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IMPERVIOUS SURFACES AS AN INDICATOR OF WATER QUALITY WITHIN SARASOTA COUNTY, FLORIDA

Permanent Link: http://ncf.sobek.ufl.edu/NCFE004737/00001

Material Information

Title: IMPERVIOUS SURFACES AS AN INDICATOR OF WATER QUALITY WITHIN SARASOTA COUNTY, FLORIDA
Physical Description: Book
Language: English
Creator: Corrao, Laurel
Publisher: New College of Florida
Place of Publication: Sarasota, Fla.
Creation Date: 2013
Publication Date: 2013

Subjects

Subjects / Keywords: Water Quality
GIS
Eutrophication
Genre: bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Impervious surfaces such as roads, rooftops, and parking lots prevent the infiltration of water into the soil. Increases in impervious surface increase runoff. Results of increased runoff include increased nitrogen and phosphorus, and other pollutants into water bodies. Pollutants can cause ecosystem eutrophication and degradation. The relationship between increased impervious surface percentages upstream from water quality test sites in Sarasota County was investigated. Sarasota County Government provided data on biological oxygen demand (BOD), chlorophyll, turbidity, color, ammonia, total Kjedahl nitrogen (TKN), total nitrogen, nitrogen oxides (NOx) in the form of NO2 and NO3, orthophosphate, total phosphorus and total suspended solids (TSS) from thirty test sites. Regression analysis of 2008 water quality data and impervious surface percentages from upstream water quality test sites showed significant results for NOx and color. Regression analysis for 2011 water quality data from upstream water quality test sites showed no relationship between the variables and impervious surface percentages. Regression analysis for water quality test areas with a one-mile radius from the test sites had significant negative linear relationships with BOD (2008), color (2008 and 2011), TKN (2008 and 2011) and TN (2008 and 2011).
Statement of Responsibility: by Laurel Corrao
Thesis: Thesis (B.A.) -- New College of Florida, 2013
Electronic Access: RESTRICTED TO NCF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE
Bibliography: Includes bibliographical references.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The New College of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Local: Faculty Sponsor: McCord, Elzie

Record Information

Source Institution: New College of Florida
Holding Location: New College of Florida
Rights Management: Applicable rights reserved.
Classification: local - S.T. 2013 C8
System ID: NCFE004737:00001

Permanent Link: http://ncf.sobek.ufl.edu/NCFE004737/00001

Material Information

Title: IMPERVIOUS SURFACES AS AN INDICATOR OF WATER QUALITY WITHIN SARASOTA COUNTY, FLORIDA
Physical Description: Book
Language: English
Creator: Corrao, Laurel
Publisher: New College of Florida
Place of Publication: Sarasota, Fla.
Creation Date: 2013
Publication Date: 2013

Subjects

Subjects / Keywords: Water Quality
GIS
Eutrophication
Genre: bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Impervious surfaces such as roads, rooftops, and parking lots prevent the infiltration of water into the soil. Increases in impervious surface increase runoff. Results of increased runoff include increased nitrogen and phosphorus, and other pollutants into water bodies. Pollutants can cause ecosystem eutrophication and degradation. The relationship between increased impervious surface percentages upstream from water quality test sites in Sarasota County was investigated. Sarasota County Government provided data on biological oxygen demand (BOD), chlorophyll, turbidity, color, ammonia, total Kjedahl nitrogen (TKN), total nitrogen, nitrogen oxides (NOx) in the form of NO2 and NO3, orthophosphate, total phosphorus and total suspended solids (TSS) from thirty test sites. Regression analysis of 2008 water quality data and impervious surface percentages from upstream water quality test sites showed significant results for NOx and color. Regression analysis for 2011 water quality data from upstream water quality test sites showed no relationship between the variables and impervious surface percentages. Regression analysis for water quality test areas with a one-mile radius from the test sites had significant negative linear relationships with BOD (2008), color (2008 and 2011), TKN (2008 and 2011) and TN (2008 and 2011).
Statement of Responsibility: by Laurel Corrao
Thesis: Thesis (B.A.) -- New College of Florida, 2013
Electronic Access: RESTRICTED TO NCF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE
Bibliography: Includes bibliographical references.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The New College of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Local: Faculty Sponsor: McCord, Elzie

Record Information

Source Institution: New College of Florida
Holding Location: New College of Florida
Rights Management: Applicable rights reserved.
Classification: local - S.T. 2013 C8
System ID: NCFE004737:00001


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IMPERVIOUS SURFACES AS AN INDICATOR OF WATER QUALIT Y WITHIN SARASOTA COUNTY, FLORIDA BY LAUREL BRIANA CORRAO A Thesis Submitted to the Division of Natural Sciences New College of Florida in partial fulfillment of the requirements for the degree Bachelor of Arts Under the sponsorship of Elzie McCord, Jr. Sarasota Florida November, 2012

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Acknowledgements First I would like to thank those who made my thes is possible. Great thanks to Julie Morris and Jennifer Shafer whose tireless dev otion to watersheds and the environment led to the grant which funded my projec t. I would also like to thank Allison Pinto, Colleen McGue and all my fellow SCOPErs. SCO PE opened up a new and wonderful world for me. My understanding of grassro ots community building has exponentially grown in the past year. The values I have gained through my experience as an intern at SCOPE will be with me and my career fo rever. I also thank Sherry Philips and Jon Perry for patiently teaching me the interwo rks of new GIS techniques and database systems. I deeply appreciate Sherry’s warm th and sincerity throughout our work together. I thank Dr. McCord for the devotion in teaching an d guiding me throughout the thesis process. He worked patiently with me in crit iquing my work and managed to make me laugh among the stressors of the process. I know that he makes many sacrifices for his students and I hope he knows that it is appreci ated by many! I would also like to thank Dr. Weber for being such a devoted scientist and educator. I have learned a great deal from the challenges presented in her classes. I also appreciate her guidance in the previous years. I also thank Dr. Shipman. I am very grateful that I was able to take his general chemistry class my first year. It was alway s upsetting knowing that I had no intention of taking any physical chemistry classes. I have always felt welcomed into your office when I needed advice. I also thank Dr. Coope r for being so constantly willing and excited to help me with my statistical data. I would also like to thank my boss Jeff Thomas for giving me two weeks off work so that I could focus on my thesis. I would also like to take a moment to thank all of my friends and classmates who cheered me on the whole time. Big thanks to Katrian a Nugent, Sivens Glaude, Sam Eastham and Leah McMacken for laughing hysterically with me during my moments of sleep deprived delirium. Also just a general thanks for being my flavorite people. Big thanks to my new roommates and friends of the high pitch palace: Sandy Werb and Kyra Berman-Gestring for being so welcoming, adorable, a nd loud this whole semester. You always make me smile. Last but certainly not least I would like to thank my parents, Lisa and Philip Corrao. Thanks for always encouraging me to reach m y dreams however I choose and always providing me with helpful advice and comfort when I needed it. I would also like to thank my brother, Justin. Thanks for accepting m y weirdness and reflecting it back at me whenever the need be.

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TABLE OF CONTENTS Acknowledgements .................................. ................................................... ....................... ii List of Figures ................................... ................................................... .......................... v-vi List of Tables .................................... ................................................... ............................ vii List of Abbreviations ............................. ................................................... ...................... viii Abstract .......................................... ................................................... ................................ ix Chapter 1 Introduction ............................ ................................................... ................... 1-29 I. Impervious Surfaces................................ ................................................... 1-10 a. Types of surfaces ................................ ................................................... 2-5 b. Important terminology ............................. ............................................ 6-10 II. Effects of Impervious Surfaces ................... ............................................. 13-14 a. Changes in stream morphology ...................... ................................... 13-14 b. Changes in water temperature ..................... ............................................ 14 c. Impervious Surfaces: laboratory setting .......... ....................................... 14 III. Biological Effects of Impervious Surfaces ........ ..................................... 1517 a. Eutrophication ................................... ................................................. 1 5-17 IV. Geographic Information Systems (GIS) .............. .................................... 15-21 a. Vector data ....................................... ................................................... ..... 18 b. Raster data ....................................... ................................................... ...... 19 c. Attribute tables .................................. ................................................... .... 20 V. Mitigation Practices .............................. ................................................... 21-29 a. Best management practices ......................... ........................................ 21-22 b. Low impact development ........................... ........................................ 22-29 Chapter 2 Methodology ......................... ................................................... .............. 30-40 I. Experimental Aims ................................. .................................................. 30-40 a. Test Variables .................................... ................................................. 3 0-33 II. Methodology ....................................... ................................................... ... 33-40 Chapter 3 Results ............................. ................................................... .................... 41-59 I. Water Quality Areas developed upstream within test site sub-basins ...... 41-47 II. Analysis between impervious surface percentage and water quality variables ................................................... ................................................... ............. 48-59 Chapter 4 Discussion .......................... ................................................... ................ 60-66 I. Impervious surfaces as an indicator of increased Nu trients ..................... 60-62

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II. Impervious surfaces as an indicator of increased se diment ...................... 62-63 III. Impervious surfaces as an indicator of increased co lor ............................ 63-64 IV. Impervious surfaces as an indicator of increased bi ological oxygen demand ................................................... ................................................... ............ 64-65 V. Constraints and Future Studies .................... ............................................. 65-66 Chapter 5 Conclusion .......................... ................................................... ................ 67-68 Chapter 6 Bibliography ........................ ................................................... ................ 69-75

