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12-2012 Sediment Based Turbidity and Bacteria Reduction Analysis in Simulated Construction Site Runoff James W. Berry III Clemson University
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SEDIMENT BASED TURBIDITY AND BACTERIA REDUCTION ANALYSIS IN SIMULATED CONSTRUCTION SITE RUNOFF
A Thesis Presented to the Graduate School of Clemson University
In Partial Fulfillment of the Requirements for the Degree Master of Science Biosystems Engineering
by James W. Berry, III December 2012
Accepted by: Dr. Calvin B. Sawyer, Committee Chair Dr. Charles V. Privette, III Dr. John C. Hayes
ABSTRACT
Large construction projects are highly vulnerable to sediment removal by erosive forces. Turbidity, resulting from this excess sediment, has gained recognition as an indicator of pollution in surface runoff from construction activities. The Environmental
Protection Agency (EPA) is currently moving toward regulations that would establish a nationwide maximum turbidity effluent limit discharged from construction sites.
Research has shown that current sediment control best management practices (BMPs) are often ineffective at controlling elevated turbidity levels in construction site discharges.
Additionally, research confirms that sediment basins may act as reservoirs for bacteria, specifically Escherichia coli (E. coli). During rainfall events, resuspension within sediment basins creates outflows containing highly turbid runoff with elevated bacterial densities.
The focus of this research is to maximize turbidity and total suspended solids
(TSS) reduction using passive polyacrylamide (PAM) applications in conjunction with excelsior sediment tube deployment, as well as to determine whether E. coli densities can be reduced using such PAM application and a sediment tube configuration aimed at reducing turbidity.
First, four different treatments were derived to evaluate PAM applications including; (i) a control with no PAM; (ii) granular PAM sprinkled in 100-g doses directly on each of five sediment tubes applied each time before five simulated runoff events; (iii) granular PAM sprinkled in 100-g doses directly on each of five sediment tubes applied only once before five simulated runoff events; (iv) granular PAM held in permeable bags
ii applied with 500-g doses. Additionally, the effect on PAM caused by dry weather after runoff events was observed.
Overall, results indicate that PAM application can be effective for turbidity reduction. Under experimental test conditions, sediment tubes without PAM application provided no observed reduction in turbidity or TSS. Sprinkled PAM applications were more effective at turbidity and TSS reductions than the permeable PAM bag over five simulated runoff events. Reapplication of granular PAM to sediment tubes after periods of dry weather and before simulated runoff events showed consistent reduction of turbidity below the proposed EPA 280 NTU effluent limit. Finally, turbidity reductions correspond to a reduction in TSS concentration based on a strong coefficient of determination value (R2 = 0.89) between turbidity and TSS.
Secondly, research also focused on whether E. coli densities can be reduced using
PAM application and a sediment tube configuration aimed at lowering turbidity. Based on prior research, reductions in turbidity and suspended sediment were maximized by applying 100 g of granular polyacrylamide (PAM) directly to each of five sediment tubes before the beginning of five simulated runoff events. PAM application successfully reduced mean turbidity and TSS by 96% and 92%, respectively, and had a mean turbidity of 80 NTU and TSS of 174 mg/L at discharge. Two E. coli densities were used as the starting concentration to determine whether E. coli density had an effect on reductions.
For the low E. coli density range (5,000–10,000 MPN/100 mL), PAM application failed to create a reduction in bacterial density, but rather an increase in E. coli was observed with an average discharge of 25,226 MPN/100 mL. Within the high E. coli density range
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(100,000–200,000 MPN/ 100mL), a 29% reduction was recorded with an average discharge of 135,270 MPN/100 mL. F-test results revealed that despite a decrease, bacterial densities across sampled position were not statistically different (F-stat= 1.5956, p = 0.2097, n = 40). Thus, PAM application caused substantial reductions in turbidity and TSS, but failed to create a corresponding reduction in E. coli density.
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DEDICATION
This thesis is dedicated to my parents, Jim and Beth Berry, who have always provided endless support, encouragement, and love; all necessary for me to achieve my dreams and aspirations. Teaching by example, they are excellent role models and have instilled countless vital characteristics which have shaped me into the person I am today.
I will be forever grateful for the opportunities they have given me. I would also like to dedicate this work to Marie, for her help as a field technician and laboratory assistant, but most of all for her love and support. Over the past five years, she’s been a constant motivator and encourager during rough times and always willing to lend a helping hand.
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ACKNOWLEDGEMENTS
I would like to thank my thesis advisor, Dr. Calvin Sawyer, for the opportunity and funding to complete a research assistantship. Additionally, Dr. Sawyer, as well as my committee members, Dr. Charles Privette and Dr. John Hayes, provided their time, support, and guidance to steer me towards successful project completion. Dr. William
Bridges, Jr. was a tremendous help with the statistical analysis portion of this project, for which I am extremely grateful.
I also want to express my sincere appreciation to several individuals for their contributions to this research project. Director of Research Farm Services, Garland
Veasey, generously gave permission to use a portion of LaMaster Dairy Center pasture for project site construction and research activities. Sam Shirley and Will Smoke with
Crop and Equipment Services helped to construct the testing channel and provided equipment and assistance when needed. Alan Estes played a pivotal role in acquiring space to store excelsior sediment tubes and kaolinite, while providing assistance when needed. Hunter Massey and Jake Fravel provided research shop assistance during fabrication of critical project components. Lance Beecher provided lab assistance and equipment during the analysis portion of the project. Finally, I would like to thank fellow graduate student Jacob Burkey for his help with sample collection and analysis during the final portion of this project.
Lastly, I would like to recognize several individuals for their willingness to donate products critical to this research project. Product Technical Manager Ernie Heins, with
Agru America, was vital in acquiring a 50 mil HDPE liner used for channel stabilization.
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Bill Blackmore, Kaolin Operations Manager in Langley, SC with Imerys North America
Ceramics, donated 3 tons of Paragon® (trade name for Kaolinite). Applied Polymer
Systems, Inc. provided several large quantities of their 700 Series Silt Stop
Polyacrylamide Erosion Control Powder® for experimental optimization and research.
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TABLE OF CONTENTS
Page
TITLE PAGE ...... i
ABSTRACT ...... ii
DEDICATION ...... v
ACKNOWLEDGEMENTS ...... vi
LIST OF TABLES ...... x
LIST OF FIGURES ...... xi
CHAPTER ...... 1
1. INTRODUCTION ...... 1
2. LITERATURE REVIEW ...... 4 A. Construction Site Erosion ...... 4 B. Turbidity as a Pollutant ...... 6 C. Polyacrylamide ...... 8 D. Temporary Erosion Control Devices ...... 10 E. Bacteria Laden Construction Site Discharge ...... 13 F. Bacterial Effluent Removal Research ...... 15
3. OPTIMIZATION OF SEDIMENT TUBE PLACEMENT AND PASSIVE POLYMER APPLICATION FOR TURBIDITY REDUCTION ...... 18 A. Abstract ...... 18 B. Introduction ...... 20 C. Procedures ...... 23 D. Results and Discussion ...... 37 E. Conclusions ...... 74
viii
Table of Contents (Continued) Page
4. EVALUATION OF PASSIVE POLYMER TREATMENT FOR TURBIDITY CORRESPONDS TO REDUCTION IN BACTERIAL DENSITY ...... 78 A. Abstract ...... 78 B. Introduction ...... 80 C. Procedures ...... 82 D. Results and Discussion ...... 99 E. Conclusions ...... 106
5. SUMMARY CONCLUSIONS ...... 109
APPENDICES ...... 112 A. Tabular turbidity and TSS data collected for turbidity reduction analysis...... 113 B. Tabular turbidity, TSS, E.coli density data collected for bacterial reduction analysis ...... 134
LITERATURE CITED ...... 147
ix
LIST OF TABLES
Table Page
3.1 Particle size distribution for kaolinite ...... 28
3.2 Mean turbidity for all sample locations within Treatment 2...... 48
3.3 Mean turbidity for all sample locations within Treatment 3...... 55
3.4 Mean turbidity for all sample locations within Treatment 3...... 62
4.1 Particle size distribution for kaolinite ...... 87
x
LIST OF FIGURES
Figure Page
3.1 Channel design ...... 23
3.2 Channel design schematic ...... 24
3.3 Collapsible tank setup ...... 25
3.4 Tank storage volume compared to water height ...... 26
3.5 Tank discharge flow rate over time ...... 27
3.6 Sediment resuspension devices ...... 29
3.7 Sediment tube deployment ...... 31
3.8 Polymer optimization ...... 32
3.9 PAM sprinkle application ...... 34
3.10 Permeable PAM bag application ...... 35
3.11 Turbidity and TSS box and whisker plots...... 38
3.12 Line fit plot TSS versus turbidity...... 39
3.13 Mean turbidity across runs for Treatment 1 ...... 40
3.14 Mean turbidity across sample locations for Treatment 1 ...... 41
3.15 Mean turbidity across sample locations for each run within Treatment 1 ...... 42
3.16 Cumulative percent reduction of turbidity for Treatment 1 ...... 43
3.17 Mean TSS concentration for all runs within Treatment 1 ...... 43
3.18 Mean TSS concentration across all sample locations for Treatment 1 ...... 44
xi
List of Figures (Continued)
Figure Page
3.19 TSS concentration across sample locations for all runs within Treatment 1 ...... 45
3.20 Cumulative percent reduction TSS across sample locations for Treatment 1 ...... 45
3.21 Mean turbidity across runs for Treatment 2 ...... 47
3.22 Mean turbidity across sample locations for Treatment 2 ...... 48
3.23 Mean turbidity across sample locations for each run within Treatment 2 ...... 49
3.24 Cumulative percent reduction of turbidity for Treatment 2 ...... 50
3.25 Mean TSS concentration across runs for Treatment 2 ...... 51
3.26 Mean TSS concentration for all sample locations for Treatment 2 ...... 52
3.27 TSS concentration across sample locations for all runs within Treatment 2 ...... 52
3.28 Cumulative TSS percent reduction for Treatment 2 ...... 53
3.29 Mean turbidity across runs for Treatment 3 ...... 54
3.30 Mean turbidity across sample locations for Treatment 3 ...... 55
3.31 Mean turbidity across sample locations for each run within Treatment 3 ...... 56
3.32 Cumulative percent reduction of turbidity for Treatment 3 ...... 57
3.33 Mean TSS concentration across all runs within Treatment 3 ...... 58
3.34 Mean TSS concentration across all sample locations for Treatment 3 ...... 59
xii
List of Figures (Continued)
Figure Page
3.35 TSS concentration across sample locations for each run within Treatment 3 ...... 59
3.36 Cumulative percent reduction TSS across sample locations for Treatment 3 ...... 60
3.37 Mean turbidity across runs for Treatment 4 ...... 61
3.38 Mean turbidity across sample locations for Treatment 4 ...... 62
3.39 Mean turbidity across sample locations for all runs in Treatment 4 ...... 63
3.40 Cumulative percent reduction of turbidity for Treatment 4 ...... 64
3.41 Mean TSS concentration across runs for Treatment 4 ...... 65
3.42 Mean TSS concentration across all sample locations for Treatment 4 ...... 66
3.43 Mean TSS concentration for all runs within Treatment 4 ...... 66
3.44 Comparing mean turbidity across sample locations for each treatment ...... 67
3.45 Comparing turbidity percent reduction for each treatment ...... 68
3.46 Turbidity 6th run comparison to previous runs for Treatment 2 ...... 69
3.47 Turbidity 6th run comparison to previous runs for Treatment 3 ...... 70
3.48 Turbidity 6th run comparison to previous runs for Treatment 4 ...... 71
3.49 Mean turbidity across sample locations for run 6 in Treatment 2, 3, and 4 ...... 72
4.1 Channel design ...... 82
xiii
List of Figures (Continued)
Figure Page
4.2 Channel design schematic ...... 83
4.3 Collapsible tank setup ...... 84
4.4 Tank storage volume compared to water height ...... 85
4.5 Tank discharge flow rate over time ...... 86
4.6 Sediment resuspension devices ...... 88
4.7 Sediment tube deployment ...... 90
4.8 Polymer optimization ...... 91
4.9 Bacteria collection ...... 93
4.10 Bacteria land application...... 94
4.11 Mean turbidity across sample locations for each run within Treatment 1 ...... 97
4.12 Mean turbidity across sample locations for each run within Treatment 2 ...... 98
4.13 Mean turbidity comparison of Treatment 1 and 2 ...... 98
4.14 Mean TSS across sample locations for each run within Treatment 1 ...... 99
4.15 Mean TSS across sample locations for each run within Treatment 2 ...... 100
4.16 Mean TSS comparison of Treatment 1 and 2 ...... 100
4.17 Mean MPN/100mL (low E. coli density range) for each treatment ...... 102
4.18 Mean MPN/100mL (high E. coli density range) for each treatment ...... 102
xiv
List of Figures (Continued)
Figure Page
4.19 Mean turbidity for the 6th run after settling for 24 hours ...... 104
4.20 Mean MPN/100mL (low density range) after settling for 24 hours ...... 104
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CHAPTER ONE
INTRODUCTION
Erosion of soils due to urban development, timber harvesting, and agricultural practices is a significant issue and topic of concern on a global scale. In 1992, soil losses from cropland due to sheet and rill erosion were projected at 1.2 billion tons annually in the United States (SCS, 1994). Estimations predict 80 million tons of sediment, from all sources, is deposited annually in US lakes, rivers, and other major waterways (Harbor,
1999). Construction sites related to urban development may pose the greatest risk for sediment removal by erosive forces on a per acre basis. Runoff carrying high volumes of suspended material transports inorganic and organic solids as well as any adsorbed chemical and biological pollutants downstream (Zhang and Lulla, 2006). Physical, biological and chemical alterations in water bodies often result after inflow of sediment- laden runoff. Elevated levels of suspended sediment introduced into surrounding water bodies from urban construction can result in both environmental and economic impacts
(Clark, 1985).
To contain sediment on site, sediment basins have been a conventional and approved method for many years. South Carolina Department of Health and
Environmental Control (SC DHEC) requires that sediment basins, for sites over five acres, retain at least 80 percent of entering sediment or achieve 0.5mL/L peak settleable concentration (SC DHEC 2003). Fecal coliform is the leading cause of impairment in waterbodies across South Carolina, which could be compounded by bacterial loading from sediment basins. Recent research also reveals that sediment basins may be acting as
1 a reservoir for pathogenic bacteria (Tempel, 2011; Sawyer, 2009; Zhang and Lulla,
2006). These studies found bacteria concentrations within sediment basins that did not meet USEPA recommended levels for contact recreation upon discharge. Sediment basins with high turbidity discharge corresponded to elevated concentrations of bacteria sampled in discharge waters (Tempel, 2011).
Currently, turbidity measurements have gained recognition as a potential regulated indicator of pollution associated with sediment-laden discharge from construction activities. Highly turbid water results from intense light scattering by fine particles with diameters smaller than 0.05 mm (Davies-Colley and Smith, 2001).
Sediment basins often provide inadequate settling times for small silt and clay sized sediment particles. Furthermore, these microscopic particles are easily resuspended during rainfall events and discharged off site. The US Environmental Protection Agency
(EPA) is presently developing new regulations that may establish a nationwide numeric turbidity effluent limit, (currently proposed at 280 NTU), to measure construction site discharge (EPA, 2010).
Research has shown that common structural sediment retention devices may be unable to reduce turbidity below the proposed EPA 280 NTU effluent limit under certain circumstances (Line and White, 2001; Haan et al., 1994; Wu et al., 1996).
Polyacrylamide (PAM) dosing systems have demonstrated effectiveness in reducing suspended sediment and turbidity through flocculation. However, knowledge of PAM usage in South Carolina for suspended sediment and turbidity control is limited. Since prior research has determined that bacteria preferentially associate with clay particles,
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PAM may serve dual function to reduce turbidity as well as bacterial density in sediment- laden runoff before impacts to surface waters occur.
The goal of this project was to evaluate passive polyacrylamide (PAM) applications in conjunction with excelsior sediment tube deployment to optimize turbidity reduction. Secondly, to determine whether E. coli densities can be reduced using PAM application and a sediment tube configuration aimed at reducing turbidity. Ultimately, this research seeks to provide information that can lead to recommendations for PAM usage on South Carolina construction sites. To achieve these goals, two objectives were established and are listed below:
1. Compare turbidity and TSS reductions using three different PAM applications
with excelsior sediment tubes. Explore the effects on turbidity in simulated
runoff events after PAM applications had become desiccated.
2. Determine whether decreases in turbidity and TSS, induced by PAM
application, correlates to a reduction in E. coli densities.
This research was aimed at further exploration of PAM usage for turbidity and sediment control, as well as potentially reducing offsite impacts to surface waters from construction sites.
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CHAPTER 2
LITERATURE REVIEW
Construction Site Erosion
Erosion occurs when soil is detached by water, wind, or ice. Ultimately, detached sediment will be transported via runoff and deposited downstream, in a process known as sedimentation (Johns, 1998). Geologic erosion occurs over time in nature, but construction practices can cause accelerated erosion and greatly increase the amount of soil removed from a site (Schwab et al., 1996).
Estimations project 80 million tons per year of sediment, from all sources, is deposited in US lakes, rivers, and other major waterways (Harbor, 1999). Construction sites introduce sediment loads, on a per acre basis, into surrounding waterbodies 2000 times more than forested lands and 10 to 20 times more than agricultural lands (USEPA,
2000; Owen, 1975). Construction sites related to urban development may pose the greatest risk for soil removal by erosional forces per acre. During construction, the land is often cleared of all trees and debris, which leaves the soil bare and exposed. After land clearing, initial site construction consists of drainage excavation and stormwater infrastructure to ensure the newly developed area will not flood during heavy rain events.
As a result, runoff is concentrated at single discharge points, which increases runoff volume collected and alters the frequency and rate of discharge. Without proper controls in place at construction sites, the United States General Accounting Office suggests that sediment loads can reach 35 to 45 tons per acre per year (USGAO, 1998). Accelerated
4 erosion from urban construction projects results in both environmental and economic impacts across a global scale.
Effects of accelerated erosion are numerous and due to detached particles that become suspended in surface waters. Erosion can transport sediment, nutrients, and other pollutants into waterbodies. Excessive sedimentation may be considered the most important factor limiting fish habitat (Judy et al., 1984). Suspended sediments have been found to damage the gills of salmonids and macroinvertebrates (Bozek and Young, 1994;
Newcombe and MacDonald, 1991). Algae growth, stimulated by excessive nutrient input, can deplete dissolved oxygen concentrations and create fish kills (EPA, 2003).
Increased sediment input into drinking water reservoirs creates high turbidities that interfere with chlorination treatment and increase treatment costs (AWWA, 1990; Le
Chevallier et al., 1981). Changes in ecosystems can lead to declines in both the recreational and commercial fishing industry (Clark, 1985). High turbidity in lakes and rivers can create a loss in aesthetic value which reduces tourism and impacts local economies. Increased sedimentation diminishes storage capacities of reservoirs (Bilotta and Brazier, 2008) and can increase the need for dredging operations to maintain navigable waters (Clark, 1985). The economic impact of accelerated erosion from urban activities has been estimated at $3.2 billion to $13 billion (Clark, 1985). Comprehensive financial impacts due to soil erosion from all causes have been estimated at $400 billion worldwide (Pimentel et al., 1995).
For urban growth and community development to occur, construction activities must take place, and thus accelerated erosion will always be an issue. To minimize
5 offsite impacts and comply with regulations, sedimentation and erosion control best management practices (BMPs) must be used effectively. Between 1982 and 1992, estimated erosion from croplands decreased 30% due to improved management practices and conservation efforts (SCS, 1994). It is estimated that deployment of turbidity control technologies and BMPs on construction sites can reduce the amount of sediment discharged by 2 million tons each year (SCDHEC, 2010).
Turbidity as a Pollutant
In general terms, turbidity refers to the cloudiness of water. Nephelometric turbidity is an index of light-scattering by suspended particles in water and can be used to quantify water clarity (Davies-Colley and Smith, 2001). Waters with high concentrations of fine suspended sediment are classified as turbid and described by having low visual clarity. According to Mitchell (2000), cloudiness of water is mostly controlled by fine sediment particles with diameters less than 0.05mm that creates intense light scattering.
Turbidity measurements quantify the optical impact on water quality by measuring light attenuation, the reduction of light transmission through water (Davies-Colley and Smith,
2001).
As discussed in the previous section, suspended sediment can create a range of environmental problems to waterbodies and aquatic life. Light attenuation caused by high turbidity levels has been documented to negatively impact aquatic biota. Turbidity specific impacts include the reduction of light penetration into water limiting productivity for photosynthetic organisms (Kirk, 1994) and reduced visual range for organisms
6 requiring sight (Vogel and Beauchamp, 1999). Impacts from elevated turbidity are not necessarily negative and can provide fish cover from predators (Gregory 1993).
One study conducted on active construction sites in North Carolina revealed that conventional sediment traps have a trapping efficiency ranging from 59% to 69%; however, turbidity fluctuates from 100 to 15,000 NTU at discharge, due to inability to retain clay and silt size particles (Line and White, 2001). These findings are consistent with earlier research that found standard sediment control structures are inadequate to retain suspended clay and silt particles, due to the low settling velocities (Haan et al.,
1994; Wu et al., 1996). In another study comparing multiple types of sediment basins, water quality is highest for the standing pool and surface skimmer designs; however, all designs discharge a maximum turbidity and TSS during peak flows (McCaleb and
McLaughlin, 2008). Although trapping efficiency can be relatively high, research suggests that conventional sediment control structures on construction sites are not sufficient to reduce elevated turbidity levels to desired levels.
