University of ConnecticutOpenCommons@UConn
Honors Scholar Theses Honors Scholar Program
Spring 4-29-2016
Predictors of Litter Pollution in Suburban ParksIlanna GibsonUniversity of Connecticut, [email protected]
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Recommended CitationGibson, Ilanna, "Predictors of Litter Pollution in Suburban Parks" (2016). Honors Scholar Theses. 489.https://opencommons.uconn.edu/srhonors_theses/489
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Ilanna Gibson
April 29, 2016
Honors Thesis Project
Predictors of Litter Pollution in Suburban Parks
ABSTRACT
Very few studies have been conducted that examine litter pollution in
terrestrial habitats. Most pollution studies are directed toward marine
environments. This study looks at the relationship between litter found in thirteen
different suburban parks in Rockland County, NY and three separate socio-economic
factors of the areas in which each of the parks are found. Using linear multiple
regression models, the abundance of litter found in each park was compared to (a)
the median income of the people in that specific area, (b) the median home value
and (c) the number of environmental programs offered in that area. Results showed
that median income of people in a town is the best predictor of the total amount of
litter found in parks within that same town. Using this model, local municipalities
can examine where to focus clean up and educational efforts in order to lower
pollution within the necessary areas.
INTRODUCTION
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Litter is one of the most significant and expensive problems facing cities
today (Roales-Nieto, 1988). It is defined as minor waste that has been disposed of
carelessly and incorrectly (Al-Khatib et al., 2009). The primary sources of litter are
pedestrians, motorists, and workers (KAB 2009). In parks, 98.5% of litter, mainly
cigarette butts and food related items, is produced by pedestrians (KAB 2009). In
and around residential areas and construction sites, the primary source of litter is
construction workers as they improperly dispose of their meals and cigarette butts
(KAB 2009).
In addition to causing the deterioration of ecosystems, litter can negatively
affect wildlife, human health, and the aesthetic value of an area. Discarded plastic
can contaminate a wide range of terrestrial, freshwater and marine environments,
with accounts of debris found even on some of the highest mountains (Thompson et
al., 2009). Glass fragments and other sharp improperly discarded items can injure
humans and wildlife (Al-Khatib et al., 2009). A 1992 study on Lorne Beach, Victoria
in Australia found that of the 211 recorded beach injuries, 19% were from beach
litter (Grenfell et al., 1992).
Improperly disposed cigarette butts can cause fires, and some chemicals
from the cigarette butts can leach into water systems contaminating drinking water
supplies (Al-Khatib et al., 2009). Cigarette butts make up 22-46% of all visible litter,
with 76% of smoked cigarettes being littered rather than disposed of properly
(Green et al., 2014). Improperly disposed cigarette butts that end up in standing
water release large amounts of nicotine, which eventually can contaminate human
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water supplies. Nicotine is easily absorbed through the skin, lungs, small intestine
and bladder (Green et al., 2014). One study found that one single cigarette butt can
contaminate 1000L of water to nicotine concentrations above the predicted
concentration at which there would be no effects (Green et al., 2014).
The cost of keeping litter pollution under control can be extremely high,
providing another reason why reducing littering habits in the general public is
particularly important (Roales-Nieto, 1988). In 2005, the cost of litter cleanup was
estimated to be $1.29 per piece of litter, when work was done by paid employees
and $0.18 cents per item when using voluntary labor under Adopt-a-Highway litter
cleanup programs (Wagner et al., 2016). Large amounts of litter can also block and
damage storm drain systems, costing between $111.95 and $167.91 per storm drain
per year to clean up (Wagner et al., 2016). Litter in neighborhoods can reduce the
property values in affected areas by more than 7% (KAB 2009).
