,
respectively) occurred when observers counted trays with zero plastic fragments present
(true zero counts); only 14% of all accurate clear plastic counts and 13% of all accurate
white plastic counts occurred when there were plastic fragments present.
3.2 Effects of Beach Characteristics
Strong trends were only observed between beach characteristics (i.e., shell density,
sand color) and the accuracies of white and clear plastic counts.
The majority of inaccurate white plastic counts were under-counts (Table 3). The
difference between the accuracy of white plastic counts in zero shell, moderate shell, and
high shell density conditions resulted in a p-value = 0.0025 (t-test). This indicated that the
accuracy associated with at least one of the shell density conditions was significantly
different. Further analysis (t-tests) showed that the accuracy of counts in high shell density
conditions was significantly different from zero shell and moderate shell density conditions
(P-value =0.0017 and P-value =0.0015, respectively). There did not appear to be a
significant difference between the accuracy of counts made in zero shell and moderate shell
density conditions (P-value =0.8790). Overall, significantly less accurate counts were
made for the high shell density condition than in the moderate and zero shell densities.
Observers’ counts of white plastic fragments were less accurate when more shell was
present, with a tendency towards under-counting. This result may be partially explained by
signal detection theory (Swets, Tanner, and Birdsall, 1961) in the following manner: As a
result of decreasing the signal (plastic fragments)-to-noise (shell fragments) ratio, through
increased shell densities, observer bias may have tended towards the expectation of shell
rather than plastic fragments, leading to greater under-counting.
21
Shell Density Categories Measurement Accuracy
Accurate counts Inaccurate counts (Under/over)
Zero shell 51% 49% (48/1)
Moderate shell 43% 57% (47/10)
High shell 33% 67% (55/12)
Table 3: White Count Accuracy vs. Shell Density
There also was a correlation between the accuracy of clear plastic counts and sand
color (Table 4). Using t-tests, the difference between the accuracy of clear plastic counts
for each of the sand colors resulted in P-value < 0.0001. Further analysis showed that
accuracy of counts was significantly different between all three colors (each of the P-values
were less than 0.0122). Brown sand resulted in the most accurate counts, while gray sand
resulted in the least accurate. One possible explanation for this result is that the clear plastic
fragments may have reflected ambient light in a way that, to the observers, their spectral
properties appeared closer to grey sand than the other sand colors.
22
Sand Color Measurement Accuracy
Accurate counts Inaccurate counts (Under/over)
Gray 39% 61% (58/3)
White/cream 40% 60% (56/4)
Brown 46% 54% (52/2)
Table 4: Clear Count Accuracy vs. Sand Color
3.3 Effects of Observer Characteristics
As completion time increased, so did median observer accuracy. The longer
observers took to log data, generally, the more accurate their counts (Figure 6). However,
regardless of how long observers spent observing the transects, their median results never
reach 100% accuracy (average observation time = 12.3 min).
Figure 6: Completion Time vs. Accuracy
23
The median observer confidence (Figure 7) in the accuracy of their results (6 out of
10) was surprisingly high when compared to the low percentage of total accurate counts
(19%). This may indicate that many observers would not have bent down to further inspect
the sand. This needs further investigation in an experiment where bending down is allowed.
Figure 7: Observer Confidence
3.4 Models/Interactions
In order to determine the most significant factors and interactions between factors
that affected overall counts, models were generated for white, clear, and blue plastic counts.
The distributions used in the generalized linear models were determined from observing
the distributions of counts of each color plastic. The generalized models, using Poisson
distributions with log link functions, characterized the variations in observations. To
determine which factors were most significant to the variability in counts of plastic, the
models considered the amount actually present. When first run, not all factors were
estimated indicating that reducing the models was necessary. A p-value cutoff of 0.30 was
used in addition to indications of no significance from other analyses (e.g., analysis of
24
variance). The models were only reduced to the point where all of the remaining factors
were estimated. Each step of the reduction removed a single factor or interaction.
Figure 8: White Plastic Model Effect Summary Report
For white plastic counts, the most significant factors were the interaction between
white plastic density and shell density, blue plastic density alone, and shell density alone
(Figure 8). The interaction between white plastic density and shell density was likely a
significant factor due to the increased potential for mistaken identity. Blue plastic density
and shell density were likely significant factors as they contributed to overall visual noise,
distracting the observer from identifying the white plastic fragments present.