Total suspended solids (TSS) have been measured to evaluate proper functioning of sediment control structures. Research shows these control structures readily retain sands and larger silt particles, whereas fine silt-sized and clay-sized particles that disproportionately affect turbidity are transferred off-site. As a result, turbidity has become a regulated pollutant in discharge from construction sites due to corresponding negative environmental impacts. Turbidity effluent guidelines were also selected based on the ability to easily measure and achieve instantaneous results. Additionally, high turbidity in waterbodies is generally what is first noticed by the public and is not thought
7 of as being aesthetically pleasing. In 2009, EPA released proposed regulations stating that discharges from construction sites disturbing 20 acres or more must comply with a numerical effluent limit of 280 NTU, beginning August 2011(EPA, 2009). The same effluent limit would apply to areas disturbing 10 acres or more by February 2014 (EPA,
2009). Due to industry outcry and potential lawsuits over possible errors in calculating the numeric effluent limit, EPA revealed that it improperly interpreted data and is currently revising the 280 NTU numeric effluent limitation (EPA, 2010).
Turbidity and suspended sediment are transported via both point and nonpoint sources. From a regulatory standpoint, construction sites are considered point sources; whereas turbidity and sediment discharged from forestry practices and agricultural lands are considered nonpoint sources. South Carolina has established a water quality standard for turbidity in which waters with more than 25 percent of samples greater than 50 NTU, collected over a five year period, are considered impaired waterbodies and listed for turbidity on South Carolina’s 303(d) list (SCDHEC, 2004). Of the 1106 impaired waterbodies on the 2010 303d list, 53 are impaired by turbidity (SCDHEC, 2010).
Polyacrylamide
Polyacrylamide (PAM) is a water-soluble synthetic polymer that has long been used in water treatment applications to induce flocculation. In general, flocculants cause aggregation of fine particles suspended in liquid to form flocs or larger particles, which more readily settle out of suspension (Ives, 77). PAM is available in several forms
(block, powder, emulsion) that can be used in a variety of applications to create
8 flocculation. In addition, PAM can be manufactured with various chain lengths and to have anionic, nonionic, or cationic charge, depending on the intended application. For erosion control and environmental applications, anionic PAM is more widely used due to its low aquatic toxicity (Sojka et al., 2007). The majority of previous research on polyacrylamide has been centered on its use as a soil conditioner in agricultural applications. Currently, focus has shifted to the use of PAM for erosion prevention and turbidity control.
North Carolina is currently promoting and regulating the use of chemical flocculants, such as PAM, for erosion and sediment control on active construction sites, specifically to aid in removal of fine suspended sediment within sediment basins.
Regulations specify that permittees can only use chemicals that are listed on the North
Carolina Division of Water Quality Approved PAMS/Flocculants List as well as suggesting a maximum recommended concentration (NCDENR, 2011). To abide by regulations, stormwater treated with chemical flocculants or polymers must be routed through sediment basins and/or settling devices to ensure sufficient removal of flocculated material prior to discharge to surface waters (NCDENR, 2011).
Previous research shows PAM can be effective in reducing soil erosion when applied to irrigation water in agricultural settings. A study completed by the USDA
Research Service experimented with PAM applications to furrow irrigation and reduced sediment in runoff by 94% (Lentz et al, 2002). In the same study, Lentz et al. (2002) also found substantial reductions (85%-99%) of sediment, phosphorous, and nitrogen losses in runoff. When applied to soil surfaces, PAM can increase infiltration rates, soil aggregate
9 stability, and decrease Mean soil loss when compared to untreated soil (Zhang and
Miller, 1996; Shainberg et al., 1990).
Research has shown the effectiveness of using PAM as a temporary soil stabilizer for erosion control on construction sites. On moderate slopes, PAM applied directly to soil reduces turbidity by 70%, sediment by 94%, and runoff by 51% when compared to results obtain with bare soil (Hayes et al., 2005). Common temporary ground covers
(mulch and hydromulch/seed) combined with PAM are more effective in reducing sediment transport. A PAM and mulch combination applied to bare soil decreased sediment loss by 93% (Roa-Espinosa, 1999), and PAM hydromulch treatment reduced turbidity by 94-99% (Tobiason et al., 2000).
Bhardwaj and McLaughlin (2008) demonstrated a 66-88% reduction in turbidity using both passive treatment (using a PAM block) and active pumping treatment (using a
PAM emulsion injection) to treat simulated construction runoff in settling basins. For construction site sediment control, PAM has been shown to be effective in reducing turbidity and other pollutants when combined with other BMPs. Research demonstrating
PAM’s effectiveness when combined with temporary sedimentation BMPs will be presented later in this paper.
Temporary Erosion Control Devices
To minimize soil loss from construction sites, both erosion prevention and sedimentation BMPs can be implemented. Erosion BMPs attempt to limit soil
10 detachment from the soil mass, whereas sedimentation BMPs trap sediment after erosion has already occurred.
To prevent erosion of soil after initial site clearing and grading, BMPs such as hydroseeding are used for temporary and permanent site stabilization. However, until vegetation can mature to stabilize the area, soil is still vulnerable to erosion. In some cases, temporary site stabilization is not applicable due to construction activity, and sedimentation BMPs are needed to limit offsite impacts from erosion. Typical BMPs found on construction sites include silt fences, sediment basins, rock check dams, and temporary erosion control devices. These products function as sediment retention devices by reducing flow velocity and allowing gravitational settling.
In the last decade, temporary erosion control devices have become widely accepted alternatives to several common structural erosion and sedimentation BMPs.
Sediment tubes, wattles, tubes, and compost socks are all examples of temporary erosion control devices that consist of compacted natural fibers encased in tubular netting.
Sediment tubes are available in various diameters depending on application, and allow water to flow through or over the fiber matrix while retaining sediment. These products are used for slope interruption, act as check dams in areas of concentrated flow, inlet protection, and construction site perimeter sediment control. For this research, temporary erosion control devices acting as ditch checks will be referred to as sediment tubes which is the same naming convention used by SC Department of Transportation (SCDOT) and
SC Department of Health and Environmental Control (SCDHEC) (SCDOT, 2011;
SCDHEC, 2005). There are three main types of natural sediment tubes that are becoming
11 widely accepted as sediment devices. Excelsior sediment tubes are made of excelsior fibers, wood slivers typically cut from aspen, poplar, and spruce. Coir sediment tubes are constructed from the shredded husk fibers of coconut. Lastly, straw sediment tubes are made of basic straw materials. These devices are less expensive than standard channel
BMPs, require less man-power for installation (unlike rock check dams), and are commonly deployed on linear projects, such as highway construction, where space is limited (McLaughlin et al., 2009).
In agricultural settings, compost filter socks, filled with composted bark and wood chips, contribute to a 49% reduction in suspended sediment concentration when applied to surface runoff from tilled corn field in grassed waterways (Shipitalo et al., 2010). A study comparing sediment control barriers for perimeter control on construction sites, found that compost socks consistently outperform mulch filter berms and straw bales in reducing suspended sediments (Faucette et al., 2009). When compared to conventional rock check dams, fiber check dams perform better in turbidity reduction and total suspended solids removal (McLaughlin, 2009). In order to quantify the effectiveness of current sediment detention devices, a side-by-side investigation compared fiber filtration tubes, straw/coconut fiber rolls, compost sock, straw wattles, and excelsior fiber rolls.
Fiber filtration tubes (containing wood and man-made fibers) perform best with 98% sediment reduction and 300 NTU turbidity, whereas other devices have sediment retention of 76-65% and average turbidity of 4500 – 7500 NTU (Theisen and Spittle,
2005). Theisen and Spittle (2005) utilized fiber filtration tubes that contain embedded
PAM at manufacturing, thus this product performs best when compared to sediment tubes
12 without PAM application. Research shows that rolled erosion control products effectively reduce suspended sediment and turbidity. In order to meet current and future turbidity effluent regulations, polymers must be incorporated.
Current research shows PAM application when combined with BMPs in construction site runoff can be essential in achieving turbidity limits within state and federal effluent limits. Several different PAM dosing methods have been explored to begin to determine how PAM can be most effectively used to reduce turbidity. Research suggests that compost filter socks significantly reduce turbidity when compared to bare soil, and addition of polymer to compost filter socks significantly reduces turbidity relative to compost filter socks without polymer (Faucette et al., 2009). A study compared multiple ditch checks in series, with and without PAM application, to find that
PAM application reduces turbidity by 61-93% when compared to untreated ditch checks
(McLaughlin and McCaleb, 2010). In the same study, excelsior sediment tubes perform better than rock ditch checks and rock ditch checks wrapped with excelsior blanket in reducing turbidity when treated or untreated with PAM (McLaughlin and McCaleb,
2010). On a roadway project in the North Carolina mountains, McLaughlin et al. (2009), found an 86% reduction in mean turbidity levels when PAM was applied to sediment tubes.
Bacteria Laden Construction Site Discharge
Public safety and prevention of water-borne illness are major factors in regulating bacteria levels in waterbodies. To maintain water quality standards, indicator bacteria,
13 such as Escherichia coli (E. coli), are measured to determine whether fecal contamination exists in water bodies. According to the 2010 Section 303(d) list of impaired waterbodies, fecal coliform represent the highest number of listed impairments at 357 in
South Carolina (SCDHEC, 2010). Elevated bacteria levels have recreational and economic impacts on communities, such as swimming advisories at beaches (SCDHEC,
2011) and shellfish bed harvest restrictions (SCDHEC, 2005).
Current research shows that increased sedimentation resulting from construction activities can contribute to microbial contamination in surrounding waterbodies. A significant portion of bacteria is associated with soil particles, thus runoff laden with suspended sediment serves as a secondary source of increased E. coli concentrations to receiving waterbodies (Sawyer, 2009; Wu et al., 2009; Jamieson et al., 2005).
Specifically, construction site sediment basins reduce a large percentage of suspended sediment from entering receiving waterbodies; however, current research shows the construction site sediment basins may contribute to the overall bacteria loading of lakes, rivers, and receiving waters.
Two research studies investigated several sediment basins in upstate South
Carolina and found these systems act as reservoirs for indicator bacteria. Sawyer (2009) found mean water column bacteria concentrations significantly higher than EPA’s recommended contact recreational water quality standards at all sediment basins tested.
Similarly, Tempel (2011) observed outflow E. coli densities higher than inflow densities in sediment basins. Additionally, E. coli levels in the water column decrease faster than
E. coli levels in sediment (Tempel, 2011). Results seemed to confirm existing research
14 that E. coli cells are more closely associated with smaller sediment particles, and deposited sediment within basins provides protection for E. coli cells (Burton et al.,
1987). Tempel (2011) reported that the highest levels of E. coli are associated with soil particles less than 0.004 mm, and the greatest density of E. coli occurs in the top 2.54 cm of sediment. These findings further support that E. coli adhere to the silt and clay fraction of soil.
Turbidity and bacteria concentration are interconnected when compared to sediment basin discharge. Silt and clay fractions influence turbidity greatly and are easily resuspended during a rainfall event. Consequently, high turbidity in the outflow caused by resuspension results in high bacteria concentrations at the outlet (Tempel 2011). The resulting discharge may exceed the recommended US EPA limit of 126 cfu/100 mL for a
5-sample geometric mean, as well as the single 235 cfu/100 mL grab sample. In order to effectively reduce bacteria concentrations discharged from construction site sediment basins, trapping efficiency should increase to include fine silt-sized and clay-sized particles.
Bacterial Effluent Removal Research
The removal of pollutants, such as bacteria, from water bodies has long been a topic relating to water and wastewater treatment processes. Due to the increase in impaired waterbodies from fecal coliform, emphasis is now focusing on removal of bacteria from stormwater.
15
Current research shows that a limited number of structural stormwater BMPs can successfully reduce fecal coliform levels in stormwater discharge. A study comparing retention ponds, detention basins, bioswales, and media filter in various states across the
US found that bioswales and detention ponds have low effectiveness in reducing bacteria levels, whereas retention basins and media filters show signs of bacterial removal (Clary et al., 2008). However, Clary et al. (2008) summarized that of the BMPs observed, none can reliably reduce bacteria in discharge. Similarly, a study in Charlotte, NC monitored nine stormwater BMPs, including one wet retention pond, two storm-water wetlands, two dry detention basins, and one bioretention area, to evaluate reductions in bacterial densities. Findings revealed that wetlands and bioretention cells reduce E. coli concentrations by greater than 50% and had effluent concentrations lower than the U.S.
EPA standard for E. coli (Hathaway et al., 2009). Davies and Bavor (2000) also found that constructed wetlands outperform wet retention ponds in terms of bacterial removal.
A study in San Francisco, CA, diverted 0.44 million L of stormwater into a natural riparian buffer before entering an adjacent lake to find lake levels of E. coli significantly lower than levels in the discharged stormwater (Casteel et al., 2005). Findings above support conclusions that BMPs effective in reducing bacteria numbers have good ambient sunlight exposure and filter stormwater through infiltration.
Construction sites pose a larger problem when dealing with bacteria removal, due to high loading rates of sediment. Because of plant life that thrive in bioretention areas and stormwater wetlands, these BMPs are not designed to handle large quantities of sediment and are usually constructed to treat post-construction stormwater. Current
16 research has focused on use of rolled erosion control products to filter and promote settling of sediment-associated bacteria particles. In a recent study, compost filter socks were found to reduce total coliforms and E. coli by 74% and 75%, respectively (Faucette et al., 2009).
In order to reduce bacteria levels, studies have explored the use of flocculants in simulated runoff. Recently, reductions in bacteria numbers from irrigation pond water were reported using three flow rates (7.5, 15.5, 22.5 L/min) in irrigation furrows where
PAM was applied directly to the soil in the first 1.0 m (Sojka and Entry, 2000). In addition, a 90% reduction in microorganisms has been observed when PAM is applied to soil where cattle, fish, and swine wastewater runoff occurs above PAM application (Entry and Sojka, 2000). A study simulating stormwater pollutant removal found filter socks combined with polymer treatment (BactoLoxx®) increase removal efficiencies of E. coli to 89 – 99% (Faucette et al., 2009). Additional research is required to determine whether bacterial reductions in construction site runoff under ‘real-world’ flow conditions are achievable with polymer application.
17
CHAPTER THREE
OPTIMIZATION OF SEDIMENT TUBE CONFIGURATION AND PASSIVE POLYMER APPLICATION FOR TURBIDITY REDUCTION
ABSTRACT
Large construction projects pose a great risk for sediment removal by erosional forces which can have significant impacts on the surrounding aquatic environment.
Turbidity, resulting from suspended fine silts and clays, has gained recognition as an indicator of sediment associated pollution in surface runoff from construction activities.
The Environmental Protection Agency (EPA) is currently moving toward regulations that would establish a nationwide maximum turbidity effluent limit discharged from construction sites. Research has shown that current sediment control best management practices (BMPs), specifically sediment basins are ineffective at controlling and reducing construction site discharges with elevated turbidity levels (Line and White, 2001; Haan et al., 1994; Wu et al., 1996).
This described research was aimed at maximizing turbidity and total suspended solids (TSS) reduction using several different passive polyacrylamide (PAM) applications in conjunction with excelsior sediment tube deployment. Four different treatments were derived to test PAM applications, including (i) a control with no PAM;
(ii) granular PAM sprinkled in 100-g doses directly on each of five sediment tubes applied each time before five simulated runoff events; (iii) granular PAM sprinkled in
100-g doses directly on each of five sediment tubes applied only once before five simulated runoff events; (iv) granular PAM held in a permeable bag applied with 500-g
18 doses. Additionally, the effect on PAM desiccation caused by dry weather after storm events was observed.
Results provide evidence to suggest that PAM application can be an effective practice for turbidity reduction in channels. Sediment tubes without PAM application provided no reduction in turbidity (F-stat = 0.0588, p = 0.9975, n = 60). PAM sprinkled was greatly more effective at turbidity and TSS reductions than the permeable PAM bag over five simulated runoff events. Mean turbidity, over five simulated runoff events, was
202 NTU, below the proposed EPA 280 NTU limit, using three sediment tubes when
PAM was sprinkled before each of five simulated runoff events. In contrast, PAM sprinkled once before five simulated runoff events required five sediment tubes reduced turbidity below the proposed 280 NTU limit, mean turbidity was 61 NTU. Reapplication of granular PAM to sediment tubes after periods of dry weather and before runoff events will consistently reduce turbidity below the proposed EPA 280 NTU effluent limit.
Finally, turbidity reductions correspond to a reduction in TSS concentration based on a strong coefficient of determination (R2 = 0.89) between turbidity and TSS.
This research provides considerable evidence that highly turbid, sediment-laden construction site runoff can be remediated using granular PAM application and sediment tube deployment. Results show that the proposed EPA 280 NTU effluent limit may be attainable through such a practice.
19
INTRODUCTION
Accelerated erosion due to construction activities accounts for a large percentage of sediment transported to surrounding water bodies each year. Construction sites have the potential to discharge excessive suspended solids in runoff, which can cause vast amounts of damage to surrounding ecosystems (Haan et al., 1994). Runoff carrying high volumes of suspended material transports inorganic and organic solids as well as any adsorbed chemical and biological pollutants downstream (Zhang and Lulla, 2006).
Physical, biological and chemical alterations in water bodies result often after input of elevated sediment-laden runoff. Since suspended sediment discharge is such an environmental impact, federal and state agencies have aimed at regulating suspended solids in construction site runoff discharge.
Regulations require sediment control structures on South Carolina construction sites to be design to retain 80% of total suspended solids (TSS) concentration before discharge. TSS is comprised of inorganic solids, sand, silt, clay sediment particles, and organic solids, algae and detritus. Laboratory analysis computes TSS by dry weight of suspended solids per unit volume of water, and TSS is usually reported in milligrams of solids per liter of water (mg/L). Much of the controversy with TSS regulations is based on design versus actual performance standards of sediment control structures.
Theoretically, sediment control structures are designed to trap a certain percentage of suspended sediment; however, trapping efficiency is difficult and costly to measure, thus federal, state, and local entities lack the resources to adequately ensure proper functioning of all sediment control structures. Additionally, TSS requires laboratory analysis and
20 cannot be easily or quickly determined in the field, which creates difficulties in detecting discharge permit violations.
Turbidity measurements are gaining recognition as a regulated indicator of pollution associated with sediment-laden discharge from construction activities.
Turbidity is an optical indication of water clarity. Highly turbid water results from intense light scattering by fine particles with diameters smaller than 0.05 mm (Davies-
Colley and Smith, 2001). Turbidity measurements are relatively easily obtained, timely, and an accurate estimation of finely sized soil particles transported in runoff. New regulations, developed by the US EPA, set a numeric effluent limit for turbidity to measure construction site discharge; however, due to discrepancies in data interruption, the numeric limit is currently under review (EPA, 2010).
Research shows common structural sediment retention devices, such as sediment basins, can meet performance specs (trapping efficiency) over time, but, may still discharge elevated turbidity levels (Line and White, 2001; Haan et al., 1994; Wu et al.,
1996). Sediment basins provide inadequate settling times for small silt and clay sized sediment particles; furthermore, these particles are easily resuspended during rain events and discharged off site. Temporary erosion control devices and polyacrylamide (PAM) dosing have demonstrated effectiveness in reducing suspended sediment and turbidity
(McLaughlin and McCaleb, 2010; McLaughlin et al., 2009; Faucette et al., 2009).
RECPs and PAM, discussed above, could be used on linear construction projects where sediment basins may not be applicable or used in conjunction with sediment basins to improve trapping efficiency and turbidity reduction.
21
This research focused on optimizing sediment tube configuration with passive
PAM application for turbidity reduction. Research was aimed at answering questions related to effectively administering PAM, as well as determining how dry weather after- runoff events affect PAM and turbidity reductions. Ultimately, the goal of this research was to provide recommendations on PAM application method, application frequency, and sediment tube configuration to achieve highest turbidity reduction.
22
PROCEDURES
Experimental Site
To replicate conditions found on a typical construction site, a 185 ft triangular channel, 12 ft wide with an average depth of 1.65 ft, at a 7% slope was constructed and lined with a 50 mil HDPE liner (Figure 3.1). In order to have correct spacing between five sediment tubes (reasoning for the selected spacing will be explained later in this section), 150 ft of channel length was needed, but a fairly steep slope at the upper portion of the channel would have resulted in a non-uniform slope between sediment tubes. A
185-ft channel allowed the sediment tubes to be positioned after the transition from a steep slope to a more uniform slope. The channel was lined to prevent scouring and erosion, which would add to the total sediment load during experimentation and compromise results.
Figure 3.1 – Channel Design. On left, upstream view of channel from bottom. On right, downstream view of channel from tank outlet during experimentation.
23
Figure 3.2 –Channel design schematic.
Since the goal of this research involved simulating construction site runoff, it was important to acquire a flow rate that was representative of flow rates found on South
Carolina construction sites. To determine a typical flow rate, 1-year, 24-hour rainfall events were averaged for Greenville, Richland, and Charleston Counties. The average 1- year, 24-hour rainfall amount was 3.4 in. A peak flow rate of 2.5 cfs was calculated for a
24 newly graded 1 acre site at a 2% slope comprised of A (50%) and B (50%) hydrologic soil groups. In order to achieve a representative flow rate, a 4,800 gallon collapsible tank was chosen to simulate runoff from construction sites. The tank was filled with water from an adjacent pond using a 5-hp semi-trash pump. The tank had a 6-in outlet controlled by a 6-in gate valve (Figure 3.3) that drained the tank in 12 minutes.