Most studies regarding litter have been conducted in marine environments,
with very few involving terrestrial environments. Litter that makes its way into
marine environments can have major impacts on wildlife. An estimated 6.4 million
tons of litter reaches the oceans annually, coming from both land-based and ocean
sources (UNEP, 2005, 2009). Of this 6.4 million tons of litter, on average 75%
consists of plastic (Nicolau et al., 2016). Marine wildlife including many species of
sea turtles, seabirds, fish and mammals are greatly affected by ocean pollution due
to ingestion of and entanglement in plastic and other floating debris (Nicolau et al.,
2016). Over 260 species have been reported to ingest or become entangled in plastic
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debris, which can result in impaired movement and feeding, reduced reproductive
output, lacerations, ulcers and death (Thompson et al., 2009). Some species have
higher incidences of ingestion as they mistake plastic items for food. For example, in
the North Sea, 95% of northern fulmars, Fulmarus glacialis, a species of seabird, that
wash up on shores dead have plastic in their guts (Gregory 2009). The ingestion of
plastic and other debris has the potential for transferring toxic chemicals such as
polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons, petroleum
hydrocarbons, organochlorine pesticides, dichloro-diphenyl-trichloroethane (DDT)
and its metabolites, and Bisphenol A (BPA) up the food chain (Thompson et al.,
2009)
In the United States, there are an estimated 12,000 park departments both
municipal and local, managing 6.0 million acres of land (Blanck et al., 2012). Public
parks provide a setting where communities can come together and benefit from
environmental and educational opportunities (Iamtrakul et al., 2005). The
conventional idea is that parks must be attractive, safe, and have adequate
amenities and features to meet the needs of people with differing interests (Cohen
et al., 2010). Parks also can provide health benefits, increasing opportunities for
both children and adults to be physically active (Blanck et al., 2012). Studies have
shown that parks and public open spaces can enhance mental health by facilitating
contact with nature and the environment, and helping with the development of
supportive community relationships (Francis et al., 2012). Other studies have
shown that by increasing access to areas where people can be physically active,
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children are less likely to be overweight or obese (Blanck et al. 2012, Cohen et al.,
2010). Damage to public parks can directly affect humans by limiting areas for
engagement in physical activity, or even by simply ruining aesthetic pleasure.
Damage such as vandalism, poor maintenance, and crime can also make a park visit
uncomfortable and unsafe, ultimately limiting park use altogether (Blanck et al.
2012).
My study explores potential predictors of how much litter is in a suburban
park, in order to guide remediation efforts. Identifying factors that correlate with
the amount of litter in an area could potentially help determine which areas are
prone to larger amounts of litter, and what might be done to reduce the problem.
Some factors that contribute to an increase in littering rates are the lack of social
pressure to inhibit littering, the absence of penalties, and lack of knowledge of the
environmental effects of littering (Al-Khatib et al., 2009). Many socio-economic
factors, such as income, can also influence public littering habits (Al-Khatib et al.,
2009).
Few studies have explored the quality of parks and recreation resources in
relation to neighborhood socioeconomic status, and thus there is little information
on this topic (Vaughn et al., 2013). A study in New Zealand found that public open
spaces located in more affluent communities had better quality environments, less
litter, more amenities and a higher availability of access to activities, than those
located in more deprived neighborhoods (Badland et al., 2010). A different study,
examining the relationship between income and race/ethnicity, and the availability,
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features, and quality of parks in Kansas City, Missouri found that parks in low
income areas had more quality concerns (e.g fewer trees, fewer water features,
more litter and more graffiti), than parks in higher income areas (Vaughan et al.,
2013).
Another study examined how effective an educational environment program
was at reducing litter pollution and keeping pollution down. The program consisted
of a beach clean-up during which inhabitants of the island of Ambon, in eastern
Indonesia, spent an entire day cleaning litter from the shores (Uneputty et al., 1998).
This environmental program was successful in changing the behaviors of the
surrounding community and reducing litter density in that area for years after the
implementation of the program (Uneputty et al., 1998). It is hard to fully understand
how this study relates to a wealthier country like the United States, hence the
importance of more studies like this being conducted. In another study, pollution
levels were compared to property values, finding that property value decreased
with higher pollution levels (e.g. more contaminated tap water, higher amounts of
leaking underground storage tanks) (Guignet 2012).
