DEVELOPMENT OF A CHLORINE DECAY AND TOTAL TRIHALOMETHANE FORMATION MODELING PROTOCOL USING INITIAL DISTRIBUTION SYSTEM EVALUATION DATA.

Transcription

1 DEVELOPMENT OF A CHLORINE DECAY AND TOTAL TRIHALOMETHANE FORMATION MODELING PROTOCOL USING INITIAL DISTRIBUTION SYSTEM EVALUATION DATA A Thesis Presented to The Graduate Faculty of The University of Akron In Partial Fulfillment of the Requirements for the Degree Master of Science James Philip Cooper December, 2009 i

2 DEVELOPMENT OF A CHLORINE DECAY AND TOTAL TRIHALOMETHANE FORMATION MODELING PROTOCOL USING INITIAL DISTRIBUTION SYSTEM EVALUATION DATA James Philip Cooper Thesis Approved: Accepted: Advisor Dr. Christopher Miller Dean of the College Dr. George K. Haritos Faculty Reader Dr. Stephen Duirk Dean of the Graduate School Dr. George R. Newkome Faculty Reader Dr. Lan Zhang Date Department Chair Dr. Wieslaw Binienda ii

3 ABSTRACT Recently many water distribution systems (WDS) developed and calibrated extended period simulation model to assist in the Initial Distribution System Evaluation (IDSE). The IDSE is the first phase of the Stage 2 Disinfectants and Disinfection Byproducts (D/DBP) Rule promulgated by the United States Environmental Protection Agency (USEPA). The purpose of the IDSE was to identify locations within the WDS for Stage 2 D/DBP compliance monitoring. This research aided two northern Ohio distribution systems in completion of the IDSE requirements and further develops the models to predict chlorine residuals and trihalomethane (THM) concentrations. Existing models were utilized as a base and updated with data collected during extensive field studies. A Form 4: Modeling Study Plan, which provides justification that the models meet minimum calibration requirements was submitted and approved by the USEPA. The updated models were utilized to select Stage 2 D/DBP future monitoring locations. Drafts of Form 5: IDSE Report for a Modeling SSS were completed for each system. First-order reaction expressions were developed to estimate the effects of bulk reactions and reactions occurring along the surface of the distribution system pipes (wall) for free chlorine decay and THM formation. Most reaction expressions that utilize a global wall reaction rate coefficient in units of inverse time do not accurately represent the proportion of reactions occurring along the wall. As a chemical travels through a iii

4 pipe, wall reactions within larger diameter pipes will have less of an effect on the overall reaction than when flowing through a smaller diameter pipe. This is because the ratio of the pipe surface to a unit volume of water is not constant throughout the distribution system. This is, however, constant given a specific flow path from the point of entry into the distribution system to any fixed point, such as a sampling location. In order to account for this wall reaction variability, a series of flow path diameter fractions are employed within the chlorine decay and THM formation expressions. A probabilistic approach using Bayesian statistics is then utilized to estimate the bulk and wall reaction coefficients. The flow path specific expressions better predicted actual data. iv

5 ACKNOWLEDGEMENTS Many thanks to my advisor, Dr. Christopher M. Miller, for his guidance on this research and analysis; and to Robert L. McNutt with ARCADIS U.S. Inc. for providing the opportunities to pursue this research with clients. Thanks to the Department of Civil Engineering at The University of Akron for financial support. Also, thank you to everyone who has provided valuable input that was critical for the success of this effort including employees of the City of Norwalk and City of Barberton, and the laboratory assistants at The University of Akron. Finally, and most importantly, a sincere thank you to my fiancé and my family for their unending support throughout this research. v

6 TABLE OF CONTENTS Page LIST OF TABLES... viii LIST OF FIGURES... ix CHAPTER I. INTRODUCTION Perspective Research Objectives Research Approach... 3 II. LITERATURE REVIEW Disinfection Byproduct Regulations Chlorine Decay Modeling Chlorinated Disinfection Byproduct Fate Modeling Water Distribution System Modeling Model Calibration Techniques III. MODEL DEVELOPMENT Initial Steady-State Hydraulic Model Development Extended Period Simulation Model Development vi

7 3.3 Chlorine Decay Rate Expressions THM Formation Rate Expressions IV. MODEL RESULTS AND ANALYSIS Extended Period Simulation Hydraulic Model Calibration Field Study and Additional EPS Model Calibration Stage 2 D/DBP Monitoring Site Selection Water Quality Model Parameter Estimation Water Quality Model Results V. SUMMARY AND RECOMMENDATIONS Summary Recommendations BIBLIOGRAPHY APPENDICES APPENDIX A. WINBUGS PARAMETER ESTIMATION MODEL APPENDIX B. FORM 4 AND FORM 5 FOR THE NORWALK WDS APPENDIX C. FORM 4 AND FORM 5 FOR THE BARBERTON WDS vii

8 LIST OF TABLES Table Page 4.1 Minimum Model Calibration Requirements per USEPA for utilization in the IDSE SSS Norwalk WDS modeled versus actual pipe length and volume for the conditions during the IDSE SSS Barberton WDS modeled versus actual pipe length and volume for the conditions during the IDSE SSS Free chlorine residual and regulated DBP concentrations at twelve locations throughout the Norwalk WDS sampled during the field study on July 31 and August 1, Free chlorine residual and regulated DBP concentrations at twelve locations throughout the Barberton WDS sampled during the field study on August 29, Bulk and wall coefficient posterior statistics for overall first-order free chlorine decay within the Norwalk WDS Bulk and wall coefficient posterior statistics for overall first-order free chlorine decay within the Barberton WDS Bulk and wall yield posterior statistics for THM formation within the Norwalk WDS Estimated first-order bulk and wall reaction coefficients for chlorine decay Flow path diameter fractions and a wall decay rate converted to inverse time for each sampling location within the Norwalk WDS Results of parameter variation and the average absolute difference between measured and modeled values for each scenario viii

9 LIST OF FIGURES Figure Page 4.1 Calculated diurnal demand pattern for the Norwalk WDS Calculated diurnal demand pattern for the Barberton WDS Tank level calibration for all storage tanks in the Norwalk WDS based on data obtained during the field study on July 31 and August 1, Tank level calibration for all storage tanks in the Barberton WDS based on data obtained during the field study on August 29, Tank water age for the tank with the highest modeled average water age (750k Tank) in the Norwalk WDS Tank water age for the tank with the highest modeled average water age (Tank T2) in the Barberton WDS Free chlorine decay model posterior convergence with a 30,000 sample simulation based on field study results from the Norwalk WDS, N= Free chlorine decay model posterior convergence with a 30,000 sample simulation based on field study results from the Barberton WDS, N= THM formation model posterior convergence with a 30,000 sample simulation based on field study results from the Norwalk WDS, N= Cl 2 residual measured versus modeled concentration based on an overall decay rate of K TOT =1.07d -1 for the Norwalk WDS, N= Cl 2 residual measured versus modeled concentration based on estimated values k b =-0.31 d -1 and k w =-0.19 ft/d for the Norwalk WDS, N= Cl 2 residual measured versus modeled concentration based on estimated values k b =-1.38 d -1 and k w = ft/d for the Barberton WDS, N= ix