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LIST OF FIGURES Figure 1 Components of runoff ................... ................................................... ................ 2 Figure 2 Pervious pavement......................... ................................................... ................... 3 Figure 3 Pervious pavement and pavers ........... ................................................... ............ 5 Figure 4 Effects of impervious surfaces .......... ................................................... .............. 8 Figure 5 The phosphorus cycle .................... ................................................... ................ 10 Figure 6 Biomagnification ......................... ................................................... ................... 11 Figure 7 Raster and vector data ................... ................................................... ................. 18 Figure 8 Vector data............................... ................................................... ....................... 19 Figure 9 Raster data .............................. ................................................... ........................ 20 Figure 10 Attribute table ......................... ................................................... ...................... 21 Figure 11 Bioretention system .................... ................................................... ................. 25 Figure 12 Sloped incline infiltration ............ ................................................... ................ 29 Figure 13 Impervious surfaces in Sarasota County ma p ................................................. 35 Figure 14 Impervious surfaces in north Sarasota Cou nty map ....................................... 36 Figure 15 Water quality test site locations map .. ................................................... ......... 39 Figure 16 Impervious surface percentages within wat er quality test sites ...................... 44 Figure 17 Pearson r correlation scatter plot for co lor and impervious surface percentage in 2008 upstream water quality test site areas .... ................................................... .......... 46 Figure 18 Pearson r correlation scatter plot for NOx and impervious surface percentage in 2008 upstream water quality test site areas........ ................................................... ........... 47 Figure 19 Impervious surface percentage in one-mile area test sites .............................. 50 Figure 20 Pearson r correlation scatter plot for BO D and impervious surface percentage in 2008 one-mile radius water quality test site are as ................................................ ....... 53 Figure 21 Pearson r correlation scatter plot for TK N and impervious surface percentage in 2008 one-mile radius water quality test site are as ................................................ ....... 54

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Figure 22 Pearson r correlation scatter plot for co lor and impervious surface percentage in 2008 one-mile radius water quality test site are as ................................................ ....... 55 Figure 23 Pearson r correlation scatter plot for TN and impervious surface percentage in 2008 one-mile radius water quality test site areas ................................................... ........ 56 Figure 24 Pearson r correlation scatter plot for co lor and impervious surface percentage in 2011 one-mile radius water quality test site are as ................................................ ....... 57 Figure 25 Pearson r correlation scatter plot for TN and impervious surface percentage in 2011 one-mile radius water quality test site areas ................................................... ........ 58 Figure 26 Pearson r correlation scatter plot for TK N and impervious surface percentage in 2011 one-mile radius water quality test site are as ................................................ ....... 59

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LIST OF TABLE Table 1 Layer details ............................. ................................................... ........................ 34 Table 2 Locations of water quality test sites ..... ................................................... ............ 38 Table 3 Area of total impervious surface and water upstream quality test sites .............. 42 Table 4 Percentage of impervious surfaces within up stream water quality test sites ...... 43 Table 5 Correlation analysis 2008 data for upstream water quality test sites .................. 45 Table 6 Correlation analysis 2011 data for upstream water quality test sites .................. 45 Table 7 Percentage of impervious surfaces within on e-mile radius water quality test sites ................................................... ................................................... .................................... 49 Table 8 Correlation analysis 2008 data for one-mile radius water quality test sites ....... 52 Table 9 Correlation analysis 2011 data for one-mile radius water quality test sites ....... 52

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LIST OF ABBREVIATIONS BOD Biological Oxygen Demand BMP Best Management Practices DCIA Disconnected Impervious Area GIS Geographic Information Systems GPS Global Positioning System IS Impervious Surface ICPR Interconnected Channel and Pond Routing LID Low Impact Development NOx Nitrogen Oxides NSP Nonpoint Source Pollution PS Pervious Surface TIA Total Impervious Area TKN Total Kjedahl Nitrogen TSS Total Suspended Solids TN Total Nitrogen TP Total Phosphorus

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IMPERVIOUS SURFACES AS IN INDICATOR OF WATER QUALIT Y WITHIN SARASOTA COUNTY, FLORIDA Laurel Briana Corrao New College of Florida, 2012 ABSTRACT Impervious surfaces such as roads, rooftops, and p arking lots prevent the infiltration of water into the soil. Increases in i mpervious surface increase runoff. Results of increased runoff include increased nitrogen and phosphorus, and other pollutants into water bodies. Pollutants can cause ecosystem eutrop hication and degradation. The relationship between increased impervious surface p ercentages upstream from water quality test sites in Sarasota County was investiga ted. Sarasota County Government provided data on biological oxygen demand (BOD), ch lorophyll, turbidity, color, ammonia, total Kjedahl nitrogen (TKN), total nitrog en, nitrogen oxides (NOx) in the form of NO2 and NO3, orthophosphate, total phosphorus and total suspen ded solids (TSS) from thirty test sites. Regression analysis of 2008 wate r quality data and impervious surface percentages from upstream water quality test sites showed significant results for NOx and color. Regression analysis for 2011 water quality d ata from upstream water quality test sites showed no relationship between the variables and impervious surface percentages. Regression analysis for water quality test areas wi th a one-mile radius from the test sites had significant negative linear relationships with BOD (2008), color (2008 and 2011), TKN (2008 and 2011) and TN (2008 and 2011). ______________________ Dr. Elzie McCord, Jr. Division of Natural Sciences

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Chapter 1Impervious Surfaces An impervious surface (IS) is any material that does n ot allow water to permeate and filter through soil (Arnold & Gibbons 1996). Pavement, rooftops, sidewalks, patios, compacted soils and other surfac es are impervious surfaces (Arnold & Gibbons 1996). In contrast to impervious surfaces pervious surfaces (PS), such as porous forest floors, have little runoff because ra inwater is able to soak into the top soil. Under these conditions, water can permeate th e soil mantle as interflow in wetlands, streams or lakes, evapotranspirate or soa k into the topsoil (Roesner et al. 2001). Interflow occurs when water moves laterally below the soil surface (Figure 1) (NWS Internet Services Team 2009). Interflow water takes longer to enter stream channels, minimizing erosion and increasing percola tion (NWS Internet Services Team 2009). Evapotranspiration is the water added t o the atmosphere through evaporation and plant transpiration. Arnold and Gibbons (1993) listed four indirect way s that impervious surfaces contribute to water pollution. Impervious surfaces: “(1) are a critical contributor to the hydrologic c hanges that degrade waterways, (2) are a major component of the intensive land use s that do generate pollution, (3) prevent natural pollutant processing in the soi l by preventing percolation, and,

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(4) serve as an efficient conveyance system transpo rting pollutants into the waterways.” Types of Surfaces Impervious surface types can be categorized into n atural and human-made. Impervious human-made surfaces include roads, drive ways, parking lots, rooftops, etc (Jacobson 2011). Human-made surfaces are constr ucted of materials such as concrete, asphalt, plastics, stone, brick, metal, e tc. Natural impervious surfaces are typically compacted soils or boulders (Weng 2008). Pervious Surfaces can be categorized similarly to natural pervious surface including forested areas, grassy areas, sandy areas and most undeveloped lands. Figure 1 – The components of runoff are shown here. When precipitation occurs, water travels to different locations using differen t processes. Water can evaporate, become stored in depressions in the earth, flow ove r the land, infiltrate (become absorbed by) the land and be added to stream flow (http://www.tankonyvtar.hu/hu/tartalom/tamop425/003 2_vizgazdalkodas/images/fig2 9.jpg )

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Human-made pervious surfaces include pervious pavem ent and pervious pavers (Figure 2)(Yang et al. 2003). Figure 2Pervious pavement is shown with water tra veling through the gaps between the large, pebble-like material (US EPA 2009). Pervious pavements and pavers are installed over a bottom layer of high infiltration soil (University of Florida 2008) (Fig ure 3). Above infiltration soil, stone aggregate is layered. An aggregate is a material fo rmed from loosely compacted fragments. Aggregates enhance infiltration capacity and create a reservoir for water retention. Above the aggregate, a layer of geo-text ile fabric is laid. Pervious pavement has a special concrete laid on top of the geo-textile fabric base layers (permeable soil, aggregate and filter fabric) (Figu re 3). Pervious concrete is designed to have a lower sand and fine particles quantity (F igure 2). Air pockets are formed between the coarse particles in the concrete leavin g void spaces between 15 and 35%. This allows water to follow freely through to the s ubstrate to the soil below (Figure 3)(Office of Wastewater Management 2009; University of Florida 2008). Pervious pavers, concrete blocks or plastic grids are laid o n top of the base layer, to facilitate

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percolation or to fill a cistern that lie beneath t he pavers (Office of Wastewater Management 2009).

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Figure 3Pervious pavement (left) and pervious pav ers (right). Pervious pavement is layered with perv ious pavement, filter fabric, reservoir later, sub-grade and type “D” curb in ord er from top to bottom. Pervious pavers are composed of paver, stone fill, stone leveling bed, filter fabric and aggregate base from top to bottom. (University of Florida 2008).