Figure 3.3 – Collapsible Tank Setup. On left, tank completely full with 6inch valve visible. On right, tank emptied.
To accurately compare previous and current research, it was important to calibrate the tank’s discharge flow rate. Given the tank’s odd shape and configuration, it was inaccurate to calculate volume in the tank based on the height of water. A TM300
Turbine Flowmeter (GP1, 2012) was used to measure flow into the tank, while height of water in the tank was recorded manually using a depth gauge. A graph (Figure 3.4) was developed using the correlation between height of water to the volume during four consecutive tank fill-ups. Then, the height of water in the tank was recorded as the tank drained four consecutive times in one minute intervals. The linear equation developed in
25
Figure 3.4 was used to correlate height of water in the tank to loss of volume. Using that data, the tank discharge flow rate was calculated over the 12 minute interval. Figure 3.5 shows how discharge from the tank mimics a partial hydrograph. The peak flow rate discharged from the tank was 1.91 cfs, and the average flow rate over 12 minutes was
0.72 cfs.
5000
4500 y = 1101.3x 4000 R² = 0.987
3500
3000 2500
2000 Volume [gals] 1500 1000 500 0 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 Water Depth [ft]
Figure 3.4. Tank storage volume compared to water height.
26
2.5
2
1.5 R² = 0.9899
1 Flow Flow Rate[cfs]
0.5
0 0 2 4 6 8 10 12 14 Time [min]
Figure 3.5. Tank discharge flow rate over time.
Although the experimental peak flow rate of the tank did not reach the target peak flow of
2.5 cfs, the experimental peak flow is still a good representation.
A homogenous sediment-water solution was needed to mimic runoff from a construction site. To achieve these conditions, kaolinite clay was chosen to be the test soil. Kaolinite is naturally occurring clay that is easily suspended in water and represents the silt/clay fraction that would be found in a South Carolina Cecil soil. For this research project, Paragon®, a trade name for kaolinite clay used by IMERYS Minerals Company, was acquired from the Langley, SC mine.
27
Table 3.1. Particle Size Distribution for Paragon (IMERYS Minerals, 2012).
PARTICLE SIZE Median (microns) 1.1 +325 Mesh (% retained) 0.3 PERCENT PASSING % < 20 (microns) 98 % < 10 (microns) 94 % < 5 (microns) 84 % < 2 (microns) 65 % < 1 (microns) 52 % < 0.5 (microns) 36 % < 0.2 (microns) 14
An 11-hp pump with a flow rate of 335 gallons per minute was used to recirculate the mixture keeping kaolinite clay suspended in the 4,800 gallon tank. A nozzle configuration was developed to increase velocity and keep particles suspended. Each nozzle produced an average velocity of 17 ft/s, which was determined mathematically using the known flow rate of the 11-hp pump and area of the 1-in nozzles. Below, Figure
3.6 shows the 3-in discharge PVC pipe reduced to 8, 1-in nozzles.
28
Figure 3.6. Sediment Resuspension Devices. On left, nozzle configuration. On right, recirculation pump.
The configuration of the recirculation nozzles created “dead zones” along the bottom of the tank where mixing would not occur well. Additionally, the recirculation pump was turned off between 8 – 9 minutes after the valve was first opened because the water level in the tank allowed the recirculation pump to begin pulling in air. For each run, target turbidity in the tank was between 1,600 and 2,000 NTU and measured by an Analite
NEP160 display with NEP260 probe handheld turbidity meter, with a range of 0-3,000
NTU (McVan Instruments, 2012). Based on the reading, the amount of Kaolinite added to the tank varied from 50 lbs to 100 lbs in an attempt to produce similar tank turbidity readings.
For this research, 20-in diameter, 10-ft American Excelsior Curlex® Sediment
Logs® were selected (American Excelsior Company, 2012). Selection of sediment tubes was governed by price and recommended products on the SCDOT Qualified Product List
57. Based on the length of the channel, five sediment tubes were used in series.
29
Following SCDOT specifications, the spacing requirement for sediment tubes acting as ditch checks on a greater than 6% slope is 25 ft (SCDOT, 2011). Following SCDOT guidelines, for this research sediment tubes were placed at 25-ft spacing (Figure 3.1).
Due to the lined channel, normal installation of the sediment tubes could not be performed. To anchor the tubes, tee posts were bent 90 degrees with a 10-in over hang and driven into the ground so that only approximately 14 in remained (Figure 3.7). Tubes were then compacted under the tee posts to ensure under cutting of the tube would not occur. SCDOT specifications dictate that for in-field installation sediment tubes should be trenched to a depth that is 20% of the sediment tube diameter (SCDOT, 2011). Thus, the effective ponding depth for this research versus SCDOT specifications is very close.
Compaction of sediment tubes may have retarded flow and increased trapping efficiency more than compared to SCDOT specifications requiring trenching in sediment tubes.
Effects of compaction were thought to be of less importance than to ensure flow was not under cutting sediment tubes. Collars in front provide structural stability and helped the tubes keep their shape. Sand bags were used to anchor the tube ends down at the top of the channel.
30
Figure 3.7 – Sediment tube deployment. On left, upstream side of sediment tube. On right, downstream side of sediment tube.
Six ISCO 3700 samplers were programed to sample the entire simulated runoff event (Teledyne ISCO, 2012). Liquid detectors activated samplers and sampling continued over 4 min time intervals. Sampling stopped when liquid detectors were inhibited. Sampling probes were placed directly at the outlet of the tank and on the downstream side of each sediment tube.
Polymer Optimization
Applied Polymer Systems, Inc. 700 Series Silt Stop Polyacrylamide Erosion
Control Powder was chosen to be flocculating agent for this project (APS, 2012). The
700 series is a polyacrylamide co-polymer powder that is tailored to be soil specific. To determine the correct polymer to use with the kaolinite, a series of laboratory scale jar tests were performed (Figure 3.8). Six polymer types within the 700 series were tested,
31 which include 705, 707, 712, 730, 740, and 745. To test each polymer, manufacture instructions for testing APS powders were followed (APS, 2012).
Figure 3.8 – Polymer Optimization. Individual jar tests determined which APS polymer worked best with kaolinite.
Jars were observed for clarity of water, largest particulate formed, and the time it took for particles to settle. Jar tests showed that kaolinite responded best to APS #705 polymer.
Polymer Tests
To test if PAM application affected turbidity, four tests varied the application of APS
#705 polymer, while keeping all other parameters constant. For this experiment, a simulated runoff event consisting of 4,800 gallons of water and complete draining of the tank will be referred to as a run. A test consisted of five separate runs aimed at determining the longevity of PAM completed within 24 hrs. All tests were duplicated for statistical accuracy. In many cases, a 6th run was added to each of the tests and completed several days after the previous run. The waiting period between the last run was to allow the tubes and PAM to dry in order to examine effects on turbidity. After
32 each test, used tubes were discarded, the channel was cleaned, and excess sediment accumulation in the tank was removed. Descriptions of the three PAM applications and control experiment are described below:
1. No application of #705 PAM served as the control and only looked at interactions
between sediment-laden water and sediment tubes. The control will be able to
summarize whether only sediment tubes have any effect on turbidity.
2. 100 g of granular #705 PAM sprinkled directly on each of the five sediment tubes
and reapplied each time before five simulated runoff events (Figure 3.9).
3. 100 g of #705 PAM sprinkled directly on each of the five sediment tubes applied
only once before five simulated runoff events (Figure 3.9).
4. The fourth test applied 500 g of #705 PAM in a 6” x 26” smooth weave 400
micron permeable bag. A bag was placed on the upstream side of each sediment
tube. Thus, the bag for tube one was placed at the outlet of the tank and the bag
for tube two was placed on the downstream side of tube one, etc. Bags remained
in place throughout a test (Figure 3.10).
33
Figure 3.9 – PAM Sprinkle Application. On top, granular PAM sprinkling on sediment tubes. On bottom, looking downstream at target area for PAM sprinkling application displayed in hatched area; PAM applied mostly on upstream face.
34
Figure 3.10 – Permeable PAM Bag Application. On left, PAM bag deployment at tank outlet. On right, bag attachment to downstream side of sediment tubes.
Sample Analysis
A Hach 2100AN Laboratory Turbidimeter (Hach, 2012) was used to measure all turbidity readings from samples following Standard Method 2130 B (APHA, 2005). The
Hach 2100AN allowed for the maximum turbidity range (0 – 10,000 NTU) without having to perform dilutions, which helped to ensure accuracy of all readings. The accuracy of the turbidity meter is as follows (Hach, 2012):
±2% of reading plus 0.01 NTU from 0-1000 NTU ±5% of reading from 1000 NTU to 4,000 NTU ±10% of reading from 4,000 NTU to 10,000 NTU
Samples were agitated by hand for five seconds before transferring the sample in the meter vials to ensure aggregates were evenly dispersed. Vials were then gently inverted
35
10 times just before being placed into the turbidity meter. Calibrations were completed using the StablCal Sealed Vial Standard provided by Hach.
In addition to turbidity, selected samples were analyzed for TSS using Standard
Method 2540 B (APHA, 2005). Typically, the first and last samples collected for a given sample location were analyzed for TSS. If the first or last sample lacked adequate volume, another sample was chosen to gather TSS data.
Statistical Analysis
Tests performed to compare mean turbidity and TSS of runs and sample positions include, regression analysis, analysis of variance, and t-tests. The statistical significance tests used an alpha value of ≤ 0.05 unless otherwise stated. Statistical calculations were performed with JMP statistics software (SAS Institute Inc., Cary, NC, USA).
36
RESULTS AND DISCUSSION
To quantify the performance of each PAM treatment, mean turbidity, mean TSS, and percent reduction for both turbidity and TSS, were evaluated. By comparing all measured turbidity and corresponding TSS values (n = 434) in a box and whisker plot
(Figure 3.11), outliers in the data were determined. The majority of outlier values were tank outlet samples (samples retrieved from sample location L0) and can most likely be attributed to accumulation of settled clay particles inside the tank outlet during tank agitation before sampling had begun. This accumulated sediment was likely immediately flushed from the tank when the valve was opened and created a spike in several initially sampled values. These turbidity and TSS outlier values were not homogenously mixed within the system and are not representative of actual, well-mixed samples. As a result, outlier values, which were shown to be statistically extreme, were removed from the overall data set.
37
n = 434 n = 434
Turbidity (NTU) Turbidity Total Suspended Solids Suspended Total (TSS) [mg/L]
Figure 3.11 – Turbidity and TSS box and whisker plots.
After outliers (n = 16) were removed from the data set, a regression analysis was performed to better understand the relationship between turbidity and TSS (Figure 3.12).
As expected, turbidity was strongly correlated with TSS (R2 = 0.89, p< 0.0001, n = 418).
A strong correlation between turbidity and TSS reveals that a reduction in turbidity corresponds with a subsequent reduction in TSS.
38
Log(Turbidity) = -1.42463 + 1.1892181*Log(TSS) R2 = 0.8856
n = 418
Turbidity (NTU) Turbidity
Total Suspended Solids (TSS) [mg/L]
Figure 3.12 – Line fit plot TSS versus turbidity.
Analysis of Treatments
To evaluate how treatments affect turbidity and TSS, a JMP model was developed to analyze response of mean turbidity and TSS across runs, sample locations, and duplicate tests. Simple means testing within each treatment determined whether change in turbidity was caused by PAM application. For this research, the sample location for the tank outlet, sediment tube 1, sediment tube 2, sediment tube 3, sediment tube 4, and sediment tube 5 will be referred to as L0, L1, L2, L3, L4, and L5, respectively. Refer to
Figure 3.2 for experimental setup schematic.
39
Treatment 1: Control
Representing the control, Treatment 1 was intended to evaluate whether sediment tubes by themselves have any effect on turbidity and TSS. With the two tests for
Treatment 1 averaged together, statistical results suggest a significant difference in mean turbidity exists between runs. F-test results show an increase in mean turbidity over 5 runs (F-stat = 10.4867, p< 0.0001, n = 60). Subsequent t-test results reveal runs 1, 3, and
5 are not connected by the same letter (Figure 3.13), which reveals these turbidity values are statistically different and an increase in turbidity occurs. For Treatment 1, an increase in turbidity across runs may be attributed to a buildup and resuspension of settled clay particles within the tank.
A
B BC C
C C
Turbidity (NTU) Turbidity
Mean
Run
Figure 3.13 – Mean turbidity across runs for Treatment 1.
40
To determine whether turbidity reductions occurred across sediment tube positions, turbidity values at each sample location were averaged for all runs. Simple means testing showed there was no statistical numeric difference in turbidity values across sediment tube position. F –test results revealed that mean turbidity across sample locations (F-stat
= 0.0588, p = 0.9975, n = 60) was not significantly different. In Figure 3.14, turbidity remains fairly constant across locations for all runs, in which locations connected by same letter are not significantly different. Had sediment tubes created a reduction, turbidity values would have been statically different across locations. Mean turbidity discharged from sediment tube 5 was 3104 NTU, which is well above the proposed EPA
280 NTU effluent limit. Results suggest that sediment tubes alone are insufficient to
reduce turbidity below proposed regulated limits.
A A A A A A
Turbidity (NTU) Turbidity
Mean
Location
Figure 3.14 – Mean turbidity across sample locations for Treatment 1.
41
A summary graph below (Figure 3.15), displays turbidity across sample locations for each run. Mean turbidity increases with runs and turbidity remains fairly constant across locations for each run. With lack of consistent reductions in turbidity it is possible to conclude that sediment tubes alone are ineffective at reducing turbidity.
Runs
Turbidity (NTU) Turbidity
Mean
Location
Figure 3.15 – Mean turbidity across sample locations for each run within Treatment 1.
Cumulative turbidity percent reduction values may appear to suggest a slight reduction in turbidity, but statistical results show that none are significantly different (p = 0.9817).
Figure 3.16 shows mean turbidity percent reductions across locations for all runs; sample locations connected by same letter are not significantly different.
42
)
% ( A A A
A A A Turbidity Percent Reduction Percent Turbidity Location
Figure 3.16 – Cumulative percent reduction of turbidity for Treatment 1.
F-test results show an increase in mean TSS over five runs (F-stat = 24.3505, p< 0.0001, n = 30). As discussed above, this increase in mean TSS across runs is believed to be due to an increase in clay content within the tank. Figure 3.17 displays mean TSS data across runs; runs not connected by same letter are significantly different.
A
B BC
C
TSS[mg/L]
D Mean
Run
Figure 3.17 – Mean TSS concentration for all runs within Treatment 1.
43
Statistical analysis failed to find a relationship between mean TSS and sample location
(F-stat = 1.2802, p = 0.3112, n = 30). Graphical results show a significant decrease at location L2 in TSS; however, due to a TSS increase at location L3, L4, and L5 it is possible to conclude that a decrease in TSS failed to occur. Figure 3.18 shows mean TSS across sample locations for all runs; runs connected by same letter are not significantly
different.
A AB AB
AB B AB
TSS[mg/L]
Mean
Location
Figure 3.18 – Mean TSS concentration across all sample locations for Treatment 1.
From Figure 3.19 below, mean TSS increases between runs support the supposition that clay concentrations increased in the tank over time. TSS data is highly variable and displays no evidence of consistent TSS reduction.
44
Runs
Mean TSS [mg/L] Mean
Location
Figure 3.19 – TSS concentration across sample locations for all runs within Treatment 1.
Graphical results appear to suggest cumulative TSS percent reduction occurs across sample locations, but statistical results show that no values are significantly different (p =
0.38). Figure 3.20 shows average TSS percent reductions across locations for all runs,
sample locations connected by same letter are not significantly different.
) A A
% A
( A A
A TSS Percent Reduction Reduction TSSPercent
Location
Figure 3.20 – Cumulative percent reduction TSS across sample locations for Treatment 1.
45
Treatment 1 results strongly suggest sediment tubes as installed provided no significant reduction in turbidity levels for simulated sediment-laden flows. Further, results did not achieve a mean turbidity value that would meet the proposed EPA turbidity effluent limit of 280 NTU. In addition, sediment tubes provided no significant reduction in finely suspended sediment that cause high turbidity levels. Lack of consistent reductions in turbidity and TSS may be attributed to the open-weave construction of the sediment tubes, which allows fine sediment to pass through and provides minimal resistance to decrease flow rate. Based on results from Treatment 1, it is possible to conclude that sediment tubes are not effective in reducing turbidity under simulated flow conditions for the experiment, which are likely to be found on linear construction projects.
46
Treatment 2: Multiple PAM Sprinkle
To test various PAM application methods, Treatment 2 applied 100-g granular
APS #705 PAM sprinkled to each of the five sediment tubes before each subsequent run.
F-test results show a constant mean turbidity (Figure 3.21) over 5 runs (F-stat = 0.3720, p= 0.8266, n = 60). Based on this result, mean turbidity did not fluctuate greatly between runs and any change in turbidity for Treatment 2, due to PAM interactions, will be
evident across sample locations.
A A A A
A
Turbidity (NTU) Turbidity
Mean
Run
Figure 3.21 – Mean turbidity across runs for Treatment 2.
F–test results revealed that mean turbidity across sampled locations (F-stat =246.95, p =
<.0001, n = 60) was significantly different. Additionally, follow-up t-test results show a significant difference in mean turbidity numbers across locations L0, L1, and L2 and failed to find a significant difference between locations L3, L4, and L5. Mean turbidity discharged from L5 is 82 NTU, well below the proposed 280 NTU limit. Based on statistical results, a significant decrease in mean turbidity is achieved with two sediment
47 tubes in series. However, turbidity levels achieve the proposed 280 NTU limit between location L2 and L3 (Figure 3.22), thus three sediment tubes would be needed to meet the proposed turbidity effluent limits based on the tested flow conditions.
Table 3.2 – Mean turbidity for all locations within Treatment 2.
Location L0 L1 L2 L3 L4 L5 2192 1311 412 202 126 82 (n=39) (n=57) (n=65) (n=67) (n=67) (n=69)
A
B
rbidity rbidity (NTU)
Tu
C
Mean D 280 NTU D D
Location
Figure 3.22 - Mean turbidity across sample locations for Treatment 2.
Figure 3.23 below illustrates how well Treatment 2 performed as evidenced by the tight grouping of turbidity values. T-tests reveal no significant difference between location and run turbidity values (p = 0.96), which suggests Treatment 2 reduces turbidity to the same level in every run.
48
Runs
Turbidity (NTU) Turbidity
Mean 280 NTU
Location
Figure 3.23 - Mean turbidity across sample locations for each run within Treatment 2.
Percent reduction allows for quantification of how the system performs and calculation of turbidity removal at each sample location. With five sediment tubes in place, mean cumulative percent reduction of turbidity is 96% (Figure 3.24). T-test results indicate a significant difference in mean turbidity percent reduction values across locations L1, L2,
L3, and L5. These results will be important to correctly determine the number of sediment tubes needed in a treatment series for turbidity.
49
)
% ( A B BA C
D
E Turbidity Percent Reduction Percent Turbidity
Location
Figure 3.24 – Cumulative percent reduction of turbidity for Treatment 2.
Unlike test results for turbidity, F-test results show a positive increase of mean TSS over five runs (F-stat = 9.9783, p<0.0001, n = 60). A statistical difference exists between multiple runs within Treatment 2 (Figure 3.25). An increase in mean TSS concentration is most likely due to an accumulation in flocculated clay particles on the upstream side of each sediment tube, which would become resuspended during the first few minutes of flow. Due to particle size, these flocs do not greatly affect turbidity, but add to TSS collected. As accumulated flocculated particles increase with every run, more become resuspended and increase sampled TSS with each run.
50
A BA CB C
D Mean TSS [mg/L] Mean
Run
Figure 3.25 – Mean TSS concentration across runs for Treatment 2.
The following graphs, Figures 3.26 and 3.27, display a continuous decrease in mean TSS across sample locations. F-test results show a decrease in mean TSS over sample locations (F-stat = 54.6003, p<0.0001, n = 60)However, Figure 3.27 also reveals an increase in mean turbidity with increasing runs, which may be to accumulation and resuspension of flocculate sediment particles. Despite an increase in TSS, mean TSS for all five runs discharged at location L5 was 319 mg/L.
51
A A
B C C
D Average TSS [mg/L] Average
Location
Figure 3.26 – Mean TSS concentration for all sample locations for Treatment 2.
Runs
Average TSS [mg/L] Average
Location
Figure 3.27 – TSS concentration across sample locations for all runs within Treatment 2.
52
Cumulative TSS percent reductions increased with sample location (Figure 3.28) as PAM interactions with clay particles created large flocs that gradually settled out of suspension.
Percent reduction data does reveal minimal TSS reduction between locations L0 and L1, which is likely due to the lack of PAM interaction and settling time between the tank
outlet and the first sediment tube.
) A % ( B B
C
D D TSS Percent Reduction Reduction TSSPercent
Location
Figure 3.28 – Cumulative TSS percent reduction for Treatment 2.
In summary, results from Treatment 2 demonstrate the effectiveness of PAM application for turbidity reduction under designed conditions. Mean discharge from location L5 of turbidity and TSS is 82 NTU and 319 mg/L, respectively. Additionally, mean cumulative reduction for turbidity and TSS is 96% and 78%, respectively. Due to the removal efficiency of Treatment 2, no significant difference in turbidity values was observed after sediment tube 2, but three sediment tubes are needed to achieve the proposed EPA 280 NTU effluent limit. Depending on turbidity regulations, sediment tubes configuration with sprinkled PAM can be modified to effectively reduce turbidity.