Figure 9: Clear Plastic Model Effect Summary Report
For clear plastic counts, the most significant factors were completion time and the
interaction between clear plastic density and sand color (Figure 9). Median accuracy and
completion time were directly related. It is likely that observers who spent more time
searching the trays increased their opportunity to identify clear plastic fragments. There
25
was a significant difference between the accuracy of counts made in trays with different
sand colors. It makes sense that the interaction between clear plastic density and sand color
was a significant factor. Clear plastic was best detected when the observer took more time
and the sand was brown.
Figure 10: Blue Plastic Model Effect Summary Report
For blue plastic counts, the most significant factors were the interaction between
blue plastic density and shell density and shell density alone (Figure 11). Both factors
contribute to overall visual noise, potentially distracting the observer from identifying the
blue plastic fragments present. Still, the overall high accuracy of blue plastic counts (95%),
diminished the importance of any of the above factors.
26
CHAPTER 4
CONCLUSIONS AND RECOMMENDATIONS
4.1 Conclusions
To the best of our knowledge, this is the first study on the effects these
characteristics have on the accuracy of beach surveys. The high accuracy of blue plastic
counts and the under-counting of white and clear plastic counts confirmed the hypothesis
that a significant amount of clear and white plastic fragments may be missed during
shoreline surveys.
The accuracy of white plastic counts was most significantly affected by increasing
shell densities due to observer bias and the low contrast between white plastic fragments
and white shell fragments. The majority of the time, white plastic fragments were mistaken
as shell (under-counts), with fewer circumstances of shell being mistaken for white plastic
fragments (over-counts). The accuracy of clear plastic counts was most significantly
affected by the color of the sand; the clear fragments contrasting best with brown sand.
There was also a correlation between higher median accuracies and slower survey
speeds. However, despite speed, surveyors never reached full accuracy.
The results of this study demonstrate that visual identification of lower size ranges
during beach surveys should be coupled with advanced beach (sand and shell) and debris
(color) characterization. Specifically, characteristics including shell cover and sand color
should be noted, as well as debris color for each item identified. If certain debris colors can
27
be further associated with known degrees of under counting in certain beach conditions,
then visual estimates can be adjusted to account for this.
The results also suggest that pre-survey advanced identity training should be further
investigated as a means of minimizing under- and over-counting. This training could
involve survey leaders setting up visual calibration plots of sand, which include a variety
of plastic and natural debris items (i.e. shells). Before beginning the survey, observers
could log the plastic debris in the calibration plot. After logging, the results could be
discussed and distinguishing factors between plastic debris and natural debris identified.
The variables tested in this experiment represent only a portion of possible beach,
observer, or debris characteristics that may be encountered in shoreline surveys. There may
well be other variables (i.e. weather/daylight conditions) that will result in drastic
differences in accuracy. By further understanding and identifying these variables, marine
debris estimates can be put in perspective and potentially improved. New survey techniques
can work to address these issues in order to obtain more accurate meso- and micro-plastic
debris estimates from visual surveys.
By further understanding the accuracy of visual observations of marine debris and
addressing it, the huge resource potential that citizen scientists represent for collecting data
on small plastic fragments can be maximized, resulting in the most accurate data possible.
28
4.2 Recommendations for Future Experiments
Future experiments could increase variation in shell density, as well as shell size
and color. In addition, other natural debris (e.g., seaweed, wood, crustacean remains), could
allow for a more realistic representation of beach characteristics.
Additional colors of plastics should be studied. For example, there are ten color
categories used by CSIRO (Black, Blue/Purple, Brown, Clear/Translucent, White, Green,
Grey/Silver, Orange, Red/Pink, Yellow). It would also be prudent to include other sizes of
plastic fragments, since the issues described in this study may apply.
This study did not obtain a sufficient number of observers with the color-blindness
trait to analyze their accuracy compared to observers lacking this trait. Therefore,
additional studies should be conducted with larger sample sizes in order to determine if
this trait significantly impacts survey accuracy, either positively or negatively.
Finally, training procedures to minimize mistaken identity between small plastic
fragments and shells should be developed and tested to improve accurate fragment
detection.
29
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APPENDICES
34
Appendix A: Literature Review
Magnitude of Marine Debris Problem
Marine plastic pollution is an urgent and wide-spread issue. A recent report
estimated that 275 million metric tons of plastic debris were produced by 192 coastal
countries in 2010, with 4.8 - 12.7 million metric tons now in the world’s oceans (Jambeck
et. al, 2015). By 2025, this amount could increase by an order of magnitude, unless
significant steps are taken to improve waste management infrastructure in coastal countries
(Jambeck et. al, 2015).