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nImportant terminology Watershed A watershed is described by Leopold (1968) as the “area of land that drains water, sediment and dissolved material to a common outlet at some point along a stream channel.” Watersheds are often referred to a s drainage basins. Drainage basins are often defined by a particular river, such as th e Mississippi river being related to the Mississippi River Basin (DeBarry 2004). Watersh eds are deconstructed into smaller sub-watersheds (or sub-basins) for the purp ose of closer analysis. Catchments are even smaller areas representing stream drainage areas (DeBarry 2004). Thus, watersheds are composed of sub-basins and sub-basin s are composed of catchments. Conveyances Conveyances are the different pathways for water t o travel in a basin. Conveyances help divert and control the flow of wat er in a canal system. Impervious surfaces increase the need for conveyances to preve nt soil erosion (Arnold & Gibbons 1996). Urban developers install systems of gutters, drains and storm sewers that feed conveyances (Booth et al. 2002). During heavy rains, areas with human-made conveyances often flood because of increased discha rge and intensity of flow (Booth et al. 2002; Brabec et al. 2002).

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Runoff Runoff is the water that collects above a surface when the surface’s infiltration rate is exceeded by the rate of precipitation. Infi ltration rate describes the rate at which water can saturate soil (Figure 4). Runoff tr aveling across a surface can collect fertilizers, pesticides, motor oil, bacteria, sedim ent and other such pollutants. The resultant polluted runoff is a source of nonpoint s ource pollution. Increases in impervious surfaces decrease infiltration rates and increase runoff (Figure 4) (Arnold & Gibbons 2007; DeBarry 2004; Paul et al. 2008; Shuster et al. 2005).

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Figure 4Effects of increased impervious surface i nclude increased runoff, decreased infiltration and decreased evapotranspiration. This image shows the rates of change observed by Arnold & Gibbon (2007). Nonpoint Pollution Nonpoint source pollution (NSP) has many contamina nt sources throughout the landscape. A general point source pollution def inition is a source that one could physically see and identify. A sewage treatment pla nt with a discharge pipe that pours chemicals into a water body can be easily identifie d as a point source, making it easy to categorize polluters. NSP is named a major contr ibutor to urban water pollution Nonpoint source pollutants in urban environments ar e found in storm-water

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rdischarges. This discharge contains sediment, nutr ients, pesticides, disease organisms and debris (Jaber 2004). Different landscapes: expected pollutants Agricultural lands are associated with sediment, a nimal waste, diseases, plant nutrients, crop residues, inorganic salts and miner als and pesticides (Basnyat et al. 2000). Organisms associated with agricultural soils include E. coli, Salmonella and Caulobacter (Jamieson et al. 2002). Excess quantities of pesticides and nutrien ts can be unloaded during a storm-water event in agricultu ral areas. Residential lawns are largely responsible for exce ss nutrients from fertilizers, pesticides, and domestic animal waste. Pet waste ca n host many diseases that are transmitted through untreated water. Plastic bags, six-pack rings and cigarette butts are also washed into storm-water when passing over impervious surfaces (Jaber 2004). Such debris are pollutants because of habita t disruption and their propensity to choke or suffocate organisms. Excess Nutrients Phosphorus pollutants are from soil particle-assoc iated (H2PO4 -and HPO4 2-) and dissolved phosphorus (Turner et al. 2007; Paul et al. 2008). Particle associated phosphorus is an inorganic form found attached to c ation particles including iron, calcium and aluminum. Phosphorus is introduced into the ecosystem by malfunctioning septic systems, pesticides, detergen ts, phosphorus mining, and fertilizers applied to agricultural and residential lands (Figure 5) (Gerritse 1995, La Velle 1975, Bennet 1999). Runoff events in areas wi th soil erosion often deposit

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phosphorus into the water bodies (Bennet 1999; DeBa rry 2004). Phosphorus that was previously stored in the soil after being applied i n the form of fertilizer will mobilize when disrupted by soil erosion (Bennet 1999). Figure 5Sources of phosphorus and processes invol ved in the phosphorus cycle (US EPA 2012). Increases in nitrogen are even higher than phospho rus within urban catchments (Paul et al. 2008). Common sources of nitrogen are from leaking septic tanks and fertilizers applied to residential and ag ricultural lands (Gerritse et al. 1995; Wernick et al. 1998). Increases in nitrogen are deposited in the form of ammonium ion (NH4 +f) and nitrate (NO3 -) (Paul et al. 2008, Turner et al. 2007; Wernick et al. 1998). Levels of pollution depend largely on the ef ficacy of wastewater treatment technology, degree of discharge, number of leaky se wer lines and amount of fertilizer used (Paul et al. 2008; Wernick et al. 1998; Bennet 1999) Metal contaminants Heavy metals are also present in polluted run-off. Lead, zinc, chromium, copper, manganese and nickel are the most commonly found metals in polluted run-

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off (DeBarry 2004). Sources of these metals include the following automobile parts: brake linings with nickel, chromium, lead and coppe r); tires with zinc, lead, chromium, copper and nickel; and engine parts with nickel, chromium, copper and manganese). These metals have been shown in higher concentrations within conveyances with high levels of organic matter (DeB arry 2004; Paul et al. 2008). Heavy metals can be toxic to biological organisms. Biomagnification of heavy metals within organisms produces toxic levels of he avy metals for predator organisms. Biomagnification is the increase in conc entration of a substance as organisms in higher trophic levels feed on metal co ntaminated species (Figure 6). Figure 6This is a visual representation of biomag nifications. Algae represents the benthic organisms that are eaten by arthropods that are consumed by fish. The fish are preyed upon by predatory birds. Contaminants accumu late at each trophic level

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producing detrimental effects in top predators, the great blue heron ( Ardea herodias ) (Seathos 2011). Pesticide contamination Urban sources of pesticides include lawns, mosquit o control, golf courses, roadsides and pet pest-treatments (Hoffman et al. 2000). Thirty percent (30%) of the pesticides used in the United States are applied as non-agricultural treatments in urban areas (US EPA 2011). Davis (2005), Hoffman et al. (2000), Paul et al. (2008) studied different stream locations and found varyin g pesticide levels within each location. The main transportation of pesticides is facilitated through NPS runoff (Foster et al. 2000). Soil Erosion Construction sites typically disrupt soil to build their project site preparations increase soil erosion and deposits sediment into ru noff (Arnold & Gibbons 1996; Jaber 2004; Leopold 1968; Roesner 2001). Urban catc hment sediment yields have been observed to be 102-104 times higher than in forested catchments (Paul et al. 2008). Suspended solids cause murky water and prevent sun light from reaching aquatic plants, preventing growth (DeBarry 2004; Ja ber 2004; Pimentel et al. 1995; Roesner 2001). Excess sediment smothers beneficial aquatic plants and settles onto stream floors smothering aquatic life (DeBarry 2004 ).

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Effects of Impervious Surface Changes in stream channel morphology Increasing levels of impervious surfaces are assoc iated with increased changes in stream channel morphology (Booth et al. 2002; Brabec et al. 2002; Arnold & Gibbons 1996). Changes in stream channel morphology involve any disturbance in the natural fluctuations of steam shape and flow ra te (Jacobson 2011; Walsh et al. 2005). During urbanization processes, native plants are removed from riparian buffer zones. Without roots to hold soil in place, soil er osion increases. Soil erosion increases the turbidity of the water, reducing the amount of light that can reach aquatic plants for photosynthesis. Aquatic plants p rovide habitat and food for many freshwater and marine species. During development, natural stream channels are st raightened and deepened. By diverting water, developers can use more space f or construction on properties in areas with streams (Booth et al. 2002) Some streams are made more hydraulically efficient by lining them with concrete (Booth et al. 2002). Human-made changes in natural stream morphology eliminate the natural dec eleration of stream flow (Booth et al. 2002). Increased speed of water traveling through stream channels reduces the available time for water to evaporate or permeate i nto the soil, resulting in most of the water traveling to larger bodies of water as runoff (Hollis 1975). Steady-state stream flow drops when groundwater le vels are decreased by increased impervious surface (Jacobson 2011). Stead y-state flow is a hydrodynamics

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term describing a reliable continuous flow. Without steady-state stream flows, ecosystems lose the foundation to sustain themselve s, leading to ecosystem collapse. Changes in water temperature Water temperatures have been observed to increase with increased impervious surface (Paul et al. 2008). Warmer temperatures within streams can be c aused by removal of riparian vegetation, the “heat-island ef fect” and decreases in groundwater recharge (Pickett et al. 2011). The “heat island effect” is a term used to describe the effect of having dense areas of urban development w ith large paved areas, buildings, and sparse green spaces. Paved areas and buildings are associated with urban heat islands that produce anthropogenic heat sources and re-radiate solar heat (Memon et al. 2008; Pickett et al. 2011). Rises or elevation in water temperature can also disturb normal fluctuations in stream processes such as lea f decomposition and invertebrate survival (Webster & Benfield 1986). Impervious surfaces: laboratory setting Pappas et al. (2008) conducted an experiment to test the impacts of impervious surface in a laboratory setting. Two run off scenarios were tested: a sloped cascade of soil with 50% impervious surface and a s imilar cascade with 0% impervious surface. The test chamber with 50% imper vious surface generated 3-5 times the sediment. The runoff rate in the area wit h no impervious surface was initially low but with continued rainfall, run-off rate increased (Pappas et al. 2008).