10 4.13 Cl 2 bulk and wall consumption based on estimated values k b =-0.31 d -1 and k w =-0.19 ft/d for the Norwalk WDS, sorted by lowest to highest water age THM bulk and wall formation based on the estimated yields of Y b =5.7 µg/mg and Y w =32.32 µg/mg for the Norwalk WDS Cl 2 bulk and wall consumption percentage based on estimated values k b =-0.31 d-1 and k w =-0.19 ft/d for the Norwalk WDS THM bulk and wall formation percentage based on the estimated yields of Y b =5.7 µg/mg and Y w =32.32 µg/mg for the Norwalk WDS...44 x

11 CHAPTER I INTRODUCTION 1.1 Perspective It is well known that chlorine disinfection of drinking water results in the formation of disinfection byproducts (DBPs). The United States Environmental Protection Agency (USEPA) serves as the regulatory authority for, among other things, limits on concentrations of disinfection byproducts. Recently DBP regulations have become more stringent and the USEPA requires most public water systems to conduct an Initial Distribution System Evaluation (IDSE). The purpose of the IDSE was to identify representative locations within the distribution system with high DBP concentrations and select specific locations for future monitoring. Currently, the two classes of regulated DBPs are four trihalomethanes (THMs) and five haloacetic acids (HAAs). Previous researchers have developed kinetic-based and mechanistic-based models to predict chlorine decay and DBP fate within the water distribution system (WDS). These models are typically based on many parameters including organic carbon content, ultraviolet absorbance (UV 254 ), temperature, and ph. (Chowdhury, Champagne, & McLellan, 2009). The models vary in detail, however many models are based on laboratory analysis of bulk water. This does not account for chlorine decay and DBP formation due to reactions along distribution system pipe walls. Many models calibrated using distribution system field sampling data also assume all reactions occur within the 1

12 bulk water phase; and because of this, these models are confounded with reactions along the surface of the pipe wall within the WDS. Some models do account for reactions along the surface of the pipe wall. Most of these models utilize field sampling results in combination with bulk decay from a lab analysis to determine a system-wide, or global, wall reaction coefficient in terms of inverse time. Distribution system modeling software packages exist with various modeling capabilities and calculation algorithms. Most models can predict water age and a single chemical concentration. Due to the IDSE regulations, many municipalities have recently developed and calibrated these distribution system models to perform the IDSE. However, to model chemical concentrations such as chlorine decay or THM formation, a global aqueous (bulk) reaction rate coefficient and a global wall (pipe surface) reaction rate coefficient must be input into the model. The process of determining the bulk and wall reaction rate coefficients for inclusion in the modeling software is typically performed by trial-and-error method with no specific protocol for accounting for the full effects of the reactions along the pipe walls. Various combinations of coefficients are input and model results are compared to field sample data until the model results most accurately match the measured values. This elementary process is faulty because more than one combination of bulk and wall reaction rate coefficients may result in similar model results. Another method often utilized is laboratory analysis of aqueous chemical reactions, referred to as bottle tests or jar tests. A bulk reaction rate is determined, the rate input into the model, and the wall rate adjusted by trial-and-error until model results are favorable. This process is more reliable than the former; however, properties of water that affect the rate of reaction are very dynamic. The model may be calibrated to best fit 1

13 a sample at one specific time that may or may not be representative of the typical reactions within the distribution system. Additionally, many distribution system DBP models are dependent on accurately predicting chlorine residuals, which may or may not be accurately modeled. Development of a mechanistic-based calibration of a distribution system model to predict chlorine decay and THM formation would provide valuable assistance to water systems making treatment and distribution system capital improvement decisions, as well as creating a pathway for future research and development of models to predict DBP formation due to both bulk and wall reactions. 1.2 Research Objectives The overall objective of this research is to develop and calibrate a model of the water distribution systems for the Cities of Norwalk and Barberton in Northern Ohio as a tool for predicting free chlorine decay and THM formation throughout the distribution system. This will provide a foundation for similar water systems to utilize the simplistic reaction expressions and procedures developed herein and extend their calibrated IDSE model to predict chlorine decay and THM formation. This overall objective will be achieved through completion of three specific objectives for each system: Objective 1: Develop, plan, and conduct an IDSE for compliance with the Stage 2 D/DBP Rule for each system. A WDS model will be developed and a system specific study will be completed for the IDSE. Objective 2: Conduct an extensive field study and further calibrate the extended period simulation hydraulic model. Calibration will be based on results of the field study. The calibrated hydraulic model will be further developed into a water quality model. 2

14 Objective 3: Develop a protocol for the estimation of global bulk and wall reaction rate coefficients based on results from field studies conducted with this research and by application of Bayesian inference Using Gibbs Sampling (BUGS). 1.3 Research Approach This research was based on full-scale distribution system studies for the Cities of Norwalk and Barberton, Ohio. The City of Norwalk is located in Huron County, covers approximately 8.3 square miles and in 2000 had a population of 16,238 (U.S. Census Bureau, 2009). The source water for the Norwalk Water Treatment Plant is a series of reservoirs: the Lower Reservoir, Upper Reservoir, and Memorial Reservoir. The distribution system consists of approximately 460,000 lineal feet of water mains ranging from 4 inches to 18 inches in diameter. The City of Barberton is located in Summit County, covers approximately 9.2 square miles and in 2000 had a population of 27,899 (U.S. Census Bureau, 2009). The source water for the Barberton Water Treatment Plant is the Barberton Reservoir, located in the Upper Wolf Creek Watershed (Cooper, 2009). Barberton also has the ability to pump and mix groundwater into the water treatment plant raw water to dilute the concentration of total organic carbon being treated through the plant. The distribution system consists of approximately 649,000 lineal feet of water mains ranging from 4 inches to 24 inches in diameter. Field studies were planned and conducted at 12 locations in each system. Samples were collected at each location four times throughout the day for the Barberton system and samples were collected at each location eight times over two days for the Norwalk system. The data collected and analyzed for each field study was utilized to 3

15 calibrate each model in EPANET, a distribution system modeling software, to predict chlorine decay and THM formation. A Bayesian statistical analysis was applied using WinBUGS software to estimate the bulk and wall chlorine decay rates (Lunn, Thomas, Best, & Spiegelhalter, 2000). This research incorporates spatial knowledge with DBP formation and has the potential to present areas of the system that need to be watched by distribution system operators. The accurately calibrated model will be useful to evaluate the effects of improvements and/or changes to the distribution system on chlorine residual and THM concentrations by running various hydraulic scenarios and observing the change in multiple water quality characteristics. 4

16 CHAPTER II LITERATURE REVIEW Researchers have developed many models to predict free chlorine decay and disinfection byproduct fate. Each model requires a unique set of parameters and is valid only within the data which it was developed. Limited research has been conducted on model development for multiple chemical species within a chlorinated water distribution system. The purpose of this review is to establish a foundation for the development of a protocol for drinking water quality model calibration. Existing models will be evaluated to further develop the established reaction rate expressions as a basis for the model development with this research. Further, a review of distribution system hydraulic model calibration techniques is performed to establish a foundation to be utilized in this research. This literature review is comprised of five sections disinfection byproduct regulations, chlorine decay modeling, disinfection byproduct fate modeling, drinking water distribution system modeling, and model calibration techniques. 2.1 Disinfection Byproduct Regulations Federal regulation 40 CFR requires that any public water system (PWS) within the United States must maintain a minimum level of disinfectant within the water distribution system at all times and also must not exceed a maximum level of disinfectant. The purpose of the disinfectant regulations is to minimize both the 5