53
Treatment 3: Single PAM Sprinkle
For Treatment 3, 100-g granular APS #705 PAM was sprinkled on each of five sediment tubes before the initial run and not applied again. F-test results indicate a significant difference in mean turbidity with across runs (F-stat = 5.0713, p = 0.0044, n =
54). Follow-up t-tests shows mean turbidity in run 4 to be significantly different than mean turbidity for run 1-3. Figure 3.29 reveals no prevailing trends with turbidity levels over runs, thus suggesting an increase in turbidity with run 4 may be attributed to a
buildup of settled clays within the tank.
A AB
B B B
Turbidity (NTU) Turbidity
Mean
Run
Figure 3.29 – Mean turbidity across runs for Treatment 3.
F-test results revealed that mean turbidity differed across sampled locations (F-stat =
114.60, p <.0001, n = 54). T-test comparisons (Figure 3.30) show a strong significant difference between sample locations L0, L1, L2, and L3 (p<0.0001). Despite a large difference in mean turbidity at sample position L4 and L5, only a weak significant difference (p = 0.053) exists. A weak significant difference between the two sample
54 positions may most likely be attributed to an overlap in turbidity measurements caused by an increase in turbidity from resuspension. Table 3.3 shows mean turbidity discharged at location L5 is 61 NTU, well below the EPA 280 NTU turbidity effluent limit. Results indicate that three sediment tubes are likely necessary to achieve turbidity levels that are significantly different; subsequent sediment tubes may not provide statistically different turbidity results.
Table 3.3 – Mean turbidity for all sample locations in Treatment 3.
Location L0 L1 L2 L3 L4 L5 2388 1796 1010 581 289 61 (n=30) (n=33) (n=54) (n=57) (n=60) (n=63)
A
B
C Turbidity (NTU) Turbidity
D E
280 NTU E Mean
Location
Figure 3.30 – Mean turbidity across sample locations for Treatment 3.
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Figure 3.31 shows that PAM application in Treatment 3 does not decrease in effectiveness with consecutive runs. Results indicate that no statistically significant difference exists between mean turbidity values at each sample location for each run
(p=0.7382).
Runs
Turbidity (NTU) Turbidity
Mean 280 NTU
Location
Figure 3.31 – Mean turbidity across sample locations for each run within Treatment 3.
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Mean cumulative percent reduction of turbidity at location L5 is 97% (Figure 3.32).
Based on t-test results, percent reduction values at all locations were significantly
different (p<0.0001).
) A %
( B C D
E
F Turbidity Percent Reduction Percent Turbidity Location
Figure 3.32 – Cumulative percent reduction of turbidity for Treatment 3.
Similar to results shown in Treatment 2, F-test results (F-stat = 11.5342, p<0.0001, n =
51) show an increase of mean TSS over five runs for Treatment 3 (Figure 3.33). An increase in average TSS concentration is most likely due to an accumulation in flocculated clay particles on the upstream side of each sediment tube, which would become resuspended during the first few minutes of flow. Due to particle size, these flocs do not greatly affect turbidity, but add to TSS collected. As accumulated flocculated particles increase with every run, more particles become resuspended and increase sampled TSS with each run.
57
A
AB AB CB
C Mean TSS [mg/L] Mean
Run
Figure 3.33 – Mean TSS concentration across all runs within Treatment 3.
The following graphs, Figures 3.34 and 3.35, display a continuous decrease in mean TSS across locations. F-test results (F-stat =7.5888, p = 0.0001, n = 51) show a decrease in mean TSS over five runs for Treatment 3 (Figure 3.34). Figure 3.35 reveals an increase in average TSS with increasing runs, which is due to accumulation and resuspension of flocculated sediment particles. Despite an increase in TSS, mean TSS for all five runs discharged at location L5 was 169 mg/L.
58
A A
B C
D
E Mean Mean TSS[mg/L]
Location
Figure 3.34 – Mean TSS concentration across sample locations for Treatment 3.
Mean TSS [mg/L] TSS Mean
Location
Figure 3.35 –TSS concentration across sample locations for all runs in Treatment 3.
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Cumulative TSS percent reductions increased with sample locations (Figure 3.36) as
PAM interactions with clay particles created large flocs that gradually settled out of suspension. Percent reduction data does reveal minimal TSS reduction between locations
L0 and L1, which is likely due to the lack of PAM interaction and settling time between the tank outlet and the first sediment tube.
A
) %
( BA
CB C
D D TSS Percent Reduction Reduction TSSPercent
Location
Figure 3.36 – Cumulative percent reduction TSS across sample locations for Treatment 3.
Results from Treatment 3, again indicate how effective PAM application is for turbidity reduction in runoff. Average discharge from location L5 of turbidity and TSS is
61 NTU and 169 mg/L, respectively. Additionally, average reduction for turbidity and
TSS is 97% and 76%, respectively. Turbidity does increase slightly with increasing runs, but results indicate these increases are not statistically different and make it difficult to conclude that turbidity increases are due to loss of PAM effectiveness. In order to achieve the proposed 280 NTU limit, five sediment tubes are needed for Treatment 3.
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Treatment 4: PAM Bag
PAM dosing for Treatment 4 consisted of 500-g granular #705 PAM in a 6” x 26” smooth weave 400 micron permeable bag placed at the tank outlet and on the downstream side of sediment tubes one thru four. F-test results show (Figure 3.37) a significant difference in mean turbidity across runs (F-stat = 8.5054, p = 0.0001, n = 58), which is primarily due to a decrease in effectiveness of the PAM application.
A BA B BA
C
Turbidity (NTU) Turbidity
Mean
Run
Figure 3.37 – Mean turbidity across runs for Treatment 4.
Analysis of variance testing showed there is a difference in turbidity values (Table 3.4) across sampled locations. F –test results revealed that mean turbidity across locations (F- stat = 48.4705, p<0.0001, n = 58) is significantly different. Mean turbidity discharged from sediment tube 5 was 915 NTU; well above the proposed EPA 280 NTU effluent limit. Figure 3.38 shows mean turbidity across sampled locations for all runs; locations connected by same letter are not significantly different.
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Table 3.4 –Mean turbidity for all sample location in Treatment 4.
Location L0 L1 L2 L3 L4 L5 3198 1738 1560 1167 1236 915 (n=34) (n=34) (n=36) (n=40) (n=36) (n=48)
A
B B C C
Turbidity (NTU) Turbidity C
Mean 280 NTU
Location
Figure 3.38 – Mean turbidity across sample locations for Treatment 4.
From Figure 3.39 below, it is evident that run 1 displays a significant decrease in turbidity, but turbidity also increases with subsequent runs. Turbidity discharged from run 1, location L5 is 152 NTU, whereas turbidity at run 5, location L5 is 1127 NTU.
Such an increase in turbidity by run may point to the overall ineffectiveness of a passive
PAM bag application over multiple runoff events. During the start of run 1 PAM inside the bag remained in granular form; however, after run 1 it was observed that the PAM swelled from interaction with water and became a solid, gelatinous log. Results show
62 that run 1 mean turbidity is well below 280 NTU, which may show that, under the described test conditions, granular PAM is most effective at reducing turbidity.
Runs
Turbidity (NTU) Turbidity
Mean 280 NTU
Location
Figure 3.39 –Mean turbidity across sample locations for each run in Treatment 4.
Figure 3.40 depicts a decrease in turbidity percent reduction over runs, which reaffirms conclusions that PAM bags become ineffective at reducing turbidity below EPA regulated limits. Mean cumulative turbidity percent reduction achieved for Treatment 4 was 71%.
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) Runs
%
( Turbidity Percent Reduction Percent Turbidity
Location
Figure 3.40 – Cumulative percent reduction of turbidity for Treatment 4.
F-test results fail to show significant increase in mean TSS over five runs (F-stat =
1.2590, p = 0.3001, n = 55). Although statistical results do not show a significant difference between run 1 and runs 2-5, numerically there is large difference between these runs which could be due to a decrease in TSS removal by the PAM bag application after run 1. Figure 3.41 displays mean TSS data across runs, runs not connected by same letter are significantly different.
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A A A A
A Mean TSS [mg/L] Mean
Run
Figure 3.41 – Mean TSS concentration across runs for Treatment 4.
TSS concentrations within Treatment 4 fail to consistently decrease across sample locations. F-test results show a significant difference in mean TSS across sample positions exists (F-stat = 5.7209, p = 0.0004, n = 55). Statistically sample location L0 is different than sample location L1-L5 (p=0.0033). After location L0, TSS concentration levels are not significantly different and appear to remain stable across locations L1 thru
L5 as shown in Figure 3.42.
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A
B B B
B B Mean TSS [mg/L] TSS Mean
Location
Figure 3.42 – Mean TSS concentration across all sample locations for Treatment 4.
Figure 3.43 illustrates how TSS concentration increases with runs. This graph further supports the theory that PAM deployed in bags creates substantial reduction in TSS during the first run, but loses reducing effectiveness with subsequent runs.
Runs
MeanTSS[mg/L]
Position
Figure 3.43 – Mean TSS concentration for all runs within Treatment 4.
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Based on results, Treatment 4 declines in effectiveness to reduce turbidity with each run. Mean turbidity and TSS discharged at location L5 was 915 NTU and 1810 mg/L, respectively. The decrease in turbidity reduction is likely attributed to change in
PAM composition from granular to a gelatinous; decreasing surface to volume ratio and opportunity for chemical reactions between PAM and suspended sediment.
Comparison of Treatments
A side by side comparison was used to determine which treatment achieved the lowest turbidity and created a significant turbidity reduction in the fewest sediment tubes.
In order to effectively compare treatments, it is essential to determine whether turbidity values from each treatment are significantly different. A graph comparing all treatments for mean turbidity across sampled locations is displayed in Figure 3.44. Similarly, Figure
3.45 compares percent reduction for each treatment.
Treatments
Turbidity (NTU) Turbidity
Mean 280 NTU
Figure 3.44 – Comparing mean turbidity across sample locations for each treatment.
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F-test results reveal a significant difference in mean turbidity across sample positions within treatments exists (F-stat = 37.7199, p<0.0001, n=232). Results did indicate a significant difference (p <.0001) in mean turbidity at location L5 between Treatment 1,
3104 NTU, and Treatment 4, 915 NTU. Statistically, Treatment 2 & 3 are different (p =
0.0002) than Treatment 4. T-test results failed to show a significant difference (p =
0.9253) between mean turbidity values at location L5 for Treatment 2, 82 NTU, and
Treatment 3, 61 NTU. When comparing mean turbidity between Treatment 2 and 3, a statistical difference (p = 0.0197) exists across locations L1, L2, and L3. Graphical results (Figure 3.42) show that Treatment 2 reaches a lower turbidity more quickly than
Treatment 3.
Treatments Turbidity Percent Reduction Percent (%) Turbidity
Location
Figure3.45 - Comparing turbidity percent reduction for each treatment.
T-test results comparing percent reduction found no significant difference (p = 0.8156) between Treatment 2 and Treatment 3 at location L5. Results did show a significant difference (p<.0001) in percent reduction across locations L2 and L3 for Treatment 2
68 compared to Treatment 3. Based on these results, it is evident that Treatment 2 creates a more rapid reduction in turbidity than the other test treatments.
PAM Desiccation Effects
To better understand turbidity reduction effects related to PAM applications becoming desiccated, a 6th run was completed several days after run 5 for Treatments 2,
3, and 4. A 6th run allowed PAM applications to dry and acted as days or weeks that normally occur between rain events. Comparison of the 6th run with runs 1 – 5 within the same treatment are displayed in Figure 3.46 – 3.48.
Runs
Turbidity (NTU) Turbidity
Mean 280 NTU
Location
Figure 3.46 – Turbidity 6th run comparison to previous runs for Treatment 2.
For Treatment 2 (PAM sprinkled on sediment tube before each run), mean turbidity discharged from location L5 on the 6th run is 100 NTU, which statistical results prove not to be significantly different than mean turbidity discharges of runs 1- 5 (p = 0.925).
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Additionally, percent reduction throughout run 6 is 97% which is not significantly different than runs 1 – 5 (p = 0.799).
Runs
Turbidity (NTU) Turbidity
Mean 280 NTU
Location
Figure 3.47 – Turbidity 6th run comparison to previous runs for Treatment 3.
Treatment 3 mean turbidity discharged from location L5 on the 6th run is 1283 NTU, which statistical results prove to be significantly different than mean turbidity discharges of runs 1- 5 (p <.0001). Percent reduction within run 6 at location L5 is 41%, which is significantly different compared to runs 1 – 5 (p <.0001).
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Runs
Turbidity (NTU) Turbidity
Mean 280 NTU
Location
Figure 3.48 – Turbidity 6th run comparison to previous runs for Treatment 4.
Figure 3.48 depicts that Treatment 4 shows a decrease in turbidity across sample locations, but shows an increase in mean turbidity with each run. Mean turbidity discharged from location L5 on the 6th run is 1863 NTU, which statistical results prove to be different than mean turbidity discharges of runs 1&2 (p = 0.0007 and p = 0.0076, respectively) but not significantly different than mean turbidity discharges of runs 3, 4, and 5 (p = 0.1298). Reduction in mean turbidity before discharge at location L5 within run #6 is 47%.
In order to effectively determine which PAM application is least effected by desiccation effects, a side by side comparison for all treatments with a run 6 is shown in
Figure 3.47 below. Statistical analysis shows a weak significant difference in average turbidity at location L5 between Treatment 2 and Treatment 3 (p = 0.073). Due to events while sampling, only one test within Treatment 2 experimented with PAM dry out effects, whereas Treatment 3 and 4 had duplicate dry out tests run. This explains why
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Treatments 3 and 4 have two turbidity values computed for an average turbidity and
Treatment 2 only has one set of turbidity values (Figure 3.49). This weak significant difference between Treatment 2 (n=38) and 3 (n=78) is primarily due to a small sample
size that may be creating error in the data.
Mean Turbidity (NTU) Turbidity Mean
Location
Figure 3.49 – Mean turbidity across sample locations for run 6 in Treatment 2, 3, and 4.
Treatment 2 shows that if PAM is reapplied to sediment tubes after subsequent
PAM applications have dried, similar turbidity reductions are still achieved. Treatment 3 and 4 lack substantial reductions in turbidity and fail to meet the EPA proposed 280 NTU effluent limit. Observations reveal that once PAM becomes wet and dries out, the outer
72 layers of PAM form a hard crust over the surface on which PAM is applied. Based on results presented above, as PAM dries out between rain events it may become less effective at flocculating sediment particles.
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CONCLUSIONS
The goal of this research was to maximize turbidity reduction with a passive PAM application and optimize excelsior sediment tubes configuration in simulated construction site runoff. Additionally, research efforts were directed at examining responses in turbidity levels when PAM applications were allowed to become desiccated. The overall goals of this research were met, and the following conclusions can be summarized from the results:
1. First, under designed conditions, sediment tubes without PAM application
provide no significant reduction in turbidity and TSS.
2. Granular PAM sprinkled directly on sediment tubes provides better reductions in
turbidity and TSS than PAM delivered through a permeable bag.
3. PAM sprinkling before each run (Treatment 2) provides a quicker decrease in
turbidity than PAM sprinkling once before the test (Treatment 3).
4. Turbidity levels meeting the proposed EPA 280 NTU effluent limit are achieved
within three sediment tubes when PAM was sprinkled before every run. PAM
sprinkled once before the beginning of the test created turbidity levels that met the
proposed 280 NTU effluent limit within five sediment tubes.
5. Once applied PAM (either in sprinkle or tube form) becomes wet from storm
events and dries out during periods of dry weather, it loses effectiveness in
reducing turbidity. As a result, runoff discharged after five sediment tubes will
likely exceed the proposed EPA 280 NTU effluent limit under certain conditions.
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6. Under designed conditions, reapplication of granular PAM to sediment tubes after
periods of dry weather and before storm events will consistently reduce turbidity
below the proposed EPA 280 NTU effluent limit.
7. Finally, although a small clay sediment size was selected for experimentation,
results reveal a strong coefficient of determination value (R2 = 0.89) between
turbidity and TSS concentration. This result suggests that reducing turbidity
through passive polymer treatment will have a corresponding reduction in
sediment loading.
Results of this research indicate that PAM application may be necessary for effective turbidity and suspended sediment reduction. This research has shown that granular PAM application directly on to sediment tubes can reduce turbidity significantly below EPA’s proposed 280 NTU turbidity effluent limit under the derived test conditions. Sprinkling of granular PAM is believed to work best for turbidity reduction because it provides a large surface area for interactions with small silt and clay particles. Depending on the future of the EPA turbidity based effluent limit, this research shows the number of sediment tubes, in conjunction with PAM application, could be manipulated to achieve a turbidity goal. Resuspension of flocculated and settled particles increased turbidity and
TSS as more clay particles were retained behind each sediment tube with each simulated runoff event. This research suggests that regular maintenance of sediment tubes to remove flocculated and settled particles will potentially limit the effects of resuspension on turbidity and TSS.
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Further research is needed to optimize PAM application rate, application timing, longevity, and potential environmental impacts. First, additional investigation is required to determine whether the PAM application of 100-g used for this research is optimal or if
PAM applied at a different rate would achieve improved or similar results. Second, to meet regulation standards PAM will need to be applied after periods of dry weather in preparation of upcoming storm events which would eliminate PAM ineffectiveness from dry out effects. Further research is need to determine whether PAM could be reapplied to water saturated sediment tubes several hours after storm events, which is typically when construction site stormwater BMPs are checked for failures and needed maintenance.
Also, research needs to address whether PAM applications, left exposed during periods between storm events, would be negatively affected by condensation, or morning dew.
Additionally, research may need to explore impacts of light rainfall, where little to no runoff occurs, on dissolving PAM applications and potentially reducing its effectiveness during subsequent storms. For example in Greenville County, SC, rainfall events between 0.01 – 0.39 inches occur on average about 20% of the days in a year and are more likely to occur than all larger rainfall events (John C. Hayes, Clemson University
School of Agriculture, Forestry and Environmental Sciences, Personal Communication,
17 September 2012). Third, little is known about the longevity of sediment tubes with
PAM applied and the point at which tubes become overloaded with sediment and ineffective. These results would be vital in project cost estimating to determine if turbidity reduction by this method is truly economically feasible. Lastly, although research has proven anionic PAM to have almost no environmental side effects, research
76 needs to determine whether the application of PAM after multiple storm events would create a concentrated source of PAM and cause possible environmental impacts downstream.
Disclaimer
Mention of a trade name and/or products does not imply endorsement of the product by
Clemson University to the exclusion of others that might be available.
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CHAPTER FOUR
EVALUATION OF PASSIVE POLYMER TREATMENT FOR TURBIDITY CORRESPONDS TO REDUCTION IN BACTERIAL DENSITY
ABSTRACT
Traditional practices for treating construction derived runoff include sediment retention basins. Current research shows sediment basins act as reservoirs for bacteria, specifically Escherichia coli (E. coli). Further, E. coli preferentially attaches to clay- sized particles and has been found in sediment basin outflows with high turbidity levels containing concentrations exceeding water quality standards recommended by the US
Environmental Protection Agency. Since research shows E. coli preferentially attach to the clay fraction within sediment, it was hypothesized that a reduction in turbidity and
TSS would create a corresponding reduction in bacterial density. Construction site sediment retention basin discharge was simulated to determine whether E. coli densities can be reduced using PAM application and a sediment tube configuration aimed at reducing turbidity. Based on prior research, reductions in turbidity and suspended sediment were maximized by applying 100 g of granular polyacrylamide (PAM) directly to each of five sediment tubes before the beginning of five simulated runoff events. PAM application successfully reduced turbidity and TSS by 96% and 92%, respectively.
Discharge after the last sediment tube had an average turbidity of 80 NTU and TSS of
174 mg/L. Since research shows E. coli preferentially attach to the clay fraction within sediment, it was hypothesized that a reduction in turbidity and TSS concentration would
78 result in a corresponding reduction of bacterial density. For the low E. coli density range
(5,000–10,000 MPN/100 mL), PAM application failed to create a reduction in bacterial density, but rather an increase in E. coli was observed with an average discharge of
25,226 MPN/100 mL. Within the high E. coli density range (100,000-200,000 MPN/100 mL), a 29% reduction was recorded with an average discharge of 135, 270 MPN/100 mL.
F-test results ultimately revealed, that despite a decrease, bacterial densities across sampled location were not statistically different (F-stat = 1.5956, p = 0.2097, n = 40).
Results make it difficult to conclude that PAM application caused a decrease in bacterial density. In both tests, E. coli densities exceeded recommended EPA water quality criteria of 126 cfu/100 mL for a 5-sample geometric mean as well as the single 235 cfu/100 mL grab sample. PAM application caused substantial reductions in turbidity and TSS concentration, but failed to create a corresponding reduction in E. coli density. Results suggest further study is needed of possible treatment practices to reduce bacterial transport from construction sites. If sediment basins are found to contribute significantly to bacterial loading which severely impacts water quality downstream, future regulation of bacterial discharge could become a reality.