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Biological effects of Impervious Surfaces Eutrophication Eutrophication is the addition of excess nutrients in water bodies that can cause increased growth of algae and plants (Kirkpat rick et al. 2004; Larsson et al. 1985). Phosphorus and nitrogen from natural or arti ficial sources contribute to eutrophication (Jaber 2004). Algal blooms are common responses to eutrophication in Florida’s marine and freshwater ecosystems (Wang et al. 1999). Typically, planktonic algal populations are limited by availability of nitrogen and phosphorus in the water. Algal population decomposition leads to a rapid drop in dissolved ox ygen and changes in bottom substrate composition resulting in light intensity decreases and narrower light spectrum from turbidity (Jrvenp & Lindstrom 2004 ). Increased turbidity prevents sea grass growth, which is necessary for the surviv al of many juvenile fish populations and invertebrate species (Jackson 2001; Wang et al. 1999). Many of the algae species associated with blooms a re harmful and are capable of producing toxins. Some toxins are capable of kil ling aquatic and terrestrial organisms (Gunter 1948). Humans experience reduced lung function, asthma and respiratory symptoms when breathing aerosolized tox ins during algal decay (Backer 2009; Carmicheal et al. 2001). Along the coast of Florida, from Tarpon Spr ings to Key West, Karenia brevis (G. Hansen & O. Moestrup) is known to cause mortal ities of marine life (Gunter 1948). Dolphins, sea turtles manatee ( Trichechus manatus )

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n and numerous fish species have were reported to hav e died following algal blooms in the Gulf of Mexico (Kirkpatrick et al. 2004; Landsberg 2002). Consumption of fish exposed to algal blooms poses r isk to humans. Fatalities occur most frequently in Asia but have been recorde d in Florida and Europe (Backer 2009; Kirkpatrick et al. 2004). M easur ing Impervious Surface Areas: techniques/mechanisms Scientists measure impervious surface areas using different techniques. Total impervious surface is the term used for all identif ied areas of impervious surface This is the most general form of measurement (Roy & Shuster 2009; Shuster et al. 2005). Other ways researchers categorize impervious surfaces include identifying impervious surfaces as effective or disconnected. Effective impervious area includes the impervious surface that is connected to a drainage system by paths of water flow from a sou rce area to a drainage system (Shuster et al. 2005). A street diverting water to a sewer drainag e system is an example of a source to drainage system. Disconnecte d impervious surfaces are also known as ineffective impervious surfaces. They are the surface that re-routes runoff from an impervious surface to a pervious one (Roy & Shuster 2009; Shuster et al. 2005). This occurs when rainwater runs off an imper vious rooftop onto a pervious patch of lawn. To compare disconnected impervious area (DCIA) and total impervious area (TIA) one must consider the accuracy and feasibilit y of the two methods. In questioning the quality of TIA versus DCIA, Roy & S huster (2009) claim that

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multiple studies have correlated increased DCIA wit h decreases in water quality, and algal, macroinvertebrate and fish assemblage succes s. DCIA considers the journey that runoff takes before reaching its depositing si te, making it a better quality indicator of environmental degradation. Calculation of DCIA relies on the existence of TIA. Development of DCIA is desirable for accura cy but undesirable for time consumption during production because one must firs t create the TIA data before getting started on the DCIA (Roy & Shuster 2009). Geographic Information Systems Geographic information systems (GIS) are systems o f hardware, software and data, which aid in capturing, managing, analyzing a nd displaying geographic data. Cartography, statistical analysis, and database tec hnologies are used to make inferences about geographical relationships. GIS ha ve the power to show spatial relationships by comparing different data types. Us es of GIS range from finding the fastest statewide evacuation routes to determining species habitat preference through Global Positioning System (GPS) tracking. GIS uses vector and raster data to coordinate lati tude, longitude and elevation. Vectors are provided as polygons, lines and points (Figure 7). Raster data are matrices that represent coordinates that are linked to attribute tables, which provide information about the different geographic features being displayed (Figure 8).

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Vector data Polygon features are used to represent areas, incl uding city boundaries, lakes, and property lines. Different colors can be assigne d to designated categories of polygons to create gradients. Gradients help visual ly classify spatial differences. Line data represents linear features. Examples inc lude rivers, streets and nature trails (Figure 8). Different linear features can be distinguished using different colored lines, dashed lines and a variety of line t hicknesses. Figure 7Representation of vector data versus rast er data. Raster data is displayed at the top as a grid and vector data is represented by polygons (Anonymous 2009)

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r Point data represents discrete data points and non adjacent features. Such data points include cities and points of interest i.e.; hospitals and schools. (Figure 8). Different shapes and colors can be assigned to poin t data. Figure 8Example showing different vector data typ es. Waianae high school is represented as a point; different streets are repre sented as lines and the parks are represented as green polygons (Anon, n.d.). Raster data Raster data, also known as grid data, represents s urfaces. Rasters display data in cells on grids. Each cell is assigned a feature value (A-E) which represents the majority of the cell (Figure 9). If one cell had t hree different values, such as forest habitat, water and development, but the forest habi tat accounted for 60% of the cell, then the cell would display 100% forest.

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Attribute Tables Attribute tables are table containing detailed asp ects of features (Figure 10). If GIS was being used to obtain data on species habita t using a GPS device, each organism being tracked could have data on its sex, age and species name. The land attribute table can show the vegetation type and el evation. This example could provide details on sex preference for different ele vations or species vegetation preference. Figure 9Raster data takes polygon data (shown lef t) and converts it to a grid representation based on the contents of the cell an d assigns the majority value to that cell (right) ( Automation 2009)

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Figure 10This attribute table shows the different features which are represented on the map. For example, the road feature is shown as the green line on the map (ESRI 2012). Mitigation Practices After an impervious surface has been identified, it is esse ntial to have a method to reduce its effects in areas with higher i mpervious surface concentrations. Different techniques have been developed to combat problems that urbanization has created for our environment (Davis 2007, Dietz 2007 US EPA 2007, Roy et al. 2010a). Best Management Practices To reduce or reverse damages incurred by urban run off, mitigation practices have been developed. Best management practices (BMP s) were created to avoid future environmental damage. BMPs branch into two d ifferent types: structural practices and nonstructural practices. Structural p ractices consist of infiltration, filtration, detention and retention systems, and we tlands ( Roesner et al. 2001; Roy et

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al. 2010 a ) Retention systems are artificial lakes used to ma nage stormwater. Detention systems are different from retention syst ems because they are used for stormwater management but drain after the rain even ts. The advantages to a structural BMP include simplicity of design, ease of construct ion, peak flow reduction, and improved water quality (Roy et al. 2010 a )[186 Roy] Nonstructural BMPs are more maintenance based, inc luding street sweeping, outreach programs, and land-use planning (Roy et al. 2010 a ) [186 Roy] Outreach programs encourage the public to prevent water qual ity problems by reducing pesticide and fertilizer use. Land-use planning is preparing new developments in a way that reduces runoff. A method of land-use plann ing includes building homes closer together and providing a conservation space which is undisturbed and therefore, pervious. Low Impact Development (LID) Low Impact Development (LID) uses natural systems t hat are capable of effectively infiltrating storm water, reducing nega tive effects of runoff (Dietz 2007). Evapotranspiration, reuse of rainwater, and infiltr ation are incorporated in LIDs to improve water quality (Dietz 2007). This technology reduces costs by decreasing the need for infrastructure by decreasing total runoff volumes (Guo & Cheng 2008). LID is a BMP that is modeled after nature (Dietz 2 007; US EPA 2007). Predevelopment LIDs can be incorporated to maintain or attempt to duplicate the site’s original hydrology (Guo & Cheng 2008). If LID is be ing applied post development,

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LID is a retrofitted design to reduce runoff volume s, pollutant loading, and overall effects of development on bodies of water (US EPA 2 007). The EPA categorizes LID practices into six practic es: conservation design, infiltration, runoff storage, runoff conveyance, fi ltration and low impact landscaping (US EPA 2007). Conservation designs Conservation design is a land use technique that a ims to leave the most land intact during development (Saunders et al. 2002). Conservation designs reduce the amount of impervious surface area and decrease runo ff (US EPA 2007). Examples include cluster development, open space preservatio n, reduced pavement widths and shared driveways (US EPA 2007). Cluster development is used to develop conservation oriented residential neighborhoods. Ho mes are built closer to each other providing residents with recreational benefit from the shared natural areas (US EPA 2007). Runoff is decreased because the amount of imperviou s surface is decreased and more pervious land goes undisturbed. This pract ice has the additional benefit of providing limited fragmentation within a habitat (M iller & Hobbs 2002; Saunders et al. 2002). Infiltration and Filtration Practices Infiltration practices are engineered landscapes d esigned to capture and infiltrate runoff (US EPA 2007). This practice redu ces runoff volumes and the need for the infrastructure to convey, treat or control runoff. Ground water recharge increases with the implementation of infiltration p ractices (Simpson & Weammert