Given the results of these previous studies, I hypothesized that parks with
lower median income values, lower median home values, and fewer environmental
programs would have more litter than parks with higher income values, higher
home values, and more environmental programs. Consequently, I predicted that
linear regression models would show significant negative correlations between the
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amount of litter and (a) median income values, (b) median home values, and (c) the
number of environmental programs.
METHODS
My study was conducted in thirteen villages, distributed throughout
Rockland County, New York (Figure 1). In each town, I chose one park that had a
walking trail at least 400 m long. Six towns did not have parks with long enough
walking trails, and were not included in the study. I used 400 m transects as several
walking studies found that a quarter mile (approximately 400 meters), is the
average distance people will walk to access community facilities (Regional Plan
Association 1997, Wolch et al., 2002, Van Herzele et al., 2003). A 2000 census found
that parents taking their toddlers out, walk on average a quarter of a mile to parks
for everyday outings and playground opportunities (Wolch et al., 2002).
I walked 400 m along the first trail I saw from the parking area in each park,
and as I walked, I scanned the area for any litter the size of a cigarette butt or larger.
I assumed that the first trail visible from the parking area would be the first trail
that most park users would see, and thus the trail that most park users would use.
When I saw a piece of litter, I recorded the distance from the center of the trail, the
distance from the start of the trail, what the litter was, and what size it classified as.
To keep track of the distance from the start of the trail, I used a rolling tape measure
wheel. Anything from the size of a cigarette butt to the size of a bottle cap was
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classified as a small piece of litter. Litter larger than a bottle cap, but smaller than a
1 liter bottle was classified as medium-sized. Anything the size of a 1 liter bottle or
larger was classified as large.
To find what environmental programs were offered in each town, I used the
county website http://rocklandgov.com/ in August of 2015. On the website, I
searched through each department’s page for all programs offered throughout the
county. I then recorded any program whose description implied that it had any
relevance to environmental issues, and where this program was offered. Using
information from the US Census Bureau website in August 2015,
http://www.census.gov/quickfacts/table/PST045215/00 , I recorded the median
household income value for each of the towns in Rockland County. I then used the
websites http://www.city-data.com and http://www.zillow.com in August of 2015,
to find the median home value for each of the thirteen towns. I used simple Pearson
correlations to compare values from the two websites and found that the estimated
median home values were well correlated (R squared = .77). I then chose the values
from the city-data website to represent my median home value data because this
website had data for more towns than did Zillow. Information on the town, village,
latitudinal and longitudinal coordinates, income, home value, and number of
environmental programs for each park can be found in Table 1.
I analyzed the data using linear multiple regressions in which the amount of
litter in each size category was the dependent variable and each of the socio-
economic factors, median income, median home value, and number of
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environmental programs, were the independent variables. I examined the socio-
economic variables separately against the amount of (a)small, (b)medium, (c)large,
and (d)total litter, and then in combination. Using R, I then compared the models
using Akaike Information Criterion small sample corrected values (AICc) to
determine which provided the best explanation of my data. All analyses were
conducted in R, with AIC statistics calculated using the package, AICcmodavg (R Core
Team 2013, Mazerolle 2016).
RESULTS
After surveying the thirteen parks, a total of 506 pieces of litter were found,
184 small, 265 medium, and 57 large pieces (Table 2). On average, a mean of 14
small, 20 medium, 4 large, and 39 total pieces of litter were found at each park. The
amount of small litter ranged from 7 pieces to 31 pieces, the amount of medium
litter ranged from 1 piece to 85 pieces, the amount of large litter ranged from 0
pieces to 21 pieces, and the amount of total litter ranged from 14 pieces to 119
pieces. The median incomes of people living in the areas surrounding these parks
ranged from $56,469 to $156,000, with a mean of $105,950. The median values of
homes in the surrounding towns ranged from $291,365 to $912,842 with a mean of
$482,070. The number of environmental programs offered in each town ranged
from 3 to 13, with a mean of 6 programs. I also found that the higher income areas
offered fewer environmental programs than the lower income areas (p =0.013, r2
=0.45) (Figure 3). Eugene Levy Park in Pomona, the town with the largest median
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income, $156,000, had the most litter, a total of 119 pieces. This park also had the
largest amount of medium sized litter, 85, and the largest amount of large sized
litter, 21, compared to all other parks surveyed.
