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PAGE 1 1 URBAN STORMWATER PARTICLE AND DISINFECTION MODELING By JOSHUA A. DICKENSON A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2011 PAGE 2 2 2011 J oshua A. D ickenson PAGE 3 3 To the glory of God and His Son, Jesus Christ PAGE 4 4 ACKNOWLEDGMENTS I thank my advising professor Dr. Sansalone, for his countless hours of advising and storm chasing and for his financial and intellectual investment in my growth and development I thank Dr. Delfino, Dr. Fregly, and Dr. Heaney for serving on my committee and for their time, critiques, and refinement of my research. I thank the undergraduate research assistants in the Urban Stormwater Lab for their tireless work and long hours in the lab and on call. I thank the professors of my classes for sharing their knowledge and equipping me with vision. I thank my colleagues for open ears and creative critiques during b rainstorming sessions I thank Ruthie for keeping the basement of Black Hall clean and for her kind words at every chance meeting. And I thank my fa mily for their love and support the thought of them always makes me smile. PAGE 5 5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 8 LIST OF FIGURES ................................ ................................ ................................ .......... 9 LIST OF ABBREVIATIONS ................................ ................................ ........................... 11 ABSTRACT ................................ ................................ ................................ ................... 13 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .... 15 Overview ................................ ................................ ................................ ................. 15 Literature Review ................................ ................................ ................................ .... 18 2 DISCRETE PHASE MODEL REPRESENTATION OF PARTICULATE MATTER FOR SIMULATING PARTICULAT E MATTER SEPARATION BY HYDRODYNAMIC UNIT OPERATIONS ................................ ................................ 23 Overview ................................ ................................ ................................ ................. 23 Objectives ................................ ................................ ................................ ............... 24 Methodology ................................ ................................ ................................ ........... 25 Selected Particle Size Distributions ................................ ................................ .. 26 Computational Fluid Dynamics (CFD) ................................ .............................. 27 Discrete Phase Model (DPM) ................................ ................................ ........... 28 Population Balance ................................ ................................ .......................... 30 Hydrodynamic Separators (HS) ................................ ................................ ........ 30 Experimental design for baffled HS tests ................................ ................... 31 Effluent sampling protocol ................................ ................................ .......... 31 Supernatant sampling protocol ................................ ................................ .. 31 Mass recovery methodology and protocol ................................ ................. 32 Laboratory analyses ................................ ................................ ................... 32 SSC methodology and protocol ................................ ................................ 33 PSD methodology and protocol ................................ ................................ 33 Head loss by manual measurement ................................ ........................... 34 Turbidity ................................ ................................ ................................ ..... 34 QA/QC ................................ ................................ ................................ ....... 35 Efficiency calculation ................................ ................................ .................. 35 CFD Model Dataset Creation ................................ ................................ ........... 36 Model Validation ................................ ................................ ............................... 38 Results and Discussion ................................ ................................ ........................... 38 Baffled HS Performance ................................ ................................ ................... 38 Model Validation Results ................................ ................................ .................. 40 PSD Discretization Res ults ................................ ................................ ............... 40 PAGE 6 6 Power Law Model (PLM): ................................ ................................ ................. 43 Computational Time ................................ ................................ ......................... 44 3 OVERALL R ATE KINETICS MODEL OF SODIUM HYPOCHLORITE DEMAND BY THE DISSOLVED AND PARTICULATE MATTER FRACTIONS IN URBAN RAINFALL RUNOFF ................................ ................................ ............................... 58 Methodology ................................ ................................ ................................ ........... 60 Catchment ................................ ................................ ................................ ........ 60 PM Fractionation ................................ ................................ .............................. 61 Batch Reactor Framework ................................ ................................ ................ 61 Batch Reactor Setup ................................ ................................ ........................ 63 Analytical Methods ................................ ................................ ........................... 64 Parallel Second Order Demand Model for Dissolved Phase ............................ 65 Second Order Potential Driving Model for the PM Fractions ............................ 68 Model Evaluation ................................ ................................ .............................. 70 Results and Discussion ................................ ................................ ........................... 70 Control Reactors ................................ ................................ .............................. 70 Kinetics Model for Dissolved Phase ................................ ................................ 70 PM Kinetic Model ................................ ................................ ............................. 73 4 SODIUM HYPOCHLORITE DISINFECTION OF INDICATOR ORGANISMS ASSOCIATED WITH URBAN STORMWATER PARTICLES ................................ 84 Methodology ................................ ................................ ................................ ........... 86 PM Fractionation ................................ ................................ .............................. 87 Microbiological Enumeration ................................ ................................ ............ 88 Batch Reactors ................................ ................................ ................................ 89 Residual Chlorine ................................ ................................ ............................. 91 Results and Discussion ................................ ................................ ........................... 92 Batch Reactor Results ................................ ................................ ...................... 95 Indicator Organism Partitioning ................................ ................................ ........ 97 5 ADVANCED COMPUTATIONAL MODELING OF FREE CHLORINE DEMAND AND DISINFEC TION IN UNIT OPERATIONS AND PRECESSES LOADED BY URBAN STORMWATER ................................ ................................ ...................... 107 Objectives ................................ ................................ ................................ ............. 108 Methodology ................................ ................................ ................................ ......... 109 Batch Reactor Setup and Initialization ................................ ............................ 115 CSBR Validation ................................ ................................ ............................. 116 Results and Discussion ................................ ................................ ......................... 116 6 CONCLUSION ................................ ................................ ................................ ...... 127 Free Chlorine Kinetics ................................ ................................ ........................... 127 Dissolved Phase Reaction Kinet ics ................................ ................................ 127 Particulate Kinetics ................................ ................................ ......................... 128 Computational Modeling ................................ ................................ ....................... 129 PM Fate and Transport ................................ ................................ .................. 129 CFD Free Chlorine Reaction Kinetics ................................ ............................. 130 PAGE 7 7 A PPENDIX: ADDITIONAL FIGURES ................................ ................................ .......... 132 LIST OF REFERENCES ................................ ................................ ............................. 136 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 144 PAGE 8 8 LIST OF TABLES Table page 2 1 Table of cumulative gamma distribution modeled gradations ............................. 45 2 2 Morsi and Alexander drag equation and coefficients for a sphere (1972) ........... 45 2 3 Hydrodynamic separator experimental run information and operational parameters ................................ ................................ ................................ ......... 46 2 4 Hydrodynamic separator performance ................................ ............................... 47 2 5 PM gradations with mean power law model parameters ................................ .... 47 2 6 Discrete phase model computational time for the baffled hydrodynamic separator ................................ ................................ ................................ ............ 48 2 7 Discrete phase model computational time for the screened hydrodynamic separator ................................ ................................ ................................ ............ 48 3 1 Summary of hydrologic and PM event mean concentration ind ices for captured events. ................................ ................................ ................................ 76 3 2 Global parallel 2 nd order demand model parameters for the dissolved phase. ... 76 3 3 Hypochlorite event based ultimate demand of urban stormwater fractions for the monitored storms. ................................ ................................ ......................... 77 4 1 Batch reactor experimental matrix of PM fractions, HOCl dose, and event date. ................................ ................................ ................................ ................... 99 4 2 Batch reactor particle granulometry. ................................ ................................ 100 4 3 Event mobilization of indicator organisms and percentage of transported organisms associated with e ach PM fraction. ................................ ................... 101 5 1 Model parameters for the dissolved parallel second order and PM potential driving force equations. ................................ ................................ .................... 120 PAGE 9 9 LIST OF FIGURES Figure page 2 1 Experimenta l validation of full scale units ................................ ........................... 49 2 2 Cumulative PSDs utilized in the study. ................................ ............................... 50 2 3 Results from the full scale experimental testing on the baffled HS ..................... 51 2 4 Results comparing influent loading concentrations on the baffled HS ................ 52 2 5 Computational results for the screened HS ................................ ........................ 53 2 6 Computational results for the baffled HS ................................ ............................ 54 2 7 CFD per particle size efficiency surfaces for both the screened HS (A) and the baffled HS (B) ................................ ................................ ............................... 55 2 8 CFD per particle size efficiency differential surface for the s creened HS and the baffled HS ................................ ................................ ................................ ..... 56 2 9 Predictive results of the power law mod el for RPD with increasing DN .............. 57 3 1 PSD of quintessen tial fractions from the batch reactors ................................ ..... 78 3 2 Physical representation of the parameters of the parallel 2 nd order demand model ................................ ................................ ................................ ................ 79 3 3 Predictive fit of the dissolved fraction parallel 2 nd order demand model ............. 80 3 4 Transient loading of COD d on the small urban catchment in north central Florida ................................ ................................ ................................ ................ 81 3 5 Maximum particle free chlorine demand ................................ ............................. 82 3 6 The modeling results of the second order PM chlorine demand model .............. 83 4 1 Event mean most probable number per 100 mL box plot for twenty five wet weather events on a small urban watershed in north central Florida ................ 102 4 2 Hypochlorite ina ctivation kinetics of particle associated coliform organisms on suspended, settleable, and sediment PM ................................ ......................... 103 4 3 Log removal of particle associated coliforms for the 04 Nov 2010 (Panel A, B) even t with an initial hypochlorite dose of 45 mg/L ................................ ........ 104 4 4 Log removal of particle associated coliforms on sediment PM across the inoculation doses of 15, 30, and 45 mg/L ................................ ......................... 105 PAGE 10 10 4 5 Partitioning of particle associated organisms to suspended, settleable, and sediment PM fractions ................................ ................................ ...................... 106 5 1 Physical batch reactor showing stirplate, aluminum foil jacket, and water quality electrodes ................................ ................................ .............................. 121 5 2 Illustration of the fluid zones within the batch reactor ................................ ....... 122 5 3 Histog ram analysis of the computational mesh CFD free chlorine concentration ................................ ................................ ................................ .... 123 5 4 Comparison of the second order CFD dissolved demand model with experimental results ................................ ................................ ......................... 124 5 5 Comparison of the second order potential driving PM CFD model with experimental results. ................................ ................................ ........................ 125 5 6 Comparison of the composite dissolved and PM CFD model with batch reactor data. ................................ ................................ ................................ ..... 126 A 1 Continuously stirred batch reactor (CSBR) schematic. ................................ ..... 132 A 2 Schematic of monitored urban sub cat chment in Gainesville, FL showing contributing impervious surface. ................................ ................................ ....... 133 A 3 Control CSBRs showing hypochlorite kinetics in Nanopure DI for 8 h at 15 mg/L and 24 h at 45 mg/L ................................ ................................ ................. 134 A 4 Control CSBRs comparing autoclave sterilized and non autocla ve sterilized stormwater Matrix ................................ ................................ ............................. 135 PAGE 11 11 LIST OF ABBREVIATION S ADB Azide dextrose broth BGB Brilliant green bile b roth BHI 6.5% NaCl brain heart infusion broth CFD Computational fluid dynamics COD Chemical oxygen demand COD d Dissolved chemical oxygen demand CS B R Continuously stirred batch reactor DN Discritization number DOC Dissolved organic carbon DPD N,N diethyl p phenylenediamine DPM Discrete phase model EMC Event mean concentration EPA The United States Environmental Protection Agency FC Fecal coliform FS Fecal streptococcus HS Hydrodynamic separator LTB Lauryl triptose broth LTB MUG Lauryl triptose broth amended with 4 methylumbelliferyl D glucuronide MBE Mass balance error MPN Most probable number MS4 Multiple separate storm sewer system NJCAT New Jersey Corporation for Advanced Technology NJDEP New Jersey Department of Environmental Protection NRMSE Normalized root mean square error PAGE 12 12 PCR P olymerase chain reaction PLM Power law model PND Particle number density PSD Particle size distribution PM Particulate matter RANS Reynolds averaged Navier Stokes RPD Relative percent difference RPD ave The average relative perc ent difference RTD Residence time distribution SE Standard error SSC Suspended sediment concentration TMDL Total maximum daily load UDF User defined function VF Volume fraction WWTP Waste water treatment plant The change in the event mean concentration PAGE 13 13 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy URBAN STORMWATER PAR TICLE AND DISINFECTION MODELING By J oshua A. D ickenson May 2011 Chair: John Sansalone Major: Environmental Engineering Sciences Urban stormwater is a component of the complex hydrologic water cycle whose genesis is the result of anthropogenic modificat ion of environmental hydrologic pathways. This hydrologic modification results in volumetric transport of water and the mobilization of potential particulate, chemical, biological, and nutrient contaminants. Moving forward with sustainable development an d re development will require identification of new technologies and novel reuse resources and an integrated design and management approach that mitigates deleterious environmental impacts of urban stormwater runoff and establishes potential reuse applicat ions to alleviate over exploitation of current environmental freshwater sources. However, due to the inherent differences in the nature and constituents of urban stormwater as compared to well studied wastewater and environmental water, research needs to characterize and identify the necessary methods to facilitate safe and sustainable potentials for reuse. This document proffers experimental research and modeling that explore s the role of particle separation with implications for the potential of utilizi ng chlorine disinfection for urban stormwater for reuse. From the experimental perspective, findings include significant loadings of planktonic and particle associated bacteriological indicator PAGE 14 14 organisms (PAOs) observed in stormwater runoff from a small u rban catchment at the University of Florida as well as high levels of chlorine demand due to dissolved and particulate constituents of the runoff Free chlorine disinfection applied to reactors with differing fractions of event mobilized particulate matte r (PM) found that sediment PM ( > 0.75 m) exerts a shielding effect that protects PAOs from disinfection at the applied doses while PAOs in the suspended PM < 25 m ) and settleable PM ( 25 m < < 75 m ) were successfully inactivated. Computational fluid dynamic (CFD) mo dels demonstrated that the Lagrangian discrete phase model required discritization numbers (DN) of heterodisperse gradations and gradations of medium dispersivity to be in the range of 8 to 16 to minimize computational error, while the median particle size the d 50m was sufficient for monodisperse distributions. An Eulerian Lagrangian CFD modeling and design methodology implementing the observed chlorine kinetics was implemented and validated utilizing batch reactor experimentation PAGE 15 15 CHAPTER 1 I NTRODUCTI ON Overview Moving into the 21 st century, appropriations of water resources and water use and re use will become an increasingly central factor in the ongoing, sustainable development of the developed and developing world. True sustainable development will require a multipronged effort including policy makers, engineers, city planners, architects and end users focusing on conservation by means of new technology and processes in addition to habit changes by the end user. An ever increasing body of evidence points to the detrimental impact of point and non point source anthropogenic pollution on the local ecologies of receiving bodies of water. Water reuse has been identified as a means to reduce this impact as well as supplement current water resources the reby mitigating environmental drain of source waters. Much of the effort dedicated to reuse to date has focused on reuse of wastewater. Reuse of wastewater has been particularly attractive due to pre existing, centralized infrastructure and the well docum ented environmental impact of wastewater nutrients on receiving bodies of water. Implementation of total maximum daily loads (TMDLs) by federal and state authorities has transcribed this environmental impact into an economical impact on local utilities, w ho have invested in reuse research and infrastructure. Traditionally, much of the reuse of wastewater has focused on non potable reuses, for example irrigation of home lawns and golf courses. However, current and predicted water scarcity in the U S has made the potable reuse of wastewater a necessity in certain regions. PAGE 16 16 Research into grey water reuse is beginning to bloom. The inchoate research and technologies have focused on localized applications of captured grey water to in home (i.e. toilet flushin g) and small outdoor irrigation projects (i.e. lawn or garden). Localized reuse of grey water potentially could reduce loads to wastewater treatment plants as well as source water demand. A downside to these technologies is that many of these application s and technologies will need to be implemented by