17 microbial agents and disinfection byproduct concentrations (Chowdhury, et al., 2006). Regulations are in place to inactivate microbes such as bacteria, viruses and protozoa due to the adverse human health effects due to consumption of these microbes. Since the 1970s, researchers have discovered many halogenated chemicals form within the WDS due to disinfection, known as disinfection byproducts. These include trihalomethanes, haloacetic acids, chloral hydrate, haloacetonitriles, haloketones, chloropicrin and N- Nitrosodimethylamine (Baribeau, et al., 2006). DBPs are formed when natural organic matter remaining in the water after conventional water treatment attaches to the pipe walls reacting with chlorinated disinfectants, typically chlorine or chloramines. It has also been found that adverse health effects can occur as a result of DBP ingestion, specifically trihalomethanes (Nieuwenhuijsen, Toledano, Eaton, Fawell, & Elliott, 2000). Due to the possible health effects, federal regulation and the USEPA have established maximum contaminant levels for various DBPs. Effective early 1999, the Stage 1 Disinfectants and Disinfection Byproducts (D/DBP) Rule established maximum annual average levels of 80 micrograms per liter for total trihalomethanes (TTHM) and 60 micrograms per liter for the sum of five haloacetic acids (HAA5) throughout the WDS (USEPA, 1998). TTHM consist of 4 chemicals: chloroform (CHCl 3 ), bromodichloromethane (CHBrCl 2 ), chlorodibromomethane (CHBr 2 Cl), and bromoform (CHBr 3 ). HAA5 consist of five chemicals: monochloroacetic acid (ClAA), dichloroacetic acid (Cl 2 AA), trichloroacetic acid (Cl 3 AA), monobromoacetic acid (BrAA), and dibromoacetic acid (Br 2 AA). The Stage 1 D/DBP Rule was replaced by the Stage 2 D/DBP Rule in

18 The Stage 2 D/DBP Rule required systems to complete an Initial Distribution System Evaluation (IDSE) and established maximum annual average levels of 80 micrograms per liter for TTHM and 60 micrograms per liter for HAA5 at each sampling location as determined by the IDSE (USEPA, 2005). This is a significantly more stringent rule as systems were previously in compliance even if some Stage 1 D/DBP monitoring site concentrations were above the annual average, provided the annual average of all monitoring locations combined was below the maximum permissible levels. The Initial Distribution System Evaluation Guidance Manual for the Final Stage 2 Disinfectants and Disinfection Byproducts Rule was published in early 2006 (USEPA, 2006). The purpose of the Manual is to assist water systems to comply with the IDSE component of the Stage 2 D/DBP Rule. The overall objective of the IDSE is to determine representative sampling sites within the system that correspond to high trihalomethane and haloacetic acid concentrations. This can be accomplished through a standard monitoring plan (SMP) or a system specific study (SSS). With the standard monitoring plan, the PWS would be required to sample at many locations throughout their distribution system on multiple occasions for a one year period. The number of required sample locations and frequency of samples was dependent on the population served by the PWS. Once the samples were all collected, analyzed, and reported to the EPA, the system would select its monitoring locations at the areas with the consistently highest disinfection by-products. This option required lesser knowledge and analysis of the system; however, it would be very expensive due to the high cost of analyzing every DBP sample. The system specific study would require significantly less DBP samples to be collected and analyzed. However, a model of the distribution system would have to be 7

19 updated and calibrated to show the locations of the highest predicted DBP formation throughout the system based on water age results of the model. The system would be required to submit proof of calibration of the model to the USEPA through various means summarized by the Form 4: Modeling Study Plan. While the SSS would require a much greater analysis effort, the long-term cost savings will outweigh the short-term effort as the PWS will likely use the model in the future to determine water quality and hydraulic results at any point in the system with minimal updates to the model. 2.2 Chlorine Decay Modeling Various chlorine decay models have been developed and most assume an overall first-order reaction within the distribution system (Chowdhury, et al., 2006). The generally accepted expression for bulk chlorine decay is shown as equation 1 (1) where k b is the bulk reaction rate coefficient and C 0 is the initial chlorine concentration. Some researchers have linked DBP formation rates to overall chlorine decay rates assuming a simple first-order chlorine decay reaction with bulk reaction rate coefficients ranging from 0.09 d -1 to 0.53 d -1 for chlorine (Boccelli, Tryby, Uber, & Summers, 2003). Another study utilized a first-order chlorine decay reaction with bulk reaction coefficients ranging from d -1 to d -1 and wall reaction coefficients ranging from 1.53 d -1 to 4.69 d -1 (Rossman, Brown, Singer, & Nuckols, 2001). Clark, 1998 linked DBP formation rates to a second-order chlorine decay reaction as shown in equation 2 (2) 8

20 where C A is the concentration of chlorine and C B is the concentration of chlorine demand (Clark, 1998). Others have developed a chlorine decay model that assumes decay through multiple pathways with an instantaneous chlorine demand input as a constant (McClellan, Reckhow, Tobiason, Edzwald, & Smith, 2000). Each of the wall reaction rate coefficients are estimated by calculation of the difference between a bulk reaction rate and the overall reaction rate as shown in equation 3 with the reaction rate in units of inverse time. (3) Distribution system modeling software such as EPANET are programmed to utilize a simple first-order reaction for a single chemical, such as chlorine, with user input firstorder reaction rate coefficients k b and k w for the bulk and wall reactions, respectively (Shang, Uber, & Rossman, 2008). The bulk reaction of a chemical within EPANET is based on the first-order bulk reaction expression previously shown as equation 1. The wall reaction, however, is dependent on the dynamic concentration of the chemical within the bulk phase as well as the dynamic pipe wall surface area. Therefore, the wall reaction expression utilized by EPANET includes a surface area per bulk volume parameter as shown in equation 4 with the rate coefficient k w in units of length per time (4) where (A/V) is equal to four divided by the pipe diameter. Values for the first-order wall reaction rate coefficient can range from 0 to 5 ft/day (Rossman, 2000). Based on equation 4, the wall reaction rate coefficient that is published in most research (in units of inverse time) must be multiplied by the (A/V) term before it can be utilized by a distribution system modeling software. However, application of a single (A/V) term is 9

21 not possible due to multiple pipe diameters present in distribution systems. This shows that wall reaction kinetics is not a function of time alone; it is a function of time and a function of the flow path taken by any given unit of water to a specific location in the distribution system. 2.3 Chlorinated Disinfection Byproduct Fate Modeling A recent review of DBP models from 1974 to 2009 found that almost 120 models to predict DBP fate have been published (Chowdhury, Champagne, & McLellan, 2009). Many factors have been found to affect the formation of DBPs. These factors include ph, temperature, pre-disinfectant type, and natural organic matter presence measured by organic carbon concentration and ultraviolet absorbance. These factors are reviewed to determine their affects on DBP fate. An increase in ph will result in an increase in THMs and a decrease in HAAs (Hua & Reckhow, 2008). Another field-scale study found that the highest HAA concentrations were observed at the lowest ph levels (Westerhoff, Reckhow, Amy, & Chowdhury, 2002). Also, it is generally accepted that increased water temperature results in elevated DBP concentrations due to increased reaction rates. However, a recent lab-scale assessment of DBP formation found that an increase in temperature elevated THM concentrations, but did not significantly affect the HAA concentrations (Hua & Reckhow, 2008). Another lab-scale study of three similar source waters found that preozonation in place of pre-chlorination significantly reduced the concentrations of THMs and HAAs (Serodes, Rodriguez, Li, & Bouchard, 2003). It is generally accepted that trihalomethane concentrations increase with an increase in water age. Trihalomethane formation occurs over time and it has not been 10