None of the linear regression models used to explain the total amount of
litter found in a park were significant (p = 0.162). The total amount of litter was best
explained by a model that included only median income, however this model left
much of the variation unexplained (r2 = 0.170; Table 3). When compared to other
models this one had the lowest AICc and a weight of 0.46. These results signify that
the median income model accounts for 46% of the model weight, and approximately
17% of the variability of the data.
The amount of small litter was best explained by median home value, with
which it was positively related (Figure 2). When compared to other models, the
model containing only the median home value variable had the lowest AICc and a
weight of 0.46, implying that of all the models, this one best explains the amount of
small litter in a park. This model was also the only one that was significant,
indicating that the amount of small litter in a park and median home value in the
surrounding town are related (p= 0.029; Table 4.). The R-squared value for this
model was found to be 0.363, which indicates that the model explains 36% of the
variability of the data (Table 4).
The amount of medium-sized litter was best explained by the model
containing only median income, however this model left much of the variation
unexplained (p = 0.16, r2 = 0.170; Table 5). When compared to other models this
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one had the lowest AICc and a weight of 0.49. This indicates that the median income
model accounts for approximately 49% of the model weight, and approximately
17% of the variability of the data.
The amount of large litter was best explained by the model containing only
median income, however this model also left much of the variation unexplained (p =
0.25, r2 = 0.12; Table 3). When compared to other models this one had the lowest
AICc and a weight of 0.39. This indicates that the median income model accounts for
almost 40% of the model weight, and approximately 12% of the variability of the
data.
DISCUSSION
Most studies on litter have been conducted in marine environments, and very
few in terrestrial environments. Of the studies regarding litter on land, most are
conducted on beaches. My study helps continue the discussion of what factors
influence litter on land and where litter clean-up programs should be implemented.
A study conducted in Monterey Bay, California, found a total of 5972 pieces of litter
over twelve study sites (Rosevelt et al., 2013). In this study, trained volunteers
collected data at the same site once a month over the course of a year. In August
they found a mean of 11 items of litter/ m2 across the twelve sites (Rosevelt et al.,
2013). In my data, a mean of 39 total pieces of litter were found across the thirteen
study sites, which equates to approximately 0.1 pieces of litter/ m2. The 2013
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California study results showed litter densities almost 100 times greater than
densities found in my study.
Another study conducted over nine years, along a 1 km transect on the
beaches along the northwestern portion of Spain, found a total of 37791 pieces of
litter (Gago et al., 2014). Along the 1 km transect, in the summer a mean of 81 pieces
of litter were found, which equates to approximately 0.08 pieces of litter/m. The
results from my study found a similar mean average of 0.1 pieces of litter/m. These
findings suggest that litter in parks should be considered as seriously as litter on
beaches, as total abundances of litter are very similar.
My results did not support my hypothesis that the parks in lower income,
and lower home value areas would have larger amounts of litter. Instead, the park
with the largest amount of litter was found in the area with the highest median
income. The largest amounts of litter were also found in the parks located in areas
with higher median home values. There are many factors that could have influenced
these results. The higher income areas offered fewer environmental programs than
the lower income areas. It may be that the people who reside in the areas with more
environmental programs are more informed and more aware of environmental
issues, and thus pay more attention to littering habits. People with higher incomes
also may have more funds to purchase more items, thus producing more garbage,
which can end up as litter in parks. There may also be population density
differences, a factor I did not look into. Higher density areas in general are found to
have larger amounts of litter (Rosevelt et al., 2013). Parks located in areas with
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higher population densities may have higher foot traffic than parks in areas with
lower population densities.
