Predictors of Litter Pollution in Suburban Parks Ilanna Gibson University of Connecticut, [email protected]

Predictors of Litter Pollution in Suburban Parks Ilanna Gibson University of Connecticut, Ilanna.Gibson@Uconn.Edu

University of Connecticut OpenCommons@UConn

Honors Scholar Theses Honors Scholar Program

Spring 4-29-2016 Predictors of Litter Pollution in Suburban Parks Ilanna Gibson University of Connecticut, [email protected]

Follow this and additional works at: https://opencommons.uconn.edu/srhonors_theses Part of the Biology Commons

Recommended Citation Gibson, Ilanna, "Predictors of Litter Pollution in Suburban Parks" (2016). Honors Scholar Theses. 489. https://opencommons.uconn.edu/srhonors_theses/489 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

1 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

2 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

3 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,

4 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,

5 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

6 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

7 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

8 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

9 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

10 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

11 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

12 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

13 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.

Figure 1. Map of the county parks and open spaces in Rockland County. The red diamonds represent the thirteen parks surveyed in this study.

30 y = 3E-05x + 0.9287 25 R² = 0.3629

10 Amount Amount of SmallLitter

0 0 100,000 200,000 300,000 400,000 500,000 600,000 700,000 800,000 900,0001,000,000 Median Home Value ($)

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).

16 14

8 y = -6E-05x + 13.054 R² = 0.4465 6

4 # Of Environmental # Environmental Of Programs

0 0 20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 180,000 Median Income ($)

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).

17 Table 1. Geographic and demographic information for each of the thirteen surveyed parks.

Table 2. Collected data for each of the thirteen surveyed parks.

20 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.

21 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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