22 observed that THM concentrations decrease at extended water ages due to decay mechanisms typically observed with HAA concentrations (Baribeau, et al., 2004). As a result, multiple models have been developed to predict THM formation based on the chlorine decay reaction rates and the total amount of chlorine consumed or chlorine demand. A first-order chlorine decay model has been applied to predict THM formation and assumes a water age time-zero THM concentration depending on the location of the time-zero sample within the treatment process (Boccelli, Tryby, Uber, & Summers, 2003). The reported values of THM yield per chlorine consumed range from 31.9 to 50.6 µg/mg. A second-order chlorine decay model has also successfully been applied to predict THM formation (Clark, 1998). A study that compared bottle test data with a physical small-scale distribution system from the same water at the same conditions found that THM formation was 15 percent higher through aged pipes than in a bottle test (Rossman, Brown, Singer, & Nuckols, 2001). This reinforces the theory that a distribution system model must include specific reaction expressions for chlorine decay and DBP formation due to reactions at the pipe wall. 2.4 Water Distribution System Modeling Many software packages are available to develop hydraulic models of distribution systems. Examples include WaterGEMS by Bentley and EPANET by the USEPA (Rossman, Clark, & Grayman, 1994). Hydraulic models may have been developed and calibrated for the IDSE if municipalities elected to perform a system specific study. If so, these models are calibrated based on a specific protocol and can easily be extended to serve as water quality models to provide significant value to water system operators. Most software packages have the ability to model a chemical reaction within the bulk 11

23 water and along the pipe wall as the water travels throughout the distribution system. These models can be developed and calibrated with adjustment to a single bulk reaction coefficient and a single wall reaction coefficient. The limitation of these software packages is the inability to model multiple chemicals throughout the WDS such as fate of disinfection by-products. Recently, an addition to the EPANET 2 software known as the Multi-Species Extension, or EPANET-MSX, has been released and it allows modeling of multiple, simultaneous chemical reactions occurring within both the bulk water and along the pipe wall (Shang, Uber, & Rossman, 2008). This software extension will prove useful in the future as it provides the ability to model chlorine decay, THM and HAA formation and fate within the distribution system by running a single scenario. This software is not yet incorporated into the standard EPANET interface and results of the multiple species can only be output to a text file. Little published research exists regarding model development of DBP fate within the distribution system. One recent unpublished study utilized EPANET-MSX to develop a preliminary water distribution system model to predict chloriminated disinfection byproducts (Alexander, Boccelli, & Kupferle, 2009); however, no studies have been performed using EPANET-MSX to model chlorinated DBP formation. The model predicted N-nitrosodimethylamine (NDMA) and THM concentrations. The THM reaction was based solely on the chlorine decay rate coefficient and the dissolved organic carbon concentration and did not explicitly include THM formation reactions occurring along the pipe wall. 12

24 2.5 Model Calibration Techniques Many researchers have identified methods to calibrate distribution system models. Most modeling software packages now include some form of a measured versus modeled correlation for many hydraulic parameters. Calibration of distribution system water quality models is more complex than hydraulic model calibration. This is due to the many unique factors that affect each chemical rate coefficient in the model. For example, when modeling chlorine decay through a distribution system, factors that affect the rate of bulk water chlorine decay (such as DOC concentration) are not always the same as those that affect the wall chlorine decay (such as velocity and pipe roughness). Due to this level of complexity, methods to calibrate a distribution system water quality model typically do not consider many individual parameters that affect the constituent being modeled. Two published calibration methods utilize genetic algorithms and Bayesian statistics. Application of Genetic Algorithms (GA) to calibrate water quality models have slowly begun to be utilized by researchers over the past few decades. Researchers have found that a GA can successfully calibrate the reaction rate coefficients of a model (Mulligan & Brown, 1998). This eliminates the trial-and-error procedure typically used to determine the bulk and wall decay coefficients for chlorine concentration throughout a water distribution system. A GA is typically not utilized in calibration of water distribution system modeling probably due to the extensive calculations required to perform a GA auto calibration. However, a GA in the form of an inverse model can successfully estimate water quality reaction parameters individually, in groups, or globally to determine the best fit, 13

25 and was recently used to perform an autocalibration of chlorine bulk and wall decay coefficients for a WDS (Munavalli & Mohan Kumar, 2006). The GA performs autocalibration by randomly generating initial values and attempts to find the best solution similar to the natural selection process of genetics (Munavalli & Mohan Kumar, 2006). GANetXL software by the University of Exeter Centre for Water Systems can be integrated into Microsoft Excel 2007 and can be further integrated to perform a GA analysis on reaction coefficients within EPANET software (Bicik, Morley, Keedwell, & Savic, 2009). It is important to note that it cannot be applied to calibration of reaction rate coefficients developed within EPANET-MSX. A GA is utilized in the Darwin Calibrator feature of WaterGEMS (Walski, DeFrank, Voglino, Wood, & Whitman, 2006). The use of a GA within WaterGEMS has been applied to distribution system analysis such as water loss detection (Wu & Sage, 2006). A subset of the standard GA, a fast messy genetic algorithm (fmga), has been applied within Water GEMS as well to perform minimal water quality calibration (Wu, 2005). These examples are all based on a proprietary GA within the WaterGEMS Darwin Calibrator feature. Because of this, the use of the GA is limited to calibration of an overall reaction rate of a single chemical. Recently the use of Bayesian statistics has been applied to estimate bulk and wall reaction rate coefficients for chlorine decay in a distribution system (Huang & McBean, 2008). Bayesian statistical modeling is computationally extensive; however, it can provide enhanced statistical analysis compared to typical regression statistics. This occurs because parameters are considered random variables that follow a prior statistical 14

26 distribution. The background of this probabilistic form of parameter estimation is based on Bayes Theorem as shown in equation 5 for any outcome A and B (Ntzoufras, 2009). (5) In order to identify values in the prior distribution, a Markov Chain Monte Carlo (MCMC) algorithm can be used. A MCMC algorithm can be used to generate values from the posterior distribution for use in the Bayesian analysis (Brooks, 1998). A common subset of the MCMC approach utilized in Bayesian modeling is the Gibbs Sampling algorithm (Ntzoufras, 2002). The complex algorithms utilized in Bayesian statistics have been compiled into the publicly available WinBUGS software, which refers to Bayesian inference Using Gibbs Sampling (BUGS) (Lunn, Thomas, Best, & Spiegelhalter, 2000). Limited research has been published utilizing WinBUGS for estimating chlorine bulk and wall reaction rate coefficients. Of the published research, WinBUGS model code utilized initial chlorine dose, ratio of TOC to chlorine, and a fraction factor of TOC to estimate the chlorine bulk decay rate for a second order model (Huang & McBean, 2007). In order to determine the chlorine wall decay rate, multiple pipe sections were investigated and many water quality parameters were measured at the inlet and outlet of the pipe sections. These parameters included inlet and outlet chlorine concentration, TOC and temperature. This research utilized WinBUGS to estimate a wall decay coefficient specific to each pipe section investigated (Huang & McBean, 2008). While this resulted in a well calibrated model, it is unreasonable to perform this level of investigation on a large WDS. It does show, however, the applicability of using WinBUGS to estimate both chlorine reaction rate coefficients. 15