The study examining the clean-up event in eastern Indonesia showed that an
environmental education program was successful in changing the littering
behaviors of a community (Uneputty et al.,1998). Although the United States and
Indonesia are very different economically speaking, the general concepts of littering
and community clean-ups are still the same. Some environmental programs such as
Keep Rockland Beautiful, in Rockland County, NY, conduct community clean-up
activities similar to that conducted in Indonesia. These programs can significantly
lower the amount of litter in an area if clean-up events occur frequently. If a clean-
up event occurred right before one of my park surveys, this could have significantly
skewed the results. In the future I would suggest researching when clean-up
activities occur, and making sure to conduct surveys before the activities. One could
even study the effectiveness of the clean-up programs by conducting litter surveys
before and after clean-up events. I would also suggest conducting the study multiple
times throughout the course of a year, similar to the Monterey Bay, California study
(Rosevelt et al., 2013).
The findings of this study suggest that the Rockland County government
should focus more of its environmental efforts in the higher income areas, than they
have in the past. In order to better understand the effectiveness of these efforts,
more studies should be conducted on how successful environmental programs
actually are. Once implemented, the programs should be monitored to see if they
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succeed in reducing litter. I would also suggest that a larger scale study be done in
the future, surveying a larger number of parks and towns. It is hard to prove or
disprove a correlation with only thirteen data points. If possible I would suggest
surveying all walking trails within the county, or even possibly a statewide survey.
This would give a greater amount of data to account for outliers and error, and
trends could be more easily assessed. With a larger amount of data, the
relationships found in this study between amount of litter and each of the three
factors, median income, median home value, and number of environmental
programs can be further evaluated.
Without studies like these, it is hard to fully understand how extensive the
litter problem is in terrestrial environments. Many studies have been conducted in
marine environments, demonstrating a major pollution problem in the oceans,
however few have looked at just how widespread this problem is. The need for
intervention is strong, and in order to understand how to resolve the issue, it is
necessary to recognize the factors that cause it.
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Figure 1. Map of the county parks and open spaces in Rockland County. The red diamonds represent the thirteen parks surveyed in this study.
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Figure 2. Graph depicting the correlation between median home value of an area and amount of small litter found in the park in that area (P =0.029).
y = 3E-05x + 0.9287R² = 0.3629
0
5
10
15
20
25
30
35
0 100,000 200,000 300,000 400,000 500,000 600,000 700,000 800,000 900,0001,000,000
Am
ou
nt
of
Sm
all
Lit
ter
Median Home Value ($)
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Figure 3. Graph depicting the correlation between median income in an area and the number of environmental programs offered in that area (P = 0.013).
y = -6E-05x + 13.054R² = 0.4465
0
2
4
6
8
10
12
14
0 20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 180,000
# O
f E
nv
iro
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ms
Median Income ($)
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Table 1. Geographic and demographic information for each of the thirteen surveyed parks.
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Table 2. Collected data for each of the thirteen surveyed parks.
20
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Table 3. Statistical data for the linear regression models comparing amount of total litter to median home value, median income, and number of environmental programs. K = a vector containing the number of estimated parameters for each model in the candidate model set.
Table 4. Statistical data for the linear regression models comparing amount of small litter to median home value, median income, and number of environmental programs. K = a vector containing the number of estimated parameters for each model in the candidate model set.
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Table 5. Statistical data for the linear regression models comparing amount of medium litter to median home value, median income, and number of environmental programs. K = a vector containing the number of estimated parameters for each model in the candidate model set.
Table 6. Statistical data for the linear regression models comparing amount of large litter to median home value, median income, and number of environmental programs. K = a vector containing the number of estimated parameters for each model in the candidate model set.
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