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2009; US EPA 2007). Examples include bioretention/r ain gardens, permeable pavement, vegetated swales, and vegetated filter st rips (Simpson & Weammert 2009; US EPA 2007). Filtration practices have similar benefits as infi ltration practices. A filtration practice filters runoff through media that will cap ture pollutants (Simpson & Weammert 2009; US EPA 2007). Pollutants are collect ed either through the physical filtration of solids and/or cation exchange of diss olved pollutants. Examples of filtration practices include bioretention/rain gard ens, permeable pavement and vegetated filter stripes/buffers (Simpson & Weammer t 2009; US EPA 2007). There is some overlap between BMPs that perform infiltrat ion and filtration. Bioretention system A bioretention system, or bioswale, is a manmade d itch area designed to improve water quality before it reaches a body of w ater. Bioswales are designed to collect rainwater runoff that would ordinarily just go to a storm drain, or water body, and remove some of the pollutants from the runoff a s the water percolates through the soil. Whereas typically water will just runoff a pe rvious surface that has become fully saturated with water, a bioswale provides a space t hat collects the water while the saturated soil drains. Bioretention systems are cap able of removing nutrients, sediments, and other variables effecting water qual ity (Suns & Davis 2007). Bioretention systems require digging up the soil i n a designated space and filling the hole with a highly permeable soil and o rganic matter designed for optimal infiltration (Figure 11) (Hsieh & Davis 2005). The selected soils should support high

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vegetative growth so that areas can be covered with native plants ( Deitz 2007; Hsieh & Davis 2005; Roy et al. 2 010a ) Plants resistant to environmental stresses are selected to extend the length of efficacy of a bior etention system. Figure 11The different components of a bioretenti on system (Roy-Poirier et al. 2010a). to minimize the need for frequent site management ( Davis 2006; Roy et al. 2010a). Bioretention area should then be covered with mulch to prevent erosion and to catch solids. Bioretention systems should have inlet stru ctures where routed runoff can

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n collect [186 Roy] Systems should have outflow structures that diver t excess runoff to prevent flooding (Figure 11) (Roy et al. 20 10a ) [186 Roy] Bioretention systems can remedy many of the negati ve effects caused by storm water runoff from impervious surfaces. Hydrologic i mpacts, nitrogen and phosphorus increases, heavy metals, total suspended solids (TSS), organic matter, pathogens, water temperature changes, pH changes an d dissolved oxygen decreases are improved with bioretention systems (Davis 2007; Dietz 2007). Bioretention systems have shown significant decrea ses in storm water runoff volumes. Some studies have shown varied levels of b ioretention effectiveness during different seasons based upon the level of evapotran spiration ( Davis 2007; Dietz 2007; Hsieh & Davis 2005; Roy et al. 2010) When evapotranspiration is low, bioretention systems can become completely saturated, diverting all excess water back into the storm-water management system ( Davis 2007; Dietz 2007; Hsieh & Davis 2005; Roy et al. 2010) Sun & Davis (2007) tested bioretention systems under laboratory conditions and showed a high removal of most Zn, Cu, Pb, and C d. The retention of Zn, Cu, Pb and Cd by the plants were approximately 94%, 88%, 9 5% and >95% respectively. A major concern with the use of bioretention to store heavy metals is their inability to retain them. Another concern is the ingestion of th ese plants, containing a great deal of heavy metals, by wildlife. More studies are need ed to evaluate this system (Sun & Davis 2007).

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Total suspended solids (TSS) measures the amount of organic and particulate matter and heavy metals are actually in water. Bior etention systems consistently remove high TSS levels from the water (Davis 2007, Hsieh & Davis 2005, Roesner et al. 2009). They can remove up to 54% of TSS (Davis 2007 ). Effective life of the system is shortened by concentrated filtrate. As mo re TSS are filtered, the less effective the hydraulic benefits are because the pe rvious fill media becomes clogged (Roesner et al. 2009). Bioretention systems were capable of buffering wat er with a pH range of 6.08.0 in a laboratory setting. More studies are neces sary to understand the efficacy of rain gardens in buffering water (Roesner et al. 2009). Bioretention systems effectively remove harmful ag ents from runoff, are aesthetically pleasing, inexpensive and require lit tle maintenance. They can be used to improve real-estate property values by adding a pleasant look (US EPA 2007). Runoff storage practices Runoff storage practices are best in areas with a large impervious surface percentage, such as a parking lot. Runoff is collec ted and stored. Stored water is reused, infiltrated, or evaporated. Reused water ca n be applied as irrigation for islands, tree boxes or rain gardens (US EPA 2007, Z hen et al. 2006). Rain barrels and cisterns can be used to collect water for reuse (Zh en et al. 2006). New College of Florida’s Academic Center, a recently constructed b uilding, collects storm-water from its courtyard and stores it in a cistern. This water is used for flushing toilets within the building.

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Green roofs are another excellent example of a run off storage strategy (US EPA 2007). Green roofs are vegetation established o n the top of a building which acts as a water storage system. Succulent plants are ro oted in several centimeters of soil, sand and absorbent medium (Davis 2005). VanWoert et al. (2005) showed a mean average of 63% retention of precipitation with diff erent media and green roof slopes. Limited studies have reported water quality changes in green roof runoff. Green roofs are widely accepted as aesthetically pleasing and e fficient water storage system (Dietz 2007; Roy et al. 20 10a; VanWoert et al. 2005). Runoff conveyance practices Run-off conveyances are used where infiltration an d storage practices are not enough to control the runoff from large storm event s (US EPA 2007; Zhen et al. 2006). Following a low impact design, runoff convey ances will ideally slow water flow velocity, lengthen the runoff time, and delay peak flows from storm events (US EPA 2007). One example of runoff conveyance practic e is eliminating curbs and gutters. Curbs and gutters can trap pollutants unti l the next rain event. It is better to have a sloped incline that promotes infiltration (F igure 12) (US EPA 2006). Runoff conveyance practices encourage infiltration, filtra tion, evaporation, solid settling and pollutant reduction.

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r Figure 12A road free of curbs and gutters. This s loped incline will promote infiltration instead of runoff during a rain event (US EPA 2006). Low impact landscaping Low impact landscaping involves designing a landsc ape with specific plants that will improve infiltration and the aesthetic qu ality of the area. Examples of this practice include planting native plants that are re sistant to environmental stress, converting turf areas to shrubs and trees, reforest ation and planting wildflower meadows. The University of Florida has a program that resea rches and promotes “Florida friendly yards.” Water use is reduced when native plants are planted in place of grass landscaping. Florida friendly yards also r educe nitrogen, phosphorus and pesticides in runoff (Garner et al. 1996) Chapter 2 Methodology

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Experimental Aims The aim of this study was to use 2008 Sarasota Cou nty data to determine if increases in impervious surfaces decrease water qua lity. I hypothesize that increased impervious surface percentage will lead to increase d levels of biological oxygen demand (BOD), chlorophyll, turbidity, ammonia, tota l Kjedahl nitrogen (TKN), total nitrogen (TN), nitrogen oxides (NOx) in the form of NO2 and NO3, orthophosphate, total phosphorus (TP) and total suspended solids (T SS) and a decreased level of color. I also examine the effectiveness of two different m ethods of calculating percentages for the water quality test sites used. Test Variables Water quality values were obtained from the Saraso ta County government. The county government tests 33 different test sites scattered around the county. Variables tested include: BOD, chlorophyll, turbidi ty, color, ammonia, TKN, TN, NOx (in the form of NO2 and NO3), orthophosphate, TP and TSS. The reasoning for selecting these locations to tes t water quality include logistical reasons and economic reasons. Water coll ection must be easy to access by car and have legal or cheap access points (spoken r ecord from Jon Perry of Sarasota County Government 2012). These sites are also ideal because of their locations being at major confluences within the different basins of Sarasota (spoken record Jon Perry 2012). A confluence is a place where two bodies of water meet. Biological oxygen demand measures the amount of ox ygen used by aerobic organisms in the metabolism of biodegradable organi cs under aerobic conditions at

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20 C during a five-day test period (DeBarry 2004). BOD represents the amount of dissolved oxygen needed by aerobic biological organ isms in a water body. Low BOD indicates that there has been an increase in microb ial populations in response to large levels of organic material. Sarasota County uses th e American Public Health Association’s 5210B standard methods (American Publ ic Health Assocation et al. 2005). Turbidity is the measure of light scattered and ab sorbed by particles in a sample. Clear water allows light to travel freely. Water containing particulate matter will have higher rates of scattered and absorbed li ght. Sources of turbidity include water discharge, runoff, algae, humic acids and iro n. Higher turbidity leads to decreased plant growth because aquatic plants need light to grow (US EPA 1999). Sarasota County uses the EPA’s 180.1 method for tes ting turbidity. Color in water comes from organic matter. Organic matter has hummic and fulvic acids which creates a yellow-brown color. Cl ays, algae, and managanese oxides also give water an appearance of color (Amer ican Public Health Assocation et al. 2005). Sarasota County measures “apparent color.” A pparent color measures the color caused by substances in the solution and due to suspended matter. High levels of color indicate high turbidity. More color can pr event the light from reaching aquatic plant life. This limits the growth of plant s and the success of the ecosystem. Sarasota County uses the American Public Health Ass ociation’s standard method 2120 B to measure apparent color (American Public H ealth Association et al. 2005).