27 CHAPTER III MODEL DEVELOPMENT 3.1 Initial Steady-State Hydraulic Model Development One of the most important steps in developing a water distribution system model is to select the software package to perform the modeling. Various software packages exist with various calculation engines and features. It is also important to consider the interoperability of the model software with geographic information systems (GIS) applications. With the significant increase in use of GIS by municipalities, many public water systems have geocoded demands directly from water billing that can be input directly into modeling software. The software package utilized for this initial model development was WaterCAD for AutoCAD version 7, build 64. This information is recorded since different versions and even different builds of the same version of software are not always compatible. It is important to note that many modeling programs do have the ability to export the model for input into other software. However, model syntax may differ between programs and minor adjustments to the main modeling file may be required. To initialize the process of completing an IDSE SSS, distribution system data must acquired. Water atlases were obtained and originally used in creating the physical model. Also construction drawings for all new distribution projects and water infrastructure upgrades since the atlases were last updated were obtained and input into 16

28 the model as necessary. Pipe information such as material and Hazen-William s roughness coefficient (C-factor) may not be listed on atlases and is needed for the model. If pipe roughness data is unknown, a field study known as C-factor testing must be performed in order to accurately represent the physical distribution system. Construction drawings of all distribution system storage are needed, specifically for elevated tanks with varying depth-to-volume ratios. Locations of any specialty valves such as pressure reducing valves (PRV) were acquired. The location and number of pumps is needed throughout the WDS. Pump curves for each pump are also input into the model. Once all information and data is obtained the physical attributes of the model are constructed in the modeling software. Following construction of the model elements, information regarding water system demands is required to be input into the model. Water treatment plant production flow rate records, water billing records, large water user specific records and unaccounted for water records are compiled. The total flow into each pressure zone, excluding large users, is divided by the total number of nodes in each pressure zone and the resulting demand is evenly distributed throughout all nodes of pressure zone. Large user demands are applied to the model node nearest to the actual location of the large user. If the PWS has geocoded demands, the demands are overlaid with the distribution system network of pipes and nodes within geographic information systems software. The actual billed demands are then assigned to the nearest node throughout the entire distribution system. This procedure results in increased model accuracy; however, neither city for this research maintains geocoded water demand data at this time. 17

29 Information regarding the typical operations of the system is required to be input into the model. Locations of closed valves in the system are input to determine pressure system boundaries. Operation of specialty valves, such as the pressure setpoint on a PRV, is input into the model. Pump operation including which pumps are lead and which are lag in pumping stations, which pumps are inoperable, and which pumps operate with variable frequency drives (VFDs) and the VFD settings are input into the model. Typical tank level ranges and average tank levels for all distribution system storage tanks are input. All operational information was input and developed as controls that can be changed with differing model scenarios. 3.2 Extended Period Simulation Model Development The existing steady-state models were further developed into extended period simulation (EPS) models. The purpose of the EPS model is to determine water quality parameters throughout the WDS that are spatially and temporally dynamic. The first step in development of an EPS model is to completely review the existing steady-state model and update it due to any water infrastructure improvements not included in the model. This is a critical step because calibration of the model without current system configuration is incorrect and will result in incorrect model output during analysis. Data collection is the basis for the model and is a vital step in creating an effective hydraulic and water quality model. Similar to the process in the steady-state model, water system demands and operation information need to be updated and input into the model. Due to the nature of an EPS model, continuously recording pumping and tank level data need to be obtained for a time period of many days. Most public water systems record their system operations using a supervisory control and data acquisition (SCADA) 18

30 system. From the SCADA data elevated tank water level, water temperature, plant production flow rate, overall system discharge pressures, and specific pressure zone demands and pressures on continuously recording five minute intervals were utilized for model development and calibration. While data collection was simplistic due to the SCADA systems, it is always important to correlate field samples with the SCADA data to ensure the SCADA equipment is recording accurate measurements. This was completed during the field study with installation of multiple hydrant pressure recorders and the SCADA data was determined to be recording at the accuracy desired for this study. The EPS model was considered to be completely developed when a diurnal pattern was applied to all system demands based on the data collected. 3.3 Chlorine Decay Rate Expressions Based on the assumption that chlorine decay due to reactions at the pipe wall is first-order, the overall chlorine decay is represented by equation 6; where C 0 is the chlorine concentration at the point of entry into the distribution system. (Huang & McBean, 2008). (6) However, based on the application of the wall reaction coefficient in the EPANET calculation algorithm, the wall reaction rate coefficient is not a function of time alone. To account for this difference, the wall reaction coefficient input with units of length per time is converted to a wall reaction coefficient in units of inverse time by equation 7. (7) The issue with combining equations 6 and 7 to determine the wall reaction coefficient for inclusion in EPANET is that water distribution systems are comprised of a complex 19

31 network of pipes that vary in diameter. To overcome this issue, it is possible to determine with relative accuracy the typical flow path of water within a well calibrated distribution system model. Based on the typical flow path to any given sample location within the system, it is also possible to determine the fraction of the flow path comprised of each pipe diameter. To model this variation in pipe diameters, a series of additional parameters, f x, which represents the fraction of the flow path length from the point of entry to a sample location through pipe of diameter x inches, is developed for inclusion in equation 7. The series of parameters can be summarized by the general form shown as equation 8 (8) where i is the pipe diameters (in inches) that comprise the distribution system. Expanding equation 8 based on the characteristics of the Norwalk WDS, for example, as shown in Table 4.2 and combining with equation 6, the resulting expression summarizes the bulk and wall chlorine decay from the point of entry into the system, through the distribution system (including storage tanks), and to the field sampling location. (9) The bulk and wall reaction coefficients in equation 9 are each in terms of the proper units for input directly into distribution system modeling software. This equation is applicable to any specific flow path. Therefore, multiple locations within a distribution system with a similar water age but different flow path are not likely to exhibit the same chemical concentration. 20

32 3.4 THM Formation Rate Expressions As discussed, multiple researchers have published THM formation models based on a linear relationship between THM formation and the amount of chlorine consumed. This indicates an overall assumed first-order THM formation with reactions occurring in both the bulk water and along the pipe wall. The reaction is similar to chlorine decay in that the overall reaction is a function of time and flow path. It is generally accepted that water at the point of entry into the distribution system already contains a concentration of DBPs due to the reaction time through the water treatment plant and clearwell to achieve the required concentration times time (CT) for disinfection. This initial concentration of THMs is determined by analysis of the water leaving the clearwell and is represented by [THM] POE in the following overall rate of THM formation shown in equation 10. (10) The parameters Y b and Y w introduced represent the bulk THM yield and wall THM yield, or micrograms of THMs formed per milligram of chlorine consumed, through each pathway. The unique bulk and wall reaction rate coefficients in both equations 9 and 10 are represented on an inverse time basis and the coefficients can be fit to a global value and input directly into the distribution system modeling software. 21

33 CHAPTER IV MODEL RESULTS AND ANALYSIS 4.1 Extended Period Simulation Hydraulic Model Calibration Model accuracy is directly related to the model s level of calibration. The purpose of this hydraulic model is to ultimately aid in the decision of future DBP monitoring locations throughout the system. Due to this, the USEPA has specified calibration levels for multiple model results. These minimum calibration requirements are presented in Table 4.1 and are specific requirements of the Stage 2 D/DBP Rule for an IDSE SSS (USEPA, 2006). Table 4.1 Minimum Model Calibration Requirements per USEPA for utilization in the IDSE SSS. Minimum Calibration Model Element Level WDS total pipe volume 75% WDS total pipe length 50% WDS pressure zones Pipes 12" and larger in diameter Pipes 8" and larger that are known to be significant conveyors of water Storage facilities Active pump stations and controls Active control valves Minimum simulation length All All All All All All 24 hours 22