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INTRODUCTION
Construction site runoff transports a variety of pollutants including, sediment, oils, heavy metals, organic matter, and bacteria, into surrounding surface waters and can cause a wide range of environmental problems. Specifically, bacteria, in elevated concentrations, can create an environment for water-borne illness and become a hazard to public health. In response to elevated bacteria levels, EPA initiated the BEACH program in 1997 to reduce risks to human health caused by exposure, either by ingestion, inhalation, and body contact, to pathogens, primarily from sewage and stormwater runoff, in recreational waters (USEPA, 2003). EPA developed criteria to regulate bacteria concentrations in recreational water, both marine and freshwater.
Traditionally, construction sites have met federal and state discharge requirements by reducing runoff and sediment loads through the use of sediment basins. Sediment basins are designed to collect stormwater from construction areas and allow sediment to settle before runoff is discharged offsite. Although sediment basins must be designed to retain 80% of sediment entering the system or achieve 0.5mL/L peak settable solids concentration (SCDHEC, 2003), fine silt-sized and clay-sized particles, which do not readily settle out of suspension, are difficult to capture and are often discharged downstream.
As water quality continues to become a greater concern, recent research reveals that sediment basins can act as reservoirs for indicator bacteria (Tempel, 2011; Sawyer,
2009; Zhang, 2006). These studies found bacteria concentrations within sediment basins that did not meet USEPA regulated levels upon discharge. Bacteria preferentially
80 associate with fine sediment particles and concentrations in the first 2.54 cm of deposited sediment are highest (Tempel, 2011). Fine silt and clay particles for which bacteria associate are easily resuspended during rain events and create high turbidity levels.
Tempel (2011) showed sediment basin discharge high in turbidity corresponded to elevated concentrations of bacteria.
Current bacterial research shows existing structural stormwater BMPs are inadequate for reducing bacteria concentrations in runoff. Constructed wetlands and bioretention were found to be relatively effective at reducing bacteria in stormwater runoff (Hathaway et al., 2009; Davies and Bavor, 2000); however, these devices are not designed to handle increased sediment loads from construction site erosion. Compost filter socks combined with PAM application have shown potential to reduce bacteria under low flow conditions (Faucette et al., 2009)
This research was aimed at determining whether a series of sediment tubes and passive PAM treatment for turbidity leads to bacterial density reduction. For testing, the experimental setup was designed to mimic highly turbid construction site discharge with typical flow rates one might see during a 1-year, 24-hour rainfall event over a 1 acre site in South Carolina.
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PROCEDURES
Experimental Site
To replicate conditions found on a typical construction site, a 185-ft triangular channel, 12 ft wide with an average depth of 1.65 ft, at a 7% slope was constructed and lined with a 50-mil HDPE liner (Figure 4.1). In order to have correct spacing between five sediment tubes (reasoning for the selected spacing will be explained later in this section), 150 ft of channel length was needed, but fairly steep slope at the upper portion of the channel would have resulted in a non-uniform slope between sediment tubes. A
185-ft channel allowed the sediment tubes to be positioned after the transition from a steep slope to a more uniform slope. The channel was lined to prevent scouring and erosion, which would add to the total sediment load during experimentation and compromise results.
Figure 4.1 – Channel Design. On left, upstream view of channel from bottom. On right, downstream view of channel from tank outlet during experimentation.
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Figure 4.2 –Channel design schematic.
Since the goal of this research involved simulating construction site runoff, it was important to acquire a flow rate that was representative of flow rates found on South
Carolina construction sites. To determine a typical flow rate, 1-yr, 24-hr rainfall events was averaged for Greenville, Richland, and Charleston Counties. The average 1-yr, 24-hr rainfall amount was 3.4 inches. A peak flow rate of 2.5 cfs was calculated for a newly
83 graded 1 acre site at a 2% slope comprised of A (50%) and B (50%) hydrologic soil groups. In order to achieve a representative flow rate, a 4,800 gallon collapsible tank was chosen to simulate runoff from construction sites. The tank was filled with water from an adjacent pond using a 5-hp semi-trash pump. The tank had a 6-in outlet controlled by a
6-in gate valve (Figure 4.3) that drained the tank in 12 minutes.
Figure 4.3 – Collapsible Tank Setup. On left, tank completely full with 6inch valve visible. On right, tank emptied.
To accurately compare previous and current research, it was important to calibrate the tank’s discharge flow rate. Given the tank’s odd shape and configuration, it was inaccurate to calculate to the volume in the tank based on the height of water. A TM300
Turbine Flowmeter (GP1, 2012) was used to measure flow into the tank, while height of water in the tank was recorded manually using depth gauge. A graph (Figure 4.4) was developed using the correlation between height of water to the volume during four consecutive tank fill-ups. Then, the height of water in the tank was recorded as the tank drained four consecutive times in 1-min intervals. The linear equation developed in
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Figure 4.4 was used to correlate height of water in the tank to loss of volume. Using that data, the tank discharge flow rate was calculated over the 12-min interval. Figure 4.5 shows how discharge from the tank mimics a partial hydrograph. The peak flow rate discharged from the tank was 1.91 cfs and the average flow rate over 12 min was 0.72 cfs.
5000 4500 y = 1101.3x 4000 R² = 0.987
3500 3000 2500
2000 Volume [gals] 1500 1000 500 0 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 Water Depth [ft]
Figure 4.4. Tank storage volume compared to water height.
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2.5
2
1.5 R² = 0.9899
1 Flow Flow Rate[cfs]
0.5
0 0 2 4 6 8 10 12 14 Time [min]
Figure 4.5. Tank discharge flow rate over time.
Although the experimental peak flow rate of the tank did not reach the target peak flow of
2.5 cfs, the experimental peak flow is still a good representation.
A homogenous sediment-water solution was needed to mimic runoff from a construction site. To achieve these conditions, kaolinite clay was chosen to be the test soil. Kaolinite is naturally occurring clay that is easily suspended in water and represents the silt/clay fraction that would be found in a South Carolina Cecil soil. For this research project, Paragon®, a trade name for kaolinite clay used by IMERYS Minerals Company, was acquired from the Langley, SC mine.
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Table 4.1. Particle Size Distribution for Paragon (IMERYS Minerals, 2012).
PARTICLE SIZE Median (microns) 1.1 +325 Mesh (% retained) 0.3 PERCENT PASSING % < 20 (microns) 98 % < 10 (microns) 94 % < 5 (microns) 84 % < 2 (microns) 65 % < 1 (microns) 52 % < 0.5 (microns) 36 % < 0.2 (microns) 14
An 11-hp pump with a flow rate of 335 gallons per minute was used to recirculate the mixture keeping kaolinite clay suspended in the 4,800 gallon tank. A nozzle configuration was developed to increase velocity and keep particles suspended. Each nozzle produced an average velocity of 17 ft/s, which was determined mathematically using the known flow rate of the 11-hp pump and area of the 1-in nozzles. Below, Figure
3.6 shows the 3-in discharge PVC pipe reduced to 8, 1-in nozzles.
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Figure 4.6 - Sediment Resuspension Devices. On left, nozzle configuration. On right, recirculation pumped.
The configuration of the recirculation nozzles created “dead zones” along the bottom of the tank where mixing would not occur well. Additionally, the recirculation pump was turned off between 8 – 9 minutes after the valve was first opened because the water level in the tank allowed the recirculation pump to begin pulling in air. For each run, target turbidity in the tank was between 1,600 and 2,000 NTU and measured by an Analite
NEP160 display with NEP260 probe handheld turbidity meter, with a range of 0-3,000
NTU (McVan Instruments, 2012). Based on the reading, the amount of kaolinite added to the tank varied from 50 lbs to 100 lbs in an attempt to produce similar tank turbidity readings.
For this research, 20-in diameter, 10-ft American Excelsior Curlex® Sediment
Logs® were selected (American Excelsior Company, 2012). Selection of sediment tubes was governed by price and recommend products on the SCDOT Qualified Product List
57. Based on the length of the channel, five sediment tubes were used in series.
Following SCDOT specifications, the maximum spacing for sediment tubes acting as
88 ditch checks on a greater than 6% slope is 25 ft (SCDOT, 2011). Following SCDOT guidelines, for this research sediment tubes were placed at 25-ft spacing (Figure 4.1).
Due to the lined channel, normal installation of the sediment tubes could not be performed. To anchor the tubes, tee posts were bent 90 degrees with a 10-in over hang and driven into the ground so that only approximately 14 in remained (Figure 4.7). Tubes were then compacted under the tee posts to ensure under cutting of the tube would not occur. SCDOT specifications dictate that for in-field installation sediment tubes should be trenched to a depth that is 20% of the sediment tube diameter (SCDOT, 2011). Thus, the effective ponding depth for this research versus SCDOT specifications is very close.
Compaction of sediment tubes may have retarded flow and increased trapping efficiency more than compared to SCDOT specifications, requiring trenching in sediment tubes.
Effects of compaction were thought to be of less importance than to ensure flow was not under cutting sediment tubes. Collars in front provide structural stability and helped the tubes keep their shape. Sand bags were used to anchor the tube ends down at the top of the channel.
89
Figure 4.7 – Sediment Tube Deployment. On left, upstream side of sediment tube. On right, downstream side of sediment tube.
Six ISCO 3700 samplers were programed to sample the entire simulated runoff event (Teledyne ISCO, 2012). Liquid detectors activated samplers and sampling continued over 4 min time intervals. Sampling stopped when liquid detectors were inhibited. Sampling probes were placed directly at the outlet of the tank and on the downstream side of each sediment tube.
Polymer Optimization
Applied Polymer Systems, Inc. 700 Series Silt Stop Polyacrylamide Erosion
Control Powder was chosen to be flocculating agent for this project (APS, 2012). The
700 series is a polyacrylamide co-polymer powder that is tailored to be soil specific. To determine the correct polymer to use with the kaolinite, a series of laboratory scale jar tests were performed (Figure 4.8). Six polymer types within the 700 series were tested,
90 which include 705, 707, 712, 730, 740, and 745. To test each polymer, manufacturer instructions for testing APS powders were followed (APS, 2012).
Figure 4.8 –Polymer Optimization. Individual jar tests determined which APS polymer worked best with Kaolinite.
Jars were observed for clarity of water, largest particulate formed, and the time it took for particles to settle. Jar tests showed that Kaolinite responded best to APS #705 polymer.
Bacteria Experimentation
The bacteria experimentation included two tests to determine if PAM application to treat turbidity would also reduce bacteria numbers in the channel. From a previous study, PAM sprinkled in 100-g doses directly on sediment tubes consistently had the greatest turbidity reductions, thus this application method was chosen for this research.
A control test, which had no PAM application, only bacteria-sediment mixture and five sediment tubes in series, included a series of five tank discharges of simulated construction site runoff completed within 24 hrs. Tests were then duplicated for statistical accuracy. A second test 100-g of PAM was sprinkled on each of the five
91 sediment tubes and reapplied with the same dose after each tank discharge. The PAM application test included five runs completed within 24 hrs; the entire test was duplicated for statistical accuracy. The first run of each test was completed one day prior of the rest, to ensure the proper dilution factor was chosen for bacteria analysis. All sediment tubes were removed and replaced with new tubes at the completion of each test.
To achieve realistic bacteria levels in the tank, two methods were explored using bovine manure from the LaMaster Dairy Center located in Clemson, SC. First, 65 gallons of wastewater (2% solids) from a dairy lagoon were chosen to be the bacteria source. By sampling the wastewater lagoon, it was estimated that 65 gallons of sludge would result in E.coli numbers between 5,000 – 10,000 MPN/100 mL within the 4,800 gallon tank. A ¼-hp sump pump was used to transfer the wastewater sludge from the lagoon to the transfer tank (Figure 4.9). Wastewater was gravity fed into the 4,800 gallon tank as it was simultaneously filling up with pond water. To achieve higher bacteria concentrations, a second method involved creating a slurry using two gallons of fresh bovine manure from a free-stall dairy barn (Figure 4.9) and a handheld drill with agitator.
The resulting mixture produced bacteria concentrations between 100,000 – 200,000
MPN/100 mL when added to the collapsible tank. Differences in E.coli density from the two sources is believed to be due to the freshness of the manure collected from the free- stall dairy barn.
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Figure 4.9 – Bacteria Collection. On left, wastewater collection from Dairy lagoon. On right, free- stall dairy barn.
The recirculation pump was turned on when the tank reached a quarter its capacity;
Kaolin clay and bacteria mixture were then added immediately. Turbidity in the tank was checked with a Hach handheld turbidity probe to ensure turbidity was within the target range of 1,600 to 2,000 NTU. The recirculation pump agitated the bacteria – sediment mixture for 20 min, before being turned off. The mixture was allowed to settle for 1-hr to allow adequate time for bacteria to become attached to clay particles. After 1hr, the recirculation pump was again turned on to resuspend settled particles. The recirculation pump agitated the mixture for 5 min before the tank value was opened and sampling commenced.
Sampling was achieved by 4 ISCO 3700 samplers. Samplers were triggered to sample by liquid detection and then sampled in 4-min intervals until the liquid detectors were inhibited. Samples were collected at the tank outlet and on the downstream side of the 1st, 3rd, and 5th sediment tube. For analysis purposes, the samples taken at the tank
93 outlet will be referred to as sample location L0, 1st sediment tube as sample location L1,
3rd as sample location L3, and 5th as sample location L5. All samples were analyzed for
E.coli MPN, turbidity, and TSS concentration. To reduce risk of cross contamination between runs, sampler tubing was rinsed three times with tap water and then three times with deionized water. Deionized water was discarded after each use on each sampler to limit contamination with residual chlorine.
To ensure bacteria testing would cause no offsite impacts, a treatment system was designed to land apply the bacteria-laden water after being discharged from the channel
(Figure 4.10). A sand bag dam, approximately 2 ft tall, was created at the base of the channel to pond water. A 5-hp semi-trash pump tank fill-up pump was placed at the bottom of the channel where the bacteria laden water was pumped 150 ft up the pasture and allowed to gradually infiltrate.
Figure 4.10 – Bacteria Land Application.
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Sample Analysis
Bacteria enumeration was completed within six hours of sample collection using the Colilert® enzyme substrate procedure, Method 9223 B, from Standard Methods for the Examination of Water and Waste Water (APHA, 2005) developed by IDEXX
Laboratories. Dilutions of collected samples were necessary to keep detectable bacteria numbers within the range of the Colilert® QuantiTray 2000. The QuantiTray system is a tray with 97 wells that determine a most probable number of viable E.coli cells. Samples were diluted with sterile water, substrate was added, and entire sample was transferred to the QuantiTray tray. For this experiment, of the 100 mL in the tray, 1 mL of sample was added, which equates to a 100 time dilution. Trays were incubated for 24 hrs at 35 degrees Celsius, and then observed under ultraviolent light to detect fluorescing wells
(IDEXX, 2011). Using the calculator developed by IDEXX, concentrations for E. coli are calculated as most probable number per 100-mL (MPN/100mL).
A Hach 2100AN Laboratory Turbidimeter (Hach, 2012) was used to measure all turbidity readings from samples following Standard Method 2130 B (APHA, 2005). The
Hach 2100AN allowed for the maximum turbidity range (0 – 10,000 NTU) without having to perform dilutions, which helped to ensure accuracy of all readings. The accuracy of the turbidity meter is as follows (Hach, 2012):
±2% of reading plus 0.01 NTU from 0-1000 NTU ±5% of reading from 1000 NTU to 4,000 NTU ±10% of reading from 4,000 NTU to 10,000 NTU
Samples were agitated by hand for five seconds before transferring the sample in the meter vials to ensure aggregates were evenly dispersed. Vials were then gently inverted
95
10 times just before being placed into the turbidity meter. Calibrations were completed using the StablCal Sealed Vial Standard provided by Hach.
In addition to bacterial enumeration and turbidity, selected samples were analyzed for TSS using Standard Method 2540 B (APHA, 2005). Typically, the first and last samples collected for a given location were analyzed for TSS. If the first or last sample lacked adequate volume, the next or previous sample, respectively, was chosen to gather
TSS data.
Statistical Analysis
Tests performed to compare mean E.coli density, turbidity, and TSS among runs and sample positions include, regression analysis, analysis of variance, and t-tests. The statistical significance tests used an alpha value of ≤ 0.05 unless otherwise stated.
Statistical calculations were performed with JMP statistics software (SAS Institute Inc.,
Cary, NC, USA).
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RESULTS AND DISCUSSION
A JMP model was created to compare turbidity, TSS, and E. coli density with differing PAM applications. Treatment 1 acted as a control, consisting of sediment tubes with no PAM application, whereas Treatment 2 involved dosing each sediment tube with
100-g of granular PAM. For this research, sample obtained at the tank outlet, sediment tube 1, sediment tube 3, and sediment tube 5 will be referred to as locations L0, L1, L3, and L5, respectively (Figure 4.2). Greater detail on experiment setup can be found in the procedures section of this paper.
Analysis of turbidity data indicates a strong difference in mean turbidity between
Treatment 1 (Figure 4.11) and 2 (Figure 4.12). Side by side data comparison for both treatments is displayed in Figure 4.13.
Runs
Turbidity (NTU) Turbidity
Mean
Location
Figure 4.11 – Mean turbidity across sample locations for each run within Treatment 1.
97
Runs
Turbidity (NTU) Turbidity
Mean
Location
Figure 4.12 – Mean turbidity across sample locations for each run within Treatment 2.
Treatments
1
2
Turbidity (NTU) Turbidity
Mean
Location
Figure 4.13 – Mean turbidity comparison of Treatment 1 and 2.
98
From F-test analysis, no significant difference (F-stat =0.4795, p = 0.7002, n=40) between turbidity values in each sample location exists in Treatment 1 (Figure 4.11). No turbidity reduction in Treatment 1 most likely shows that sediment tubes alone provide no ability to reduce turbidity. Treatment 2 displays (Figure 4.12) a significant decrease in average turbidity across sample locations (F-stat = 59.4258, p< 0.0001, n = 44).
Turbidity at location L5 for all runs within Treatment 2 was not significantly different and combined for a mean discharged turbidity of 80 NTU. In a side by side comparison
(Figure 4.13), a substantial reduction is observed in Treatment 2 versus Treatment 1; in which Treatment 2 reduces turbidity by 96%.
As expected, TSS concentrations followed similar trends as turbidity and reductions only occurred when PAM was present. TSS concentration comparisons for each treatment are shown in Figures 4.14 – 4.16.
Runs
Mean TSS [mg/L] Mean
Location
Figure 4.14 – Mean TSS across sample locations for each run within Treatment 1.
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Runs
Mean TSS [mg/L] Mean
Location
Figure 4.15 – Mean TSS across sample locations for each run within Treatment 2.
Treatments
1
2 Mean TSS [mg/L] Mean
Location
Figure 4.16 – Mean TSS comparison of Treatment 1 and 2.
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For Treatment 1 (Figure 4.14), statistical analysis showed no significant difference in
TSS concentration between sample locations (F-stat = 1.9442, p = 0.1550, n = 40).
Results confirm that sediment tubes alone provide no decrease in TSS concentration within Treatment 1. Statistical results validate a large decrease in TSS across sample locations (F-stat = 33.2155, p< 0.0001, n = 44) within Treatment 2 (Figure 4.15). TSS discharged at location L5 was 174 mg/L which equates to a 92% reduction. In a side by side comparison (Figure 4.13), a substantial reduction is observed in Treatment 2 versus
Treatment 1.
Bacterial Results
Bacterial results analyzed E. coli density within each treatment and were reported as most probable number (MPN) per 100 mL. For this experiment, two E. coli densities were used as the starting concentration for the tank to determine whether E. coli density had an effect on reductions. The two E. coli densities were comprised of a low range
(5,000 – 10,000 MPN/100 mL) and a high range (100, 000 – 200,000 MPN/100 mL).
The variable nature of E. coli samples caused high standard errors within bacterial results. The following figures (4.17 – 4.18) best show E. coli density trends for each treatment.
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Treatments 1
2 MPN/100mL
Location
Figure 4.17 – Mean MPN/100 mL (low E. coli density range) for each treatment.
Treatments 1
2 MPN/100mL
Location
Figure 4.18 – Mean MPN/100 mL (high E. coli density range) for each treatment.
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Results from Treatment 1 reveal sediment tubes provide no significant bacterial reduction across sample locations for both the low (Figure 4.17) and high (Figure 4.18) E. coli density ranges. PAM treatment for the low E. coli density range (Figure 4.17) created no reduction in bacterial numbers. T-test results revealed a statistically significant difference (p = 0.0123) between E. coli densities at location L0 and L5. Increasing E. coli density across sample locations suggests that PAM treatment was ineffective at reducing the low E. coli density range. PAM treatment for the high E. coli density range
(Figure 4.18) produced a 29% reduction. However, densities across sample locations were determined to not be statistically different (F-stat = 1.5956, p = 0.2097, n = 40).
To determine whether inconsistent E. coli reductions were brought on by insufficient association between bacteria cells and clay particles, a 6th run (Figure 4.19) was added to Treatment 2 for the low density range. For the 6th run, bacteria and clay mixture remained un-agitated for a period of 24 hours to ensure adequate time for E. coli cells to associate with soil particles. Turbidity results (Figure 4.19) show similar reductions with a mean turbidity of 100 NTU at sample location L5 and a cumulative percent reduction of 94%.
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Runs
Turbidity (NTU) Turbidity
Mean
Location
Figure 4.19 – Mean turbidity for the 6th run after settling for 24 hours.
Runs
MPN/100mL
Location
Figure 4.20 – Mean MPN/100mL (low density range) after settling for 24 hours.