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When dissolved solids are tested, solids pass thro ugh a filter when water is evaporated. Total suspended solids (TSS) include so lids remaining on the filter after all water has been evaporated from the testing vess el. Total suspended solids are indicators of the amount of sediment within the wat er column. Increased total suspended solids are associated with increased turb idity. Sarasota County uses the American Public Health Association’s standard metho d 2540 B to measure TSS (American Public Health Association et al. 2005). Ammonia (NH3) is a precursor to most nitrogen-containing compou nds. Decomposed organisms and fertilizers are the main s ources of ammonia (Randall & Tsui 2002). Aquatic organisms suffer from convulsio n, coma and death when exposed to high levels of ammonia (Randall & Tsui 2 002). Large numbers of fish will die when high ammonium levels interact with increas ed pH, increased temperature and/or decreased dissolved oxygen (Camargo & Alonso 2006). Total Kjeldahl nitrogen (TKN) is the sum of ammoni a (NH3), ammonium (NH4 +) and organic nitrogen. Total nitrogen (TN) is TKN plus nitrate and nitrite (US EPA n. d.). Sarasota County tests nitrogen oxides ( NOx) including nitrite (NO2 -) and nitrate (NO3 -). When testing NOx all of the nitrite is converted to nitrate and the total nitrate is reported. Testing TKN and TN helps show nitrogen levels (US EPA n.d.; DeBarry 2004). Knowing these levels helps track eut rophication. Sarasota County uses the EPA’s 351.2 method for testing TKN and the EPA’s 353.2 method for testing NOx.

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Total phosphorus is a measure of the different for ms of phosphorus in a sample, including orthophosphate, condensed phospha te and organic phosphate. Orthophosphate is the term used to describe a singl e phosphate molecule. Water samples being tested for orthophosphate are not fil tered and include both the dissolved and suspended orthophosphate (US EPA 2012 ). Testing these forms of phosphorus are helpful in indicating eutrophication Sarasota County uses EPA 365.3 for total phosphorus and orthophosphate. Methodology Impervious surfaces were calculated for all of Sa rasota County, Florida. Three types of impervious surfaces were digitized w ithin the 464,000-acre county: pavement, parking lots and rooftops (Table 1)(Figur es 13 and 14). Pavement included roads, but not including driveways. The pavement la yer was digitized by taking a previously developed roads layer and checking the s hape and location all of these roads in Sarasota County (11,042.30 acres total). Parking lots were created by selecting all of the commercially zoned property ar eas from Sarasota County’s property zones layer. The parking lots were confirm ed and redrawn based on their correspondence with satellite images (5010.82 acres total). The rooftop layer was developed by a group contracted by Sarasota County to map all the rooftops (2.51 acres).

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Table 1Different layers and their sources, the ye ar they represent, their description and how they were developed "" #$ % & '())$*$* )+,+!"+, $* ""$ '!$" !% ./-"" 0 1+ % & '())$*$* )+,+!"+, $* rr 2 3"$+"$' !4+*#"+ 55 45* $6+-"' 3 $6+ 7 $"" + % "+-"+ 4/"+ 4+++ "$*04/++!' "$ 3/$" $6+ %" /$"% 8+"' ""6+ *9""-'/$" !'-++ +!4'+* :$6+ 9-"" % %+$+" -" / % & ) /'!$ !-"!+ -+ ./-#%3:! +"

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Figure 13Impervious surfaces in Sarasota County.

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n Figure 14Impervious surfaces in north Sarasota Co unty.

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Links of waterflow were taken from the Interconnec ted Channel and Pond Routing Model (ICPR) stormwater modeling system. IC PR storm water models help solve flooding problems by showing the network of i nterconnected storm water ponds. The components of ICPR models include basins nodes and links. Nodes represent ponds, channels, streams, rivers and junc tions in pipe systems. Links represent pipes, channel segments, weirs and bridge s. Using two different methods water quality test sit e areas were developed. The first technique aimed to represent the possible sto rmwater runoff routes prior to the water quality test site by selecting sub-basins con nected by upstream water bodies and links of water flow (Table 2 and Figure 15). Th ese links show water flow from and to nodes. These selections were made into their own layers us ing the “Create Selection as New Layer” tool in GIS. A new field wa s created in each new layer for the area (acres). Using the field calculator, the a rea in acres was calculated in each new field. The thirty-three layers were merged into one layer, using the “merge” tool. The dissolve tool was used to break the areas divid ed by sub-basins into one solid area for each test site. The second method developed for assigning the wate r quality test site area aimed to have a more uniform size and shape. These test sites were used to reduce the possible error in having different size test areas as in the “upstream” test sites. In order to create the uniform water quality test site area, the “buffer” tool was used to create a circle surrounding each test site. These c ircles were developed to have one-

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mile radius. A new field was added in the attribute table and the areas were calculated using the “calculate geometry” tool. Table 2Locations of water quality test site by lo ngitude and latitude. nr r 9 2*r *n 92 2*r %9. 2*n %9.2 2*r %9.2 2*n *r %%2 2* %%2 2*rr %; 2*r %;2 2* *r %32 2*r *r %32 2* *n %32 2*rn *n < 2* *r 7;:2 2* 7;:2 2*r *r &;.2 2* n*r &;.2 2*r n*r 9.2 2*rn *rrr 9.2 2* *n =2 2* =2 2*n *n =2 2*n >9.2 2*n >9.2 2* ;: 2*r *r :1027 2*rr *n :102 2*n :102; 2* *n ;=2 2*n *rn 0 2 2* *nr

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r Figure 15 Different water quality areas used to a pproximate impervious surface percentage

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The completed water quality test site area and the impervious surfaces area were combined in a union. This produces a layer com posed of the different impervious surface polygons cut in the shape of the test site areas (Figure 15). A new field was created and the area of these new test si te polygons was calculated. The areas of the total water quality test site and the impervious surfaces within the test sites were exported into an Excel file. Percentage of impervious area was determined by dividing the area of impervious surface within e ach test site by the total area of each test site. A correlation-regression analysis was performed in SAS. The correlations were between the impervious surface percentage and the water quality variable values for each test site in 2008.

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Chapter 3Results Water Quality Areas developed upstream within test site sub-basins Impervious surface percentages were determined for the 33 water quality test areas by dividing the total impervious areas and the water quality areas (Table 3). The lowest percentage of imperviou s surface area was 0% in test site CPS-3 and the highest was 18% in test sit e WOD-2 (Table 4) (Figure 16). The average percentage within the test sites w as 7.8%. Different impervious surface percentages are represented for each test site area in Figure 16.

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Table 3Areas of total impervious area and water q uality test area 9 r5rn5rr 55 92 r55n 55r %9. 55nr r5r5 %9.2 5 55n %9.2 rr5n 55 %%2 5n5rn 55rr %%2 r5 5r5r %;2 n5n 5n5 %32 55 5nr5n %32 55r n55r %32 5rr5r 5nr5 < 5n5n 55 7;:2 55r n55 7;:2 5 5n5nr &;.2 55 r55 &;.2 5n5r n55n 9.2 55rr 5n5rr 9.2 55 55 =2 5r5 n5r5r =2 5r5r 5nrr5r =2 55 5r5 >9.2 5n5r n55 >9.2 5r5 r5r5 ;: 5nnr5r 55n :12;,3 21 5nr5r 5rr5n5 ;=2 n55n 55n 0 2 55r 5n5n 0 2 55r 55 0; 5nr5r 5n5r 0;2 5n5nn r55

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Table 4Percentage of impervious surface within ea ch upstream water quality test site areas. The lowest percentage of impervious sur face area was 0% in test site CPS3 and the highest was 18% in test site WOD-2. n 9 ? 92 ? %9. ? %9.2 ? %9.2 ? %%2 ? %%2 ? %;2 ? %32 ? %32 ? %32 ? < n? 7;:2 ? 7;:2 ? &;.2 ? &;.2 ? 9.2 ? 9.2 ? =2 ? =2 ? =2 ? >9.2 r? >9.2 ? ;: ? :12;,3 21 ? ;=2 ? 0 2 ? 0 2 ? 0; ? 0;2 ?

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Figure 16Impervious Surface percentages within wa ter quality test sites. Yellow has the least percen tage impervious and red has the highest percentage imper vious.

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The Pearson r correlation coefficients for the 2008 and 2011 water quality variables compared with the impervious surfaces per centages are found in Tables 5 and 6. In 2008 the NOx and color values showed sign ificant correlation with impervious surface percentages (NOx: r-value = 0.43 44, probability of H0=0.0185; color: r-value = -0.41726, probability of H0=0.0243) (Figures 17 and 18). In 2011 there were no significant correlations found betwee n the water quality variables and the impervious surface percentages. Table 5Pearson r correlation coefficient values f or variables of water quality and impervious area percentage in 2008 within upstream water quality area sites. Asterisk indicates significant r-value. Variable (2008) r-Value Probability of H 0 Biological Oxygen Demand 0.15561 0.4202 Color* -0.41726 0.0243 NH 4 -0.19425 0.3126 NO x 0.43437 0.0185 Orthophosphate 0.13494 0.4852 Total Kjeldahl Nitrogen -0.06394 0.7418 Total Nitrogen 0.00370 0.9848 Total Phosphorus 0.13614 0.4813 Total Suspended Solids 0.12011 0.5348 Turbidity 0.19835 0.3023 Table 6Pearson r correlation coefficient values f or variables of water quality and impervious area percentage in 2011 within upstream water quality test sites. Variable (2011) r-Value Probability of H 0 Biological Oxygen Demand 0.07375 0.7038 Color -0.29720 0.1174 NH 4 -0.13196 0.4950 NO x 0.24435 0.2014 Orthophosphate 0.07160 0.7121 Total Kjeldahl Nitrogen -0.13917 0.4715 Total Nitrogen -0.00118 0.9951 Total Phosphorus 0.08321 0.6678 Total Suspended Solids -0.08547 0.6593 Turbidity -0.09253 0.6331

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n Figure 17Pearson r correlation between 2008 color and percent of impervious surface in the upstream site area (r-value = -0.41726, probability of H0=0.0243)

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Figure 18Pearson r correlation between 2008 NOx and percent of impervious surface in the upstream site area (r-value = -0.43437, probability of H0=0.0185).