the end user on a local scale due to lack of current grey water infrastructure and the high cost of infrastructure retrofitting. A potential alternative reuse source is urban stormwater. Increasing develop ment of impermeable infrastructure continues to increase the volumetric flow of urban stormwater. Traditionally urban stormwater is volumetrically contained to prevent localized flooding and subsequently returned to the water cycle via evaporation, infilt ration, or directly into surface waters. Urban stormwater has been identified as non point source nutrient and silting pollution and municipalities are at the beginning stages of being required to treat urban stormwater to comply with established TMDL reg ulations for impaired water bodies. The physical chemical constituents of urban runoff differ from the traditionally treated environmental waters and wastewater. The particulate matter (PM) of environmental waters and wastewater are primarily organic part icles, whereas a significant portion of the entrained particulate matter is inorganic. Some of the inorganic particulate matter especially in the runoff from impermeable pavement is made up of heavy metal debris from rubber tire wear. Fecal microbial pollution of urban stormwaters is possible due to animal deposits, inappropriate application of non PAGE 17 17 chemical fertilizers, or possible sanitary sewer overflows. However, the direct anthropogenic fecal contaminant link is not present in raw urban stormwate r as it is naturally in untreated wastewater reducing the likelihood of certain opportunistic human pathogens. In addition, urban stormwater PM tends to be denser, coarser, and more hetero disperse than wastewater. Urban stormwater is also distinct from e nvironmental and wastewater flows by its stochastic and transient nature. Urban stormwater flows can vary greatly between events and during the duration of a single event. With respect to PM, stormwater flows tend to be front loaded flushing out much o f the PM during the initial phase of the storm. The transient and hetero disperse nature of urban stormwater flows will require advanced modeling technologies and techniques to account for their time sensitive behavior. Many of the established technologi es and processes for the treatment of environmental waters and wastewater need to be re evaluated for the purpose of urban stormwater treatment due to the aforementioned inherent differences in constituents sient nature. Sound research is necessary prior to the development and application of urban stormwater non potable reuse in and around human populations and for potential potable reuse. Following this macroscopic motivation for urban stormwater research for reuse, the specific focus of the proposed research is the investigation of the kinetics of free chlorine demand and disinfection of particle laden urban stormwater and the subsequent advanced modeling of these processes in computational fluid dynamics with the special treatment for the hetero disperse nature of PM in urban stormwater. PAGE 18 18 The dual pronged focus will elucidate fundamental biological, chemical, and physical properties of the involved kinetic and particulate processes and couple these propert ies with direct application to pertinent and realizable design and modeling methodology. The fundamental hypothesis for the quantification of free chlorine demand is that urban stormwater will have a unique particulate and soluble based chlorine demand tha t differs from both environmental water and wastewater. Particulate matter has been shown to shield associated organisms from microbial inactivation processes. These studies have been primarily performed on organic particulate matter, thus a concrete stu dy illuminating the effect of inorganic particulate matter shielding of associated microorganisms is warranted. In addition, the effects of gradation characteristics such as uniformity on discrete computational particle modeling will be elucidated to ensu re accurate modeling with minimal computational cost. The primary motivation for the development and incorporation of advanced computational modeling is the likelihood of potentially complex and specific retrofit designs and to reduce unnecessary over des ign of disinfection systems. Literature Review Disinfection by chlorination is the most utilized form of microbial inactivation employed in the world today (Hrudey and Hrudey 2004). Due to its relative low cost and current saturation of installed infrastru cture, it can be expected that this trend will continue for some time. As a primary factor in the effectiveness and cost in this disinfection process, chlorine demand has been much studied and modeled. This chemical concept of chlorine demand has also be en correlated to disinfection byproduct formation (Clark 1998). Frequently, chlorine demand has been modeled as first order decay, however, second order, nth order power law, and parallel first order kinetics PAGE 19 19 have been employed to better fit the data unde r situations where additional chlorine significant role in reaction rates has been illustrated in the chlorine demand concept by modifying the first order reaction rate cons tants by a factor of 2.5 over a range of 10C (Powell et al. 2000). PM has a deleterious effect on the disinfection process. LeChevallier (1981) documented the hindering effect of PM, by turbidity as a surrogate, on the disinfection of environmental surfa ce waters in Oregon. This study identified two mechanisms of impediment. The first, inherent oxidant demand of the PM decreased the free chlorine available for disinfection. Second, the PM shielded bacteria that were attached to its internal pore struct ure from the free chlorine and potentially from detection using the membrane filtration technique Berman (1988) documented similar findings in the organic rich PM in wastewaters. In addition Berman contributed observations that the free chlorine permeat ed particles of diameter <7m at a rate faster than it permeated particles of larger diameter. Dietrich (2003) extended these findings and modeled the permeability of PM to free chlorine with a radial diffusion model. The effect of initial concentration of free chlorine was shown to be a primary driver in the maximum particle size that chlorine could effectively disinfect a size identified as the critical particle diameter. The effect of PM and organics during chlorine disinfection of grey water for reu se was recently studied by Winward (2008). This investigation demonstrated that for grey water, organic material quantified by total organic carbon only affected the disinfection process by creating chlorine demand, whereas PM was implicated in shielding particle PAGE 20 20 associated coliforms from the disinfectant. Similar to Dietrich, this study documented the effect of initial concentration on the ability of the disinfectant to penetrate particles of increasing size. The above mentioned previous research on the interaction of PM and disinfectant on the microbial inactivation process has exclusively focused on organic wastewater and environmental PM. Significant differences between wastewater and urban stormwater PM have been documented. Kim and Sansalone (20 10 ) compared PM from an urban stormwater event to influent at a wastewater treatment plant (WWTP) in Baton Rouge, Louisiana. The study found that the influent PM to the WWTP was relatively fine (d 50m = 26 m), mono disperse (d 80m /d 20m = 3.1), had a specific gravity of 1.5, and had a ratio of volatile suspended solids to total suspended solids of 76% (a surrogate indicator of organic content). In comparison, the urban stormwater event mean was relatively course (d 50m = 136 m), hetero disperse (d 80m /d 20m = 50 .5), had a specific gravity of 2.3, and had a ratio of volatile suspended solids to total suspended solids of 27%. These differences potentially indicate a distinction in behavior for the interaction of urban stormwater PM with chlorination. The more het ero disperse coarse gradation indicates that particle shielding of particle bound microorganisms could be a possible hindrance to the efficacy of the process (albeit the coarseness and density a boon to gravitational treatment). In addition the primarily inorganic nature of urban stormwater particles should indicate reduced instantaneous chlorine demand as well as a reduction in potential disinfectant byproduct formation as compared to wastewater. In addition to the above disparity in constituent makeup of PM between wastewater and urban stormwater, the delivery and loading mechanisms likewise differ. PAGE 21 21 These differences have implications for representative characterization as well as modeling. The delivery and volume of wastewater flows are generally predi ctable. The delivery of PM in urban stormwater flows is governed by flow rate and watershed PM loading (Sansalone and Kim 2008) and thereby not known a priori (Sansalone and Buchberger 1997). The flow volume of a storm coupled with the available dry depo sition of the watershed dictates if the flow is mass or flow limited (Christina and Sansalone 2003) affecting the temporal delivery of PM. These differences dictate necessary characterization of entire events. Pollutant yields are also non uniformly dist ributed over PM partitions. More metal mass tends to be present on coarse PM, whereas a higher concentration of metal pollutants tends to be present on finer PM (Sansalone and Ying 2008). These pollutant properties require representative sampling of PM c oncentration (Sansalone and Kim 2008) and characterization of particle size distribution (Kim and Sansalone 2008). The presence of microbial indicator organisms has been identified in urban stormwater flows with a portion of the indicator organisms existin g in a particle bound state (Charaklis et al. 2005). The presence of particle associated microorganisms has significant implications for the importance of particle treatment technologies such as filtration for suspended fractions (< 25 m) or gravitational settling for sediment (> 75 m) or settleable (< 75 m, > 25 m) fractions. While particle associated indicator organisms have not shown similar frequency across organisms to centrifugal fraction partitioning, there was no significant intra storm variati on of particle associated partitioning for individual microbial indicators (Krometis et al. 2007). However, as particle loading is not uniform across the duration of a storm and tends to be PAGE 22 22 concentrated toward the rising limb of the hydrograph, likewise p article associated microbial loading is not uniform across the storm (Krometis et al. 2007). In addition, research has shown that the particle partitioning of Clostridium perfringens spores mimics that of the protozoan cysts of Giardia and oocysts of Cryp tosporidium for which it has been proposed as an indicator organism (C h izek et al. 2008). Computational Fluid Dynamics (CFD) is a state of the art tool for modeling complex fluid flows, particle settling phenomena, and simulating reaction kinetics spatiall y and transiently. CFD has been previously used to model steady and transient settling in urban stormwater unit operatio ns (Pathapati and Sansalone 2009 ) as well as in the optimization of a potable water settling basin (Goula et al. 2008). CFD has also b een utilized by Baawain (2006) as an optimization tool for the design of a disinfectant storage contact chamber. This study sought to improve the performance of the contact chamber by geometrically improving the mixing and flow characteristics of the basi n. S Surface Water Treatment Rule of the Ct 10 concept and sought to maximize t 10 but did not apply CFD to model the oxidant demand or integrate localized Ct values across the domain PAGE 23 23 CHAPTER 2 DISCRETE PHASE MODEL REPRESENTATION OF PA RTICULATE MATTER FOR SIMULATING PARTICULA TE MATTER SEPARATION BY HYDRODYNAMIC UNIT OPERATIONS Overview Particulate matter (PM) is a common pollutant in rainfall runoff ( Heaney and Huber 1984 ) and surface waters ( De lfino 1977 ). As historically practiced, gravimetric indices such as total suspended solids (TSS) and suspended sediment concentration (SSC) do not provide particle size distribution (PSD) representation. However, PM discretization methods allow examination of chemical and biological interactions with PSDs, for example with metals ( Chellum and Wiesner 1997 ). Silt and clay size PM provide habitat and a protective matrix for microbes ( Lnsdorf et al 2000 ) and is a mobile substrate that provides disinfection resistance ( LeChevallier et al. 1981 ). As a result, PM clarification is common for treatment of stormwater ( Small and DiToro 1979 ) and wastewater ( Tchobanoglous et al 2003 ) or for water treatment optimization ( Boccelli et al. 2004 ). PM discretization as a function of hetero dispersivity is important in modeling treatment and fate of PM and PM bound pollutants. A common analysis for PM separation by treatment unit operations is the overflow rate theory with the inherent assumption of Type I gravitational s ettling, where PM settles discretely with negligible particle particle interaction and impact on the fluid flow field. This basic theory can be combined with a constitutive model of PM settling w and PSD discretization, while less common, permits a more complete description of discrete PM settling irrespective of the continuous fluid phase dynamics. The most basic PSD PAGE 24 24 discretization and separation representation is using a mass based median size (d 50m ), with increasingly accurate representation generated by higher PSD discretization. provides a more accurate description of the discrete PM phase, a major shortcoming o f the basic overflow rate concept is the lack of a quantitative coupling with the potential hydrodynamic complexity of the fluid phase. Coupling the dynamics of the fluid phase can range from semi Hazen 1 904, Fair et al. 1968 ) to a fundamental description with Navier Stokes equations ( Pathapati and Sansalone 2009 ). Computational fluid dynamics (CFD) has significantly improved modeling of PM separation and fate for complex geometries, non ideal flow fields, and transient flows ( Patruno et al 2009, Pougatch et al. 2009) However, the level of PM discretization as a function of PSD hetero dispersivity is required when modeling unit operations with a Lagrangian Eulerian CFD approach. PM laden flows represent a challenging modeling phenomenon with discrete PM sizes, as PSDs can be considered a continuum. Using a discrete phase model (DPM) to simulate PM separation requires discrete PM sizes for a continuous PSD. As hetero dispersivity increases, the particle nu mber exponentially increases with increasing computational effort. Identifying PSD discretization needed for acceptable results and computational economy is important. Objectives A primary study objective is the examination of PM discretization requirement s in a CFD model for selected levels of granulometric size hetero dispersivity. Additionally, this study illustrates the impact of gradation uniformity and overall gradation fineness on PSD discretization requirements for CFD modeling of two different hydr odynamic PAGE 25 25 separators (HS) commonly utilized worldwide for treatment of urban drainage. Using controlled physical modeling of a baffled HS and screened HS shown in F igure 2 1 to validate the CFD model, the study hypothesized that the error in modeling PM se paration is a function of the PSD discretization. Methodology A granulometric attribute of PM is the PSD. Urban drainage PM in wet weather (rainfall runoff) or dry weather (wastewater) flows is hetero disperse. To explore the effect of PSD dispersivity on PSD discretization requirements a rubric is needed to characterize the gradation uniformity. Folk and Ward ( 1957 ) proposed a sorting coefficient ( I ) for this granulometric attribute. With the sorting coefficient, gradations of similar uniformity can be generated at a chosen d 50m Equation 2 1 presents the sorting coefficient modified by a negative sign since percentiles are reported as % finer by mass whereas Folk and Ward present them as % greater. (2 1) I n this expression is the phi scale particle size gradation parameter ( McManus 1966 ), and is defined by Equation ( 2 2). (2 2) W here n is the percentile, n is the phi parameter of the n th finer percentile, d n is the PM diameter of the n th finer percentile, and d 0 is the unit length to non dimensionalize the equation. In order to systematically study the effect of PSD discretization for size gradations of differing I and d 50m a methodology is needed to generate PSDs that vary only in PAGE 26 26 these two parameters while simultaneously generating representative urban drainage PM gradations. Previous studies ( Sansalone and Ying 2008, Lin et al. 2009 ) have utilized a two parameter cumulative gamma distribution to model hetero disperse urban drainage PSDs. The shape and scale factor parameters in the cumulative gamma I and d 50m Equations 2 3 and 2 4 represent the gamma distribution ( f(x) ) and cumulative gamma distribution ( F(x) ), respectively. (2 3) (2 4) W here k is the gamma shape factor; is the gamma scale fac tor; and x is the particle diameter. Selected Particle Size Distributions Table 2 1 and F igure 2 2 describe the chosen gradations for this study. Gradation characteristics are selected to elucidate the effects of PSD dispersivity and also the PSD d 50m on t he CFD simulation results for different levels of PSD discretization using a discretization number 3 ; silica sand). The nine matched the 3x3 gradation matrix of mono disperse ( I < 0.35; in this study: I = 0.11), moderately dispers e ( I I = 1.03) and hetero disperse ( I > 2.00; in this study: I = 2.64) PSDs. The gravimetric median sizes for these PSDs are coarse ( d 50m = 100 m), fine ( d 50m = 66.7 m), and very fine ( d 50m = 33.3 m). PAGE 27 27 Computational Fluid Dy namics (CFD) CFD simulates behavior of unit operations at a variety of scales, from pilot scale behavior to smaller scale phenomena, such as the internal flow field velocities and pressure distributions as well as PM transport and fate. CFD is utilized to model behavior of each HS as a function of granulometric attributes (PSD, d 50m and I ) and flow. CFD is based on numerical solutions to Navier Stokes equations across a domain. Specifically, to model the flow fields in this study, Reynolds Averaged Navier Stokes (RANS) equations ( Ferziger and Peric 2002 ) are utilized with Fluent 6.3.26. The RANS equations decompose the bulk, time independent fluid flow from the transient turbulent fluctuations. Averaged over a time scale much larger than the time scale of the fluctuations, the transient turbulent fluctuations become zero leaving only the bulk fluid flow and stationary tu rbulence structures. Equations 2 5 and 2 6 succinctly describe the steady state RANS continuity and momentum equations respectively. (2 5) (2 6) In these equations is fluid density; x i is the i th direction vector; is the Reynolds averaged velocity in the i th direction; is the Reynolds averaged pressure; and g i is the sum of body forces in the i th direction (i.e. gravity in the negative z direction). The decomposition of the non linear convection in the momentum eq uation results in Reynolds Stresses The Reynolds Stresses are unknown quantities and can be modeled wit h the semi 2 7 through 2 8) that was PAGE 28 28 developed by Shih et al. ( 1995 ) which performs well for rot ating homogeneous (screened HS) and boundar y free (baffled HS) shear flows. (2 7) (2 8) (2 9) T he constants: k = 1.0, = 1.2, and C 2 = 1.9. In Equations 2 7, 2 8, and 2 9 k is the tur bulent kinetic energy; is the turbulent energy dissipation rate; S is the mean strain rate; T is the eddy viscosity; is the fluid viscosity; and and are defined as above. The velocity and pressure flow fields in the co mputational domain are solved by upwind cellular approximations on a tetrahedral meshing scheme. The geometric mesh size of the screened HS is 1.3 million cells and 3.1 mil lion cells for the baffled HS. Discrete Phase Model (DPM) To model transport of PM within HS units, a mixed mode Eulerian Lagrangian reference frame is utilized where fluid velocity and pressure flow fields are modeled in an Eulerian or control volume re ference frame and PM is modeled as discrete particles in a Lagrangian or particle tracking reference frame. PM transport modeled in the discrete phase is integrated across the fluid velocity and pressure flow fields modeled in the Eulerian reference frame. This does not account for particle influence on velocity and pressure flow fields and is restricted to dilute fluid flows with <10% volume fraction (VF) ( Brennen 2005 ). Dilute flows are governed by Type 1 settling. Even with this PAGE 29 29 restriction, many flow s ituations, including the HS units can be successfully modeled as dilute flows (this study: VF << 10%). Furthermore, as a dilute, non agglomerating flow, particle collisions are negligible and the resulting CFD model is independent of concentration. In the discrete phase, particles are tracked by calculating particle acceleration as a particle moves in the flow field. This is an evaluation of New enumerated in E quations 2 10 through 2 13 were i represents the three dimen sional direction (i. e. x, y, z). (2 10) (2 11) (2 12) (2 13) The formulati on of Equation 2 10 is particle acceleration is equal to the summation of the forces per unit particle mass. is the drag force per unit particle mass; and is buoyancy/gravitation al force pe r unit particle mass. Equation 2 12 is the definition of the relative Reynolds number for flo w around a sphere. In E quation 2 11 and 2 12 p d p is particle diameter; v p_i is particle velocity in the i th direction; v i is the localized fluid velocity in the i th direction; and is the dynamic viscosity. Morsi and Alexander ( 1972 ) present Equation 1 13 as a mathematical correlation for the drag coefficient of spherical PAGE 30 30 particles that predicts expe rimentally obtained values to within 2% up to Re = 50,000. Coefficients to Equation 2 13 are in T able 2 2. Population Balance The computational DPM is linked to quantification of the HS performance by the PM population balance equation. In this study the unit operation behavior is state conditions, assuming no particle coalescence, breakage, nucleation or destruction, a population balance equation is utilized (Equation 2 14). (2 14) Where f d is a discrete particle number density (PND) function with the internal particle diameter coordinate ; and x