34 The total percent of pipe volume and length are presented in Tables 4.2 and 4.3 for Norwalk and Barberton, respectively. Both Norwalk and Barberton exceed the total pipe length and volume minimum model requirements. Is important to note the general difference in the proportion of larger diameter pipes to average size pipes for each system, particularly for pipes greater than 12 inches in diameter. Table 4.2 Norwalk WDS modeled versus actual pipe length and volume for the conditions during the IDSE SSS. Length (ft) Volume (ft 3 ) Diameter (inches) Total Not in Model In Model % in Model Total Not in Model In Model % in Model 4 57, , , , , , , , , , , , , , , , , , , , , , , , Total 458, , , , Table 4.3 Barberton WDS modeled versus actual pipe length and volume for the conditions during the IDSE SSS. Diameter (inches) Length (ft) Volume (ft 3 ) Not in Model In Model % in Model Not in Model In Model % in Model Total Total 4 17,454 5,787 11, , , , , , , , , , , , , , , , , , , , , , , , , , , Total 654,471 5, , , ,

35 The next phase of EPS hydraulic model calibration was to calibrate storage tank diurnal level patterns with actual tank level data from the SCADA systems while maintaining a total water balance as well. In order to calibrate the tank level patterns, minor adjustments were made to pipe roughness factors, pump flow rates and diurnal patterns. The tank level calibration results for all storage tanks in each WDS are a required element of the Form 4 and are included in Appendices B and C. Additional model calibration results required by Form 4 are the average water age for all nodes in the model and a graph of the consistently repeatable water age for the tank in the system with the highest average water age. These calibration results are also included in Appendices B and C. Each model was calibrated, based on data during the month of historical peak THM formation in 2007, to exceed the requirements of the USEPA for an IDSE SSS model. A Form 4 was completed and submitted to the USEPA electronically by the October 1, 2007 deadline for each WDS and were both approved for utilization in the selection of Stage 2 D/DBP monitoring locations. The complete Form 4: Modeling Study Plan, including all extended period simulation hydraulic model calibration results, is presented as Appendices B and C for the Norwalk WDS and the Barberton WDS, respectively. 4.2 Field Study and Additional EPS Model Calibration The calibrated EPS hydraulic model was used as the base model for this phase of research. Using this model, an extended period simulation of 504 hours for the Norwalk WDS and 1,000 hours for the Barberton WDS was initiated and water age throughout each system was calculated. The long simulation period is required based on IDSE guidance that all model results must be presented based on water age reaching a stable 24

36 pattern, which will only occur after an extended period and as shown on various figures as a part of the Form 4 submittal. In an effort to be proactive towards the IDSE SSS official sampling the following year, a field study was conducted during the month of peak THM formation in The purpose of the field study was to simulate the official IDSE sampling event and proactively improve distribution system operations to reduce the concentrations of regulated DBPs throughout the WDS. Based on the water age results throughout each system and an overall knowledge of system operations, twelve sample sites were selected for each WDS to represent a range of water ages and geographical regions of the distribution system. With the assistance of the city staff, a field study was conducted on July 31- August 1, 2008 for the Norwalk WDS and on August 29, 2009 for the Barberton WDS. Typical system operations during the field study and no atypical water quality flushing activities immediately prior to the field study was confirmed. Field analysis included free chlorine (Hach Method 8021) and static pressure (recorded by Telog hydrant pressure recorders for the Norwalk WDS only). Field samples were collected and analyzed for TTHM (EPA Method 551.1) and HAA (EPA Method 552.2) speciation and concentration with the assistance of University of Akron laboratory staff. Tables 4.4 and 4.5 present the free chlorine residual and DBP concentration results for the Norwalk WDS and the Barberton WDS, respectively. Tank level data and pump flow rates and pressures for this time period were recorded with a SCADA system provided by each city. The hydrant pressure recorders installed throughout the Norwalk WDS were removed and reviewed for fluxuations in pressure throughout the field study. 25

37 Table 4.4 Free chlorine residual and regulated DBP concentrations at twelve locations throughout the Norwalk WDS sampled during the field study on July 31 and August 1, Cl 2 Haloacetic Acids (μg/l) Trihalomethanes (μg/l) Total DBP Location Date Time (mg/l) MCAA MBAA DCAA TCAA DBAA CHCl 3 BDCM CDBM CHBr 3 HAA 5 THM 4 Water 7/31 9:20a 1.92 BDL BDL BDL BDL Treatment 7/31 10:58a 1.88 Plant 7/31 12:15p 1.92 BDL BDL BDL BDL J-10 7/31 1:18p /31 2:37p 1.83 BDL BDL BDL BDL /1 9:12a 2.01 BDL BDL BDL BDL /1 10:44a /1 12:06p 2.04 BDL BDL BDL BDL Shaker 7/31 9:48a 1.11 BDL BDL BDL BDL Village 7/31 11:11a 1.11 Apts. 7/31 12:21p 1.08 BDL BDL BDL BDL J /31 1:36p /31 2:50p 0.92 BDL BDL BDL BDL /1 9:25a 0.66 BDL BDL BDL BDL /1 10:57a /1 12:17p 1.21 BDL BDL BDL BDL Sanitation 7/31 10:02a 0.00 BDL BDL BDL BDL Building 7/31 11:20a 0.05 J /31 12:33p 0.00 BDL BDL BDL BDL /31 1:46p /31 3:03p 0.00 BDL BDL BDL BDL /1 9:37a 0.02 BDL BDL BDL BDL /1 11:10a /1 12:26p 0.00 BDL BDL BDL BDL Eagle 7/31 10:45a 0.79 BDL BDL BDL BDL Creek 7/31 12:08p 0.89 J /31 1:08p 0.80 BDL BDL BDL BDL /31 2:19p /31 3:38p 0.24 BDL BDL BDL BDL /1 10:30a 0.93 BDL BDL BDL BDL /1 11:52a /1 1:11p 0.94 BDL BDL BDL BDL AAA 7/31 10:30a 1.24 BDL BDL BDL BDL J-550 7/31 11:55a /31 1:00p 1.16 BDL BDL BDL BDL /31 2:10p /31 3:21p 1.06 BDL BDL BDL BDL /1 10:16a 1.20 BDL BDL BDL BDL /1 11:41a /1 12:58p 1.13 BDL BDL BDL BDL Eschen 7/31 10:20a 0.51 BDL BDL BDL BDL Residence 7/31 11:40a 0.62 J-790 7/31 12:47p 0.00 BDL BDL BDL BDL /31 2:00p /31 3:12p 0.46 BDL BDL BDL BDL /1 9:57a 0.66 BDL BDL BDL BDL /1 11:28a /1 12:44p 0.52 BDL BDL BDL BDL Austin's 7/31 12:17p 0.64 BDL BDL BDL BDL Sunoco 7/31 1:19p 0.65 BDL BDL BDL BDL J /31 2:16p