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Analysis between impervious surface percentage and water quality variables Regression analysis was used to test the potential relationship between impervious surface percentage and the different wat er quality variables (Tables 4 & 5). In 2008 two variables had linear relationships in the regression analysis. Color (Pearson’s r = -0.41726; probability of H0= 0.0243) and NOx (Pearson’s r = 0.43437 probability of H0= 0.0185) had linear relationships. These results i ndicate a correlative relationship between impervious surface percentage and color or NOx. Biological oxygen demand, NH4, orthophosphate, total Kjeldahl nitrogen, total nitrogen, total phosphorus, total suspended solids and turbidity had non-linear relationships. In 2011 no correlation relationships were found when a regression analysis was performed. All relationships were nonlinear. Water Quality Areas with one-mile radius circles ar ound test sites Impervious surface percentages were determined for the 33 water quality test areas by dividing the total impervious areas and th e water quality areas (Table 7). The lowest percentage of impervious surface area was 0% in test site CPS-3 and the highest was 42% in test site HUD-2 (Table 7) (Figur e 19). The average percentage within the test sites was 23.3%. Different impervio us surface percentages are represented for each test site area in Figure 19.

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r Table 7Percentage of impervious surface within ea ch one-mile radius circle water quality test site areas. n 9 ? 92 ? %9. ? %9.2 ? %9.2 ? %%2 ? %%2 ? %; ? %;2 ? %32 ? %32 r? %32 ? < ? 7;:2 ? 7;:2 ? &;.2 ? &;.2 ? 9.2 ? 9.2 ? =2 ? =2 ? =2 ? >9.2 ? >9.2 ? ;: ? :1027 r? :102 ? :102; n? ;=2 ? 0 2 ? 0 2 ? 0 2 ? 0; ? 0;2 ?

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Figure 19Impervious Surface percentages within wa ter quality test site areas with a 1-mile radius fr om the test site. Yellow has the least percentage impervious and red has the highest percentage impervious.

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The Pearson r correlation coefficients for the 2008 and 2011 water quality variables compared with the impervious surfaces per centages are found in Tables 8 and 9. In 2008 the BOD, color, TKN, and TN values s howed significant correlation with impervious surface percentages (BOD: r-value = -0. 41636, probability of H0=0. 0159; color: r-value = -0. 66431, probability of H0 <0.001; TKN: r-value = -0.51409, probability of H0= 0.0022; TN: r-value = -0.47108, probability of H0= 0.0057) (Table 8) (Figures 20-23). These results indicate that BOD color, TKN and TN had negative linear relationships with impervious surface percen tage in 2008. Non-linear relationships were observed between impervious surf ace percentage and NH4, NOx, orthophosphate, TP, TSS, and turbidity. In 2011 color, TKN and TN showed significant correl ations with impervious surface percentages (color: r-value = -0.60141, pro bability of H0 =0.002; TKN: rvalue = -0.57412, probability of H0= 0.0004; TN: r-value = -0.53196, probability of H0= 0.0012) (Table 9) (Figures 24-26). This indicatin g that color, TKN, and TN had a negative linear relationship with impervious surfac e percentage in 2011. Nonlinear relationships were observed between impervious surf ace and BOD, NH4, NOx, orthophosphate, TP, TSS, and turbidity.

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Table 8 Pearson r correlation coefficient values for variables of water quality and impervious area percentage within a one-mile radius of the test site in 2008. Asterisk indicates significant r-value. Variable (2008) r-Value Probability of H 0 Biological Oxygen Demand* -0.41636 0.0159 Color* -0.66431 <0.001 NH 4 -0.27560 0.1206 NO x 0.32045 0.0690 Orthophosphate -0.17015 0.3438 Total Kjeldahl Nitrogen* -0.51409 0.0022 Total Nitrogen* -0.47108 0.0057 Total Phosphorus -0.15014 0.4043 Total Suspended Solids -0.17385 0.3333 Turbidity 0.11061 0.5400 Table 9Pearson r correlation coefficient values f or variables of water quality and impervious area percentage within a one-mile radius of the test site in 2011. Variable (2011) r-Value Probability of H 0 Biological Oxygen Demand -0.20611 0.2422 Color* -0.60141 0.0002 NH 4 -0.22503 0.2007 NO x 0.24316 0.1658 Orthophosphate -0.17327 0.3271 Total Kjeldahl Nitrogen* -0.57412 0.0004 Total Nitrogen* -0.53196 0.0012 Total Phosphorus -0.10766 0.5445 Total Suspended Solids -0.32714 0.0590 Turbidity -0.14960 0.3984

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Figure 20Pearson r correlation between 2008 BOD a nd percent of impervious surface in the one-mile te st site area ( r-value = -0. 41636, probability of H0=0.0159) Percent 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 BOD5_MGL 0.500.751.001.251.501.752.002.252.502.753.003.25

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Figure 21Pearson r correlation between 2008 TKN a nd percent of impervious surface in the one-mile te st site area (r-value = 0.51409, probability of H0= 0.0022) Percent 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 TKN_MGL 0.40.60.81.01.21.41.61.8

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Figure 22Pearson r correlation between 2008 color and percent of impervious surface in the one-mile test site area (r-value = -0.66431, probability of H0 <0.001). Percent 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 COLOR_PCU 5060708090100110120130140150160170

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n Figure 23 Pearson r correlation between 2008 TN and percent o f impervious surface in the one-mile test site area (r-value = -0.47108, probability of H0= 0.0057) Percent 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 TN_MGL 0.40.60.81.01.21.41.61.8

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Figure 24Pearson r correlation between 2011 color and percent of impervious surface in the one-mile test site area (r-value = 0.60141, probability of H0 =0.002). PERCENT 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 COLOR_PCU 30405060708090100110120130140150

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Figure 25Pearson r correlation between 2011 TN an d percent of impervious surface in the one-mile tes t site area (r-value = -0.53196, probability of H0= 0.0012). PERCENT 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 TN_MGL 0.60.70.80.91.01.11.21.31.41.51.61.71.8

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r Figure 26Pearson r correlation between 2011 TKN a nd percent of impervious surface in the one-mile te st site area (r-value = 0.57412, probability of H0= 0.0004). PERCENT 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 TKN_MGL 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8

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n Chapter 4Discussion Impervious Surfaces as an Indicator of Increased Nu trients This study showed that there was no positive corre lative relationship between nutrient loads and increased impervious surfaces. M ost eutrophication indicators (NH4, TKN, TN, TP, and OP) did not show positive statis tical relationships as hypothesized. In 2008, upstream water quality tests showed positive relationships between NOx and impervious surface percentages. Relationships between NOx and impervious surface percentages were unexpected, considering that many experts have show n increased runoff with increased impervious surfaces(Arnold & Gibbons 2007 ; DeBarry 2004; Lee et. al 2012; Paul et al. 2008; Shuster et al. 2005; Walsh et. al 2005 ). Lee et. al (2012) did not find a positive correlation between impervious surface percentages and NO3 -, while NO2 did have a strongly significant positive correlati on with impervious surface percentages. A comparison of results betwee n the Lee et. al (2012) study and this study was difficult because the Lee et al (2012) study separated the NO2 and NO3 while this study combined them. In addition, Nagy et. al (2012) observed higher concentrations of NO3 in areas with higher impervious surface percentage s. Furthermore, in previous studies, indicators of eu trophication have increased with increased impervious surface percentages (Arno ld & Gibbons 2007; DeBarry 2004; Lee et. al 2012; Paul et al. 2008; Shuster et al. 2005; Walsh et. al 2005 ). Runoff increases the amount of nutrients in the wat er because it picks up phosphorus and nitrogen from residential lawns and agricultur al lands (Gerritse 1995, La Velle