is the mass fraction of a particle with diameter Therefore, the summations represent the total mass fractions of PM injected in the in fl = 1. From this analysis, the computationally modeled HS performance is given by = 1 effl infl ) 100%. An in depth presentation of the population balance equation can be found in Jakobsen ( 2008 ). Hydrodynamic Separators (HS) PM separat ion of a baffled HS and screened HS is used to examine the role of PSD discretization for differing flow fields with physically validated CFD models. The screened HS is a 1.8 m dia. unit designed to include a potential swirling mechanism and a 2400 m scre en to deflect PM from the flow stream. The baffled HS is a 1.8m dia. unit designed to provide volumetric isolation of settled PM, trap oil, grease and floatables, and settle PM. Units are illustrated in F igure 2 1. PAGE 31 31 E xperimental d esign for baffled HS test s The parameters selected in the experimental design include: Particle concentration (100 and 300 mg/L), as suspended sediment conc. (SSC) Flow rate (2%, 5%, 10%, 25%, 50%, 75%, 100% and 125% of the given design flow rate) Influent particle gradation b ased on an NJCAT gradation ( Total 2003 ). Two gravimetric concentrations of NJCAT influent particles (100 mg/L and 300 mg/L) are examined in this analysis, in order to investigate the effect of influent solid concentration on the separation performance of the baffled HS across the range of hydraulic loading capacities considered. Table 2 3 presents the complete experimental design. Effluent sampling p rotocol The sampling is conducted according the following procedure. During the test running time, twenty r epresentative effluent samples are taken manually as discrete samples in 1L wide mouthed bottles. Samples are collected in duplicate at constant intervals through the entire duration of the run at the effluent section of the unit. The sampling interval tim es spanned from 2 minutes to 48 minutes for the range of operating flow rates from 2% to 125% of design flow rate. Supernatant sampling p rotocol The sampling protocol examining the supernatant PSD s consists of taking one duplicate sample at the geometri c midpoint of the supernatant after overnight settling. An investigation was performed to confirm the sampling methodology adopted is an appropriate procedure to adequately quantify the PSD of PM remaining entrained within the supernatant after a period greater than 8hrs. This analysis intended to verify the PSD of PM remaining in suspension at various depths in the supernatant after overnight PAGE 32 32 settling after an event based test on the baffled HS. In particular, four PSD samples were sampled from the drai ning supernatant of the baffled HS at 4 separate times and were calculated so that the samples were taken at four evenly spaced intervals of height of the draining supernatant. The result proved there is not a significant deviation among the four samples i n terms of supernatant incremental PSD after overnight settling. During this same investigation, four 4L SSC samples taken at the same time as the PSD sample were analyzed and the result determined that there was a significant variation in the SSC through out the vertical profile within the unit, thus it was concluded that the best sampling approach for SSC would be a 4L composite SSC sample made from four 1L samples taken at the same time as the PSD samples. The supernatant mass represents approximately 7 % of the event captured mass. Mass recovery methodology and p rotocol After the supernatant sample has been collected the wet slurry from the system is recovered from the bottom of the unit by manually sweeping it through the washout into buckets and taken to the laboratory where they are allowed to stand for quiescent settling and dried in glass trays at 110 degrees Celsius in an oven. After the slurry completely dries the dry silica is disaggregated and collected in pre weighed glass bottles and the gross weight is recorded to find the overall efficiency of the system based on mass and for the mass balance. Laser diffraction analysis for the collected dry sample is then performed to analyze the PSD of the captured PM. Laboratory a nalyses During a run a tot al of forty 1L samples were collected. The laboratory analyses consisted of SSC, PSD of the aqueous phase and the dry phase of the captured mass using the Malvern Mastersizer 2000, and the mass balance for the efficiency of the PAGE 33 33 system. Twenty samples were used for the SSC analysis and 20 for the PSD analysis. The SSC analysis was performed as duplicate composite samples (A and B), while the PSD analysis was performed on each 1L sample to enable quantification of the PSD over the duration of the experiment al run. These methodologies are elaborated below. SSC methodology and p rotocol SSC analysis was performed to quantify particle concentration for each effluent composite sample as collected from each run and to calculate the effluent mass load for the oper ating flow rates. In addition to the 10L composite samples, an additional 1L was added to the event mean concentration (EMC) calculation to act as an initial sample to accommodate the fact that the samples were taken as end interval samples instead of mid interval samples. Since the run was conducted under steady flow conditions and the samples collected at equal intervals, the particle concentration in the composite sample represents the mean effluent concentration. The protocol specifically followed for this laboratory analysis is the ASTM D 3977 ( 2002 ). PSD methodology and p rotocol Effluent and supernatant PSD measurement in aqueous p hase A large variety of instruments have been developed for particle size determination. A relatively new, state of the art, high resolution method is a laser diffraction analyzer. The Malvern Mastersizer 2000 is a commercial laser diffraction analyzer and was utilized in this experimental analysis to characterize particle sizes. The instrument technology is based on laser diffraction, occurring when a laser beam passing through a dispersion of particles in air or in a liquid is diffracted at the particle surface. The angle of diffraction is influenced by the size and the shape of the particle. As the particle size decrease s, the scattering angle increases ( Jillavenkatesa et al. 2001 ). The Mastersizer 2000 detects PAGE 34 34 particle sizes in the range of 0. 02 assuming a spherical diameter. The 10 duplicate samples are analyzed independently in the Mastersizer 2000 as 20 on e liter samples. The sample collection, handling, and particle analysis procedure followed Standard Method 2560 ( Eaton et al. 1998 ). Captured particulate PSD m easurement In order to representatively sub sample the dry mass the silica is uniformly mixed t o obtain a sub sample as representative as is physically obtainable. Duplicate 20 gram samples are taken for the dry phase of the laser diffraction analyzer. The dry dispersion cell is connected to the laser diffraction analyzer and the dry sample is meas ured by forming a PM aerosol with a high pressure, high velocity air stream. The PSDs measured are observed for stability and averaged. Head loss by manual m easurement The head loss through the system was measured by manual tape measurement. This was acc omplished by measuring the approximately static head over the inlet and outlet sections of the fiberglass insert. These measurements were performed in the same spatial location for each run and were trued to a level to account for the difference in vertic al height of the surface of the insert at the inlet and outlet sections. Turbidity Effluent and influent turbidity were measured by a portable turbidimeter probe (YSI 600OMS). Three point calibration curve was determined to calibrate the probe by using the turbidimeter calibration standard solution. The turbidity of the effluent was characterized throughout the run by an in situ YSI 600OMS probe installed before each run in the outlet riser. The average value of the turbidity once a steady state was obtaine d was designated as the effluent turbidity. For this study, turbidic steady state is PAGE 35 35 defined as a turbidity measurement at 95% of the first maxima. Another in situ turbidity meter was installed in the inlet drop tee and to avoid the restriction of flow in the orifice plate. This data recorded by this inlet turbidity meter was significantly besmirched by turbulent flow in the inlet and possibly light scattering from the water surface above the orifice plate. In addition, trends in the data from this turbi dity meter indicated particle settling on the surface of the small angle lens used in this device and further obscured the data. Large scale turbidity tests were performed to measure the influent turbidity by using a YSI turbidimeter. The test was carried out on 150L samples in which a specific amount of NJCAT mass, depending on the concentration considered (100 mg/L and 300 mg/L), was added. Initially, a turbidity measurement of the background was taken. Then, the NJCAT gradation mass was added to the sam ple. After vigorously mixing the sample and ensuring the cleaning of the instrument lenses, two discrete turbidity readings were recorded. The same procedure is followed for both sediment concentration of 100 mg/L and 300 mg/L. QA/QC Verification of mass balance for each experimental r un The PM mass balance was calculated from dried captured mass, effluent mass load, and supernatant mass load. The mass balance error (MBE) criterion is 10% MBE and deter mined by Equation 2 15. (2 15) Ef ficiency c alculation The methods selected to estimate the particle removal efficiency of the unit are described in the present section. The first approach used is efficiency ratio or percent PAGE 36 36 removal. This measure is based on the change in event mean concen and is defined in Equation 1 16. (2 16) Where INF EMC is defined as the total influent solid mass loading divided by the total treated influent volume and EFF EMC represents the average effluent SSC obtained from composite samples collected in the field The second methodology used to evaluate the removal efficiency is based on the captured PM recovered from the unit (Equation 2 17). (2 17) CFD Model Dataset Creation To investigate the PSD discretization e ffect, a computational dataset of PM separation based on particle size is modeled for the HS units as a function of flow rate. Datasets contain separation efficiencies calculated for PM from 1 to 1000 m in 1 m increments for a series of flow rates (0.36, 0.91, 1.8, 4.5, 9.1, 13.6, 18.1, 22.7, 28.3, and 34.0 L/s). Intermediate values were determined, when needed, by linear interpolation. For all gradations particle sizes ranged from 1000 m or 1m and s eparation efficiencies are based on this range of particle sizes. PSD discretization is performed on a symmetric, gravimetric basis on an arithmetic scale. For the first increment, DN = 1 and the entire PSD distribution was represented by the PSD d 50m S ubsequently for DN = 2, the PSD was subdivided into two ranges and each was represented by the representative gravimetric median particle diameter for each range, the d 25m and d 75m quartiles. Sequentially subdividing each PAGE 37 37 range further in a similar fashion resulted in PSD representation by 2 (n 1) discrete particles giving DNs of 1, 2, 4, 8... The DN was increased until the model converged sufficiently, as defined by the model discretization error. Advantages of symmetric, gravimetric discretiza tion is that each discrete particle size range is equally independent of flow rate and HS. However, this discretization scheme could also expend computational resources on particles that are larger than the particle completely removed under given conditions. Other discretization schemes that are customized for a particular unit operation over a more narrow flow rate range potentially offer greater accuracy for similar DNs or lower computational effort by focusing computational resources on a PM range of relevance for the unit operation and flow rate. The computational model discretization error is error introduced into the modeled PM sizes. As the DN approaches infinity, the model will converge to a simulated he model PSD discretization error for a specific DN is characterized by an absolute Relative Percent Difference (RPD) as compared to DN = represented by a surrogate DN_MAX (Equation 2 18). (2 18) DN_MAX is the calculated surrogate of the theoretical model at DN = based on a calculated overall mean RPD of DN_MAX and DN_MAX 1 of less than 0.1% for all models and HS units. PAGE 38 38 Model Validatio n To ensure that a CFD model produces physically representative results, modeled PM separation is validated in the laboratory by pilot scale physical models loaded by a hetero disperse regulatory gradation ( 23 ) for a series of flow rates (Baffled HS: 0.4, 0.9, 1.8, 4.5, 9.1, 13.6, 18.1, 22.7 L/s and Screened HS: 2.3, 5.7, 11.4, 17.2, 22.9, and 28.6 L/s). For the CFD model, the PSD was represented by the hetero disperse NJDEP gradation ( Total 2003 ) with a d 50m R 2 = 0.99). Model fit is examined based o n mean and maximum absolute RPD (Equation 2 19). (2 19) An overall mean RP D of < 10% is considered to be an acceptable fit between the physical and numerical models. An additional rubric identified as the bias error was calculated to give an indication of the fit of the model to the physical model results. The bias error is esse ntially the mean RPD excluding the absolute value in the calculation. The bias error gives an overall indication of the tendency for the model to over or under predict the physical results. Results and Discussion Baffled HS Performance The treatment perf function of flow rate and for both aforementioned concentrations is reported in T able 2 4 and F igure 2 3 The figure also correlates a given experimental run to a MBE, which confirms that each accepte d run remained within 10% MBE in order to fulfill the QC protocol. PAGE 39 39 As expected the performance of the unit demonstrates exponentially increasing mass removal as the flow rate tends towards zero flow. At 2% design flow rate, the unit exhibits removal ef ficiency nearly equal to 90%. This efficiency rapidly decreases with increased flow up to the flow rate of 142gpm at 50% design flow. At this point the rate of decrease of efficiency with respect to the flow rate diminishes and eventually the efficiency reaches a minimum of approximately 50% removal at about 355gpm or 125% design flow. This phenomenon corresponds to the exponentially decreasing settling velocity of PM with respect to particle size. Since the influent PM gradation is heterodisperse and contains a significant portion of finer particles it is expected that the unit, primarily using the mechanism of gravitational settling, will not reach high efficiencies unless it is operating at very low flow rates. For the higher flow rates the unit is primarily capturing coarse PM while the fine material is passing through the unit. In order to investigate the influence of the influent sediment concentration on the overall removal efficiency of the system, the experimental results corresponding to th e concentrations of 100 mg/L and 300 mg/L are reported respectively for EMC and Mass in F igure 2 4 As depicted in the plots, the removal efficiency performed by the system for the two NJCAT sediment concentrations considered (100 mg/L and 300 mg/L) across the entire range of flow rates tested is nearly comparable. This outc ome demonstrates that for these dilute concentrations particle particle interactions are negligible. Therefore, the removal efficiency does not depend on the influent solid loading concentrations as long as they remain within this range and can be consider ed dilute. PAGE 40 40 Model Validation Results The criteria outlined for model discretization error is utilized and DN_MAX is incremented for the screened and baffled HS for each of the nine PSDs until a mean overall absolute modeled RPD of < 0.1% is obtained for eac h PSD tested. For these HS units and PSDs, the criteria are satisfied with the DN = 128, a PSD discretization greater than provided by most PM analysis. Model validation was performed and results illustrated in F igure 2 1 The screened HS model had an ov erall mean absolute RPD of 6.5%, a maximum absolute RPD of 11.7% (at 2.3 L/s) and a n over prediction bias error of 5.0%. The baffled HS had an overall mean absolute RPD of 4.6% (100 mg/L) and 4.1% (300 mg/L), a maximum absolute RPD of 9.2% (100 mg/L at 1. 8L/s) and 7.3% (300 mg/L at 18.1 L/s), and an over prediction bias error of 1.1% (100mg/L) and 0.7% (300mg/L). The low bias likely indicates that a majority of the error is likely from physical data collection. The CFD modeled HS units satisfy the requir ements for model validation. PSD Discretization R esults Figures 2 5 and 2 6 illustrate the PSD discretization results for the screened HS and the baffled HS, respectively. Results illustrate an exponential decline approaching zero with increasing DN. The role of the gradation sorting on the error due to discretization is apparent especially for low DNs. Of primary importance is the DN gradations centered on 66.7 m and 100 m th e maximum modeled RPD is less than 2.1% at a DN of 8. For a DN of 8 and the three gradations centered on 33.3m, the maximum modeled RPD is 7.5%. However, at a DN of 16 the maximum modeled RPD is 1.1%. PAGE 41 41 The use of the 1m PSD increment for each HS datas et facilitated subsequent analysis of unit behavior. This high resolution CFD output generated large CFD particle tracking files that are post processed as batch script files and further processed through user defined coding. An advantage of utilizing th is methodology is the time intensive CFD output did not require model re creation to further explore unit behavior for different PM size gradations that are within the limits of the original high resolution PSD range; including sampled gradations from phys ical modeling. The drawback to this methodology is the necessity of linear interpolation to evaluate PM sizes that are not explicitly solved for by the CFD simulation. However, since the resolution of the output is high compared to the required analysis resolution, this impact is minimal. High resolution, per particle size removal datasets offer a fundamental perspective on the behavior of any unit operation, in this study, two similar HS units. These datasets, illustrated in figure s 2 7 and 2 8 provide a three dimensional surface of flow PSD domain. Currently, regulatory requirements for approval of unit operations such as these HS units rely on the physical modeli ng of a given unit, under specific flow conditions, for a specific PM influent gradation. These gradations vary widely between regulations as illustrated by the NJDEP (hetero disperse) and Indianapolis (mono disperse) gradations in F igure 2 2 A disadvan tage in such disparate regulations is the required physical modeling needed for diverse regulatory frameworks. One solution is for regulators to allow full scale testing of a unit operation to physically model the per particle size behavior with a hetero disperse PSD with high resolution PSD monitoring, such as a laser diffraction analysis given a demonstration of mass and volume PAGE 42 42 balances. Such high resolution validation datasets can be utilized in CFD post processing to comply with diverse or regional re gulatory frameworks while still providing meta Most urban drainage PSDs are hetero disperse as represented by the NJDEP regulatory PSD and therefore must be examined at DN levels that reasonably minimize the RPD between measured and modeled results. However, a significant conclusion of this study is that only mono disperse gradations are reasonably measured and modeled by a DN = 1; the d 50m Given a constant density across the PSD and a given flow field, this result mat ches the expectation driven by particle fluid dynamic theory. Across the narrow PM sizes in mono disperse PSDs, particles will remain within the same flow regime (turbulent/transitional/laminar) and the non linear relationship of particle size to particle drag force will have negligible impact on the final result due to the minimal diameter range encountered with uniform PSDs. However, as the PSD becomes hetero disperse or dense, particles will no longer reside in the same flow regime and the non linear rel ationship between particle size and particle drag force greatly impacts the accuracy of using the singular gravimetric median as the sole representative particle. The uniformity of the gradation also influenced the rate of convergence with regard to the DN, as hypothesized in this study. For uniform gradations, convergence is almost immediate as when a PSD is well represented by the d 50m The convergence rate of the medium gradations centered on 66.7 and 100 m is located between the convergence rate of the hetero disperse and mono disperse gradation in figures 2 5 and 2 6 This result can be explained based on the hetero dispersivity of the PSD. However, for the finest PSDs, those centered on 33.3 m, the convergence rate of the medium and PAGE 43 43 hetero dispe rse gradations are more similar than for coarser PSDs illuminating the additional influence of overall fineness of the gradation on the convergence rate. For these fine PSDs a significant portion of the gradation is below the effective separation potentia l of both HS units for a majority of the studied flow rates. This results in a largely suspended PM with more opportunity for variability in modeled behavior because of poorly represented gradations and low separation potential by both units. The DN is the driving factor in the modeling discretization error as well as a significant contributor to computational time. In general, doubling the DN doubled the computational time while providing diminishing improvement in model accuracy. Gradation uniformity and fineness impact the discretization error at DNs in the range of 1 to 8. As DNs increase beyond 16, the effect of gradation uniformity and overall fineness are essentially negligible for all tested gradations as shown in figures 2 5 and 2 6 For the g radations centered on 66.7 m and 100 m model convergence is achieved at a DN of 8 regardless of gradation uniformity. Based on the results, a discretization at a DN of 8 to 16 is generally provides a discretization error that for many applications can p rovide acceptable results for coarser gradations. For very fine gradations, similar to the gradations centered on 33.3 m, a DN of 16 to 32 is recommended. Power Law Model (PLM): For this study mean and maximum RPD are modeled as a function of DN. Result s are examined with a power law model (PLM) ( Cristina and Sansalone 2003 ) across all granulometric variations (Equation 2 20). (2 20) PAGE 44 44 The power law constants and exponents c and m are summarized in T able 2 5 and the model fit is pres ented graphically in F igure 2 9 Physically, the power law constant (10 c ) represents the RPD of the d 50m The m values (slopes) are all negative and are an index for the rate of convergence to zero discretization error with increasing DN. Higher negativ e slopes indicate a higher convergence rates and therefore lower DN to provide a given RPD. The coefficients for the PLM are generated by the least squares fit of the CFD RPD results as well as a linear transcription of the least squares fit to the upper 9 5% confidence interval for a more conservative model. In general, the power law model presented should extend well as an initial guidance for other particle fluid systems that maintain the underlying assumptions of the provided computational analysis. Com putational Time Table s 2 6 and 2 7 present the processor computational time for executing the steady state DPM particle tracking model for a few select flow rates as total processor seconds for the baffled HS and screened HS, respectively These results i llustrate that the computational time approximately doubles by doubling the DN. These results were obtained using a Dell Precision 690 with dual quad core Intel Xeon E5345 CPUs at 2.33 GHz and 16 GB of RAM running Fluent 6.3.26 on Microsoft Windows XP Pro fessional x64. PAGE 45 45 Table 2 1 Table of cumulative gamma distribution modeled g radations Gradation # # 1 a # 2 a # 3 a # 4 b # 5 b # 6 b # 7 c # 8 c # 9 c k 162.9 177. 