38 Table 4.4 Free chlorine residual and regulated DBP concentrations at twelve locations throughout the Norwalk WDS sampled during the field study on July 31 and August 1, 2008 (Continued). Cl 2 Haloacetic Acids (μg/l) Trihalomethanes (μg/l) Total DBP Location Date Time (mg/l) MCAA MBAA DCAA TCAA DBAA CHCl 3 BDCM CDBM CHBr 3 HAA 5 THM 4 (Continued) 7/31 3:17p 0.59 BDL BDL BDL BDL /1 9:17a 0.45 BDL BDL BDL BDL /1 10:30a /1 11:36a 0.62 BDL BDL BDL BDL County 7/31 12:27p 1.06 BDL BDL BDL BDL Highway 7/31 1:26p 1.09 BDL BDL BDL BDL Garage 7/31 2:22p 0.70 J /31 3:27p 0.87 BDL BDL BDL BDL /1 9:29a 0.80 BDL BDL BDL BDL /1 10:40a /1 11:47a 1.09 BDL BDL BDL BDL Wal-Mart 7/31 11:30a 1.33 BDL BDL BDL BDL J /31 12:35p 1.48 BDL BDL BDL BDL /31 1:44p /31 2:33p 1.36 BDL BDL BDL BDL /1 9:41a 1.34 BDL BDL BDL BDL /1 10:52a /1 12:03p 1.50 BDL BDL BDL BDL WWTP 7/31 11:41a 1.17 BDL BDL BDL BDL J /31 12:49p 1.30 BDL BDL BDL BDL /31 1:52p /31 2:40p 1.19 BDL BDL BDL BDL /1 9:54a 1.11 BDL BDL BDL BDL /1 11:01a /1 12:13p 1.20 BDL BDL BDL BDL Van 7/31 11:55a 1.47 BDL BDL BDL BDL Dresser 7/31 1:00p 1.71 BDL BDL BDL BDL Building 7/31 2:00p 1.68 J /31 2:53p 1.71 BDL BDL BDL BDL /1 10:05a 1.72 BDL BDL BDL BDL /1 11:16a /1 12:28p 1.63 BDL BDL BDL BDL Fire 7/31 12:06p 1.31 BDL BDL BDL BDL Department 7/31 1:10p 1.52 BDL BDL BDL BDL J /31 2:09p /31 3:01p 1.68 BDL BDL BDL BDL /1 10:17a 1.32 BDL BDL BDL BDL /1 11:24a /1 12:40p 1.51 BDL BDL BDL BDL Note: BDL indicates concentration below detection limit. 27

39 Table 4.5 Free chlorine residual and regulated DBP concentrations at twelve locations throughout the Barberton WDS sampled during the field study on August 29, Cl 2 Haloacetic Acids (μg/l) Trihalomethanes (μg/l) Total DBP Location Time (mg/l) MCAA MBAA DCAA TCAA DBAA CHCl 3 BDCM CDBM CHBr 3 HAA 5 THM 4 WTP 8:30a BDL BDL BDL :00a BDL BDL BDL Summit 11:45a BDL BDL BDL :10p BDL BDL BDL Circle K 8:52a BDL BDL BDL :20a BDL BDL BDL Wooster 12:05p BDL BDL BDL :05p BDL BDL BDL Holland Oil 9:05a BDL BDL BDL :33a BDL BDL BDL st 12:40p BDL BDL BDL :50p BDL BDL BDL Circle K 9:20a BDL BDL BDL :45a BDL BDL BDL Wooster 1:03p BDL BDL BDL :20p BDL BDL BDL Holland Oil 9:35a BDL BDL BDL :58a BDL BDL BDL Cleve-Mass 1:32p BDL BDL BDL :37p BDL BDL BDL Arby's 9:45a BDL BDL BDL BDL BDL :08a BDL BDL BDL BDL BDL 15 0 Greenwich 1:50p BDL BDL BDL BDL BDL :53p BDL BDL BDL BDL BDL 17 0 Fa-Ray's 8:35a BDL BDL BDL :23a BDL BDL BDL Wooster 11:44a BDL BDL BDL :30p BDL BDL BDL Fire Station 8:48a BDL BDL BDL :34a BDL BDL BDL West 12:18p BDL BDL BDL :46p BDL BDL BDL Holland Oil 9.05a BDL BDL BDL :46a BDL BDL BDL th 12:27p BDL BDL BDL :50p BDL BDL BDL CVS 9:30a BDL BDL BDL :55a BDL BDL BDL Robinson 12:40p BDL BDL BDL :02p BDL BDL BDL C. C. Supply 9:43a BDL BDL BDL :11a BDL BDL BDL S. Van Buren 12:52p BDL BDL BDL :13p BDL BDL BDL Red Cross 9:59a BDL BDL BDL :23a BDL BDL BDL W. Park 1:07p BDL BDL BDL :34p BDL BDL BDL Note: BDL indicates concentration below detection limit. 28

40 As previously stated, usefulness of a model is directly related to its level of calibration. Therefore both models were further calibrated to the WDS conditions at the time of the field studies. Calibration activities were similar to what was performed during the initial EPS hydraulic model calibration for each model and is summarized as follows: 1. Calibration of the model to measured flow rates for the high service pumps at each Water Treatment Plant: Adjustment to overall system demand and diurnal flow pattern were used as calibration parameters. The base diurnal pattern was determined for each pressure zone by adding flow into the zone and subtracting flow in or out of the storage tank in each zone for each 30 minute time step. Finally, calculation of a demand peaking factor based on the water usage during each time step compared to the total water usage over a 24 hour period resulted in a peaking factor for each time step. 2. Calibration of the model to measured storage tank water levels: Adjustments to overall system demands and diurnal patterns were used as calibration parameters. 3. Calibration of the model to measured static pressures: Adjustments to overall pressure zone demands were used as the calibration parameter. Due to the additional field data and subsequent model calibration, the WDS model results were no longer what was submitted to and approve by the USEPA. As a result of the model deviation, model calibration was required to be proven again through submission of model results similar to what was required with Form 4. This is the last stage of hydraulic model calibration performed in the research and the current state of the 29

41 hydraulic models. For this reason, model calibration results are presented herein. To simulate the dynamic nature of the water system, the diurnal flow pattern was adjusted and is presented as Figures 4.1 and 4.2 for the Norwalk WDS and the Barberton WDS, respectively. Figures 4.3 and 4.4 present the storage tank level modeled versus measured data for both systems. The Norwalk tank level modeled values show an average absolute difference of 1.6 feet and a maximum difference of 2.0 feet. The Barberton tank level modeled values show an average absolute difference of 1.4 feet with a maximum difference of 4.4 feet. A model should accurately predict tank levels to within three to six feet (Walski, Chase, Savic, Grayman, Beckwith, & Koelle, 2003). The models are clearly simulating the dynamic nature of the storage. A model simulating water age begins with a water age of zero throughout the distribution system and tanks. To avoid model analysis error that occurs at points in the system where the water age is not consistent, a figure of the water age at the storage tank with the highest residence time is required to be submitted to the USEPA (USEPA, 2006). Figures 4.5 and 4.6 present the stable, repeatable water age pattern for the tank with the highest water age in both the Norwalk WDS and the Barberton WDS, respectively. All analysis of model results were based on the last 24 to 48 hours of model simulation during which a consistent, repeatable water age exists. 30

42 Base Demand Multiplier Average = Figure 4.1 Simulation Time (Hours) Calculated diurnal demand pattern for the Norwalk WDS Base Demand Multiplier Average = Figure 4.2 Simulation Time (Hours) Calculated diurnal demand pattern for the Barberton WDS. 31