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Grab samples collected at the end of 24-hrs showed low bacteria levels and low turbidity at the surface, whereas high bacteria level and high turbidity were found near the bottom of the tank. On the basis of this data, it can be assumed that E. coli cells have attached to clay particles and are migrating towards the bottom due to gravitational settling. Despite the additional settling time to allow for association, PAM treatment failed to show a positive reduction and E. coli density actually increased 127%.
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CONCLUSIONS
The overall goal of this research was to simulate runoff discharged from sediment basins containing high turbidity and bacterial density, and to determine whether PAM treatment to reduce turbidity levels would result in a subsequent reduction in bacterial density. The overall goals of this research were meet, and several conclusions can be summarized from the results.
Based on theory, E. coli cells attached to clay particles should have settled out from PAM interaction and created a decrease in total E. coli density sampled throughout the treatment process. PAM application produced an average turbidity discharge of 80
NTU with a total reduction of 96%. Similarly, TSS was reduced by 92% with an average discharge of 174 mg/L. Despite PAM application, no reduction in bacterial density was observed for the low E. coli density range, rather E. coli displayed a positive increase across locations to 25,226 MPN/100 mL at discharge. A 29% reduction was recorded for the high E. coli density range with an average discharge of 135,270 MPN/100 mL; however, reductions within the high E. coli density range were determined to be not statistically different (F-stat = 1.5956, p = 0.2097, n = 40). Results of this research show that while PAM application results in turbidity and TSS reductions, under research conditions, granular PAM fails to reduce E. coli levels to the US EPA limit of 126 cfu/100mL for a 5-sample geometric mean as well as the single 235 cfu/100mL grab samples.
As discussed earlier in this paper, previous research demonstrated bacterial reductions using PAM, anionic flocculent (BactoLoxx), and compost filter socks.
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Faucette et al. (2009) reported final E. coli reductions to 0.57 x 1010 MPN/100mL (75%) using only filter socks and 0.02 x 1010 MPN/100mL (99%) using BactoLoxx. The experiment above produced 2.9 in/hr of simulated rainfall-runoff for 30 minute durations on bare soil conditions to create 0.0747 gallons/min or 0.0002 cfs of runoff (Faucette et al, 2009). PAM application reduced fecal coliforms by 90% in cattle (1.81 x 104 bacteria/100 mL), fish (2.80 x 104 bacteria/100 mL), and swine (1.35 x 106 bacteria/100 mL) wastewater in furrow 8.6 L/min (0.005 cfs) surface flow (Entry and Sojka, 2000).
Although both these studies recorded substantial reductions, discharged bacterial densities remained extremely elevated when compared to the US EPA limit. These studies were also conducted as scale models, which could point to why high reductions occurred and may not be a valid representation of “real world” conditions.
A possible explanation of lack of consistent reduction in E. coli densities is that a percentage of bacterial cells preferentially remained suspended in the water column rather than associating with clay particles. PAM treatment for bacteria would only remove those cells associated with clay particles, through flocculation. Research shows that E. coli cells do not reproduce within sediment basins (Tempel, 2012). Since the described testing conditions were designed to mimic sediment basin runoff, the increase in E. coli density was assumed not caused from reproduction. Increases in E. coli density across sample positions may result from PAM spread across sediment tubes acting as a sort of biofilm, allowing E. coli cells to attach and creating a source of E. coli cells within each sediment tube. Increases in E. coli density across sample locations may be attributed to disassociation of E. coli cells in accumulated flocculated clay particles
107 during resuspension occurring at the beginning of each run. Disassociation, resulting from the impact of water, may also occur at the sediment tube.
In the future, regulatory agencies may develop bacterial density effluent limits as part of the construction general permit to better regulate sediment basin discharge.
Regulatory strengthening will hinge on whether bacterial loading from sediment basins is found to significantly degrade water quality downstream. Further research evaluating alternative treatment processes will be needed to adequately reduce E. coli. Constructed wetlands and bioretention are alternatives to sediment basins and promote infiltration, but are costly, require space, and cannot handle typical sediment loading rates found on construction sites. Developing a small scale water treatment plant with chlorination and/or ultraviolent light could be other possible techniques.
Disclaimer
Mention of a trade name and/or products does not imply endorsement of the product by
Clemson University to the exclusion of others that might be available.
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CHAPTER 5
SUMMARY CONCLUSIONS
Two main goals for this research project are centered on reducing offsite impacts from construction site erosion. First, research aimed to maximize turbidity reduction while testing several passive PAM applications with excelsior sediment tubes in simulated construction site runoff. Secondly, the application that performed best was then applied to simulated runoff discharged from sediment basins containing high turbidity and bacterial density to determine whether PAM treatment would result in a subsequent reduction in bacterial density. Both goals of this research project were met.
Under the design conditions established for testing, the following conclusions can be made from the results:
1. Sediment tubes without PAM application provide no reduction in turbidity and
TSS under test conditions.
2. For test conditions, granular PAM sprinkled directly on sediment tubes provides
better reductions in turbidity and TSS than PAM delivered through a permeable
bag.
3. Granular PAM sprinkled in 100-g doses directly on each of five sediment tubes
applied each time before five simulated runoff events (Treatment 2) provides a
quicker decrease in turbidity than granular PAM sprinkled in 100-g doses directly
on each of five sediment tubes applied only once before five simulated runoff
events (Treatment 3).
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4. Turbidity levels meeting the proposed EPA 280 NTU effluent limit are achieved
within 3 sediment tubes when PAM is sprinkled before every run. PAM sprinkled
once before the beginning of the test creates turbidity levels that meet the
proposed 280 NTU effluent limit within five sediment tubes.
5. Once applied PAM (either in sprinkle or bag form) becomes wet from storm
events and becomes desiccated during periods of dry weather, it loses
effectiveness in reducing turbidity. As a result, runoff discharged after five
sediment tubes will exceed the proposed EPA 280 NTU effluent limit.
6. Under test conditions, reapplication of granular PAM to sediment tubes after
periods of dry weather and before storm events will consistently reduce turbidity
below the proposed EPA 280 NTU effluent limit.
7. Finally, although a small clay sediment size was selected for experimentation,
results reveal a strong coefficient of determination value (R2 = 0.89) between
turbidity and TSS. The conclusion can be made that turbidity reductions
corresponds to a reduction in sediment load.
After optimization of the PAM application, the following conclusions can be made from the bacterial results:
1. PAM application creates a reduction in average turbidity of 96% with an average
discharge of 80 NTU. Similarly, TSS is reduced by 92% with an average
discharge of 174 mg/L.
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2. No significant reduction in bacterial density is observed for the low E. coli density
range; rather, E. coli displays a positive increase across sediment tube positions to
25,226 MPN/100mL at discharge.
3. A 29% reduction is observed for the high E. coli density range with an average
discharge of 135,270 MPN/100mL; however, reductions within the high E. coli
density range are determined to be not statistically different (F-stat = 1.5956, p =
0.2097, n = 40). Based on statistical results, it is difficult to conclude that
reductions in bacterial density are in response to PAM application.
4. PAM application results in turbidity and TSS reductions, but fails to reduce E.
coli levels to the US EPA limit of 126 cfu/100mL for a 5-sample geometric mean
as well as the single 235 cfu/100mL grab samples.
Due to the steep slope and selected soil, results of this research were gathered under what could be considered worst case scenario conditions. Ultimately, this research could aid in improvement of construction site BMPs as well as provide information for future regulatory compliance.
Disclaimer
Mention of a trade name and/or products does not imply endorsement of the product by
Clemson University to the exclusion of others that might be available.
111
APPENDICES
112
APPENDIX A
Tabular turbidity and TSS data collected for turbidity reduction analysis
Appendix A contains raw turbidity and TSS data collected for turbidity reduction analysis section (Chapter 3). Blank spaces within the data set indicate samples were not taken for that time period. More samples were gathered on some tests due to increased drainage time from sediment tubes limiting more flow. Some samples were missed due to sampling equipment malfunction.
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Table A.1: Turbidity data for turbidity removal analysis
Average Time Time Time Time Time Time Time Sample Percent Treatment Duplicate Run Turbidity 0 4 8 12 16 20 24 Location Reduction (NTU) (min) (min) (min) (min) (min) (min) (min) 1 1 1 L0 3004.8 2728 2910 3351 3030 0.00 1 1 1 L1 3018.0 2769 2840 3413 3050 -0.44 1 1 1 L2 3036.0 2746 2908 3366 3124 -1.04 1 1 1 L3 2969.3 2708 2845 3307 3017 1.18 1 1 1 L4 2912.5 2750 2829 3133 2938 3.07 1 1 1 L5 2879.8 2668 2777 3005 3069 4.16 1 1 2 L0 3179.4 2859 3117 3409 3041 3471 0.00 1 1 2 L1 2986.8 2773 2902 3037 3235 6.06 1 1 2 L2 2967.3 2790 2853 3185 3041 6.67 1 1 2 L3 3013.8 2929 2800 3256 3070 5.21
114 1 1 2 L4 2995.3 2960 2774 3166 3081 5.79
1 1 2 L5 2979.5 2955 2742 3140 3081 6.29
1 1 3 L0 3718.5 3737 3700 0.00 1 1 3 L1 4048.0 4559 3391 4112 4130 -8.86 1 1 3 L2 3825.8 3449 4070 4109 3675 -2.88 1 1 3 L3 3819.8 3800 3384 4028 4067 -2.72 1 1 3 L4 3647.3 3371 3560 4011 1.91 1 1 3 L5 3845.2 4230 3356 3812 4017 3811 -3.41 1 1 4 L0 3421.0 2070 6430 1763 0.00 1 1 4 L1 1999.3 2178 1937 2033 1849 41.56 1 1 4 L2 2387.8 2229 1944 2108 1895 3763 30.20 1 1 4 L3 2046.5 2297 1908 2055 1926 40.18 1 1 4 L4 2298.2 3717 2122 1843 1974 1835 32.82 1 1 4 L5 2285.2 2571 1944 2070 1948 2893 33.20 1 1 5 L0 4300.5 3971 4290 4465 4476 0.00 1 1 5 L1 4601.0 4348 4406 5017 4633 -6.99 1 1 5 L2 4474.0 4517 4331 4421 4627 -4.03
Average Time Time Time Time Time Time Time Sample Percent Treatment Duplicate Run Turbidity 0 4 8 12 16 20 24 Location Reduction (NTU) (min) (min) (min) (min) (min) (min) (min) 1 1 5 L3 4443.5 4500 4137 4609 4528 -3.33 1 1 5 L4 5190.0 7312 4349 4480 4619 -20.68 1 1 5 L5 4497.3 4556 4306 4441 4686 -4.58 1 2 1 L0 2329.8 2659 2037 2309 2314 0.00 1 2 1 L1 2353.0 2476 1923 2698 2315 -1.00 1 2 1 L2 2259.0 2013 1909 2625 2538 2210 3.04 1 2 1 L3 2306.2 2013 1950 2714 2375 2479 1.01 1 2 1 L4 2347.6 1896 1921 2661 2360 2900 -0.77 1 2 1 L5 2191.2 1936 1957 2449 2356 2258 5.95 1 2 2 L0 2737.2 2581 2495 2560 2550 3500 0.00 1 2 2 L1 2876.8 2573 2575 2543 2609 2964 3997 -5.10 1 2 2 L2 2893.3 2775 2568 2624 2650 2888 3855 -5.70
115 1 2 2 L3 2967.3 2897 2650 2714 2700 2921 3922 -8.41
1 2 2 L4 3041.2 3127 2561 2628 2648 2921 4362 -11.11
1 2 2 L5 2970.2 3284 2651 2683 2698 2864 3641 -8.51 1 2 3 L0 3626.6 2905 2329 6097 3322 3480 0.00 1 2 3 L1 2929.8 2350 2367 3341 3274 3317 19.21 1 2 3 L2 3000.0 2428 2750 3114 3372 3336 17.28 1 2 3 L3 2989.2 2564 2378 3256 3416 3332 17.58 1 2 3 L4 2990.2 2620 2337 3271 3334 3389 17.55 1 2 3 L5 2884.6 2903 2279 2689 3291 3261 20.46 1 2 4 L0 3521.3 3449 3491 3565 3580 0.00 1 2 4 L1 2818.0 2872 2764 19.97 1 2 4 L2 3437.4 3044 2871 4109 3778 3385 2.38 1 2 4 L3 3413.3 3039 2812 4092 3710 3.07 1 2 4 L4 3365.4 3048 2859 3901 3724 3295 4.43 1 2 4 L5 3234.0 3129 2717 3353 3695 3276 8.16 1 2 5 L0 5339.6 10000 4237 4657 3745 4059 0.00 1 2 5 L1 3353.8 3002 2709 4032 3672 37.19
Average Time Time Time Time Time Time Time Sample Percent Treatment Duplicate Run Turbidity 0 4 8 12 16 20 24 Location Reduction (NTU) (min) (min) (min) (min) (min) (min) (min) 1 2 5 L2 3344.0 2931 2695 3609 3775 3710 37.37 1 2 5 L3 3298.0 2939 2725 3858 3670 38.24 1 2 5 L4 3347.8 3000 2765 3801 3825 37.30 1 2 5 L5 3281.0 2894 2709 3921 3600 38.55 2 1 1 L0 2004.5 1785 1818 1807 2608 0.00 2 1 1 L1 1135.8 512 781 872 1918 1596 43.34 2 1 1 L2 562.5 478 463 550 1019 555 310 71.94 2 1 1 L3 112.9 148 144 131 186 101 44.4 36.1 94.37 2 1 1 L4 103.0 101 189 139 99.7 81.7 74.8 35.5 94.86 2 1 1 L5 62.6 68.2 105 85.4 59.6 56.8 30.7 32.2 96.88 2 1 2 L0 2131.8 1865 1988 2660 2014 0.00 2 1 2 L1 820.8 885 875 1185 589 570 61.50
116 2 1 2 L2 387.4 746 723 892 220 54.3 45.5 31.1 81.83
2 1 2 L3 142.4 382 279 201 43 34 30.6 27.2 93.32
2 1 2 L4 120.1 493 132 79 38.8 26.8 49.6 21.2 94.37 2 1 2 L5 111.1 280 73 315 35.2 28.1 24.7 21.5 94.79 2 1 3 L0 2271.8 2112 2092 2769 2114 0.00 2 1 3 L1 1296.2 928 1195 2033 1243 1082 42.94 2 1 3 L2 399.0 721 867 878 208 53.6 37.1 28.2 82.44 2 1 3 L3 223.0 478 463 362 132 52.6 42.7 30.5 90.19 2 1 3 L4 146.3 360 284 190 75.7 46 38.8 29.8 93.56 2 1 3 L5 83.6 222 132 81.2 52.9 35.2 34.2 27.7 96.32 2 1 4 L0 1969.3 1697 2012 2235 1933 0.00 2 1 4 L1 1173.8 787 1200 1705 1091 1086 40.39 2 1 4 L2 377.6 802 817 768 79 68 56.8 52.5 80.82 2 1 4 L3 270.9 606 647 341 130 72.8 56.4 43 86.24 2 1 4 L4 188.0 469 445 206 74.2 48.1 41.1 32.6 90.45 2 1 4 L5 100.7 299 186 82.7 43.5 35.1 30.7 28.2 94.88 2 1 5 L0 1875.5 1520 1617 2175 2190 0.00
Average Time Time Time Time Time Time Time Sample Percent Treatment Duplicate Run Turbidity 0 4 8 12 16 20 24 Location Reduction (NTU) (min) (min) (min) (min) (min) (min) (min) 2 1 5 L1 1238.6 653 1074 1677 1336 1083 1257 1590 33.96 2 1 5 L2 410.1 809 636 960 195 104 86.1 80.6 78.13 2 1 5 L3 280.3 529 632 435 145 79.3 75 66.8 85.05 2 1 5 L4 225.5 626 457 299 68.1 45.1 42.3 40.8 87.98 2 1 5 L5 120.9 352 257 84.5 46.4 36.3 38 32.1 93.55 2 2 1 L0 2050.0 1550 1530 2684 2436 0.00 2 2 1 L1 1512.3 429 1022 2351 2247 26.23 2 2 1 L2 372.3 249 404 540 296 81.84 2 2 1 L3 141.1 127 183 142 154 99.3 93.12 2 2 1 L4 52.0 56.2 94.7 60.7 46.3 34.4 19.6 97.46 2 2 1 L5 34.0 45 40.9 36.7 30.1 30.1 21.3 98.34 2 2 2 L0 2747.5 2490 2525 3113 2862 0.00
117 2 2 2 L1 1791.2 872 1224 1980 2403 2477 34.81
2 2 2 L2 487.3 600 805 975 390 83.1 70.6 82.26
2 2 2 L3 180.1 309 372 296 120 55.4 57.5 51.1 93.44 2 2 2 L4 84.1 190 165 92.1 55 32.2 25.1 29 96.94 2 2 2 L5 52.3 130 77.7 51.9 31.3 26.1 23.8 25.4 98.10 2 2 3 L0 2287.7 2465 2199 2199 0.00 2 2 3 L1 1467.2 977 888 2022 1701 1748 35.86 2 2 3 L2 346.5 680 803 634 185 50 36.3 37.1 84.85 2 2 3 L3 225.2 645 510 246 61.9 44 39.4 30.1 90.16 2 2 3 L4 106.3 279 252 97.3 40.2 25.3 26 24.2 95.35 2 2 3 L5 70.7 198 125 80.9 30.2 16.8 19.3 25 96.91 2 2 4 L0 2341.8 2370 2272 2484 2241 0.00 2 2 4 L1 1447.3 922 1114 1936 1980 1681 1051 38.19 2 2 4 L2 417.8 958 894 732 209 62.2 37.8 31.8 82.16 2 2 4 L3 229.4 625 477 157 49 32.2 36.3 90.20 2 2 4 L4 79.4 286 50.2 26.7 17.6 16.7 96.61 2 2 4 L5 88.0 262 147 80.6 43.2 31.7 26 25.5 96.24
Average Time Time Time Time Time Time Time Sample Percent Treatment Duplicate Run Turbidity 0 4 8 12 16 20 24 Location Reduction (NTU) (min) (min) (min) (min) (min) (min) (min) 2 2 5 L0 2250.3 2630 2022 2264 2085 0.00 2 2 5 L1 1232.2 662 1109 1475 1525 1390 45.24 2 2 5 L2 364.3 720 682 749 244 82.9 46 26 83.81 2 2 5 L3 223.4 399 598 371 112 42.7 25.5 15.5 90.07 2 2 5 L4 160.6 408 364 205 62.3 35.1 26.7 22.9 92.86 2 2 5 L5 98.9 308 167 97.6 44.7 33 21.6 20.6 95.60 2 2 6 L0 4000.8 8205 2190 2902 2706 0.00 2 2 6 L1 1938.5 828 1702 2512 2257 2384 1948 51.55 2 2 6 L2 729.8 860 1098 1846 736 322 152 94.4 81.76 2 2 6 L3 426.5 588 576 1101 330 273 75 42.8 89.34 2 2 6 L4 213.5 401 464 445 90.7 35.3 28.6 29.9 94.66 2 2 6 L5 100.2 253 171 133 63 33 25 23.7 97.49