2 163.0 2. 3 2.3 2.3 0. 6 0. 6 0. 6 0.2 0. 4 0.6 17.0 33.8 50.3 116.3 232.6 353. 2 I 0.1 0.1 0.1 1.0 1.0 1.0 2.6 2.6 2.6 3 ) 2. 7 2. 7 2.7 2.7 2.7 2.7 2.7 2.7 2.7 d 95m ( m) 37.8 75.3 113.4 88.4 176.8 262.8 241.9 483.2 730.6 d 84m ( m) 35.9 71.8 107.9 62.5 125.2 186.1 128.9 257.3 388. 4 d 50m ( m) 33.3 66.7 99.9 33.3 67.0 99.4 33.2 66.1 99.3 d 16m ( m) 30.8 61.8 92.3 15.1 30.6 45.4 3.8 7.5 11.2 d 5m ( m) 29.2 58.8 87.6 8.1 16.3 24.2 0.5 0.9 1.4 a U niformly distributed; b Gradations of medium distribut ion ; c H etero disperse. The c haract eristics of I ) that are utilized in this study Gradation #8 is the gamma curve fit of the NJDEP gradation ( R 2 = 0.99). The other gradations were selected to systematic ally explore the effect of uniformity and d 50m on discretization error and were I < 0.23), the medium gradations transect the boundary between moderately sorted and poorly sorted I = 1.0), and the other hetero disperse gradations (#7, #9) have similar sorting to the NJDEP distribution (#8). The distribution indices (percentiles) necessary for the calculation of the sorting coefficient are also included. Table 2 2. Morsi and Alexan der drag equation and coefficients for a sphere (1972) Reynolds Number K1 K2 K3 <0.1 24.0 0 0 0.1 < Re < 1.0 22.73 0.0903 3.69 1.0 < Re < 10.0 29.1667 3.8889 1.222 10.0 < Re < 100.0 46.5 116.67 0.6167 100.0 < Re < 1000.0 98.33 2778 0.3644 10 00.0 < Re < 5000.0 148.62 4.75 X 10 4 0.357 5000.0 < Re < 10,000.0 490.546 57.87 X 10 4 0.46 10,000.0 < Re < 50,000.0 1662.5 5.4167 X 10 6 0.5191 PAGE 46 46 Table 2 3 Hydrodynamic separator e xperimental r un information and operational parameters Run # Des ign flow rate (%) Actual flow rate (gpm) Target influent concentration (mg/L) Actual influent concentration (mg/L) Influent mass load (g) Mean influent turbidity (NTU) 1 50 149.1 300 266.1 9000 32.2 2 100 296.0 300 287.5 9986 30.8 3 75 224.3 300 288. 4 10124 30.8 4 125 365.1 300 297.0 10124 30.8 5 100 294.0 100 97.8 3375 12.4 6 25 73.8 300 73.8 10124 32.2 7 125 364.6 100 98.5 3375 10.6 9 10 28.9 300 300.0 10124 30.8 11 10 29.3 100 97.8 3375 12.4 13 25 73.2 100 96.5 3375 12.4 14 75 220.1 100 96.9 3375 12.4 15 5 15.7 300 273.5 7817 30.8 16 2 5.4 100 106.0 1042 10.6 17 50 146.8 100 97.8 3375 10.6 18 5 16.6 100 86.3 2606 10.6 19 2 5.9 300 291.2 3127 32.2 Experimental matrix of treatment runs for the baffled HS unit loaded by NJCA T gradation under 100 and 300 mg/ L and various operating flow rates PAGE 47 47 Table 2 4 Hydrodynamic separator performance Run # Effluent concentration (mg/L) Effluent mass load (g) Total mass captured( g) b EMC(%) Mass balance error (%) Mean turbidity reducti on (NTU) 1 104.8 3550 4803 60.6 7.2 2.7 2 121.3 4213 4760 57.8 10.1 0 c 3 134.2 4709 5475 53.5 0.6 0 c 4 146.0 4977 4809 50.8 3.3 0 c 5 48.0 1655 1802 51.0 2.4 0.3 6 108.0 3749 7161 63.0 7.8 2.8 7 46.3 1588 1747 53.0 1.2 1.5 9 84.6 2874 794 7 71.8 6.9 5.4 11 29.8 1028 2660 69.5 9.3 4.1 13 36.0 1242 2225 62.7 2.7 2.0 14 44.5 1551 1885 54.1 1.8 0.5 15 62.1 1775 6119 77.3 1.0 17.3 16 11.1 109 917 89.5 1.5 6.6 17 37.6 1295 2318 61.6 7.1 3.3 18 14.8 448 2186 82.8 1.1 7.4 19 30.5 328 2817 89.5 0.6 20.6 a Effluent event mean concentration ; b Total mass captured is the sum of suspended PM in supernatant and settled PM recovered as wet slurry from the unit ; c The actual value is slightly less than 0, since mean turbidity reduc tion is within the range of instrument resolution it is considered essentially as zero Table 2 5 PM g radations with mean power law model parameters M odel p arameter Uniform mean Medium mean Hetero disperse mean Uniform upper 95% Mediu m upper 95% Heter o disperse upper 95% Mean RPD : c 0.012 1.24 1.46 0.55 1.64 1.75 Mean RPD : m 1.07 1.51 1.66 1.07 1.51 1.66 Maximum RPD : c 0.46 1.54 1.69 0.92 2.02 2.00 Maximum RPD : m 1.04 1.43 1.56 1.04 1.43 1.56 The mean model parameters represent t he power law parameters that are the result of the least square fit of the residuals. The upper 95% model parameters represent the upper bound of the 95% confidence interval for the parameter values. PAGE 48 48 Table 2 6 Discrete phase model computational time f or the baffled hydrodynamic separator Flow Rate (L/s) DN1 DN2 DN4 DN8 DN16 DN32 DN64 DN128 0.4 4 11 16 30 58 114 227 452 18.1 8 20 41 77 151 302 599 1200 Times are in seconds. The results show that doubling the DN generally doubles the computational time. This result combined with the diminishing convergence rate of the RPD for higher DNs demonstrates the exponentially increasing computational time to achieve the same improvement in the models RPD error due to discritization. Table 2 7 Discrete p hase model computational time for the screened hydrodynamic separator Flow Rate (L/s) DN1 DN2 DN4 DN8 DN16 DN32 DN64 DN128 4.5 5 6 16 31 61 121 241 481 22.9 4 8 15 30 60 118 235 469 Times are in seconds. The results show that doubling the DN genera lly doubles the computational time. This result combined with the diminishing convergence rate of the RPD for higher DNs demonstrates the exponentially increasing computational time to achieve the same improvement in the models RPD error due to discritiza tion. PAGE 49 49 Figure 2 1. Experimental validation of full scale units Experimental validation of the change in event mean concentration for the screened HS ( A ) and baffled HS ( B ) CFD models from experiments conducted on full scale 1.8m (6 ft) diameter unit s under laboratory conditions, loaded with the hetero disperse NJDEP regulatory gradation (gradation #8). As required, the CFD model results were linearly interpolated to provide data points at concurrent flow rates for RPD calculation. Range bars show ex perimental mass balance recovery (%). Influent flow is demarcated by Q in each subfigure. Reported volumes are calculated from the water level under static conditions. PAGE 50 50 Figure 2 2. Cumulative PSDs utilized in the stu dy. The cumulative PSDs selected to explore the effect of gradation dispersity ( I ) and d 50m on modeling error due to PSD discretization. The vertical axis represents the percentage by mass of particles that are finer than the diameter on the corresponding horizontal axis. There are three sets of three gradations. Each set of gradat ions is centered on a common focus (33.3m, 66.7m, or 100m). Each gradation set contains gradations with I = 0.11, 1.03, and 2.64. From an experimental perspective, the hetero disperse gradation centered on 66.7m (#8) is the gamma curve fit of the Ne w Jersey (NJDEP) gradation ( R 2 = 0.99) and the uniform gradation centered on 100m (#3: I = 0.11, d 50m = 100) is analogous to an OK 110 gradation ( I = 0.21, d 50m = 110). PAGE 51 51 Figure 2 3. Results from the full scale expe rimental testing on the baffled HS. Baffled HS treatment performance based on EMC and Mass for influent mass loading concentrations of 300 mg/L and 100 mg/L and matched to a corresponding mass balance error ( MBE ) PAGE 52 52 Figure 2 4. Results comparing influent loading concentrations on the baffled HS. This figure demonstrates the b affled HS treatment performance by comparing EMC efficiency and Mass efficiency across the two influent mass loading concentrations The resu lts show that the unit performs similarly for both influent mass loadings and that at the tested concentrations that particle particle interaction is negligible validating the use of the discrete phase model without accounting for particle collisions. PAGE 53 53 Figure 2 5 Computational results for the screened HS. Mean and maximum RPDs based on DN as calculated against DN = 128. In this manner, the RPD is a characterization of error due solely to PSD discretization. Resul ts show rapidly decreasing error for higher DNs as well as the effect of I at low DNs for the mono disperse ( I = 0.11), moderately dispersed ( I = 1.03), and hetero dispersed ( I = 2.64) gradations. Results for DNs higher than 16 are not displayed due to high convergence. The d 50m is only a reasonable characterization and model input for mono disperse gradations. PAGE 54 54 Figure 2 6 Computational results for the baffled HS M ean and maximum RPDs based on DN as calculated against DN = 128. In this manner, the RPD is a characterization of error due solely to PSD discretization. Results show rapidly decreasing error for higher DNs as well as the effect of I at low DNs for the mono disperse ( I = 0.11), moderately dispersed ( I = 1.03), and hetero dispersed ( I = 2.64) gradations. Results for DNs higher than 16 are not displayed due to high convergence. The d 50m is only a reasonable characterization and mod el input for mono disperse gradations. PAGE 55 55 Figure 2 7 CFD p er particle size efficiency surf aces for both the screened HS (A) and the baffled HS (B performance of the solids separator at steady flow rates across the spectrum of designed flows. The top plane in each surface represents particles that are completely captured and the bottom plane in each surface represents particle sizes that are negligibly captured by the solids separators. PAGE 56 56 Figure 2 8 CFD per particle size efficiency differential surface for the screened HS and the baffled HS. This represents the performance differential per particle size for the two units positive indicating better performance from the baffled HS. PAGE 57 57 Figure 2 9 Predictive results of the power law model for RPD with increasing DN. Cumulative baffled HS and screened HS convergence data modeled by the power law. Panels (a) through (c) are model mean RPDs in increasing order of he tero dispersivity and panels (d) through (f) are model maximum RPDs in increasing order of hetero dispersivity. The convergence model is based on a least squares linear regression of the log log plot of the data. An upper bound model (Upper 95% CM) cons tructed from the standard deviation of the regression residuals is given for more conservative estimation of RPD. PAGE 58 58 CHAPTER 3 OVERALL RATE KINETIC S MODEL OF SODIUM HY POCHLORITE DEMAND BY THE DISSOLVED AND PARTIC ULATE MATTER FRACTIO NS IN URBAN RAINFALL RUN OFF Urban runoff is a significant source of hetero disperse PM, chemical and microbial loadings to receiving waters and combined sewer overflows. Effective treatment and reuse options are needed given urban water demands and regulations such as total maxi mum daily loads ( Code of Federal Regulations 2001 ), and numeric nutrient criteria in Florida ( USEPA 2010 ). Urban runoff microbial loads can impair receiving waters ( Jin et al. 2004, USEPA 1984) and in reuse applications pose a potential public health risk if untreated ( DEC 2006 ). Chlorination is the most commonly utilized disinfectant worldwide ( Hrudey and Hrudey 2004 ) and is utilized to provide a residual in reclaimed wastewater utilized for irrigation in the built environs. However, the efficacy of chl orine disinfection is highly dependent on the available residual over time ( Chick 1908, Fair et al. 1948 ). Many kinetic studies of free and total chlorine demand in source waters and wastewater have been undertaken. Taras ( 1950 ) model chlorine demand of inorganic and organic substances with a power law function. Hass and Karra ( 1984 ) evaluate chlorine demand in wastewater utilizing first order, power law and parallel first order models. Clark describes chlorine demand in drinking waters utilizing a sec ond order model ( 1998 ) and, in conjunction with Sivaganesan, describes chlorine demand with a parallel second order model for raw and finished waters ( 2002 ). Huang and McBean e model parameters ( 2007 ). Warton et al. utilize piecewise first order functions and dissolved organic carbon (DOC) to normalize models to provide dose independence PAGE 59 59 ( 2006 ). However, there are fewer chlorine demand kinetic models for urban runoff or wet weather combined sewer overflows (CSOs). While the potential for DBPs exists for waters with natural organic matter ( Rook 1977 ), an EPA study concludes that chlorination/dechlorination is preferred in decentralized CSO treatment. The study cited high cos ts of decentralized ozone generation for intermittent wet weather events, PM shielding in ultraviolet inactivation schemes, and the public health hazard associated with Cl 2 gas storage required for chlorine dioxide generation ( USEPA 2003 ). The constituen ts of urban runoff differ from potable water sources and wastewater. Kim and Sansalone ( 2010 ) compared PM from untreated runoff to wastewater treatment plant (WWTP) influent. The study demonstrated that influent PM was relatively fine (median diameter: d 5 0m disperse (80 th to 20 th percentile dispersivity index: d 80m /d 20m = 3.1 ), with a specific gravity of 1.5, and a volatile fraction of suspended PM of 76% (a surrogate indicator of organic content) and is transport is relatively steady wit h regular diurnal fluctuations in flow. In comparison, the urban runoff gradation is relatively coarse (d 50m disperse (d 80m /d 20m = 50.5 ), with a specific gravity of 2.3, a volatile fraction of suspended PM of 27% and is transported spa tio temporally by episodic events. It is hypothesized that these urban runoff differences will provide distinct chlorine demand kinetics as compared to first order decay models commonly utilized for wastewater ( Haas and Karra 1984 ). It is hypothesized th at the temporally varying loading rate with multiple potential limiting factors require a second order model, similar to ( Clark and Savaganesan 2002) but with the inclusion of PM demand, and correlation of the model parameters to runoff chemistry paramete rs. Furthermore, as runoff can be the dominant volumetric factor in PAGE 60 60 combined sewerage flows, by a factor of 100 in a Philadelphia study ( USEPA 1973 ), chlorine demand kinetics in runoff has direct application to CSOs. Quantifying chlorine demand of PM fra ctions allows decision making with respect to PM separation and advanced computational modeling ( Dickenson and Sansalone 2009 ) of unit operations and processes. Therefore the objective of this study is to model chlorine demand kinetics in urban runoff and compare the kinetics for dissolved and PM fractions. Results elucidate the primary phase or PM fractions in urban runoff for chlorine demand. Methodology Catchment The catchment for this study is an urban source area located in Gainesville, FL. The lan d use is a 13,000 m 2 carpark field site of which a 500 m 2 catchment was delineated, instrumented and monitored for this study (Figure A 2) The surface area of the carpark is 75% asphalt pavement and 25% raised vegetated islands with mature trees and deli neated by vertical concrete curbs. The catchment has an average daily traffic loading of 530 vehicles (observed). Physical (PM), chemical (nutrients, metals and organic compounds) and microbial loading sources are anthropogenic (tire, vehicular and pavem ent abrasion and urban litter) and biogenic (leaf litter, grass clippings, insects, and small urban animals and bird feces). The catchment drains to a catch basin modified to allow all flow to be diverted for full cross sectional flow manual sampling. Ra infall is measured with a tipping bucket rain gage and flow measured with a calibrated Parshall flume, an ultrasonic transducer and data logger. Runoff is sampled at volumetrically spaced intervals. PAGE 61 61 PM Fractionation During the monitoring phase of the stu dy, four runoff events are sampled. The hydrologic and PM indices are presented in Table 3 1. Approximately 100 L of runoff is sampled for each event and immediately (30 minutes) transferred to the laboratory for analysis. Prior to each of these events additional runoff from a previous event loading the same catchment is collected and filtered (0.45 m membrane). This filtered runoff served as a dissolved matrix for re suspending each PM fraction. The dissolved matrix is autoclaved at 121C for one hou r to render the dissolved matrix biologically inert The runoff matrix is then stored at 4C. runoff. PM is fraction ated by wet sieving and microfiltration from the sampled 100 L volume into gross solids (>4250 m, Rushton 2007 ), sediment PM (4250 75 m, Kim and Sansalone 2008 ), settleable PM (75 25 m, Kim and Sansalone 2008 ), suspended PM (25 0.45 m, Kim and Sa nsalone 2008 ), and dissolved fractions (<0.45 m, Kim and Sansalone 2008 ). The gross solids are dried and weighed to characterize their contribution to runoff PM, but are not included in the batch reactors for chlorine kinetic analysis. Sediment and sett leable PM fractions were immediately re suspended in 1 L of runoff matrix as PM concentrate and 25 m sieve filtrate is set aside as the suspended PM fraction. Batch Reactor Framework The batch reactor framework for chlorine demand kinetics utilizing the f irst runoff event consists of two replicate analysis of three sodium hypochlorite doses (15, 30, and 45 mg/L 2 mg/L) for each runoff fraction (sediment PM, settleable PM, suspended PM, and the dissolved fraction) for a total of 24 batch reactors. Subseq uently, three PAGE 62 62 additional storm events are sampled and batch reactors are run with a single hypochlorite dose across the four runoff fractions with the hypochlorite dose varying for subsequent events (nominally 15, 30, and 45 mg/L) for a total of 8 additio nal batch reactors per storm. In addition to these reactors, control batch reactors are run to determine the effects of autoclaving the runoff matrix and environmental losses to the atmosphere by volatilization or UV breakdown of HOCl/OCl Finally, an a dditional 22 batch reactors are run at very dilute PM fractions to enhance the resolution of the ultimate chlorine demand by PM. For each reactor, samples are taken at 1, 5, 10, 20, 40, 120, and 480 min. with an additional sample taken at 24 h for the dilu te PM fraction reactors. Batch reactors, stirbars, and color spectrophotometer cuvettes are prepared to be chlorine demand free by immersion in 60 mg/L HOCl for a minimum of one hour (Eaton et al 1998) Subsequently, the glassware is triple rinsed with ch lorine demand free water (Barnstead NanoPure). Batch reactors containing the dissolved and suspended fractions are filled with 1700 mL of their respective fractions and reactors containing sediment and settleable PM fractions are reconstituted by adding 15 0 mL of PM concentrate to a sufficient volume of runoff matrix to achieve a final volume of 1700 mL. Prior to initiation of each reactor run, the batch reactors are brought to room temperature of 25 C 2 C. The batch reactors are enclosed in an alumin um foil jacket to eliminate free chlorine decomposition due to light and sealed with a lid to reduce HOCl volatization to the atmosphere. The continuously mixed batch reactors are dosed with sufficient stock hypochlorite solution to meet the requirements of the experimental construct (mixing speed 1000 rpm for first 30 s, 350 rpm thereafter). The PAGE 63 63 stock hypochlorite solution s are formulated to be approximately 1000 mg/L and standardized by sodium thiosulfate titration (Eaton et al. 1998) For these experi ments the stock hypochlorite solution ranged from 962 1017 mg/L. The experimental construct for the first baseline storm event consisted of replicate analysis of 3 sodium hypochlorite doses (nominally 15, 30, and 45 mg/L) across the 4 aforementioned st ormwater fractions (sediment, settleable, suspended, and dissolved) for a total of 24 batch reactors. Subsequently, three additional storm events were sampled and batch reactors were run with a single sodium hypochlorite dose across the 4 stormwater fract ions with the hypochlorite dose varying with each storm (nominally 15, 30, and 45 mg/L) for a total of 8 additional batch reactors per storm. In addition to these reactors, control batch reactors were run to determine the effects of autoclaving the storm water matrix and environmental losses to the atmosphere by volatilization or UV breakdown of HOCl/OCl Batch Reactor Setup Batch reactors, stirbars, and color spectrophotometer cuvettes were prepared to be chlorine demand free by immersion in 60 mg/L HOCl for a minimum of one hour (Eaton et al 1998). Subsequently, the glassware was triple rinsed with chlorine demand free water (Barndstead NanoPure). Batch reactors containing the dissolved and suspended fractions were filled with 1700 mL of their respecti ve fractions and reactors containing sediment and settleable fractions were reconstituted by adding 150 mL of PM concentrate to sufficient stormwater matrix to achieve a final volume of 1700 mL. Prior to initiation of the experiment, the batch reactors we re brought to room temperature of 25 C 2 C in a water bath at 45 C. The batch reactors were enclosed in an aluminum foil jacket to eliminate free chlorine decomposition due to light and PAGE 64 64 sealed with a lid to reduce HOCl volatization to the atmosphere. Continuously mixed batch reactors were