Environmental, Social, and Economic Impacts of Marine Debris
The staggering amount of plastic debris currently in the world’s oceans has resulted
in a wide range of environmental, social, and economic damages. For example, at least 170
marine species have been documented ingesting plastics. (Carr 1987, Laist 1987, Bjorndal
et al. 1994, Derraik 2002, Ceccarelli 2009, Boerger et al. 2010, Jacobsen et al. 2010, Baulch
& Perry 2012, Fossi et al. 2012, Schuyler et al. 2012, Besseling et al. 2013). The species-
level impacts of this occurrence include gut perforation, gut impaction, dietary dilution,
interference with development, and toxin introduction. (Ryan 1988a, Bjorndal et al. 1994,
McCauley & Bjorndal 1999, Mader 2006, Teuten et al. 2009, van Franeker et al. 2011,
Gray et al. 2012, Tanaka et al. 2013).
Studies have shown that plastics not only can contain harmful plasticizers
introduced during manufacturing of the virgin material (Meeker et al. 2009), but once
present in the marine environment, they can adsorb heavy metals such as Copper and Zinc
(commonly leached into seawater from antifouling paint used in shipping) (Brennecke et
35
al. 2016). The measured heavy metal concentrations in marine plastics can reach toxic
levels, far above concentrations commonly found in the water column (Brennecke et al.
2016). Also, the hydrophobicity of persistent organic pollutants (POPs) enables their
adsorption on meso-/microplastic fragments and leads to concentrations on these plastics
that are multiple orders of magnitude higher than concentrations in sea water (Andrady,
2011).
Marine plastics also have a greater tendency to become bio-fouled by introduced
species (Whitehead et al. 2011), than by native species (Wyatt et al. 2005, Glasby et al.
2007, Tamburri et al. 2008). This characteristic, coupled with the durability and persistence
of plastic in the marine environment, results in the significant prospect of increased non-
native species transportation (Vegter et al. 2014).
Humans are not exempt as a species effected by marine debris. Social and economic
damages occur via disruption to shipping, tourism, and fisheries. In 2011, the total yearly
cost of debris-related damage to marine industries in the Asia-Pacific rim countries was
estimated to be US $1.26 billion per year (Hall 2000, McIlgorm et al. 2011). In South
Korea, a single period of intense rainfall during July 2011 resulted in significantly
increased coastal debris, leading to a 63% decrease in tourism (Jang et al. 2014). The
damages caused by fouling of water bodies go beyond the economics of social well-being
and include an increase in emotional stress, and loss of relaxation, insight, self-reflection
and creativity (White et. al, 2010).
Addressing the Marine Debris Problem
In order to improve waste management on a global scale, the problem needs to be
recognized, understood, and addressed on both international and local levels. The United
36
Nations Environment Program (UNEP) developed the Regional Seas Programme (RSP) to
respond to the marine debris issue (UNEP 2009) and to collect and distribute information.
(Vegter et al. 2014). In total, there are 18 regions recognized within the RSP, but only 12
have participated in UNEP supported marine debris activities (Vegter et al. 2014). The
majority of these regions have minimal data on the extent of the issue, do not have data
standards, and few acknowledge marine debris as a problem (Vegter et al. 2014).Without
a scientifically based global understanding of plastic pollution, there will be insufficient
governmental and public drive to make the changes urgently needed to address this
problem.
Marine Debris Surveying
One of the most common and established methods for marine debris data collection
is shoreline or “beach” surveying (Cheshire et al. 2009). Shoreline surveys are capable of
providing estimations of debris loads in coastal waters (Thiel et al. 2013), and are far more
economically viable than at-sea surveying (Morishige et al. 2007). Shoreline surveys are
often conducted by volunteers or “citizen scientists” and can be highly effective in
increasing social awareness of the issue (Vegter et al. 2014).
A recent study in Australia analyzed the difference between volunteer or “citizen
science” data from shoreline surveys and data collected by scientists from the
Commonwealth Scientific and Industrial Research Organization along the same shoreline
areas. The study found there to be no significant difference between the two datasets
(Hardesty et al. 2014). This highlighted the effectiveness of involving citizen scientists in
data collection. This effectiveness combined with the social and economic benefits of
involving volunteers, make beach surveys conducted by citizen scientists an important
37
method in addressing the marine debris issue. However, according to a recently published
list of global research priorities to mitigate plastic pollution impacts on wildlife, one of the
greatest limitations to the quantification of marine plastic debris loadings is the dependence
on the human eye (Vegter et al. 2014). The goal of this research is to understand the
potential error associated with visual identification of plastic fragments during shoreline
surveys.
38
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