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n 1975, Wernick et al 1998, Bennet 1999). Increases in TN and TP have bee n observed with increased levels of impervious surfaces in pre vious studies (Griffin et. al 1980 May et al. 1997). May et al (1997) observed TP having a positive linear relati onship with impervious surface percentage. Lee et. al (2012) observed a positive TP, TN, NH3 and PO4 3to have significant positive correlations with imp ervious surface percentage. Nagy et. al (2012) observed a higher positive correlation betwe en impervious surface percentages and TP but a negativ e correlation with total dissolved nitrogen (TDN). In this study, the significant positive correlatio n between NOx and impervious surface percentages in the upstream test site area (2008) could indicate increased pollutants from fertilizers containing potassium ni trate and ammonium nitrate (US EPA n.d.). Yet in this study, results were inconclu sive due to the insignificant correlation between NOx and impervious surface percentage in the one-mile test sites (2008 and 2011) and the upstream test site area (20 11). Forested wetlands in tropical and subtropical clim ates are defined as functional nitrogen and phosphorus sinks (Elder 198 5). Increased impervious surfaces are located in lands with more urbanized areas and reduced forested wetlands. Thus, results were expected to have higher eutrophication indicator levels correlated with higher impervious surface percentages. The largest source of nutrient loads in Sarasota B ay is stormwater runoff (Tomasko et. al 2005). No correlation existed between the eutrophic ation indicators and percent of impervious surface because of a redu ction in nutrient loads. From 1988

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n to 2010 nitrogen loads decreased 64% (Sarasota Bay Estuary Program 2010) because a fertilizer ordinance was implemented on February 8th, 2008, setting maximum nitrogen and phosphorus levels and banning fertiliz er application during the wetseason, June 1September 30 (Whittle 2007). Fewer nutrients from fertilizers reduces levels of nutrients in stormwater runoff, s o effects of impervious surfaces are harder to detect. Impervious Surfaces as an Indicator of Increased Se diment In this study impervious surfaces were not shown t o be an indicator of increased sediment. The indicators of increased sed imentation (TSS and turbidity) were not positively correlated with impervious surf ace percentages. These results were unexpected because previous stu dies show that indicators of increased sedimentation increase with increased impervious surface percentages (Arnold & Gibbons 1996; Leopold 1968; Paul et al. 2008). In areas with higher levels of impervious surface, which are often urbanized, t here are higher rates of construction sites. Construction projects often inv olve disrupting soil and removing the plants that hold the soil in place (Arnold & Gi bbons 1996; Jaber 2004; Leopold 1968; Roesner 2001). The amount of sediment found in urban catchments has been observed to be 102-104 times higher than in forested catchments (Paul et al. 2008). May et. al (1997) observed a positive correlation between imp ervious surface percentages and TSS. Nagy et. al (2012) observed that watersheds with impervious surface percentages of 5%-10% or >10% had significa ntly higher levels of TSS.

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n Typically, increased sediments will increase levels of TSS and turbidity; however, these indicators had no significant correlations in this study. In 2007, the majority of jobs lost in Sarasota Cou nty were in the construction sector, indicating that construction was at an all time low during this time (Scruggs & Associates LLC 2009). Low economic activity has con tinued from 2007-2011. This decrease in construction could have decreased avail able sediment in urbanized areas which had higher rates of impervious surfaces. Dete cting significant positive correlations between impervious surface percentages and indicators of increased sediment is more difficult given the decreases in c onstruction which reduce sedimentation. Decreases in sedimentation could be responsible fo r the increase in sea grasses. Higher levels of sediment in water reduce the turbidity. Light limitation has been identified as the largest factor in loss of se agrasses in Sarasota Bay (Mote Marine Laboratory 1995). The Sarasota Bay Estuary p rogram (2010) found that seagrass coverage increased from 7% to 11% during 1 988-1994. Thus, correlations between impervious surface percentages and sediment indicators may not have been observed because of decreases in sedimentation. Fur ther studies are necessary to understand this relationship. Impervious Surfaces as an Indicator of Color Impervious surfaces were an indicator for color fo r the upstream areas in 2008 and both the 2008 and 2011 one-mile radius test sit es. A negative correlation was observed between impervious surface percentages and color.

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n Negative correlation between color and impervious surface percentages were expected because color increases with increased lev els of humic and fuvic acids (American Public Health Association et al. 2005). Higher levels of humic and fuvic acids result from higher amounts of decomposing org anic matter. Bodies of water surrounded by wetland forested area are expected to have higher organic matter from decaying leaves, branches, and tree trunks which ru ns off into the water. Areas with higher impervious surface percentages are more urba nized and have less trees, reducing the amount of organic matter entering the water. Impervious Surfaces as an Indicator of Biological O xygen Demand This study did not show impervious surfaces to be an indicator of increased biological oxygen demand. BOD showed no significant correlation for the upstream water quality test sites in 2008 and 2011 and for t he one-mile radius for 2011 values. The 2008 one-mile radius showed significant negativ e correlation. Previous research showed positive correlation with BOD and impervious surface percentages (Keefer et al. 1979; Klein 1979; Lee et. al 2012; Paul et al. 2008). Lee et. al (2012) observed a significant positive correlati on between BOD and impervious surface percentage. A greater than avera ge BOD was found in over 40% of 104 urban streams (Keefer et al. 1979). Positive correlation with BOD was expected because increased impervious surfaces redu ce forested lands. With less forest, there will be less decaying organic matter leaching into the water (Keefer et al. 1979; Paul et al. 2008). In response to increased organic matter, mi crobial populations increase, reducing the level of BOD (De Barry 2004).

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n Another reason these results were surprising is th e significant negative correlation between color and impervious surface pe rcentage in the upstream water quality test sites in 2008 and the one-mile radius test sites of 2008 and 2011. Color decreased as impervious surfaces increased. This ob served decrease is attributed to the decreases in humic and fuvic acids resulting fr om lack of decomposing organic matter in the water. Thus, one would expect that if color had a significant negative correlation with impervious surface percentage, BOD would also have a significant positive correlation with impervious surface percen tage. Constraints and Future Studies Water quality test sites were chosen based on Sara sota County’s budget and the ease of access. Future studies should select te st sites with more variance in the amount of surrounding impervious surface areas in o rder to distinguish between the test site’s impervious surface percentages. Potenti ally the studies could obtain more significant and meaningful results. Additionally, creation of impervious surface maps was tedious, limiting the scope of the study. Future studies with more time c ould map multiple years of impervious surface and compare water quality test s ites. Even if impervious surface maps had been created from maps from 20 or 30 years ago, these data could easily be skewed by the increased awareness about nonpoint po llution and point source pollution today. Thus, the best option would be to update the current impervious surface map and compare results to current maps. Re sults would be more reliable because the same test site could be compared to its elf during the multiple years instead of comparing the different test sites again st each other as in this study.

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nn Future studies should consider using the more unif orm method of creating the water quality test site areas. The “upstream” metho d had too much variability among the test site sizes with a 2,906,950,113-acre diffe rence between the largest and the smallest test sites. The larger test site areas end ed up skewing some of the data because there was very little impervious surface su rrounding the test site. Thus, the one-mile radius test sites had much higher rates of impervious surface percentage and produced more significant results. The amount of rainfall reaching each test site is important in this study. Rain is required in order to have stormwater runoff and thus pollution. In 2008, the average rainfall was 42.46 inches of rainfall and in 2011 t he average rainfall was 84.62 inches of rain, a 50.17% difference. The lowest of rainfal l at any test sites was 30.0 inches of rain in 2008 and 60.0 inches of rain in 2011. While there is no concern around the lack of rainfall for these test sites, there is cer tainly a disparity between rainfall in 2008 and 2011. Future comparative studies that use multiple years of impervious surface data against multiple years of water qualit y data will control for effects of differences in rainfall levels. This study mapped only roads, parking lots, and ro oftops. Further studies could evaluate the effects of sidewalks, courtyards and compact soils. Field studies could calculate the average surface cover of sidewa lks, courtyards and compact soils to add to the impervious surfaces map. This could i ncrease the accuracy of the impervious surfaces coverage.

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n Chapter 5Conclusion The results of this study did not show impervious surfaces as an indicator of water quality. Although most of the variables showe d the opposite of expected or neutral results, color was decreased as impervious surfaces increased. These results can be attributed to decreases in fuvic and humic a cids, which are produced from organic matter (American Public Health Association et al. 2005). Lesser organic matter is expected in an area with higher imperviou s surface percentage because there is less forest cover and more pavement cover. Unexp ected results may be explained by efforts made in Sarasota County to reduce nutrie nt loads and the decrease in construction from 2007 to current day (2012) (Saras ota Bay Estuary Program 2010; Scruggs & Associates LLC 2009). Runoff contains nitrogen and phosphorus from agric ultural and residential fertilizer applications (Gerritse et al. 1995; Wernick et al. 1998). Sediment increases in urbanized areas because of soil disruption durin g construction (Arnold & Gibbons 1996; Jaber 2004; Leopold 1968; Roesner 2001). The health of Sarasota’s freshwater and marine ecosystems is dependent on the reduction of pollutants in runoff. Eutrophication of waterways leads to algal blooms that can lead to rapid decreases in dissolved oxygen (Jaber 2004; Jrvenp & Lindstrom 2004; Wang et al. 1999). Algal blooms and increased sediment increase the turbidity in the water, decreasing the amount of light reaching the seagras ses on the shallow sea floor. Sea grasses are crucial to supporting the survival of j uvenile fish and invertebrate species (Jackson 2001; Wang et al. 1999).

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n Continued research on impervious surfaces is impor tant for improved water quality. Environmental research will provide develo pers with strategies to increase mitigation practices that will decrease runoff poll ution and their effects. Additionally, increased impervious surface research can help crea te new mitigation practices. As urbanization continues to increase, this research w ill become more necessary to protect ecosystems.

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