dosed with sufficient stock hypochlorite solution to meet the requirements of the experimental construct (mixing speed 1000 rpm for first 30 s, 350 rpm thereafter). The stock hypochlorite solution was formulated to be approximately 1000 mg/L and was standardized by sodium thiosulfate titration (Eaton et al 1998 ). For these experiments the stock hypochlorite solution ranged from 962 to 1017 mg/L. Analytical Methods Residual free chlorine is determined from reacto r samples by measuring absorbance at 530 nm with a color spectrophotometer (Hach DR2800) utilizing N,N diethyl p phenylenediamine (DPD, Hach Chemical) as the reagent (Eaton et al. 1998) A custom absorbance curve is developed with prepared free chlorine s tandards to ensure the accuracy of the supplied DPD batch as well as to extend the range of the analysis. At each sampling interval, two aliquots of reactor constituents are removed for residual free chlorine analysis to perform the method in replicate. The volume of the aliquots were selected for each individual reactor and diluted to 10 mL with chlorine demand free water to ensure that the sample is within the analytical range of the DPD reagent and absorbance curve. In addition, at time zero, a third aliquot of sample is removed to check for manganese oxide interference. Manganese oxide interference is accounted for by adding 3 drops of 30 g/L potassium iodide to the analyte in the cuvette, waiting one minute, and then adding three drops of 5 g/L sodi um arsenite. DPD is then added to the aliquot and the absorbance is measured at 530 nm. The absorbance from the interference test is then subtracted from the previously measured absorbance and the result utilized to calculate the residual free chlorine. PAGE 65 65 The PM granulometry for each reactor is determined by low angle laser light scattering (LALLS, Malvern Mastersizer 2000) to ensure consistent loading conditions within the reactors and validate the fractionation process. LALLS employs the principle of M ie scattering to iteratively estimate the particle size distribution (PSD) (ISO 2009) PSD determination is carried out in the aqueous phase and measurements are made in triplicate to ensure that the measurements are reproducible and stable. Figure 3 1 p resents the PM fractions visually as they are applied within the framework of this study. PM mass loading within the batch reactors is determined as suspended sediment concentration (SSC) method (ASTM 2002) The entire contents of the reactor are filtere d through a nominal 1 m glass fiber filter, dried at 105 C, cooled in a desiccator, and weighed to the nearest 0.1 mg. The volatile fraction of the PM is calculated by volatization at 550 C, cooling in a desiccator, and weighed. Water chemistry measure ments of temperature, pH, specific conductivity, and oxidation and reduction potential (ORP) are made utilizing calibrated electrodes and dissolved chemical oxygen demand (COD d ) is calculated utilizing color photospectrometry and the USEPA approved Hach Me thod 8000 (Federal Register 1980) Parallel Second Order Demand Model for Dissolved Phase Urban stormwater runoff contains a complex matrix of dissolved inorganic and organic species (Dean et al. 2005) (2002) parallel second orde r model demonstrated applicability to the complex chemistry of both raw and finished waters This model is chosen to represent the kinetics of free chlorine demand in urban runoff. The model assumes that the chlorine demand kinetics is the result of two p arallel reactions. PAGE 66 66 (3 1) In these reactions, and p n are stoichiometric coefficients, P n are products, and are the concentration of free chlorine parti cipating in each reaction and and are the concentration of reactants in the solution that react with the free chlorine. An analytical solution of Equation 3 1 is Equation 3 2 (3 2) (Note that in Equation 18 in Clark and Savaganesan (2002), and as presented in Equation 3 2). In this expression, Cl(t) = chlorine concentration at time, t ; Cl 0 is the initial concentration of free chlorine ( + ; X = ( ; k 1 and k 2 are rate constants; and R n is defined in Equation 3 3. (3 3) Substituting Equation 3 3 into Equation 3 2 gives Equation 3 4. (3 4) To aid in the development and physical significance of the model a hypothetical ultimate chlorine de mand term is introduced, C B0 which is the sum of the initial concentrations of and and reacts on a one to one stoichiometric basis with free chlorine ( Note that the 1:1 stoichiometric relationship g ives Utilizing these relationships gives Equation 3 5. PAGE 67 67 (3 5) In addition, C B0 is assumed to be a fractional component represented by the dissolved chemical oxygen demand (COD d ). COD d is a reasonable model parameter given the ease of measurement and that the basis of the measurement is an oxidation reaction. Substituting C B0 = fCOD d into the model of Clark and Savaganesan returns a solution of the form in Equation 3 6 (3 6) In this expression the physical parameters are the initial chlorine dose as Cl 0 [ mg/L ], COD d [ mg/L ] and time, t The four model parameters are f the fractional component of the COD d that represents the ultimate chlorine demand (unitless), X (unitless), the proportion of the chlorine demand that reacts quickly at rate k 1 and k 1 and k 2 the quick and s low, respectively, reaction rate constants with the units of [ L 1 mg 1 min 1 ]. Figure 3 2 illustrates the physical significance of the parameters of the second order demand model for the dissolved phase. To determine the model parameters, the non linear cu rve fitting using the Levenberg Marquardt algorithm (Marquardt 1963) to estimate the parameters of the non linear functions as well as the parameter standard error. In addition, the model parameters are combined for sixteen datasets to obtain globally bes t fitting parameters based on the catchment runoff data. The remaining datasets are utilized as non influencing verification datasets of the model. PAGE 68 68 Second Order Potential Driving Model for the PM Fractions As a source water, urban rainfall runoff contains a heterodisperse PM phase that from a treatment perspective contains three significant PM fractions, suspended, settleable, and sediment based on size and mechanistic delimiters. The chlorine demand for each PM fraction is determined by subtracting the c hlorine demand of the dissolved runoff matrix from the overall chlorine demand of the batch reactor containing a PM fraction. A model of the dissolved phase demand is determined from two replicate reactors from the same dissolved runoff matrix source and dosed with the same initial hypochlorite concentration. Studies have demonstrated the applicability of a second order potential driving model for kinetics of adsorption in runoff for dissolved metals (Liu et al. 2005) and phosphorus (Wu and Sansalone 2011 ) for simulating overall mass transfer. The second order potential driving model has the following form (Liu et al 2005) (3 3) In this expression C t is the concentration of free chlorine at time, t ; C e is the concentration of free chlorine at equilibrium; S t is the number of active reaction sites on the PM at time, t ; S e is the number of active reaction sites on the PM at equilibrium; and k PM is the mass tra nsfer rate constant. Equation 3 3 models the instantaneous free chlorine demand from PM as a function of the available PM reaction sites and the concentration of th e available free chlorine. Substituting: (3 4) (3 5) PAGE 69 69 Substituting Equations 3 4 and 3 5 into Equation 3 3 and linearizing gives Equation 3 6 (Liu et al. 2005 ) : (3 6) In this expre ssion is the sorben t(PM)/solute(HOCl) ratio. From E quation 3 6, the final mass transfer form of the model (Equation 3 10) is obtained by substituting Equations 3 7 through 3 9 in Equation 3 6 (Wu and Sansalone 2011) (3 7) (3 8) (3 9) W here W/V is the mass of PM [g] over the volume of the reactor [L], which is the SSC [g/L]; q t [mg/g] is the ratio of the mass of free chlorine transferred to the mass of PM in the reactor at time, t [min]; and q e [mg/g] is the ratio of the mass of free chlorine transferred to the mass of PM in the reactor at equilibrium. (3 10) Equation 3 10 is the linearized second order potential driving model for the overall mass t ransfer of free chlorine from the solute phase into surface reactions on constituent PM Initial values for k PM [g 1 mg 1 min 1 ] and q e are determined experimentally by plotting t / q t versus t. Resulting parameter values favor the experimental endpoint due to the linearization, and are further refined by minimizing the normalized root mean square error (NRMSE). PAGE 70 70 Model Evaluation NRMSE is utilized to evaluate model performance. (3 11) In this expression O i is the observed value at measurement i ; E i is the modeled value at measurement i ; n is the total number of measurements; and C l o is the initial chlorine dose Res ults and Discussion Control Reactors The results from the control reactors demonstrate that no detectable environmental losses of free chlorine due to UV light or volatization occur during the duration of the batch reactor experiments (Figure A 3) In add ition, autoclaving the runoff matrix resulted in no detectable change to the model parameters (Figure A 4) Kinetics Model for Dissolved Phase Figure 3 3 panel A, and Table 3 2 summarize the kinetic model parameters for the inter event CSBR dataset of the dissolved phase (n = 16). The fractional component of COD d reacting on a 1:1 basis with free chlorine as chlorine demand, f is 0.36 0.025 (95% C.L). X, which represents the portion of f COD d that reacts rapidly are 0.39 0.035. k 1 is 0.07 0.033 (L 1 mg 1 min 1 ) and k 2 is (2.93 0.79) X 10 4 (L 1 mg 1 min 1 ). Figure 3 3 also demonstrates the predictive capability for the second order demand model for the dissolved phase using four non influencing datasets. The data, second order model, and a 95% confidenc e bands for model parameters are shown. From the figure, the multi modal decay rates are visibly apparent from the datasets confirming the selection of a parallel model for the analysis (c.f. Panel D). One of the PAGE 71 71 strengths of the parallel second order di ssolved model is the characterization of the initial phase of chlorine demand, thus, allowing the entire curve to be modeled as band illustrates the sensitivity of the model to the four model parameters with the parameter sensitivity being f > X > k 2 > k 1 The model parameters are developed on an inter event basis. Thus, the given model parameters and ranges represent loading characteristics which are consistent for the catchm ent and event independent. The rate constants, k 1 and k 2 demonstrate that there are parallel reactions with separate rate constants and that these rate constants differ by two orders of magnitude. This difference provides additional validation for the u se of a parallel model in identification of two separate reaction rates of disparate values. The parameter, X, indicates that approximately 39% of the chlorine demand is exerted by dissolved components that react rapidly with free chlorine. This proporti onality is consistent (3.5%) for the catchment across multiple events and results from the dissolved inorganic and organic loads. Further research is needed to elucidate the value of X for similar and dissimilar loadings on differing watersheds and, in pa rticular, to illuminate if X is related to the ratio of DOC to COD. The fraction, f of the COD that exerts a chlorine demand is consistent (2.5%) across events for the catchment. Similar to X this fraction may be a result of the type and proportion of the biogenic and anthropogenic loadings for a catchment. The NRMSE of the dissolved model on the non influencing datasets is < 6% in each case. The use of non influencing datasets is an important reliability indicator of the second order chlorine demand model. As with any model, it is necessary to predict PAGE 72 72 the behavior of a system given initial conditions and system reaction rates. Predicting the chlorine residual concentration over time in urban runoff can be determined by this model. From a system desig n standpoint, this is a powerful tool in the development of disinfection unit process in a runoff treatment train. Figure 4 4 illustrates the intra event mass transport of COD d which is a primary parameter of the second order, parallel chlorine demand mode l summarized in Equation 2. The 21 August 2010 event is a low intensity, low volume, short duration event that is flow limited with respect to COD d In contrast the 27 September 2010 event is a high intensity, high volume, long duration event of low previ ous dry hours (PDH). The long duration of the low intensity falling limb of intermittent runoff results in dissolution of particulate bound COD into the dissolved phase. This increases the mass loading of the dissolved phase at the end of the event and a ccounts for the inverted cumulative distribution of COD d during the tail end of the event. The 04 November 2010 event is of moderate intensity, moderate duration, moderate volume, and 910 PDH. The extended dry time increases buildup of COD d on the catchme nt and this event transports the highest cumulative mass of COD d The 16 November 2010 event is low volume, low intensity and low duration. The cumulative distribution indicates that this event is weakly mass limited. The inter event variation of COD d lo ad demonstrates the influence of hydrologic parameters on transport and illustrates the intra and inter event temporal variability of chlorine demand. As a result, a treatment water quality volume (WQV) cannot be defined for COD d a priori and corroborate s a previous study for COD d on a disparate watershed (Sansalone et al. 2005) of differing land use. PAGE 73 73 PM Kinetic Model The ultimate free chlorine demand of the reactor constituents is potentially limited by the chlorine dose if the theoretical demand exce eds the dose value. For the second order potential driving model for free chlorine demand this ultimate free chlorine demand is represented by q e Figure 3 5 presents the results of plotting the modeled q e versus the maximum q e that that is available to the PM fraction, q e_max given the initial hypochlorite dose, the SSC of the reactor, and the dissolved kinetic chlorine demand. As can be seen from the figure, for under chlorinated reactors q e = q e_max as the maximum chlorine demand per PM mass cannot e xceed the available free chlorine in the reactor. As the chlorine available in the reactor increases, the relationship between q e and q e_max enters a transitional region, where increasing the q e_max continues to increase the value of q e but the values ar e not equal. This transitional region asymptotically converges on a maximum value for q e which is the maximum mass transfer of the chlorine out of the dissolved phase to reactions with PM. Suspended PM achieves a maximum modeled value of approximately 35 0 Cl 2 / g PM settleable material achieves a maximum modeled value of approximately 320 mg/g, and sediment PM reaches an asymptotic maximum of 72 0 mg/g. The proximity of the maximum q e value of the suspended and settleable PM fractions is attributed to the similar siliceous, low organic crystalline structure of these fractions. For the sampled events, PM in these fractions have a low volatile fraction with a range of 24 58%. However, the sediment PM fraction is much more organic in nature, by the surrogate measurement of the volatile fraction of the SSC, and has volatile fractions from 59 75%. The reaction rate constant, k PM also varies with q e_max This parameter governs the rate of the reaction and lowers as q e_max increases. This is a result of the model PAGE 74 74 governing an overall mass transfer rate. For lower values of q e_max the chlorine reacts with the easiest to access sites on the PM. For higher values of q e_max in addition to reacting with the most accessible sites, the free chlorine also reacts t hrough the macro pore structure of the PM involving a diffusion process that slows the overall reaction rate as measured experimentally. Thus, the second order diffusion model does not account for this variability. The range of k PM values for this experi mental construct is 3.95 X 10 6 g 1 mg 1 min 1 (super chlorinated reactors) to 9.2 X 10 3 g 1 mg 1 min 1 However, the variability is the strongest for under chlorinated reactors, thus appropriate design of the chlorination process given the criteria of SSC allo ws for a predictable k PM Figure 3 6 demonstrates the model fit of the second order PM kinetic model. The results indicate the model fit the experimental data with R 2 values ranging from 0.8 9 to 0.97 for the different fractions. This indicates the applic ability of the second order potential driving model to the overall mass transfer of hypochlorite from the dissolved phase to the particulate phase. Table 3 3 presents a comparison of the modeled demand of the dissolved and particulate fractions on an event mean basis for the monitored storms. The sediment PM fraction accounts for greater than 55% of all potential hypochlorite demand for each event and up to 93.6% of the potential hypochlorite demand for the 27 September 2010 event. In addition, the demand from all PM fractions for the monitored events represent over 77% of the potential chlorine demand illustrating the potential benefits of treatment by sedimentation or filtration prior to chlorination from a chlorine demand perspective. The dissolved pha se demand, represented by f COD d exerts a range of hypochlorite demand from 7.6 mg/L to 88.7 mg/L. Previous authors (Clark 1998) have linked PAGE 75 75 chlorine demand to disinfectant by product formation, thus, providing motivation for demand removal prior to treat ment with hypochlorite. Effective dissolved phase demand reduction can be accomplished through the use of membrane treatment. Overall the two models developed in this study provide a methodology for estimating the kinetic chlorine demand of urban rainfall runoff PM fractions and dissolved phase. Results indicate a significant chlorine demand by each PM fraction, although the predominance of the demand is from the sediment PM fraction of higher organic content as compared to suspended or settleable PM frac tions. This study models the chlorine demand kinetics of PM fractions and the dissolved phase of runoff by combining rainfall runoff event monitoring utilizing COD d the gravimetric based PSD of PM fractions, and a batch reactor framework for the generat ion of chlorine demand parameters. Results enable the evaluation of hypochlorite disinfection as a unit process for the conditioning of urban rainfall runoff for reuse. Implementation of these results allows the balancing of primary and secondary unit op eration requirements for source area runoff reuse with hypochlorite demand by each PM fraction and the dissolved runoff phase. PAGE 76 76 Table 3 1. Summary of hydrologic and PM event mean concentration indices for captured events. E vent (2010) PDH (h) Rainfall dur ation (min) Total runoff volume (L) Q med (L/s) Q p (L/s) PM fractions (mg/L) Suspended Settleable Sediment 7 Aug a 24 48 2623 1.01 4.3 13.1 (3 50) 32.2 (8 99) 222.5 (6 21414) 21 Aug b 83 31 299 0.03 1.5 2.2 (0.5 4) 36.8 (6 192) 301.1 (18 3295) 2 7 Sep 10 388 3842 0.01 10.9 44.5 (16 190) 50.0 (1 289) 874.1 (2 6035) 4 Nov 910 43 996 0.13 3.5 93.6 (15 319) 51.5 (4 225) 486.6 (5 18145) 16 No v 286 34 307 0.01 1.8 123.2 (30 247) 137.8 (4 340) 332.2 (24 3208) a The event on 7 Aug ust 2010 was utiliz ed for stormwater matrix collection. b Baseline event. PDH is previous dry hours; Q med is the median runoff flow rate; and Q p is the peak runoff flow rate. Table 3 2 Global parallel 2 nd order demand model parameters for the dissolved phase. Parameter Units Mean SE a k 1 L 1 mg 1 min 1 0.070 0.015 k 2 L 1 mg 1 min 1 2.93 X 10 4 3.66 X 10 5 X 0.39 0.016 f 0.36 0.012 The global adjusted R 2 = 0.988. The results indicate that the rate constant k 1 is over two orders of magnitude greater th an k 2 In addition, X indicates that approximately 40% of the chlorine demand is exerted by dissolved components that react quickly with free chlorine. f indicates that the estimated ultimate chlorine demand is approximately 36% of the COD d a Standard Er ror = where s is the standard deviation of the sample mean and n = 16. PAGE 77 77 Table 3 3. Hypochlorite event based ultimate demand of urban stormwater fractions for the monitored storms. COD (mg/L) PM (mg/L) PM HOCl Demand (mg/L) HOCl Demand (%) Event (2010) COD d f COD d Suspended Settleable Sediment Suspended Settleable Sediment Dissolved Suspended Settleable Sediment 7 Aug 19.5 7.0 13.1 32.2 222.5 4.6 10.3 160.2 3.9 2.5 5.7 88.0 21 Aug 80.5 29.0 2.2 36.8 301.1 0.8 11.8 216.8 11.2 0.3 4.6 83. 9 27 Sep 35.7 12.9 44.5 50.0 874.1 15.6 16.0 629.4 1.9 2.3 2.4 93.4 4 Nov 166.5 59.9 93.6 51.5 486.6 32.8 16.5 350.4 13.0 7.1 3.6 76.2 16 Nov 227.5 81.9 123.2 137.8 332.2 43.1 44.1 239.2 20.1 10.6 10.8 58.6 Calculations utilize f = 0.3 6 suspe nded q e = 350 mg/g, settleable q e = 320 mg/g, and sediment q e = 720 mg/g. Results indicate that the constituents of the sediment PM fraction exert the largest portion of HOCl demand on an event mean basis. PAGE 78 78 Figure 3 1 PSD of quintessential fractions from the batch reactors. The figure demonstrates validation by laser diffraction analysis of the wet sieve fractionation procedure. Note that the upper bound of the laser diffraction analysis is 2mm. PAGE 79 79 Figure 3 2 Physical representation of the parameters of the parallel 2 nd order demand model. Cl o is the initial concentration of free chlorine; C B0 is the ultimate free chlorine demand of the reactor (note a/b = 1) which is estimated by a fractional comp onent, f of the reactor initial dissolved COD; X is the ratio of the ultimate chlorine demand that reacts quickly with the second order rate constant k 1 ; (1 X) is the ratio of the ultimate chlorine demand that reacts slowly with the second order rate cons tant k 2 ; and C* represents the remaining free chlorine concentration at equilibrium. Note that as a parallel model, the effects of the slower reaction, k 2 are also exerted during the initial portion, but as the time scale of this reaction is several ord ers of magnitude slower than the quick initial reaction, the initial interval is well illustrated by the parameters k 1 and X PAGE 80 80 Figure 3 3 Predictive fit of the dissolved fraction parallel 2 nd order demand model. The model parameters are derived from the global best fit of the experimental dataset and the demand model shown utilizes these parameters and the initial conditions of the reactor. Models are shown within a 95% C.L. The experimental data are four datasets excluded from the parameter estimat ion analysis to independently verify the derived model for the watershed. NRMSE is reported for each panel and show that for all models NRMSE is < 6%. PAGE 81 81 Figure 3 4 Transient loading of COD d on the small urban catchment in north central Florida. With respect to COD d the 21 Aug 2010 event illustrates an event that is flow limited and the extended event on 27 Sep 2010 illustrates potential dissolution of PM COD during the low flow period at the end of the event. The November storms illustrate mass limit ed events. This panoply of COD d loadings is indicatory of the nature of antecedent conditions and hydrologic parameters on COD d transport, thus, a treatment WQV cannot be defined for this parameter a priori PAGE 82 82 Figure 3 5 Maxim um particle free chlorine demand. For under chlorinated reactors, the modeled chlorine demand at equilibrium, q e is limited by the maximum chlorine available in the reactor, q e_max For the over chlorinated reactors, q e for the suspended PM reaches an as ymptotic maximum of 350 mg/g, q e for the settleable PM reaches an asymptotic maximum of 320 mg/g, and q e for the sediment PM reaches an asymptotic maximum of 720 mg/g. In the reactors chlorinated near the plateau point there is a transitional region where q e increases up to the asymptotic maximum. PAGE 83 83 Figure 3 6 The modeling results of the second order PM chlorine demand model. The results indicate an excellent model fit for all PM fractions (R 2 > 0. 