118 3 1 1 L0 2248.0 2108 2069 2751 2064 0.00
3 1 1 L1 1509.3 829 1304 1887 2017 32.86
3 1 1 L2 688.5 448 952 1273 499 546 413 69.37 3 1 1 L3 171.2 193 370 472 74.6 34.6 24.9 29 92.39 3 1 1 L4 61.0 80.1 126 95.5 47 31.6 29.7 17.1 97.29 3 1 1 L5 31.1 56.5 38 28.9 23.3 27.2 22.6 21 98.62 3 1 2 L0 2568.8 2235 2177 2812 3051 0.00 3 1 2 L1 1446.3 1425 1596 1438 1326 43.70 3 1 2 L2 658.9 950 1404 1681 300 103 85.5 88.8 74.35 3 1 2 L3 278.1 498 823 492 73 20.1 15.8 25 89.17 3 1 2 L4 105.9 188 333 134 33.2 19.2 17.8 15.9 95.88 3 1 2 L5 41.1 110 87.8 30.9 20.7 13.4 12.3 12.4 98.40 3 1 3 L0 1800.0 1800 1800 1800 1800 0.00 3 1 3 L1 1429.8 1707 1524 1510 978 20.57 3 1 3 L2 925.1 1808 1547 1728 575 281 275 262 48.60 3 1 3 L3 727.1 1928 1136 1003 183 91.4 21.3 59.60 3 1 3 L4 291.0 912 542 416 103 26.3 24.3 13.7 83.83
Average Time Time Time Time Time Time Time Sample Percent Treatment Duplicate Run Turbidity 0 4 8 12 16 20 24 Location Reduction (NTU) (min) (min) (min) (min) (min) (min) (min) 3 1 3 L5 53.9 177 100 33 25.1 16.8 11.8 13.7 97.00 3 1 4 L0 2100.0 2100 2100 2100 2100 0.00 3 1 4 L1 1866.5 2110 2124 1978 1254 11.12 3 1 4 L2 1270.3 2550 2070 2224 789 500 395 364 39.51 3 1 4 L3 1074.6 2809 1762 1331 328 164 53.4 48.83 3 1 4 L4 694.2 94 1334 726 234 1756 20.9 66.95 3 1 4 L5 81.6 194 190 110 26.2 22.3 14.4 14.6 96.11 3 1 6 L0 2983.5 2750 2770 2999 3415 0.00 3 1 6 L1 2162.8 1873 2215 2386 2177 27.51 3 1 6 L2 1879.4 1940 2041 2751 1599 1066 37.01 3 1 6 L3 1654.0 2795 1934 2635 1682 988 792 752 44.56 3 1 6 L4 1741.9 3769 1888 2246 1540 1050 841 859 41.62
119 3 1 6 L5 1496.1 4360 1561 1379 1143 780 613 637 49.85
3 2 1 L0 2397.3 1765 2378 2710 2736 0.00
3 2 1 L1 1834.0 1021 1860 2621 23.50 3 2 1 L2 1347.8 743 1290 1576 1782 43.78 3 2 1 L3 494.5 387 646 674 271 79.37 3 2 1 L4 138.2 118 241 431 67.4 36.6 28.2 45.1 94.24 3 2 1 L5 38.4 65.2 50.7 45.9 38 29 24.3 15.6 98.40 3 2 2 L0 4285.0 10000 2333 2537 2270 0.00 3 2 2 L1 1909.0 1497 1880 2350 55.45 3 2 2 L2 1199.3 872 1403 1550 972 72.01 3 2 2 L3 407.1 708 887 951 183 51.3 44.6 25 90.50 3 2 2 L4 187.6 346 359 382 141 40.1 22.7 22.4 95.62 3 2 2 L5 52.1 139 78 52.8 39.9 22.2 17.8 15.3 98.78 3 2 3 L0 2059.3 1944 2250 2043 2000 0.00 3 2 3 L1 1606.3 1325 1561 1933 21.99 3 2 3 L2 922.0 909 1118 1873 692 484 456 55.23 3 2 3 L3 514.6 1210 819 797 179 56.3 26 75.01
Average Time Time Time Time Time Time Time Sample Percent Treatment Duplicate Run Turbidity 0 4 8 12 16 20 24 Location Reduction (NTU) (min) (min) (min) (min) (min) (min) (min) 3 2 3 L4 253.6 693 466 382 145 41.2 26.6 21.2 87.69 3 2 3 L5 64.0 199 97.3 46 32.5 32.1 21.6 19.3 96.89 3 2 4 L0 4935.3 10000 3919 2922 2900 0.00 3 2 4 L1 2577.0 2313 2430 2760 2805 47.78 3 2 4 L2 1178.0 1798 2164 1029 956 593 528 76.13 3 2 4 L3 789.0 1623 1882 1526 311 57.6 77 46.1 84.01 3 2 4 L4 342.8 1184 489 161 103 64.9 55 93.05 3 2 4 L5 66.8 125 133 73.3 39 26.2 32 39 98.65 3 2 5 L0 2695.0 3772 2200 2308 2500 0.00 3 2 5 L1 1993.0 1613 1835 2184 2340 26.05 3 2 5 L2 903.9 1559 1602 1317 755 435 350 309 66.46 3 2 5 L3 778.7 2205 1602 1126 326 98.4 36.5 57.1 71.11
120 3 2 5 L4 535.6 1323 1003 591 139 89.9 67.7 80.13
3 2 5 L5 126.8 283 307 152 46.8 29.5 22.6 46.7 95.29
3 2 6 L0 1609.8 1479 1733 1599 1628 0.00 3 2 6 L1 1657.0 1676 1687 1608 -2.94 3 2 6 L2 1389.4 1793 1529 1440 1097 1088 13.69 3 2 6 L3 1260.1 2402 1567 1477 1046 891 771 667 21.72 3 2 6 L4 1293.4 2579 1657 1397 1268 747 642 764 19.65 3 2 6 L5 1071.3 2707 1204 934 905 656 583 510 33.45 4 1 1 L0 2430.0 2016 2399 2875 0.00 4 1 1 L1 1283.7 859 1572 1420 47.17 4 1 1 L2 844.5 452 1118 1087 721 65.25 4 1 1 L3 427.3 372 510 717 110 82.42 4 1 1 L4 100.00 4 1 1 L5 181.8 255 213 217 42.2 92.52 4 1 2 L0 3228.8 5060 2600 2625 2630 0.00 4 1 2 L1 1289.0 1456 1904 1706 89.9 60.08 4 1 2 L2 899.5 825 1310 1292 171 72.14
Average Time Time Time Time Time Time Time Sample Percent Treatment Duplicate Run Turbidity 0 4 8 12 16 20 24 Location Reduction (NTU) (min) (min) (min) (min) (min) (min) (min) 4 1 2 L3 632.3 563 921 928 117 80.42 4 1 2 L4 100.00 4 1 2 L5 277.9 500 381 427 58.4 23.2 91.39 4 1 3 L0 3264.5 3800 2959 3050 3249 0.00 4 1 3 L1 1852.5 2588 2393 2196 233 43.25 4 1 3 L2 1847.0 2843 2146 2080 319 43.42 4 1 3 L3 1452.8 2337 1738 1603 133 55.50 4 1 3 L4 876.8 1888 1427 180 12 73.14 4 1 3 L5 910.1 3420 1297 616 70.9 33.4 23 72.12 4 1 4 L0 3493.3 5294 3399 2450 2830 0.00 4 1 4 L1 1542.0 2242 1984 1723 219 55.86 4 1 4 L2 1583.8 2618 1740 1629 348 54.66
121 4 1 4 L3 1311.0 2563 1244 1269 168 62.47
4 1 4 L4 1236.4 4800 1282 1130 125 45.3 36.2 64.61
4 1 4 L5 1425.7 5909 1211 1271 86.2 45 32.2 59.19 4 1 5 L0 3036.8 3716 2400 2861 3170 0.00 4 1 5 L1 1572.1 2041 2008 2171 68.2 48.23 4 1 5 L2 1499.8 2678 2176 1019 126 50.61 4 1 5 L3 1121.9 2440 1358 1616 132 63.4 63.06 4 1 5 L4 1177.4 4020 1481 1294 115 92.1 62.4 61.23 4 1 5 L5 787.8 3470 890 249 47.9 39 31 74.06 4 1 6 L0 3028.8 2498 2531 3341 3745 0.00 4 1 6 L1 2241.3 2525 2130 3040 1270 26.00 4 1 6 L2 2256.8 2574 2055 2871 1527 25.49 4 1 6 L3 2380.8 3550 1985 2825 1163 21.39 4 1 6 L4 1701.4 3055 1899 2762 463 328 43.83 4 1 6 L5 1895.0 4741 1684 2067 662 321 37.43 4 2 1 L0 3826.0 6621 2275 3117 3291 0.00 4 2 1 L1 1677.7 1030 1773 2230 56.15
Average Time Time Time Time Time Time Time Sample Percent Treatment Duplicate Run Turbidity 0 4 8 12 16 20 24 Location Reduction (NTU) (min) (min) (min) (min) (min) (min) (min) 4 2 1 L2 1076.8 503 1287 2073 444 71.86 4 2 1 L3 618.0 160 904 1291 117 83.85 4 2 1 L4 354.0 75 117 1069 155 90.75 4 2 1 L5 123.8 37.8 84.5 377 74.8 45.1 96.76 4 2 2 L0 3556.0 4588 2900 3180 0.00 4 2 2 L1 2075.3 1716 2204 2306 41.64 4 2 2 L2 1869.3 1480 1863 2265 47.43 4 2 2 L3 1721.7 1452 1807 1906 51.58 4 2 2 L4 1505.0 1936 2017 1894 173 57.68 4 2 2 L5 858.9 2100 1001 961 196 36.5 75.85 4 2 3 L0 2767.3 4748 1653 2481 2187 0.00 4 2 3 L1 1693.0 1590 1740 1749 38.82
122 4 2 3 L2 1611.7 1570 1530 1735 41.76
4 2 3 L3 1216.8 1840 1429 1488 110 56.03
4 2 3 L4 1249.3 3303 1307 1401 182 53.5 54.85 4 2 3 L5 1671.5 6350 1000 788 154 65.7 39.60 4 2 4 L0 2700.3 3330 2600 2304 2567 0.00 4 2 4 L1 1695.0 1581 1885 1619 37.23 4 2 4 L2 1778.7 1682 1895 1759 34.13 4 2 4 L3 1287.8 1701 1752 1565 133 52.31 4 2 4 L4 1581.5 4646 1716 1331 174 40.5 41.43 4 2 4 L5 1451.3 5100 1232 800 74.5 49.8 46.25 4 2 5 L0 3683.3 4300 3416 3400 3617 0.00 4 2 5 L1 2707.0 2544 2777 2800 26.51 4 2 5 L2 2596.3 2468 2841 2480 29.51 4 2 5 L3 1885.3 2320 2570 2491 160 48.82 4 2 5 L4 1915.4 4490 2578 2122 334 53 48.00 4 2 5 L5 1466.8 4825 1511 755 154 88.9 60.18 4 2 6 L0 4368.8 10000 2716 2279 2480 0.00
Average Time Time Time Time Time Time Time Sample Percent Treatment Duplicate Run Turbidity 0 4 8 12 16 20 24 Location Reduction (NTU) (min) (min) (min) (min) (min) (min) (min) 4 2 6 L1 2310.0 2579 2501 1850 47.12 4 2 6 L2 2213.3 2298 2374 1968 49.34 4 2 6 L3 1637.3 2250 2309 1722 268 62.52 4 2 6 L4 1674.3 2243 2317 1814 323 61.68 4 2 6 L5 1832.8 4842 2085 1719 380 138 58.05
123
Table A. 2: TSS data for turbidity removal analysis
Average Time Time Time Time Percent Treatment Duplicate Run Location TSS 0 4 8 12 Reduction (mg/L) (min) (min) (min) (min) 1 2 1 L0 1690.0 1880.0 1550.0 1670.0 1660.0 0.00 1 2 1 L1 1632.5 1740.0 1420.0 1810.0 1560.0 3.40 1 2 1 L2 1557.5 1460.0 1450.0 1770.0 1550.0 7.84 1 2 1 L3 1552.5 1470.0 1410.0 1770.0 1560.0 8.14 1 2 1 L4 1557.5 1400.0 1430.0 1810.0 1590.0 7.84 1 2 1 L5 1530.0 1450.0 1410.0 1650.0 1610.0 9.47 1 2 2 L0 1790.0 1760.0 1820.0 0.00 1 2 2 L1 1820.0 1820.0 -1.68
1 2 2 L2 1825.0 1850.0 1800.0 -1.96 124
1 2 2 L3 1905.0 1950.0 1860.0 -6.42
1 2 2 L4 2075.0 2180.0 1970.0 -15.92 1 2 2 L5 2140.0 2230.0 2050.0 -19.55 1 2 3 L0 2640.0 1790.0 4250.0 1880.0 0.00 1 2 3 L1 1735.0 1570.0 1900.0 34.28 1 2 3 L2 1730.0 1550.0 1910.0 34.47 1 2 3 L3 1760.0 1610.0 1910.0 33.33 1 2 3 L4 1710.0 1580.0 1840.0 35.23 1 2 3 L5 1805.0 1730.0 1880.0 31.63 1 2 4 L0 2150.0 2250.0 2050.0 0.00 1 2 4 L1 1790.0 1790.0 16.74 1 2 4 L2 1840.0 1680.0 2000.0 14.42 1 2 4 L3 1865.0 1670.0 2060.0 13.26 1 2 4 L4 1805.0 1690.0 1920.0 16.05
Average Time Time Time Time Percent Treatment Duplicate Run Location TSS 0 4 8 12 Reduction (mg/L) (min) (min) (min) (min) 1 2 4 L5 1790.0 1790.0 16.74 1 2 5 L0 9930.0 17610.0 2250.0 0.00 1 2 5 L1 2105.0 1950.0 2260.0 78.80 1 2 5 L2 2095.0 1950.0 2240.0 78.90 1 2 5 L3 2080.0 1910.0 2250.0 79.05 1 2 5 L4 2080.0 1920.0 2240.0 79.05 1 2 5 L5 2125.0 1990.0 2260.0 78.60 2 1 1 L0 1475.0 1270.0 1680.0 0.00 2 1 1 L1 1195.0 820.0 1570.0 18.98 2 1 1 L2 940.0 780.0 1100.0 36.27
125 2 1 1 L3 240.0 250.0 230.0 83.73
2 1 1 L4 160.0 210.0 110.0 89.15
2 1 1 L5 85.0 120.0 50.0 94.24 2 1 2 L0 1275.0 1340.0 1210.0 0.00 2 1 2 L1 1050.0 1300.0 800.0 17.65 2 1 2 L2 930.0 1540.0 320.0 27.06 2 1 2 L3 480.0 890.0 70.0 62.35 2 1 2 L4 480.0 880.0 80.0 62.35 2 1 2 L5 380.0 690.0 70.0 70.20 2 1 3 L0 1460.0 1570.0 1350.0 0.00 2 1 3 L1 1690.0 1790.0 1590.0 -15.75 2 1 3 L2 905.0 1490.0 320.0 38.01 2 1 3 L3 695.0 1190.0 200.0 52.40 2 1 3 L4 540.0 960.0 120.0 63.01 2 1 3 L5 370.0 570.0 170.0 74.66
Average Time Time Time Time Percent Treatment Duplicate Run Location TSS 0 4 8 12 Reduction (mg/L) (min) (min) (min) (min) 2 1 4 L0 1340.0 1350.0 1330.0 0.00 2 1 4 L1 1495.0 1760.0 1230.0 -11.57 2 1 4 L2 1200.0 2250.0 150.0 10.45 2 1 4 L3 1095.0 1980.0 210.0 18.28 2 1 4 L4 980.0 1830.0 130.0 26.87 2 1 4 L5 485.0 900.0 70.0 63.81 2 1 5 L0 1360.0 1240.0 1480.0 0.00 2 1 5 L1 1510.0 1670.0 1350.0 -11.03 2 1 5 L2 970.0 1680.0 260.0 28.68 2 1 5 L3 845.0 1450.0 240.0 37.87
126 2 1 5 L4 1150.0 2160.0 140.0 15.44
2 1 5 L5 640.0 1200.0 80.0 52.94
2 2 1 L0 1516.5 1320.0 1713.0 0.00 2 2 1 L1 1210.0 730.0 1690.0 20.21 2 2 1 L2 470.0 400.0 640.0 370.0 69.01 2 2 1 L3 230.0 250.0 210.0 230.0 84.83 2 2 1 L4 115.0 150.0 80.0 92.42 2 2 1 L5 110.0 130.0 90.0 92.75 2 2 2 L0 1940.0 2040.0 1840.0 0.00 2 2 2 L1 1525.0 1380.0 1670.0 21.39 2 2 2 L2 990.0 1440.0 540.0 48.97 2 2 2 L3 572.5 810.0 690.0 580.0 210.0 70.49 2 2 2 L4 335.0 570.0 400.0 240.0 130.0 82.73 2 2 2 L5 205.0 330.0 230.0 160.0 100.0 89.43 2 2 3 L0 1580.0 1580.0 0.00
Average Time Time Time Time Percent Treatment Duplicate Run Location TSS 0 4 8 12 Reduction (mg/L) (min) (min) (min) (min) 2 2 3 L1 1460.0 1500.0 1420.0 7.59 2 2 3 L2 950.0 1850.0 790.0 210.0 39.87 2 2 3 L3 715.0 1680.0 790.0 330.0 60.0 54.75 2 2 3 L4 450.0 830.0 70.0 71.52 2 2 3 L5 232.5 550.0 210.0 120.0 50.0 85.28 2 2 4 L0 1565.0 1720.0 1410.0 0.00 2 2 4 L1 1630.0 1720.0 1540.0 -4.15 2 2 4 L2 1370.0 2450.0 290.0 12.46 2 2 4 L3 646.7 1040.0 680.0 220.0 58.68 2 2 4 L4 355.0 640.0 70.0 77.32
127 2 2 4 L5 295.0 760.0 240.0 140.0 40.0 81.15
2 2 5 L0 1570.0 1770.0 1370.0 0.00
2 2 5 L1 1640.0 1910.0 1370.0 -4.46 2 2 5 L2 1305.0 2710.0 1280.0 900.0 330.0 16.88 2 2 5 L3 1445.0 2730.0 160.0 7.96 2 2 5 L4 865.0 1640.0 90.0 44.90 2 2 5 L5 390.0 990.0 360.0 130.0 80.0 75.16 2 2 6 L0 2 2 6 L1 2 2 6 L2 2 2 6 L3 2 2 6 L4 2 2 6 L5 3 1 1 L0 1375.0 1520.0 1230.0 0.00 3 1 1 L1 1350.0 1340.0 1360.0 1.82
Average Time Time Time Time Percent Treatment Duplicate Run Location TSS 0 4 8 12 Reduction (mg/L) (min) (min) (min) (min) 3 1 1 L2 640.0 710.0 570.0 53.45 3 1 1 L3 210.0 330.0 90.0 84.73 3 1 1 L4 115.0 180.0 50.0 91.64 3 1 1 L5 110.0 160.0 60.0 92.00 3 1 2 L0 1685.0 1530.0 1840.0 0.00 3 1 2 L1 1540.0 1820.0 1260.0 8.61 3 1 2 L2 895.0 1410.0 380.0 46.88 3 1 2 L3 475.0 840.0 110.0 71.81 3 1 2 L4 250.0 430.0 70.0 85.16 3 1 2 L5 120.0 190.0 50.0 92.88
128 3 1 3 L0
3 1 3 L1 1305.0 1520.0 1090.0
3 1 3 L2 1270.0 1840.0 700.0 3 1 3 L3 1285.0 2310.0 260.0 3 1 3 L4 890.0 1610.0 170.0 3 1 3 L5 210.0 340.0 80.0 3 1 4 L0 3 1 4 L1 1545.0 1870.0 1220.0 3 1 4 L2 1505.0 2190.0 820.0 3 1 4 L3 1470.0 2610.0 330.0 3 1 4 L4 385.0 50.0 720.0 3 1 4 L5 185.0 350.0 20.0 3 1 6 L0 1980.0 1950.0 2010.0 0.00 3 1 6 L1 1640.0 1690.0 1590.0 17.17 3 1 6 L2 1385.0 1730.0 1040.0 30.05
Average Time Time Time Time Percent Treatment Duplicate Run Location TSS 0 4 8 12 Reduction (mg/L) (min) (min) (min) (min) 3 1 6 L3 1755.0 2280.0 1230.0 11.36 3 1 6 L4 2100.0 3040.0 1160.0 -6.06 3 1 6 L5 2280.0 3640.0 920.0 -15.15 3 2 1 L0 1485.0 1370.0 1600.0 0.00 3 2 1 L1 1590.0 1470.0 1710.0 -7.07 3 2 1 L2 1010.0 1010.0 31.99 3 2 1 L3 410.0 540.0 280.0 72.39 3 2 1 L4 135.0 190.0 80.0 90.91 3 2 1 L5 85.0 120.0 50.0 94.28 3 2 2 L0 4340.0 7320.0 1360.0 0.00
129 3 2 2 L1 1530.0 1430.0 1630.0 64.75
3 2 2 L2 1080.0 1250.0 910.0 75.12
3 2 2 L3 615.0 1020.0 210.0 85.83 3 2 2 L4 330.0 490.0 170.0 92.40 3 2 2 L5 170.0 270.0 70.0 96.08 3 2 3 L0 1305.0 1420.0 1190.0 0.00 3 2 3 L1 1305.0 1200.0 1410.0 0.00 3 2 3 L2 990.0 1270.0 710.0 24.14 3 2 3 L3 940.0 1680.0 200.0 27.97 3 2 3 L4 725.0 1270.0 180.0 44.44 3 2 3 L5 185.0 310.0 60.0 85.82 3 2 4 L0 6310.0 10860.0 1760.0 0.00 3 2 4 L1 1750.0 1890.0 1610.0 72.27 3 2 4 L2 1455.0 1950.0 960.0 76.94 3 2 4 L3 1195.0 2030.0 360.0 81.06
Average Time Time Time Time Percent Treatment Duplicate Run Location TSS 0 4 8 12 Reduction (mg/L) (min) (min) (min) (min) 3 2 4 L4 1030.0 1870.0 190.0 83.68 3 2 4 L5 150.0 230.0 70.0 97.62 3 2 5 L0 1490.0 1490.0 0.00 3 2 5 L1 1450.0 1460.0 1440.0 2.68 3 2 5 L2 1135.0 1460.0 810.0 23.83 3 2 5 L3 1275.0 2180.0 370.0 14.43 3 2 5 L4 3 2 5 L5 310.0 530.0 90.0 79.19 3 2 6 L0 3 2 6 L1
130 3 2 6 L2
3 2 6 L3
3 2 6 L4 3 2 6 L5 4 1 1 L0 2010.0 2010.0 0.00 4 1 1 L1 1175.0 1110.0 1240.0 41.54 4 1 1 L2 740.0 700.0 780.0 63.18 4 1 1 L3 340.0 580.0 100.0 83.08 4 1 1 L4 100.00 4 1 1 L5 160.0 160.0 92.04 4 1 2 L0 3530.0 5230.0 1830.0 0.00 4 1 2 L1 800.0 1510.0 90.0 77.34 4 1 2 L2 1185.0 1070.0 1300.0 66.43 4 1 2 L3 4 1 2 L4
Average Time Time Time Time Percent Treatment Duplicate Run Location TSS 0 4 8 12 Reduction (mg/L) (min) (min) (min) (min) 4 1 2 L5 425.0 760.0 90.0 87.96 4 1 3 L0 2575.0 3020.0 2130.0 0.00 4 1 3 L1 1175.0 2090.0 260.0 54.37 4 1 3 L2 1360.0 2370.0 350.0 47.18 4 1 3 L3 1305.0 2440.0 170.0 49.32 4 1 3 L4 4 1 3 L5 1760.0 3440.0 80.0 31.65 4 1 4 L0 3020.0 4210.0 1830.0 0.00 4 1 4 L1 995.0 1770.0 220.0 67.05 4 1 4 L2 1235.0 2100.0 370.0 59.11
131 4 1 4 L3 1295.0 2410.0 180.0 57.12
4 1 4 L4 2525.0 4930.0 120.0 16.39
4 1 4 L5 2750.0 5390.0 110.0 8.94 4 1 5 L0 2480.0 2480.0 0.00 4 1 5 L1 900.0 1640.0 160.0 63.71 4 1 5 L2 1100.0 2030.0 170.0 55.65 4 1 5 L3 1155.0 2140.0 170.0 53.43 4 1 5 L4 1805.0 3430.0 180.0 27.22 4 1 5 L5 1655.0 3240.0 70.0 33.27 4 1 6 L0 1680.0 1680.0 0.00 4 1 6 L1 1465.0 1970.0 960.0 12.80 4 1 6 L2 1575.0 2000.0 1150.0 6.25 4 1 6 L3 1805.0 2680.0 930.0 -7.44 4 1 6 L4 1505.0 2560.0 450.0 10.42 4 1 6 L5 2270.0 3930.0 610.0 -35.12
Average Time Time Time Time Percent Treatment Duplicate Run Location TSS 0 4 8 12 Reduction (mg/L) (min) (min) (min) (min) 4 2 1 L0 6400.0 6400.0 0.00 4 2 1 L1 2500.0 1440.0 3560.0 60.94 4 2 1 L2 885.0 1060.0 710.0 86.17 4 2 1 L3 350.0 440.0 260.0 94.53 4 2 1 L4 335.0 370.0 300.0 94.77 4 2 1 L5 255.0 320.0 190.0 96.02 4 2 2 L0 4 2 2 L1 3495.0 2960.0 4030.0 4 2 2 L2 3110.0 2320.0 3900.0 4 2 2 L3 2305.0 1990.0 2620.0
132 4 2 2 L4 1235.0 2140.0 330.0
4 2 2 L5 1565.0 2780.0 350.0
4 2 3 L0 4160.0 4160.0 0.00 4 2 3 L1 2575.0 2180.0 2970.0 38.10 4 2 3 L2 1735.0 1770.0 1700.0 58.29 4 2 3 L3 1150.0 2030.0 270.0 72.36 4 2 3 L4 1640.0 2920.0 360.0 60.58 4 2 3 L5 3705.0 7020.0 390.0 10.94 4 2 4 L0 2880.0 2880.0 0.00 4 2 4 L1 1835.0 2050.0 1620.0 36.28 4 2 4 L2 1965.0 1730.0 2200.0 31.77 4 2 4 L3 1355.0 2390.0 320.0 52.95 4 2 4 L4 2990.0 5580.0 400.0 -3.82 4 2 4 L5 3335.0 6390.0 280.0 -15.80 4 2 5 L0 4440.0 4440.0 0.00
Average Time Time Time Time Percent Treatment Duplicate Run Location TSS 0 4 8 12 Reduction (mg/L) (min) (min) (min) (min) 4 2 5 L1 2750.0 3200.0 2300.0 38.06 4 2 5 L2 2290.0 2370.0 2210.0 48.42 4 2 5 L3 1825.0 3310.0 340.0 58.90 4 2 5 L4 2430.0 4310.0 550.0 45.27 4 2 5 L5 2490.0 4630.0 350.0 43.92
133
APPENDIX B
Tabular turbidity, TSS, E. coli density data collected for bacterial reduction analysis
Appendix B contains raw turbidity, TSS, E. coli data collected for turbidity reduction analysis section (Chapter 4). Blank spaces within the data set indicate samples were not taken for that time period. More samples were gathered on some tests due to increased drainage time from sediment tubes limiting more flow. Some samples were missed due to sampling equipment malfunction.