89 ). PAGE 84 84 CHAPTER 4 SODIUM HYPOCHLORITE DISINFECTION O F INDICATOR ORGANISM S ASSOCIATED WITH URBA N STORMWATER PARTICL ES Water resources and reuse thereof are increasingly central themes of sustainable development for the developed and developing world. Future water needs will be addressed by integrating syste ms that reduce water consumption, reusing water discharges and simultaneously moving towards hydrologic restoration. In urban areas rainfall runoff relationships have a significant impact on the hydrologic cycle and in such areas have been subject to sign ificant anthropogenic modification due to urban activities such as traffic and the high imperviousness of such watersheds. Rainfall runoff transports particulate matter (PM), microbial, chemical and nutrient loadings and can impair receiving waters (Heane y and Huber 1984, House et al. 1993) With respect to hydrology urban modification of the rainfall runoff relationship diverts a significant volume of water annually from pre developed pathways (Marselek et al. 1993) with the commensurate increases in pea k flow, volume and transported load. Such scenarios in urban areas makes runoff a critical hydrologic component for an integrated management approach involving low impact development and reuse. Hatt et al (2006) reports that the current research base is inadequate to support the present implementation of urban runoff reuse. With respect to urban water reuse there is the need for identification of PM associated microbial distribution and the appropriate unit operations and processes for PM separation an d corresponding disinfection in order to minimize public health risks. The U.S. EPA has documented the use of indicator organisms as surrogate indices of pathogenic organisms in receiving waters through epidemiologic correlations of illness and issued amb ient water chemistry criteria (USEPA 1984) Jin et al. (2006) PAGE 85 85 investigated indicator organism loadings by urban rainfall runoff vectors into Lake Pontchartrain and subsequent recreational water closings. Charaklis et al. (2005) has documented that indica tor organisms in urban runoff exist as both planktonic and particle associated organisms, and Krometis et al. (2007) has documented that there is no significant variation in intra event partitioning of indicator organisms in runoff, but that there is a sig nificant variation in intra event microbial loading rates. Finally, He et al. (2008) examined runoff for reuse and found that a retention basin could produce water of acceptable microbial levels during dry weather periods, but that runoff events mobilized significant microbial loadings in excess of public reuse guidelines in Alberta, Canada Chlorination has been in use for over a century and is the most common form of disinfection in practice today (Hrudey and Hrudey 2004) However, urban rainfall runof f is a complex matrix of dissolved and heterodisperse PM fractions (Sansalone and Kim 2008, Kim and Sansalone 2010) With respect to the impact of PM on disinfection LeChavallier et al. (1981) documented the hindering effect of PM, using turbidity as a su rrogate, on the disinfection of environmental surface water in Oregon Berman et al. (1988) documented similar findings for the organic PM in wastewaters and in addition found that chlorine permeated smaller PM faster than PM of larger diameter. Dietrich (2003) extended these findings and modeled the intra particle transport of free chlorine with a radial diffusion model. Winward et al. (2008) extrapolated this research to the use of chlorine as a disinfectant for grey water reuse applications. In addit ion, studies have modeled the kinetics of inactivation for bacteriological and protozoan organisms utilizing the Chick Watson (Chick 1908) the Hom (Hom 1972) a modified Hom (Finch PAGE 86 86 et al. 1993, Haas and Joffe 1994) and rational models (Gyurek and Finch 1 998) Contributions to this existing body of knowledge are the distribution of indicator organisms as a function of PM fractions and the efficacy of chlorination as a function of PM fractions in urban runoff. Given the heterodispersivity, granulometry, or ganic content and distribution of nutrients for urban runoff PM fractions (Dickenson and Sansalone 2009, Berretta and Sansalone 2011) the present study examines the association of indicator organisms with PM fractions and the efficacy of disinfection for t hese PM fractions. Specifically it is hypothesized that organisms do not distribute equally across the PM gradation. Furthermore it is hypothesized that disinfection efficacy is not equal for each PM fraction. Methodology The catchment for this study i s an urban source area located in Gainesville, FL. The land use is a 13,000 m 2 carpark field site of which a 500 m 2 catchment was delineated, instrumented and monitored for this study (Figure A 2) The surface area of the carpark is 75% asphalt pavement and 25% raised vegetated islands with mature trees and delineated by vertical concrete curbs. The catchment has an average daily traffic loading of 530 vehicles (observed). Physical (PM), chemical (nutrients, metals and organic compounds) and microbial loading sources are anthropogenic (tire, vehicular and pavement abrasion and urban litter) and biogenic sources (leaf litter, grass clippings, insects, and small urban animals and bird feces). The catchment drains by sheet and gutter flow to a catch basin that was modified to allow all flow to be diverted for full cross sectional flow manual sampling during a monitored rainfall runoff event. Rainfall is measured with a tipping bucket rain gage and flows are measured PAGE 87 87 real time with a calibrated Parshall fl ume, an ultrasonic transducer and data logger. During a rainfall event runoff is sampled at volumetrically spaced intervals in replicate and each set of replicates composited to construct paired event based replicates for an event. PM Fractionation Durin g the monitoring phase of the study a series of rainfall runoff events were sampled. The hydrologic and PM indices are presented in Table 4 1 Approximately 100 L of runoff was sampled for each event and immediately (within 30 minutes) transferred to the laboratory for analysis. Prior to each of these events additional runoff from a previous event loading the same catchment was collected and filtered (0.45 m membrane). This filtered runoff served as a dissolved matrix for re suspending each PM fraction The dissolved matrix was autoclaved at 121C for one hour to render the dissolved matrix biologically inert. The runoff matrix was then stored at 4C. In this P M was fractionated by wet sieving and microfiltration from the sampled 100 L volume into, sediment PM (4250 75 m, Kim and Sansalone 2008 ), settleable PM (75 25 m, Kim and Sansalone 2008 ), and suspended PM (25 0.45 m, Kim and Sansalone 2008 ). Sedim ent and settleable PM fractions were immediately re suspended in 1 L of runoff matrix as PM concentrate and filtrate from the 25 m sieve is set aside as suspended PM. For each of the events analysis included microbial enumeration for total coliform, E. c oli fecal streptococcus, and enterococcus organisms partitioned to each PM fraction in replicate composite samples for each runoff events. Batch reaction testing elaborated the chlorine inactivation kinetics for total coliform partitioned to each PAGE 88 88 PM frac tion (Kim and Sansalone 2008) of the runoff events. In all cases, microbial samples are analyzed immediately or maintained at 4C and analyzed within six hours. Microbiological Enumeration Microbiological enumeration of the organisms utilizes the multiple tube fermentation, the most probable number (MPN) method. This method is selected due to its applicability to turbid waters (Eaton et al. 1999) For the event microbial monitoring, total coliforms, E. coli fecal streptococcus and enterococcus organisms are enumerated according to Standard Methods 9221B, 9223B and 9230B (Eaton et al 1998) For total coliform organisms and E. coli samples are inoculated aseptically into a five row by five dilution tube bank of lauryl triptose broth amended with 4 methyl umbelliferyl D glucuronide (LTB MUG) and incubated at 35C. At 24 and 48 h the cultures were checked for lactose fermentation (gas bubbles in an inverted vial) and fluorescence under a 366 nm UV light. Lactose fermentation in LTB incubated at 35C repr esents a glucuronidase enzymatic activity (Feng and Hartman 1982) confirming the presence of E. coli The most dilute row with all positive lactose fermenting tubes and all po sitive tubes of higher dilution were transferred aseptically to brilliant green bile broth (BGB) and incubated at 35C. At 24 and 48 h the cultures were checked for lactose fermentation and positive tubes represented confirmed positives for total coliform organisms. Reference microbiological controls were simultaneously processed for quality assurance including E. coli (ATCC: 25922, positive lactose fermentation, positive UV fluorescence), E. aerogenes (ATCC: 13048, positive lactose fermentation, negative UV fluorescence), E. faecalis (ATCC: 29212, negative lactose fermentation, negative UV fluorescence), and a non inoculated blank. PAGE 89 89 Fecal streptococcus and enterococcus organisms were enumerated using samples inoculated aseptically into a five row by fou r dilution tube bank of azide dextrose broth (ADB) and incubated at incubated at 35C. At 24 and 48 hrs the cultures were checked for turbidity with positive samples aseptically transferred to agar results in a black halo around the colonies and is characteristic of fecal streptococci (Isenberg et al 1970) Positive fecal streptococci plates are aseptically transferred to 6.5% NaCl brain heart infusion broth (BHI) and incu bated at 45C, the Sherman criteria for enterococcus organisms. At 24 and 48hrs the BHI broth was checked for turbidity, with positive tubes indicating the presence of enterococcus organisms. Reference microbiological controls were simultaneously process ed for quality assurance including E. coli (ATCC: 25922, negative ADB turbidity, negative esculin hydrolysis, negative BHI turbidity), E. faecalis (ATCC: 29212, positive ADB turbidity, positive esculin hydrolysis, positive BHI turbidity), and a non inocula ted blank. For both bacteriological enumeration schemes, most probable numbers (MPNs) are calculated according to standard method 9221C (Eaton et al 1998) Batch Reactors Table 4 1 outlines the batch reactor experimental matrix. For the runoff events, a reference storm on 21 August 2010 was sampled and batch reactors are initialized in 2 replicate reactors (Figure A 1) for the three PM fractions at sodium hypochlorite doses of 15, 30 and 45 mg/L for a total of eighteen batch reactors. Three additional st orms are sampled on 27 Sep 2010, 4 Nov 2010, and 16 Nov 2010 and are initialized in 2 replicate reactors across the three PM fractions at a single sodium hypochlorite dose of 30, 45, and 15 mg/L, respectively, for an additional eighteen batch reactors. Fo r the PAGE 90 90 experimental analysis, additional rainfall runoff is collected on 7 Aug 2010 and 5 Sept 2010, micro filtered through 0.45 m nylon filters, and autoclaved at 121C for 60 minutes. This filtered and sterilized rainfall runoff is utilized to reconstit ute the separated PM into the batch reactors and is referred to as stormwater matrix. PM is immediately fractionated from the rainfall runoff sample by a sterile wet sieve procedure. Stormwater sample is poured through sterilized 4750, 75, and 25 m sieve s. PM remaining on the 4650 m sieve is considered gross solids (Rushton et al. 2007) and is dried and characterized, but not included in batch reactor experiments. PM remaining on the 75 and 25 m sieves are reconstituted in 1 L stormwater matrix as sed iment and settleable fractions, respectively, and stored at 4C until reactor initialization. PM passing through the 25 m into a sterile container is considered suspended material and is stored at 4C until reactor initialization. The batch reactors ar e 2 L nominal glass jars with a 4 cm stir rod that were prepared to be chlorine demand free (Eaton et al. 1998) and autoclaved at 121C for 20 minutes. Sediment and settleable PM reactors are filled with 150 ml of respective PM concentrate and 1650 ml of dissolved stormwater matrix and suspended PM reactors are filled with 1800 ml of 25 m sieve filtrate. At time zero, batch reactors are brought to 25C 2C, initial water quality measurements of pH, temperature, and conductivity are recorded, a 100 ml al iquot of sample is removed for bacteriological analysis and the reactor is dosed with a standardized sodium hypochlorite solution according to the dosing schedule. A t each time interval listed in T able 4 1, water quality measurements are made, a 100 ml al iquot is removed for bacteriological analysis, and 2 replicate aliquots are removed to analyze the free chorine residual. Sterile syringes and pipette PAGE 91 91 tips are used at each time interval and water quality electrodes are chlorine sterilized before immersio n in the reactor. In the interim between samples, an LDPE lined lid is tightly secured on the reactors. Aliquots utilized for bacteriological analysis are processed in a sterile blender at 22,000 rpm (Bar maid, manufacturers reported specifications) to d issociate particle attached organisms from PM (Borst and Selvakumar 2003) prior to total coliform enumeration. A laboratory blank of sterile DI water was also processed through the blender and microbiologically enumerated to ensure no cross contamination from the blending procedure. In addition to total coliform enumeration for the events on 27 Sep 2010, 4 Nov 2010, and 16 Nov 2010, the time zero measurements for each batch reactor were additionally enumerated for E. coli Fecal Streptococcus, and Enteroco ccus to determine the partitioning of each organism in the suspended, settleable, and sediment fractions. Following the experiment, batch reactors are analyzed for total (SSC) and volatile (VSSC) suspended sediment concentration (ASTM 2002) and particle s ize distribution (ISO 2009) to characterize the PM loading and validate the wet sieve fractionation procedure. The granulometry of each reactor is reported in Table 4 2. Residual Chlorine Residual free chlorine is analyzed utilizing a DR2800 (Hach Chemical ) color spectrophotometer measuring the absorbance of N,N diethyl p phenylenediamine (DPD, Hach Chemical) at 530 nm. Replicate 2 ml (5:1 dilution), 5 ml (2:1), or 10 ml (1:1) aliquots, depending on anticipated residual chlorine, are removed from the batch reactor and diluted to 10 ml with chlorine demand free water (Barndstead Nanopure). A custom absorbance curve is obtained for the DPD reagent to increase the range of the analysis and the measurement has an accuracy of 0.1 mg/L for a 1:1 dilution ratio. PAGE 92 92 Results and Discussion Microbiological controls and laboratory blanks ensured sterility of microbiological growth media, sterility of the process, and appropriate media response to reference organisms. The laboratory blender blank demonstrated no cross c ontamination reference organisms are utilized as comparators for UV fluorescence and turbidity where appropriate. Figure 4 1 presents the results from the monitoring study of ev ent mean bacterial densities of indicator organisms for twenty five wet weather events. The results indicate that, as expected, total coliform organisms are ubiquitous in the watershed and have a median density in excess of 10 6 organisms per 100 ml. E. c oli densities range from 30 MPN/100ml to greater than 10 4 MPN/100ml with a median density of 1300 MPN/100ml. From these storms, E. coli represented <1% to 21% of total coliform organisms present. The fecal streptococci densities ranged from 3000 MPN/100ml to 10 5 MPN/100ml with a median of 5 X 10 4 MPN/100ml and the enterococcus densities ranged from 10 3 to 10 5 MPN/100ml with a median value of 10 4 MPN/100ml. Epidemiological studies of disease due to contact exposure of the types experienced in reuse appli cations with organisms transported by rainfall runoff acting as the etiological agents do not currently exist. The state of Florida has water quality criteria regulations regarding the restricted and unrestricted reuse of reclaimed water. However, the n umeric bacteriological criteria in the regulations are specifically for the reuse of water from domestic wastewater treatment facilities and do not apply to the reuse of urban rainfall runoff. In the event of the design and implementation of urban rainfal l runoff reuse in an urban setting in Florida, the designer is required to PAGE 93 93 demonstrate that the reuse application will not impair water quality criteria at or near the site of application and are required to demonstrate that the reuse application of urban rainfall runoff does not impair the public health or welfare (Personal communication, Eric Livingston, Florida Dept. of Environmental Protection). This is accomplished through the implementation of best management practices. However, it is useful in the absence of specific, numeric regulatory guidance regarding the bacteriological water quality of urban rainfall runoff as a source water to consult the regulations regarding water quality criteria for recreational waters and wastewater reuse for restricted and unrestricted urban reuse (He et al. 2008) The U.S. Environmental Protection Agency (EPA) and many state agencies, including the Florida Department of Environmental Protection, issue water quality criteria for recreational waters, waters utilized as f eed waters to drinking water treatment plants, and reuse/reclaimed waters. The EPA bacteriological water quality criteria for freshwater recreational water use are, as geometric means, 126 MPN/100ml for E. coli and 33 MPN/100ml for enterococci and for bra ckish/saltwater recreational water use is 35 MPN/100ml for enterococci (USEPA 1984) note that the EPA does not recommend the use of E. coli as an indicator organism in brackish/saltwaters. As can be readily observed from F igure 4 1, urban rainfall runof f exceeds E. coli water quality criteria in 19/25 events and enterococcus water quality criteria in 25/25 events when sampled at the head of the rainfall runoff conveyance system. Thus, for recreational waters, stormwater runoff functionally impairs the ba cteriological quality and the dilution ratio and organism die off governs whether the recreational water as a whole will exceed the standard water quality criteria. In Florida, the restricted and unrestricted reuse of wastewater requires high level PAGE 94 94 disinf ection with an effluent bacteriological density of fecal coliforms below the detection limit of 2 MPN/100 ml as a geometric mean and below 25 MPN/100 ml for any single sample. As can be seen from Figure 4 1, urban rainfall runoff exceeds the regulatory re quirement for water quality criteria for both E. coli (as a member of the fecal coliform family) and enterococcus indicator organisms in regards to recreational waters and urban reuse applications. In addition, previous authors have suggested a link bet ween the ratio of fecal coliforms (FC) to fecal streptococcus (FS). High ratios of FC/FS, those greater than one, indicated potential anthropogenic sources of fecal contamination, whereas low ratios of FC/FS potentially indicate contamination from animal and bird sources (Geldreich and Kenner 1969) This rubric, however, should only be applied to recent bacterial loadings as the die off rates for the indicator organisms vary (Maier et al. 2009) As fecal coliforms were not explicitly measured in this stu dy a direct comparison to the work of these authors cannot be made. However, as E. coli is the primary bacteria from human origins that constitute fecal coliforms (Kott 1977) the ratio of E. coli to FS is useful to comment on. For every observed event, the ratio of E. coli to FS is < 1, corroborating the expected loadings of contamination from animal, bird or insect sources. This result is substantiated by the potential microbial loading sources of the watershed and corroborates Clausen et al. (1977) on the FC to FS ration for urban stormwater. As a primary source, sampled from the entrance to municipal separate storm sewer system, there are no identified anthropogenic fecal influences. There are no nearby septic systems and no potential cross connecti ons due to the location of sampling. Thus, as observed, any fecal contamination is from small animals, birds and PAGE 95 95 insects. However, as such, this fecal contamination is pertinent to human health as there is the potential for cross species communication of disease such as giardiasis (Majewska 1994) and cryptosporidiosis (Beach 2008) and as wet weather events have been shown to mobilize waterborne pathogens present in the environmental watershed (Hrudey and Hrudey 2004). Batch Reactor Results The results fro m the batch reactor experiments illuminate the level of shielding actuated by PM on particle associated organisms. Figure 4 2 presents the results from the batch reactor inactivation experiments which demonstrate that the finer particles of the suspended and settleable fractions are readily permeated by the hypochlorite and a high level of inactivation is demonstrated in each case. Corroborating this finding, Figure 4 3 plots the log removal of the 04 Nov 2010 event in the suspended, settleable and sedime nt PM fractions and shows the rate of the inactivation (Panels A and B). In all cases, the initial rate of inactivation rapidly reaches a plateau of maximum inactivation. For the suspended and settlable fractions, this plateau occurs near the maximum rem oval rate observable by the experiment. This denotes that the hypochlorite readily penetrates the PM in those fractions at 45 mg/L and that shielding of associated organisms is not occurring. In addition, a percentage of maximum log removal is displayed and is defined as the observed log removal over the maximum potential log removal for the reactor. For the sediment fraction (Panel C), the reactors rapidly achieve a maximum log inactivation of 20 60% reactor inactivation potential which corresponds to f inal bacterial densities of approximately 10 3 MPN/100 ml for these reactors (Figure 4 2). Thus, there is shielding of particle associated organisms in the sediment PM fraction. PAGE 96 96 Figure 4 4 extends these results to demonstrating the log removal of the sedi ment fractions for various initial concentrations of hypochlorite. As can be readily observed from the figure, increasing the hypochlorite dose in the reactor increases the final level of inactivation achieved. This result corroborates the work of other authors who find similar results in highly organic grey waters (Winward et al. 2008) but is tempered by the fact that shielding is observed at all hypochlorite doses. The shielding in the sediment PM fraction may be attributed to several governing factors The primary driver of shielding is the large particle size which represents a physical barrier to disinfectant penetration. In addition, the volatile fraction of PM is a surrogate indicator of the organic content. During the monitored events the volat ile fraction (VF) of the SSC for the sediment PM was in the range of 59 79%, whereas the VF of the suspended and settleable PM was in the range of 24 58%. Thus, the carbonaceous material may represent a localized chlorine sink conveying additional protect ion to particle associated organisms. Finally, the constituents of the suspended and settleable PM fractions contain more siliceous, crystalline material than the sediment PM fraction. The internal pore structure of the crystalline material may be less h ospitable to particle associated organisms, resulting in a surface orientation for associated organisms. This potentially explains the similarity of the rate of disinfection for the suspended and settleable fractions with respect to particle diameter. PM shielding of associated organisms evokes a deleterious effect on inactivation by chlorination of sediment PM. However, chlorination at all levels was able to significantly inactivate PM associated organisms in the suspended and settleable fractions. PM f ractions are separated into suspended, settleable, and sediment PAGE 97 97 categories from a functional, treatment perspective. Sediment material readily separates by gravitational quiescent settling. Settleable material is the material that will settle in 60 min i n an Imhoff cone and suspended material is the PM remaining in the supernatant and 60 minutes. Thus, these categorical definitions indicate the applicability to treatment of urban rainfall runoff by gravitational settling. The correlation of these catego rical labels to particle size ranges is imperative to the quantification and descriptive analysis that is required in many studies. This correlation also enables extrapolations of these PM ranges to treatment process of differing nature, such a filtration Many implementations of urban rainfall reuse will require the detention of stormwater for storage before reapplication as reuse water. This is advantageous in that the detention of the rainfall runoff simultaneously provides clarification of PM espec ially of the sediment PM exuding the strongest shielding capability. In addition, the use of a diatomaceous earth or sand filter pre chlorination can reduce the effects of particle associated shielding as well as the required chlorine dose. Indicator Orga nism Partitioning Figure 4 5 presents the gravimetric density of the indicator organism partitioning to rainfall runoff. The bacterial density for each enumerated organism is the highest in the suspended fraction. In general, this is followed by the settl eable and the sediment fractions in relative orders of magnitude of bacterial density. Of particular note is the low E. coli density of less than 25 MPN/mg PM for the sediment fractions as compared to the densities of the suspended and settleable fraction s. However, this result is contrasted with the fact that the sediment fraction of urban stormwater runoff has the largest PM mass of the three categories on an event mean basis. For the monitored storms, the suspended fraction had a PM geometric mean of 41.8 mg/L, the settleable PAGE 98 98 had an SSC geometric mean of 47.2 mg/L and the sediment fraction had a SSC geometric of 572.5 mg/L. Table 4 3 presents the percentage of mobilized organisms associated with each PM fraction as weighted by PM. E. coli is most mob ilized in the suspended and settleable fractions for the monitored events, whereas enterococcus organisms are most mobilized in the suspended and sediment fractions. In conclusion, wet weather events result in the bacteriological mobilization of indicat or organisms on a small, impervious urban catchment in north central Florida. Batch reactor experiments on particle associated coliforms demonstrated that organisms in the suspended and settleable fractions are readily inactivated at the applied hypochlor ite doses, but that particle associated coliforms in the sediment fraction are shielded by host PM. In sediment PM, microbial inactivation increased with increasing hypochlorite dose. However, disinfectant shielding is observed at all hypochlorite doses a nd pretreatment and removal of sediment PM is recommended for any practical design implementation of hypochlorite inactivation for urban reuse purposes. PAGE 99 99 Table 4 1 Batch reactor experimental matrix of PM fractions, HOCl dose, and event date. Two replicate reactors are utilized for each experiment to ensure reproducibility. x represents batch reactor initial concentration for the event. HOCl Dose Sediment Settleable Suspended (mg/L): 15 30 45 15 30 45 15 30 45 Time (min) Contact time (mg min/L) 0 0 0 0 0 0 0 0 0 0 1 15 30 45 15 30 45 15 30 45 5 75 150 225 75 150 225 75 150 225 10 150 300 450 150 300 450 150 300 450 20 300 600 90 0 300 600 900 300 600 900 40 600 1200 1800 600 1200 1800 600 1200 1800 120 1800 3600 5400 1800 3600 5400 1800 3600 5400 480 7200 14400 21600 7200 14400 21600 7200 14400 21600 Event Date 21 Aug 2010 x x x x x x x x x 27 Sep 2010 x x x 4 Nov 2010 x x x 16 Nov 2010 x x x PAGE 100 100 Table 4 2. Batch reactor particle granulometry. E vent HOCl PM d 10 a d 50 b d 90 c d [4,3] d (2010) Fraction (mg/L) (mg/L) (m) (m) (m) (m) 21 August 2010 Suspended 15 50.6 2.1 10.1 29.8 13.7 Suspended 15 24.5 1.6 7.7 29.0 16.0 Suspended 30 25.9 1.7 6.9 39.0 15.4 Suspended 30 105.5 4.1 15.1 42.4 23.5 Suspended 45 47.9 2.2 9.3 24.4 19.8 Suspended 45 22.1 1.9 8.2 27.2 14.2 Settleable 15 188.6 14.9 44.3 90.7 49.3 Settleable 15 176.3 14.5 39.5 91.0 49.1 Settleable 30 139.2 12.9 42.8 69.9 48.2 Settleable 30 206.8 14.5 44.2 93.0 49.9 Settleabl e 45 147.9 13.4 43.1 89.7 48.2 Settleable 45 197.8 14.0 43.6 90.7 51.7 Sediment 15 284.8 53.8 244.9 958.3 390.4 Sediment 15 787.6 62.4 323.5 427.0 480.2 Sediment 30 Sediment 30 419.8 72.6 268.0 1059.9 438.1 Sediment 45 211.4 39.3 193. 9 948.5 366.1 Sediment 45 391.6 66.3 310.8 1120.4 467.1 27 Sept 2010 Suspended 30 36.8 2.2 9.5 25.1 12.0 Suspended 30 30.2 2.3 9.9 28.0 15.7 Settleable 30 187.0 13.0 39.2 83.2 44.4 Settleable 30 149.4 11.8 37.7 82.1 45.8 Sediment 30 330.9 41.8 3 89.1 1165.7 501.9 Sediment 30 285.1 50.8 393.7 1288.6 561.8 4 Nov 2010 Suspended 45 32.9 2.1 9.4 29.6 14.0 Suspended 45 39.6 2.2 10.0 28.5 14.9 Settleable 45 83.1 8.4 35.0 61.9 41.6 Settleable 45 75.7 8.1 35.8 100.5 53.2 Sediment 45 330.4 54.0 2 03.5 887.7 347.0 Sediment 45 353.8 38.4 189.3 842.5 334.9 16 Nov 2010 Suspended 15 89.3 1.8 8.7 27.3 15.8 Suspended 15 98.0 1.9 9.8 30.0 14.5 Settleable 15 67.3 5.9 27.8 50.3 33.2 Settleable 15 67.5 7.7 40.6 245.0 88.7 Sediment 15 272.0 73.3 207 .9 670.7 302.6 Sediment 15 232.2 75.9 218.4 769.9 344.0 a Characteristic particle size of 10% finer by volume. b Characteristic particle size of 50% finer by volume. c Characteristic particle size of 90% finer by volume. d De Brouckere volumetric mean: which is analogous to the number mean volume size. PAGE 101 101 Table 4 3. Event mobilization of indicator organisms and percentage of transported organisms associated with each PM fraction. 27 Sept 2010 4 Nov 2010 16 Nov 2010 27 Sept 2010 4 Nov 201 0 16 Nov 2010 [%] [%] [%] Suspended PM g 170.8 92.3 36.2 4.6 15.0 20.8 T. Coliform 4 549 1303 179 55.4 69.4 77.4 E. Coli 4 32.3 1.7 11.9 95.1 32.6 94.6 F. Strep 4 89.1 417 48.3 51.1 62.1 83.4 Enterococcus 4 20.4 107 10.9 71.1 47.5 75.4 Settleable PM g 192.0 38.4 40.4 5.2 6.3 23.3 T. Coliform 4 19.1 179 28.6 1.9 9.5 12.4 E. Coli 4 0.5 2.6 0.7 1.4 50.5 5.2 F. Strep 4 4.8 70.0 4.2 2.8 10.4 7.2 Enterococcus 4 1.9 18.8 2 .5 6.5 8.4 17.1 Sediment PM g 3357 483.5 97.5 90.2 78.7 56.0 T. Coliform 4 424 395 23.9 42.7 21.1 10.3 E. Coli 4 1.2 0.9 <0.1 3.5 16.9 0.2 F. Strep 4 81.0 185 5.4 46.2 27.5 9.3 Enterococcus 4 6.4 99.0 1.1 22. 4 44.1 7.5 Percentages are weighted by PM loading. Results indicate that organisms are highly mobilized in the suspended fraction relative to mobilization in the settleable and sediment fractions, with the exception of E. coli during the 04 November 2010 event. PAGE 102 102 Figure 4 1. Event mean most probable number per 100 mL box plot for twenty five wet weather events on a small urban watershed in north central Florida. Comparative EPA regulatory guidance for freshwater recreation al ambient bacteriological density is shown as (A), 126 MPN/100 ml (geometric mean) for E. coli Comparative regulatory guidance in Florida for unrestricted urban reuse for wastewater effluent is shown as (B), 25 MPN/100 ml (single sample) for fecal colif orms. Comparative regulatory g uidance for brackish/saltwater recreational use is shown as (C), 35 MPN/100 ml for Enterococcus organisms. Comparative Australian regulatory guidelines for urban runoff reuse are : (L1), <1 MPN/100 ml for non potable residentia l reuse ; (L2), <10 MPN/100 ml for reuse in area with un restricted access; and (L3), <1000 MPN/100 ml for reuse in areas with restricted access. PAGE 103 103 Figure 4 2. Hypochlorite inactivation kinetics of particle associated coliform organisms on suspended, set tleable, and sediment PM. Results indicate shielding of bacteria on sediment PM throughout the duration of the experiment and rapid inactivation of particle associated coliform organisms on the settleable and suspended fractions. Sediment fractions maint ained bacterial densities on the order of 103 MPN/100 ml at the end of the 8 h HOCl reactor experiment. PAGE 104 104 Figure 4 3 Log removal of particle associated coliforms for the 04 Nov 2010 (Panel A, B) event with an initial hypochlorite dose of 45 mg/L. Result s indicate that particle associated coliforms on suspended and settleable PM and rapidly achieve maximum log removal. Particle associated coliforms on sediment material (Panel C) only achieve 20 60% of the maximum potential log removal for the reactor, de monstrating particle shielding of associated coliforms. PAGE 105 105 Figure 4 4 Log removal of particle associated coliforms on sediment PM across the inoculation doses of 15, 30, and 45 mg/L. Results indicate that increasing chlorine d oses penetrate sediment PM with increasing efficaciousness. Oscillations in removal are attributed to the effects of sampling a heterodisperse particle size distribution within the sediment fraction. PAGE 106 106 Figure 4 5 Partitioning of particle associated organisms to suspended, settleable, and sediment PM fractions. For each organism, the suspended PM fraction contains the highest bacterial density followed by the settleable and sediment fractions. In particular, the sediment PM f raction, which exhibits the greatest organism shielding potential contains the lowest density of the indicator E. coli ( < 25 MPN/mg PM) and enterococcus ( < 300 MPN/mg PM) organisms. PAGE 107 107 CHAPTER 5 ADVANCED COMPUTATION AL MODELING OF FREE CHLORINE DEMAND AND DISINFECTION IN UNIT OPERATIONS AND PRECE SSES LOADED BY URBAN STORMWATER Urban rainfall runoff is a water which has come under increasing scrutiny for an integrated management approach (Heaney and Sample 2000) Development in the United States and elsewhe re has resulted in increased volumetric transport of water with constituent microbiological, particulate, and nutrient loadings to receiving waters (House et al 1993) In order to reverse this trend, technologies, research, and integrated management syste ms need to continue to be developed to reduce, treat, and reuse urban rainfall runoff. Urban rainfall runoff exhibits temporally varying water volume and quality and transports constituent particulate, microbial, heavy metal, and nutrient environmental lo adings in both dissolved and particulate fractions (Sansalone and Kim 2008, Sansalone and Buchberger 1997, Christina and Sansalone 2003, Kim and Sansalone 2010 ) The implementation of a source for reuse effectuates the need for the consideration of public health and safety. Chlorination as a form of microbial inactivation is the oldest chemical oxidizing reaction utilized for the public health of drinking waters and waste waters and is currently the most widely used inactivation process world wide (Hrudey and Hrudey 2004) From the earliest use of this process, researchers documented the importance of the concentration of disinfectant over time, the contact time (CT), to the level of microbial inactivation (Chick 1908, Fair et al. 1948) Continued studies developed generalized batch inactivation models as the Hom model (Hom 1972) and the incomplete gamma Hom model (Haas and Joffe 1994) as organisms were shown to exhibit disparate inactivation kinetics as compared to the Chick Watson model and for PAGE 108 108 utilizati on on water with disinfectant demand. Extension of batch reactor data to full scale flow through reactors has been demonstrated (Haas et al. 1998) as well as the utilization of reactor residence time distributions (RTD) to determine reactor CT values (Bel lamy et al. 1998) The most advanced modeling technique of inactivation kinetics in potable water applied to date is computational fluid dynamics (CFD) (Greene et al. 2004, Baawain et al. 2006, Goula et al. 2008, Greene et al. 2004) whereby it is utilized to improve reactor design and model first order chlorine demand. CFD is the numerical solution of the fundamental equations of fluid motion involving the simulation of transient flow fields, chemical reactions, and particle fate and transport in spatiall y complex geometry. In urban stormwater, CFD has enhanced the modeling of PM separation for transient flows (Sansalone and Pathapati 2009) and heterodisperse particle size distributions (Dickenson and Sansalone 2009) as well as re entrainment of PM by scou ring mechanisms (Pathapati and Sansalone 2011 ) In addition, in C hapter 3, urban stormwater batch reactor experimentation indicated parallel second order chlorine demand kinetics for the dissolved fraction and a second order potential driving force model for particulate fractions. As a result, there is the requisite need to formulate discretized finite rate chlorine kinetic dissolved and PM equations for modeling transient and complex flows encountered in urban stormwater runoff. Objectives The objective o f this study is to discretize the analytical parallel second order dissolved and potential driving PM finite rate free chlorine demand models for utilization in CFD. The computational dissolved, PM, and composite CPD model of sodium hypochlorite kinetic de mand in urban rainfall runoff is validated by batch reactor data PAGE 109 109 Methodology CFD is the numerical solution of the fundamental partial differential equations that govern fluid flow and particle and chemical transport. CFD can be implemented in both Lagran gian, fluid particle tracking schemes, or Eulerian, fluid flux through control volume schemes. CFD is capable of modeling both laminar and turbulent flows, where the turbulent flow characteristics are numerically simulated through direct numerical simulat ion (DNS) or modeled by solving the bulk equations of motion coupled with a turbulent flow closure model, an example of which is the Reynolds averaged Navier Stokes (RANS) equations with a variant of the k two equation turbulent model. Contradistinguishing between DNS and RANS are computational time and turbulent scale resolution. DNS is computationally expensive, particularly at high Reynolds numbers and for many industrial flows (White 2006) RANS si mulations are computationally less expensive than DNS, but the simulations generalize detailed turbulent structure information. A third available option is a large eddy simulation (LES). LES employs a scaling filter that delineates turbulent eddy length scales larger than the scaling filter for direct numerical resolution and models turbulent structures smaller than the filter length scale. This technique has been shown to be superior to RANS k models in disinfection flow through reactors (Wols et al. 2010). The LES filtered continuity (Equation 5 1) and momentum (Equation 5 2) equations are: (5 1) (5 2) PAGE 110 110 where is fluid density; x i is the i th direction vector; is the filtered velocity in the i th direction; is the filtered pressure; is the kine matic fluid viscosity; and is the turbulent viscosity. In the present implementation, the Smagorinsky model is utilized to model the turbulent viscosity of the filtered turbulent eddies: (5 3) where C s = 0.1 is the S magorinsky constant; L s is the sub grid characteristic filter length scale of the finite volume mesh; and is the local strain rate tensor. The reader is encouraged to refer to Lesieur and Metais (1996) for a more comprehensive development of the LE S Smagorinsky turbulent model. To model the transport of PM, a mixed mode Eulerian Lagrangian reference frame is utilized where the fluid velocity and pressure flow fields are modeled in an Eulerian reference frame and the PM is modeled as discrete particl es in a Lagrangian reference frame. PM transport modeled in the discrete phase is integrated across the fluid velocity and pressure flow fields modeled in the Eulerian reference frame. This scheme does not account for particle influence on the velocity and pressure flow fields and, thus, is restricted to dilute fluid flows of < 10% volume fraction (VF) (Brennen 1996) Even with this restriction, many flow situations, including the batch reactors in this present study, are successfully modeled as dilute flows (the concentrations of interest for the present study are less than 1% as VF). second law (Equation 5 4) for representative particles across the numerical fluid domai n. PAGE 111 111 (5 4) (5 5) (5 6) (5 7) The formula tion of equation ( 5 4) is particle acceleration equal to the summation of the forces per unit particle mass. The quantity is drag force per unit particle mass; and the quantity is buoyancy/gravitational force per unit particle mass. Equation ( 5 6) is the definition of the relative Reynolds number for flow around a sphere. In equation ( 5 5) and ( 5 6) p density; d p is particle diame ter; v p_i is particle velocity in the i th direction; v i is the localized fluid velocity in the i th direction; and is the dynamic viscosity. Equation ( 5 7) is the drag coefficient for spherical particles with the constants K 1 K 2 and K 3 defined in Morsi and Alexander (1972) In a transient Eulerian Lagrangian solution framework the discrete phase is numerically solved by iteration in interwoven intervals with the Eulerian solution space. The computational modeling of the free chlorine concentration, tra nsport, and decay is a coupled set of Eulearian Lagrangian equations extending the analytical work of dissolved and particulate chlorine demand in urban stormwater in C hapter 3 whereby chlorine reactions are broken into three component reactions: a second order, fast PAGE 112 112 demand reaction with the dissolved fraction (rate constant: k 1 ), a second order, slow demand reaction with the dissolved fraction (rate constant: k 2 ), and a potential driving PM model (rate constant: k pm ). The kinetic governing equations imple mented in C hapter 3 are the second order parallel dissolved model (Equation 5 8 ) and the PM potential driving model (Equation 5 9 ): (5 8) (5 9) whe re C t is the total free chlorine concentration; C f is the fast acting free chlorine concentration; C s is the slow acting free chlorine concentration; DMD f is the concentration of the chlorine demand reacting with the fast acting free chlorine; DMD s is t he concentration of the chlorine demand reacting with the slow acting free chlorine; q e is the mass of free chlorine consumed in PM surface reactions per mass PM at equilibrium; and q t is the mass of free chlorine consumed in PM surface reactions per mass PM at time t The differential equation set is formulated on the following assumptions: The dissolved reactions (fast/slow) are parallel reactions without interaction The dissolved chlorine reactions involve a 1:1 reaction with a theoretical chlorine deman d (fast/slow) The initial dissolved chlorine demand is a fraction, f of the dissolved chemical oxygen demand, COD d PM chlorine demand is driven by a chlorine reaction potential and is limited by the local available free chlorine PAGE 113 113 Potential particulate chlo rine demand dissolution into the dissolved fraction is accounted for in the PM demand At a uniform temperature, the fast dissolved free chlorine demand reaction is modeled by a combined Eulerian Lagrangian mass transport equation: (5 10) where Y Cl F is the mass fraction of the free chlorine in the fast