134
Table B.1: Turbidity data for bacterial reduction analysis
Average Time Time Time Time Percent Treatment Duplicate Run Location Turbidity 0 4 8 12 Reduction (NTU) (min) (min) (min) (min) 5 1 1 L0 2693.8 3801 3597 1542 1835 0.0 5 1 1 L1 1401.0 1293 1476 1434 48.0 5 1 1 L3 1342.3 1275 1452 1300 50.2 5 1 1 L5 1300.0 1258 1327 1315 51.7 5 1 2 L0 2200.0 2200 0.0 5 1 2 L1 1792.7 1670 1833 1875 18.5 5 1 2 L3 1874.7 1790 1951 1883 14.8 5 1 2 L5 1909.0 1980 1862 1885 13.2
5 1 3 L0 2290.0 2034 2518 2318 0.0 135
5 1 3 L1 2295.7 2205 2500 2182 -0.2
5 1 3 L3 2331.7 2250 2513 2232 -1.8 5 1 3 L5 2324.0 2398 2300 2274 -1.5 5 1 4 L0 3461.3 4366 3055 2974 3450 0.0 5 1 4 L1 2931.7 2828 3129 2838 15.3 5 1 4 L3 2968.3 3022 3150 2733 14.2 5 1 4 L5 3016.7 3085 3085 2880 12.8 5 1 5 L0 1705.5 1632 1538 1562 2090 0.0 5 1 5 L1 1734.7 1675 1906 1623 -1.7 5 1 5 L3 1767.0 1764 1888 1649 -3.6 5 1 5 L5 1932.0 2261 1870 1665 -13.3 5 2 1 L0 2788.8 2626 2537 2609 3383 0.0 5 2 1 L1 2683.3 2638 2790 2622 3.8 5 2 1 L3 2620.3 2658 2636 2567 6.0
Average Time Time Time Time Percent Treatment Duplicate Run Location Turbidity 0 4 8 12 Reduction (NTU) (min) (min) (min) (min) 5 2 1 L5 2585.3 2604 2600 2552 7.3 5 2 2 L0 2602.7 3093 2418 2297 0.0 5 2 2 L1 2274.0 2130 2458 2234 12.6 5 2 2 L3 2315.0 2444 2270 2231 11.1 5 2 2 L5 2327.0 2538 2283 2160 10.6 5 2 3 L0 1930.0 2800 1520 1470 0.0 5 2 3 L1 1367.0 1338 1403 1360 29.2 5 2 3 L3 1493.0 1692 1397 1390 22.6 5 2 3 L5 1478.0 1670 1365 1399 23.4 5 2 4 L0 1544.0 1651 1479 1502 0.0
136 5 2 4 L1 1557.0 1507 1589 1575 -0.8
5 2 4 L3 1580.0 1810 1465 1465 -2.3
5 2 4 L5 1548.0 1672 1478 1494 -0.3 5 2 5 L0 1996.3 2132 1832 2025 0.0 5 2 5 L1 2084.7 2089 2110 2055 -4.4 5 2 5 L3 1991.3 2342 1844 1788 0.3 5 2 5 L5 2027.3 2269 1870 1943 -1.6 6 1 1 L0 2647.0 3175 2380 2386 0.0 6 1 1 L1 1719.0 1411 2134 1612 35.1 6 1 1 L3 203.8 313 332 135 35 92.3 6 1 1 L5 39.8 51 50 38 20 98.5 6 1 2 L0 3242.3 5709 2026 1992 0.0 6 1 2 L1 836.3 447 1041 950 907 74.2 6 1 2 L3 171.5 355 261 43 27 94.7 6 1 2 L5 60.3 118 83 25 15 98.1
Average Time Time Time Time Percent Treatment Duplicate Run Location Turbidity 0 4 8 12 Reduction (NTU) (min) (min) (min) (min) 6 1 3 L0 2530.7 4000 1748 1844 0.0 6 1 3 L1 1064.0 887 1089 956 1324 58.0 6 1 3 L3 263.0 486 414 115 37 89.6 6 1 3 L5 95.0 204 117 36 23 96.2 6 1 4 L0 2823.7 2692 2736 3043 0.0 6 1 4 L1 1735.8 1004 2046 2458 1435 38.5 6 1 4 L3 390.8 682 691 151 39 86.2 6 1 4 L5 110.0 248 137 40 15 96.1 6 1 5 L0 2985.5 5496 1900 2555 1991 0.0 6 1 5 L1 1473.0 826 1328 1789 1949 50.7
137 6 1 5 L3 356.5 767 484 120 55 88.1
6 1 5 L5 112.0 307 86 33 22 96.2
6 2 1 L0 3319.0 3750 2128 2875 4523 0.0 6 2 1 L1 1792.0 1290 1191 2895 46.0 6 2 1 L3 346.8 241 248 682 216 89.6 6 2 1 L5 56.0 73 59 60 32 98.3 6 2 2 L0 2863.3 1728 1710 3682 4333 0.0 6 2 2 L1 997.0 335 975 1681 65.2 6 2 2 L3 148.0 265 146 139 42 94.8 6 2 2 L5 60.5 111 67 34 30 97.9 6 2 3 L0 2112.8 1769 1642 1990 3050 0.0 6 2 3 L1 828.3 705 730 860 1018 60.8 6 2 3 L3 182.5 329 273 101 27 91.4 6 2 3 L5 67.8 140 80 30 21 96.8 6 2 4 L0 1500.0 1350 1393 1757 0.0
Average Time Time Time Time Percent Treatment Duplicate Run Location Turbidity 0 4 8 12 Reduction (NTU) (min) (min) (min) (min) 6 2 4 L1 742.5 625 880 936 529 50.5 6 2 4 L3 225.3 369 356 138 38 85.0 6 2 4 L5 96.8 195 123 45 24 93.6 6 2 5 L0 1143.7 943 1154 1334 0.0 6 2 5 L1 793.3 516 748 1247 662 30.6 6 2 5 L3 189.3 388 203 134 32 83.5 6 2 5 L5 108.5 230 134 47 23 90.5 6 2 6 L0 1595.7 1270 1433 2084 0.0 6 2 6 L1 843.8 527 1174 648 1026 47.1 6 2 6 L3 296.3 507 399 227 52 81.4
138 6 2 6 L5 100.7 198 67 37 93.7
Table B.2: TSS data for bacterial removal analysis
Average Time Time Time Percent Treatment Duplicate Run Location TSS 0 4 8 Reduction (mg/L) (min) (min) (min) 5 1 1 L0 2045 2870 1220 0.0 5 1 1 L1 1120 1150 1090 45.2 5 1 1 L3 1120 1130 1110 45.2 5 1 1 L5 1135 1150 1120 44.5 5 1 2 L0 1435 1320 1550 0.0 5 1 2 L1 1315 1300 1330 8.4 5 1 2 L3 1295 1290 1300 9.8 5 1 2 L5 1385 1420 1350 3.5
5 1 3 L0 1655 1660 1650 0.0 139
5 1 3 L1 1555 1610 1500 6.0
5 1 3 L3 1565 1670 1460 5.4 5 1 3 L5 1620 1740 1500 2.1 5 1 4 L0 2670 3380 1960 0.0 5 1 4 L1 1825 1900 1750 31.6 5 1 4 L3 1855 1980 1730 30.5 5 1 4 L5 1965 2110 1820 26.4 5 1 5 L0 1375 1410 1340 0.0 5 1 5 L1 1230 1300 1160 10.5 5 1 5 L3 1280 1360 1200 6.9 5 1 5 L5 1555 1830 1280 -13.1 5 2 1 L0 1830 1940 1720 0.0 5 2 1 L1 1655 1720 1590 9.6 5 2 1 L3 1630 1700 1560 10.9
Average Time Time Time Percent Treatment Duplicate Run Location TSS 0 4 8 Reduction (mg/L) (min) (min) (min) 5 2 1 L5 1620 1680 1560 11.5 5 2 2 L0 1965 2330 1600 0.0 5 2 2 L1 1520 1570 1470 22.6 5 2 2 L3 1570 1690 1450 20.1 5 2 2 L5 1615 1790 1440 17.8 5 2 3 L0 1780 2470 1090 0.0 5 2 3 L1 1070 1110 1030 39.9 5 2 3 L3 1200 1380 1020 32.6 5 2 3 L5 1160 1280 1040 34.8 5 2 4 L0 1235 1380 1090 0.0
140 5 2 4 L1 1115 1140 1090 9.7
5 2 4 L3 1200 1380 1020 2.8
5 2 4 L5 1175 1300 1050 4.9 5 2 5 L0 1415 1500 1330 0.0 5 2 5 L1 1375 1430 1320 2.8 5 2 5 L3 1440 1700 1180 -1.8 5 2 5 L5 1435 1590 1280 -1.4 6 1 1 L0 2100 2550 1650 0.0 6 1 1 L1 1385 1390 1380 34.0 6 1 1 L3 200 390 90.5 6 1 1 L5 130 130 93.8 6 1 2 L0 3035 4690 1380 0.0 6 1 2 L1 885 980 70.8 6 1 2 L3 370 720 87.8 6 1 2 L5 135 250 95.6
Average Time Time Time Percent Treatment Duplicate Run Location TSS 0 4 8 Reduction (mg/L) (min) (min) (min) 6 1 3 L0 2255 3100 1410 0.0 6 1 3 L1 1100 1110 51.2 6 1 3 L3 485 950 78.5 6 1 3 L5 185 360 91.8 6 1 4 L0 1920 1930 1910 0.0 6 1 4 L1 1685 1720 12.2 6 1 4 L3 685 1320 64.3 6 1 4 L5 260 500 86.5 6 1 5 L0 2960 4830 0.0 6 1 5 L1 1560 1520 47.3
141 6 1 5 L3 605 1180 79.6
6 1 5 L5 265 520 91.0
6 2 1 L0 3025 2730 0.0 6 2 1 L1 1435 1090 1780 52.6 6 2 1 L3 340 380 88.8 6 2 1 L5 60 110 98.0 6 2 2 L0 2125 1300 0.0 6 2 2 L1 1080 670 1490 49.2 6 2 2 L3 240 430 88.7 6 2 2 L5 140 230 93.4 6 2 3 L0 1720 1280 0.0 6 2 3 L1 1025 1110 40.4 6 2 3 L3 345 650 79.9 6 2 3 L5 145 280 91.6 6 2 4 L0 1190 1080 1300 0.0
Average Time Time Time Percent Treatment Duplicate Run Location TSS 0 4 8 Reduction (mg/L) (min) (min) (min) 6 2 4 L1 755 930 36.6 6 2 4 L3 500 940 58.0 6 2 4 L5 200 370 83.2 6 2 5 L0 985 830 1140 0.0 6 2 5 L1 745 780 24.4 6 2 5 L3 365 670 62.9 6 2 5 L5 220 390 77.7 6 2 6 L0 1225 1050 1400 0.0 6 2 6 L1 1035 1070 15.5 6 2 6 L3 470 830 61.6
142 6 2 6 L5 200 300 83.7
Table B.3: E. coli density data for bacterial reduction analysis
Time Time Time Time Average MPN Percent Treatment Duplicate Run Location 0 4 8 12 (MPN/100mL) Reduction (min) (min) (min) (min) 5 1 1 L0 4573.5 3145 5475 5539 4135 0.0 5 1 1 L1 4729.0 4412 5731 4044 -3.4 5 1 1 L3 3588.0 3931 3986 2847 21.5 5 1 1 L5 3843.3 4654 3692 3184 16.0 5 1 2 L0 1906.7 306 1869 3545 0.0 5 1 2 L1 6367.3 11874 4044 3184 -234.0 5 1 2 L3 23029.7 57481 6053 5555 -1107.8 5 1 2 L5 86475.0 241960 11910 5555 -4435.4
5 1 3 L0 1715.3 1223 2133 3405 100 0.0 143
5 1 3 L1 3060.7 4787 3986 409 -78.4
5 1 3 L3 5681.0 9842 4284 2917 -231.2 5 1 3 L5 13628.0 31301 5448 4135 -694.5 5 1 4 L0 5110.0 6314 7634 2653 3839 0.0 5 1 4 L1 5652.3 5204 5122 6631 -10.6 5 1 4 L3 5563.0 6695 6155 3839 -8.9 5 1 4 L5 10181.7 19349 4882 6314 -99.2 5 1 5 L0 4200.5 3592 4077 4479 4654 0.0 5 1 5 L1 4480.7 4798 4165 4479 -6.7 5 1 5 L3 5301.0 6266 5863 3774 -26.2 5 1 5 L5 7599.7 14137 4585 4077 -80.9 5 2 1 L0 241960.0 241960 241960 241960 241960 0.0 5 2 1 L1 241960.0 241960 241960 241960 0.0 5 2 1 L3 241960.0 241960 241960 241960 0.0
Time Time Time Time Average MPN Percent Treatment Duplicate Run Location 0 4 8 12 (MPN/100mL) Reduction (min) (min) (min) (min) 5 2 1 L5 241960.0 241960 241960 241960 0.0 5 2 2 L0 241960.0 241960 241960 241960 241960 0.0 5 2 2 L1 241960.0 241960 241960 241960 0.0 5 2 2 L3 241960.0 241960 241960 241960 0.0 5 2 2 L5 241960.0 241960 241960 241960 0.0 5 2 3 L0 79455.0 43930 129965 64470 0.0 5 2 3 L1 227515.3 198629 241957 241960 -186.3 5 2 3 L3 241959.0 241960 241960 241957 -204.5 5 2 3 L5 241958.0 241960 241957 241957 -204.5 5 2 4 L0 70737.3 7310 155312 49590 0.0
144 5 2 4 L1 17817.7 11158 18372 23923 74.8
5 2 4 L3 37254.3 75555 12400 23808 47.3
5 2 4 L5 213072.7 241960 198629 198629 -201.2 5 2 5 L0 147572.0 141361 120333 198629 129965 0.0 5 2 5 L1 161304.3 155312 173289 155312 -9.3 5 2 5 L3 198633.7 241960 198629 155312 -34.6 5 2 5 L5 227515.3 241960 198629 241957 -54.2 6 1 1 L0 129210.7 111987 155312 120333 0.0 6 1 1 L1 123543.7 120333 129965 120333 4.4 6 1 1 L3 40571.0 32554 61314 51721 16695 68.6 6 1 1 L5 36514.5 37844 48844 24890 34480 71.7 6 1 2 L0 179543.3 141361 155312 241957 0.0 6 1 2 L1 100151.3 120333 111987 81641 86644 44.2 6 1 2 L3 95829.3 111987 86644 72699 111987 46.6 6 1 2 L5 92212.8 86644 92084 92084 98039 48.6
Time Time Time Time Average MPN Percent Treatment Duplicate Run Location 0 4 8 12 (MPN/100mL) Reduction (min) (min) (min) (min) 6 1 3 L0 241958.5 241960 241960 241957 241957 0.0 6 1 3 L1 241957.8 241957 241960 241957 241957 0.0 6 1 3 L3 241959.3 241960 241960 241957 241960 0.0 6 1 3 L5 241959.3 241960 241960 241960 241957 0.0 6 1 4 L0 198634.7 111987 241957 241960 0.0 6 1 4 L1 220293.0 198629 241957 241957 198629 -10.9 6 1 4 L3 170633.8 129965 198629 198629 155312 14.1 6 1 4 L5 167148.8 241957 155312 129965 141361 15.9 6 1 5 L0 205976.8 241960 198629 141361 241957 0.0 6 1 5 L1 203127.8 129965 241960 241957 198629 1.4
145 6 1 5 L3 172723.5 241960 173289 120333 155312 16.1
6 1 5 L5 138514.8 241960 72699 141361 98039 32.8
6 2 1 L0 72433.5 241960 14830 21416 11528 0.0 6 2 1 L1 11958.3 11446 9599 14830 83.5 6 2 1 L3 8085.5 6382 8361 10426 7173 88.8 6 2 1 L5 7989.3 13344 6504 7541 4568 89.0 6 2 2 L0 6546.8 3355 5731 11370 5731 0.0 6 2 2 L1 6087.3 5204 8624 4434 7.0 6 2 2 L3 7469.5 7894 8162 8361 5461 -14.1 6 2 2 L5 6595.8 5291 5461 6828 8803 -0.7 6 2 3 L0 9729.3 8803 10460 11528 8126 0.0 6 2 3 L1 16152.5 15853 11874 10758 26125 -66.0 6 2 3 L3 78763.0 241960 32554 24890 15648 -709.5 6 2 3 L5 110935.5 241960 129965 30759 41058 -1040.2 6 2 4 L0 8071.3 5908 10144 8162 0.0
Time Time Time Time Average MPN Percent Treatment Duplicate Run Location 0 4 8 12 (MPN/100mL) Reduction (min) (min) (min) (min) 6 2 4 L1 9103.5 9086 11190 7491 8647 -12.8 6 2 4 L3 19049.3 43517 12740 14209 5731 -136.0 6 2 4 L5 25554.3 62940 17821 12809 8647 -216.6 6 2 5 L0 6296.0 5381 5204 6437 8162 0.0 6 2 5 L1 5625.5 6127 4568 6766 5041 10.6 6 2 5 L3 13022.3 27551 7976 7227 9335 -106.8 6 2 5 L5 18734.0 46111 12591 9867 6367 -197.6 6 2 6 L0 1532.7 1464 2034 1100 0.0 6 2 6 L1 1946.5 2405 2011 2034 1336 -27.0 6 2 6 L3 2420.8 2954 2307 2433 1989 -57.9
146 6 2 6 L5 3486.7 5833 2182 2445 -127.5
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