reaction; Y DMD F is the mass fraction of the chlorine demand in the fast reaction; is the species density; u i is the local velocity vector; D is the diffusion coefficient of the species; t is the turbulent viscosity; Sc t is the turbulent Schmidt number; k is the dissolved second order rate constant with units [L 3 M 1 T 1 ]; c pm is the local concentration of PM as re presented by discrete particles; k pm is the PM potential driving model rate constant with units [M 1 M 1 T 1 ]; and (5 11) where is limited by the lower value of the PM potential driving model and the local free chlorine concentration. q e is the mass of free chlorine consumed i n PM surface reactions per mass PM at equilibrium with units [M 1 M 1 ]; q t is the mass of free chlorine consumed in PM surface reactions per mass PM at time t with units [M 1 M 1 ]; and is defined as Equation 5 12. (5 12) where Y Cl T is the sum of the mass fractions of the free chlorine in the fast and slow ( Y Cl S ) reactions and ensures the proportionate removal of free chlorine from the PAGE 114 114 dissolved fractions. The convection, turbulent diffusion, and dissolved demand terms are sol ved in Eulerian space, and the PM demand is solved in Lagrangian space with the time rate of change solved in both reference frames. Similarly, the mass transport equation for the slow reaction is defined in Equation 5 13 : (5 13) and two additional transport equations for chlorine de mand for j = F,S are defined as Equatio n 5 14. (5 14) Numerical requirements for the mixed transport and reaction Eulerian Lagrangian dissolved and PM species model require that the PM chlorine demand does not exceed the local species mass within the numerical cell within the Lagrangian timestep advancement when the reactor is not in an overall limiting condition (when the volumetric me an Cl T >> ). Qualitatively, this is governed by mesh spacing, overall chlorine concentration, PM mass loading, the number of representative particles in the discrete phase, and the timestep of the simulation. In the absence of experimenta l data validating data, the solution must demonstrate mesh, particle number, and timestep independence. The numerical Eulerian Lagrangian kinetic chlorine model is computationally simulated in ANSYS Fluent 13.0 where the demand reaction terms in the above PAGE 115 115 equations are programed in C computer code and compiled as external dynamic linked libraries. Batch Reactor Setup and Initialization The batch reactors are modeled as three dimensional continuously stirred batch reactors (CSBRs) of 1700 ml volume with a d iameter of 140 mm, height of 130 mm, and a flat bottom with a 20 mm fillet with the sidewalls. The mesh has 84 thousand tetrahedral cells resulting in a mean cell volume of 0.02 ml. The CSBRs consist of three distinct fluid zones a 50 ml free chlorine injection zone, a rotating zone containing the stir rod, and a bulk fluid zone. The mixing within the batch reactor is motivated by a 6 mm by 40 mm stir rod set within a hemispherical rotating mesh at the bottom center of the reactor. The hemispherical m oving mesh has a sliding interface with the bulk fluid zone and rotates at 350 rpm. The free chlorine injection zone is contained within the 1700 ml of the batch reactor, which is illustrated in F igure 5 1. The reactor is initialized with all species fra ctions set to zero and the velocity and pressure field are brought to periodic steady state by solving 60 s of flow time at a 1 s timestep. The initial values of the chlorine species are then patched to the free chlorine injection zone to mimic the physic al hypochlorite injection of the physical CSBRs with the concentration of the patched zone scaled over the volume of the reactor to provide a mean concentration of the initial experimental value which was nominally 15, 30, or 45 mg/L depending on the model ed batch reactor. Chlorine demand species that react on a 1:1 basis are patched to the CSBR domain where Y DMD F = d and Y DMD S = (1 d where COD d is the dissolved chemical oxygen demand and X and f are model parameters is the second order dissolved model defined for a small urban catchment in Gainesville, FL in Table 5 1. For CSBRs with PM interaction, 1400 representative discrete particles PAGE 116 116 are injected at t = 61s along the viewing plane that bisects the CSBR in F igure 5 1. Timesteps for the CSBR are 1 s for the first minute and 5 s thereafter. CSBR Validation CSBR validation is performed on a dataset of 9 e xperimental batch reactors from C hapter 3 with runoff from a small paved urban catchment for events that occurred during the fall of 2010. A control batch reactor with hypochlorite addition to chlorine demand free water is used as a control for the mixing of the simulation. Four of these batch reactors elaborate performance on dissolved urban rainfall runoff and validate the CFD finite rate parallel dissolved kinetic model and the remaining batch reactors validate the PM potential driving model and the com posite PM and dissolved model. The criteria of the model performance for the CFD species model is the normalized root mean square error (NRMSE): (5 15) where O i is the observed value at measurement i ; E i is the modeled value at measurement i ; n is the total number of measurements; and C o is the initial chlorine dose. For the experimenta l dataset, n = 8. The NRMSE defined as in equation ( 5 15 ) illuminates the model performance relative to the initial chlorine dose of the reactor. For both the dissolved and PM laden reactors, the volumetric mean of Y Cl T of the reactor is utilized as the estimate of the chlorine concentration at the sample time. Results and Discussion Figure 5 3 presents a histogram mixing analysis of the CSBR of the initial mixing phase of a simulated hypochlorite injection, of reactor average value C o with no PAGE 117 117 chlorine demand. Initially 96.5% of the cells within the CSBR contain no chlorine species with the remainder containing the high chlorine dose of the injection. At 5 s, the 89% of the CSBR volume elements contain chlorine concentration with 5 mg/L of C o At 15 s, 100% of the CSBR volume elements contain a chlorine concentration with 1 mg/L of C o The experimental batch reactor found the target C o concentration at the earliest sample time of 1 min and the CFD simulation corroborates this finding. Figure 5 4 presents the validation of the parallel dissolved model under disparate initial conditions. The correlation of the dissolved chlorine demand in urban stormwater to the COD d is apparent comparing panels C and D, which illustrates a high COD d high demand s ample, and a low COD d low demand sample, respectively. Overall the data is illustrative of the second order nature of the dissolved demand wherein the kinetic rate is dependent on the concentration of both the free chlorine and dissolved demand and may b e limited by either constituent depending on the initial chlorine dose and the water quality of the sample. In each case in the dissolved CSBRs there is a clear initial demand followed by a chlorine demand of slower timescale and the CFD kinetic model cor rectly predicts the exhaustion of the fast rate demand portion with respect to the experimental data. The NRMSE of the reactors are 3.3%, 4.0%, 3.8%, and 5.4%, for panels A through D, respectively. The modeled reactors utilized in the validation of the CF D implementation of the second order parallel analytical rate expression for the dissolved phase were non influential in the derivation of the model constants. Thus, these reactors are examples of the predictive capability of the CFD model on typical load ings given the COD d of the runoff and C o for the small urban catchment in Gainesville, FL with the characteristic model parameters in Table 5 1. These PAGE 118 118 characteristic parameters are a result of the typical event particulate and dissolved loadings of the cat chment and were found to be consistent on an inter event basis. Figure 5 5 presents the validation of the potential driving PM kinetic model with CSBRs laden with PM in a dissolved stormwater matrix. The dissolved demand is subtracted from the total de mand to produce the PM demand in the batch reactor. This PM demand is modeled by the potential driving model which is governed by the available reaction sites on the PM mass within the CSBR and the available free chlorine dose. These characteristics are modeled through q e and k pm NRMSEs for the CFD PM kinetic demand model are 7.1%, 7.9% 2.6% and 10.3%, for panels A through D with a slight modification where C o = q e in the determination of the NRMSE. As can be seen from the figure, the timescale of the PM potential driving model is on the order of the slow second order dissolved demand reaction. It is also important to note that the capacity of PM chlorine demand is high with respect to PM mass and represents a pronounced potential chlorine sink in urba n rainfall runoff at even low PM loadings. The results from the composite particulate and dissolved CFD kinetic model are presented in F igure 5 6. The NRMSEs for the composite model are 2.4%, 4.3%, 2.6%, and 3.9% for panels A through D, thus the composit e model is capable of reproducing the experimental CSBR kinetic reaction utilizing all three simultaneous reactions. The extension of the CFD composite kinetic model to a flow through chlorine contactor for urban rainfall runoff incorporates a few impor tant considerations. PM transport is essential in modeling urban stormwater disinfection processes as sediment PM shielding of associated organisms has been established in stormwater runoff in C hapter 4 Thus, a modeled unit operation and process must en sure that all particles PAGE 119 119 greater than 75 m are captured by the unit. The dissolved species transport model should not require significant modifications for flow through reactors as implemented in this study. To directly model bacteriological, viral, or p rotozoan transport and inactivation, a generalized scalar equation can be solved utilizing a kinetic inactivation sink term such as the Hom model (Greene et al. 2004) overlaying the composite CFD kinetic model presented in this study. The analytical poten tial driving model, from which the CFD model is derived for the PM surface reaction is derived for initial hypochlorite doses to find the chlorine demand at equilibrium. Additional research investigating the maximum q e values for PM fractions with continu al exposure to low doses of free chlorine is warranted to investigate the performance of the PM model under continually limiting chlorine application. However, even with this limitation, the implementation of the PM demand model in this study would either equal the reaction under a continual low dose chlorination experiment or exceed the value and remain a conservative estimator of the chlorine concentration in this case. PAGE 120 120 Table 5 1. Model parameters for the dissolved parallel second order and PM potential driving force equations. Dissolved PM k 1 0.07 L 1 mg 1 min 1 Reactor V4 R13 V5 V1 k 2 2.9 L 1 mg 1 min 1 k pm 0.83 2.19 1.00 1.80 g 1 mg 1 min 1 f 0.39 q e 102 105 85 154 m g/g X 0.36 PAGE 121 121 Figure 5 1. Physical batch reactor showing stirplate, aluminum foil jacket, and water quality electrodes. Utilized reactor volume is 1700 ml and the stirplate is set to 350 rpm. The batch reactor is sealed tightly with a lid w hen water quality measurements are not being made. PAGE 122 122 Figure 5 2. Illustration of the fluid zones within the batch reactor. The hemispherical rotating zone is shown containing the stir rod at a single frame. The hypochlorite injection region is a cylindrical zone bisected by the viewing plane and is represented by the red region. The bulk fluid zone is shown in blue. Reactor volume is 1700 ml and is comprised of approximately 41 thousand tetrahedral cells. HOCl Injection Region Bulk Fluid Zone Stir Rod in Rotating Fluid Zone PAGE 123 123 Figure 5 3 Histogram analysis o f the computational mesh CFD free chlorine concentration during the initial mixing phase in a batch reactor with an overall initial Co = 45 mg/L. After 5s of mixing, 90% of the computational mesh exhibits a free chlorine concentration within 5 mg/L. Aft er 15 s of mixing, 100% of the computational mesh exhibits a free chlorine concentration within 1 mg/L. The uniformity of the chlorine concentration in the reactor enables increasing the timestep of the simulation. PAGE 124 124 Figure 5 4 Comparison of the sec ond order CFD dissolved demand model with experimental results. NRMSE values are reported and indicate that the CFD model accurately (NRMSE < 6%) accounts for the complex reaction dynamics of the urban stormwater demand reactor containing dissolved matrix of disparate initial water quality conditions and demand. PAGE 125 125 Figure 5 5 Comparison of the second order potential driving PM CFD model with experimental results. PAGE 126 126 Figure 5 6 Comparison of the composite dissolved and PM CFD model with batch reacto r data. NRMSEs and RPDs are reported and are less than 5% in each case. Results validate the finite rate CFD kinetic model developed for free chlorine demand in urban stormwater. PAGE 127 127 CHAPTER 6 CONCLUSION Urban stormwater particle transport and disinfectio n reactions are complex phenomena with coupled transport and reaction kinetics across both solid and liquid phases. In addition, stormwater volumetric and particle transport are the result of stochastic rainfall events that render the volumetric and parti culate matter (PM) loading difficult to determine or estimate a priori Free Chlorine Kinetics Dissolved Phase Reaction Kinetics The reaction kinetics of the dissolved phase of the urban stormwater runoff exhibit parallel second order characteristics. T he ultimate modeled chlorine demand of the water in a batch reaction is determined to correlate well with the dissolved chemical oxygen demand (COD d ). COD d is a simple and expedient analytical procedure and it is possible to process many samples simultane ously. However, certain formulations of COD reagents generate hazardous waste thus the suitability and regulatory usability of non hazardous waste formulations remains to be determined. The bimodal proportionality of the parallel reaction of the second order kinetics is stable on the inter event reference frame for the small paved urban watershed utilized in this study for runoff collection. This result is expected to be function of the ratio of the inorganic to organic loadings on the watershed. This loading ratio can be expressed in terms of the dissolved organic carbon ratio (DOC) to COD d This ratio has been determined to be consistent and a characteristic parameter of the watershed. However, as the experimental matrix only included a single catch ment, the full elaboration of the relationship between the bimodal proportionality of the reaction is beyond the resolution PAGE 128 128 of the experimental construct. Thus, future research regarding the relationship of this proportionality factor, X, to DOC/COD d for similar and dissimilarly loaded catchments is warranted. Particulate Kinetics The reaction kinetics of free chlorine with the various particulate matter (PM) fractions mobilized by urban stormwater runoff are well modeled by a second order potential drivin g model. This model relates the kinetic parameters to the available surface reaction sites and a driving potential of available free chlorine. As free chlorine is an added component to the kinetic reference frame, there are three general resulting cases for reaction whereby the batch reactor is under chlorinated, super chlorinated, or transitionally chlorinated. There remains a case where the reactor is severely under chlorinated, where the dissolved demand immediately quenches all available chlorine and this is most suitably modeled by the dissolved model without particulate consideration. In the under chlorinated model the parameter of maximal mass transfer, q e is limited by the maximum value of chlorine mass available for reaction with the particulat e phase, q e max q e max is the initial mass of chlorine inoculated into the reactor less the chlorine that reacts with the dissolved phase normalized of the PM mass contained within the reactor. In the super chlorinated reactors, the chlorine mass transf er is limited by the available reaction sites on the PM. This in effect exhausts the chlorine demand of the PM fraction of the reactor. This maximum sorbance per PM unit mass is characteristic of the PM fraction with the suspended and settleable fraction exerting similar maximal demand due to their similar low volatile fractions and siliceous composition. The maximum sorbance per PM unit mass for the sediment fractions is PAGE 129 129 double the sorbance for the suspended and settleable fraction and is attributed to the high volatile fraction and, thereby, the increased organic density of that fraction. Computational Modeling PM Fate and Transport The dispersivity of the g r anulometry of the PM phase influences the computational accuracy of a La grangian Eulerian comput ational fluid dynamics (CFD) model. The fundamental fluid mechanics driving this phenomenon are non linear drag forces with respect to particle diameter, exerted on mobilized PM entrained in the flow. In the framework of a Lagrangian Eularian PM fate an d transport model PM is represented by discrete particle sizes and the non linear nature of the mechanics reduces the computational accuracy of the when the simulated particle sizes inadequately represent a disperse influent gradation. The computational s tructure for the investigation involves a convergence study that incrementally increases the number of representative particle sizes, the discritization number (DN), by a factor of 2 until solution difference is negligible achieved at a DN of 128. Avera ge relative percent difference (RPD) calculations ascertain the influence of gradation dispersivity on computational accuracy. Mono disperse, or uniformly disperse gradations, are well represented by a median particle size, the d 50m which correlates to a DN of 1. However, gradations of medium dispersivity and heterodisperse gradation require a DN of 8 to 16 depending on overall gradation fineness, for computational modeling. Overall gradation fineness influences the DN requirement mechanistically as the non linearity of particle drag forces increases for very small particle diameters. Thus, a DN of 8 suffices for particle granulometries PAGE 130 130 with d 50m = 66.7 m and d 50m = 100 m, however, a DN of 16 is required for finer gradations near a d 50m of 33.3 m. Th e Lagrangian Eulerian CFD model also enabled the development of a novel CFD rubric for the per formance evaluation of hydrodynamic and baffled separators. A composite dataset of the per particle removal efficiency across a spectrum of flow rates generates a performance surface for the unit. The performance surface is akin to a unit density. Mathematical comparison of the performance surfaces of two distinct separat ors yields a multi dimensional, differential performance evaluation that extends the depth found in typical performance studies utilizing a singular gradation. In addition, the data from the performance surface can be utilized to model the efficiency of a unit operation given a specific gradation without the need for additional computational time. CFD Free Chlorine Reaction Kinetics Advanced modeling of the fate, transport, and kinetics of free chlorine in a transient simulation of urban stormwater runoff is possible in CFD. The coupled dynamics of the chlorine kinetics with the dissolved an d PM phases under transient loading conditions generate a dynamic physical chemical network with multiple reaction pathways. CFD models the fundamental equations of mot ion for PM, chemical species, and fluid flow. The analytical models developed in C hapter 3 are successfully ported to discrete equivalents in differential space for computational modeling in CFD and validated on the dataset generated in the CSBRs for rain fall runoff events captured in the fall of 2010 in Gainesville, FL and generated NRMSEs of less than 6% in all cases. Advanced CFD modeling utilizing validate d CFD models allow s for the integrated and iterative design of treatment systems without the need for pilot scale testing of each PAGE 131 131 design variant. This reduces research and development expenses for such testing and permits exploration of non traditional design implementations which were previously cost prohibitive. PAGE 132 132 APPENDIX A ADDITIONAL FIGURES Fig ure A 1. Continuously stirred batch reactor (CSBR) schematic. PAGE 133 133 Figure A 2. Schematic of monitored urban sub catchment in Gainesville, FL showing contributing impervious surface PAGE 134 134 Figure A 3. Control CSBRs showing hypochlorit e kinetics in Nanopure DI for 8 h at 15 mg/L and 24 h at 45 mg/L. Results indicate no detectable environmental loss of hypochlorite due to UV or volatilization du ring the experimental timeframe (p < 0.05). PAGE 135 135 Figure A 4. Control CSBRs comparing autoclave sterilized and non autoclave sterilized stormwater Matrix. 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Rietveld, L., and van Dijk, J. The weakness of a k eddy simulation for the prediction of UV dose distributions and disinfection. Chemical Engineering Journal 162, 528 536. Wu, T., Sansalone, J. J. Mass transfer to filter media from aqueous m atrices II: Kinetics. Journal of Environmental Engineering in Review. PAGE 144 144 BIOGRAPHICAL SKETCH Joshua Dickenson was born and raised in Jacksonville FL and was homeschooled from 3 rd through 12 th grade s In 2005, Joshua graduated Cum Laude from the University of F lorida with a Bachelor of S cience in Mechanical Engineering. After his bachelor s, Joshua spent 7 months in Bundibugyo, Uganda working with a Christian non governmental organization implementing water development projects. While there he developed a pass ion for providing clean water to the poorest of the world. In 2007, Joshua matriculated at the University of Florida in the Environmental Engineering Science s Depart ment in a combination master s and doctoral program. In May 2010, Joshua received a Maste r of Engineering degree from the University of Florida. In May 2011, Joshua received Doctor of Philosophy Degree in Environmental Engineering and Science from the University of Florida. Joshua pursues life to the fullest, loves hi s family deeply, enjoys deep, intimate relationships, and ultimately owes all to God and His Son, Jesus Christ. 