43 k Tank Measured 400k Tank Measured Model Results Tank Level (ft.) :00 4:00 8:00 12:00 16:00 20:00 0:00 Time (hrs.) Figure 4.3 Tank level calibration for all storage tanks in the Norwalk WDS based on data obtained during the field study on July 31 and August 1, Tank Level (ft.) Figure Model Results Summit Road (T1) 20 Eastern Road (T2) Bowers Hill (T3) East Side (T4) 0 00:00 04:00 08:00 12:00 16:00 20:00 00:00 Time (hrs.) Tank level calibration for all storage tanks in the Barberton WDS based on data obtained during the field study on August 29,

44 Tank Water Age (hrs.) Figure Model Simulation Time (hrs.) Tank water age for the tank with the highest modeled average water age (750k Tank) in the Norwalk WDS Tank Water Age (hrs.) Figure Model Simulation Time (hrs.) Tank water age for the tank with the highest modeled average water age (Tank T2) in the Barberton WDS. 33

45 4.3 Stage 2 D/DBP Monitoring Site Selection The model calibration results presented in the previous section indicate a level of calibration acceptable based on multiple references. The models updated and calibrated for this research were then utilized for selection of Stage 2 D/DBP monitoring locations based on a selection procedure outlined by the USEPA (USEPA, 2006). A Form 5: IDSE Report for a Modeling SSS was completed for each WDS. The Form 5 includes the official IDSE sampling results for a modeling SSS, justification for Stage 2 D/DBP monitoring site selection, and model calibration information. A draft Form 5 was completed and is included in Appendices B and C for the Norwalk WDS and the Barberton WDS, respectively. 4.4 Water Quality Model Parameter Estimation Data obtained during the field study was used to estimate chlorine decay and THM formation rate coefficients. The objective was to determine the effect of the variation in flow path fractions on concentrations. As shown in Table 4.5, free chlorine residuals at multiple sites within the Barberton WDS consistently reported values near zero milligrams per liter. With this analysis that compares water age to chlorine residual, significant error would result if the data from these sample locations were included in the parameter estimation. As a result, any site with an average free chlorine residual less than 0.0 mg/l was excluded from the data set utilized for parameter estimation. Overall first-order flow path specific chlorine decay and THM formation coefficients were input into WinBUGS software. This software utilizes Bayesian statistics to converge on a parameter value based on input data and a prior distribution of 34

46 the parameter. Equations 9 and 10 developed in the previous chapter were input into WinBUGS along with the following data: 1. Free chlorine residuals (Norwalk and Barberton) and THM concentrations (Norwalk) for multiple times of day at each sample location as presented in Tables 4.4 and Estimated water age output from the calibrated EPS hydraulic model corresponding to the specific sample times at each sample location. 3. The flow path diameter fractions, which were unique to each sample location but constant over time. The initial simulation length for all WinBUGS estimation models was set at 10,000 samples with three chains. Each chain represents a unique set of initial conditions from with the parameter estimation begins. The initial samples are considered the burnin period and are eliminated to avoid influence of the final parameter estimates (Ntzoufras, 2009). Each model was run for another 20,000 samples and the posterior convergence data was obtained from the last 10,000 samples. Due to the limited number of sites and overall sense of reliability of the chlorine residual data for the Barberton WDS, coupled with the chlorine and THM model interdependency, THM formation parameter estimation for the Barberton WDS was excluded from this research. While complex software exists to analyze WinBUGS output for convergence, the process of verifying simulation convergence can be assumed when multiple chains of the model appear overlapping and in close proximity. If individual model chains are visibly separate, then the model is not converging. Both chlorine models and the THM model do show clear posterior convergence as presented in Figures 4.7 through 4.9, respectively. 35

47 Figure 4.7 Free chlorine decay model posterior convergence with a 30,000 sample simulation based on field study results from the Norwalk WDS, N=80. Figure 4.8 Free chlorine decay model posterior convergence with a 30,000 sample simulation based on field study results from the Barberton WDS, N=14. 36

48 Figure 4.9 THM formation model posterior convergence with a 30,000 sample simulation based on field study results from the Norwalk WDS, N=54. Tables 4.6 through 4.8 present the bulk and wall coefficient results for chlorine decay within the Norwalk and Barberton systems and THM formation within the Norwalk system. The complete model syntax for each parameter estimation model is included as Appendix A. The water age data that WinBUGS referenced was in units of hours, therefore the parameter units of time are hours. An important measure of convergence is the Monte Carlo (MC) error reported in the following tables. A smaller value for the MC error compared to the posterior standard deviation of the sample set infers an increasingly precise estimation. The mean value presented in the following tables is the estimated value for each parameter. The bulk reaction rate coefficients are presented in units of inverse hours and the wall reaction rate coefficients are presented in units of feet per hour. 37

49 Table 4.6 Bulk and wall coefficient posterior statistics for overall first-order free chlorine decay within the Norwalk WDS. Parameter Mean St. Dev. MC error 2.50% Median 97.50% Samples k b E E ,000 k w E ,000 Table 4.7 Bulk and wall coefficient posterior statistics for overall first-order free chlorine decay within the Barberton WDS. Parameter Mean St. Dev. MC error 2.50% Median 97.50% Samples k b E ,000 k w E E ,000 Table 4.8 Bulk and wall yield posterior statistics for THM formation within the Norwalk WDS. Parameter Mean St. Dev. MC error 2.50% Median 97.50% Samples Y b ,000 Y w ,000 Based on the convergence of the WinBUGS model, the global bulk and wall coefficients for chlorine decay and THM formation are estimated for input into distribution system modeling software and are summarized in Table 4.9. The wall decay rate coefficient reported in units of inverse time is based on an average of the flow path fraction parameters to each of the sampling locations. The value is reported for comparison to published values and is not input into distribution system modeling software. 38

50 Table 4.9 Estimated first-order bulk and wall reaction coefficients for chlorine decay. Distribution System Model k b (d -1 ) k w (ft/d) k w (d -1 ) Norwalk Chlorine Decay Barberton Chlorine Decay In addition to the results presented in the previous tables, a parameter estimation model was run to determine an overall THM formation reaction rate coefficient for comparison to previously reported THM yields. The model converged with a mean of 19.67, a standard deviation of and a MC error of The overall THM formation yield of µg/mg is low compared to published values. However, the point of entry THM concentration of 48.5 µg/l is higher than in previously published THM formation yield models and explains the low overall yield value. 4.5 Water Quality Model Results The reaction rate coefficients presented in the previous section were input into EPANET and multiple water quality simulations were run. Figures 4.10 through 4.13 show the correlation between measured values obtained during the field study and modeled values for free chlorine residual. Both Norwalk and Barberton show a good correlation between measured and modeled values. The correlation coefficient for measured versus modeled values was 0.64 when estimating and modeling a first-order overall decay reaction rate coefficient for the Norwalk WDS as shown on Figure When estimating and modeling both and bulk and wall reaction rate coefficients the correlation coefficient increases to 0.72 as shown on Figure

51 2.5 r ² % confidence intervals 2.0 Modeled (mg/l) Figure 4.10 Measured (mg/l) Cl 2 residual measured versus modeled concentration based on an overall decay rate of K TOT =1.07d -1 for the Norwalk WDS, N= r ² % confidence intervals 2.0 Modeled (mg/l) Measured (mg/l) Figure 4.11 Cl 2 residual measured versus modeled concentration based on estimated values k b =-0.31 d -1 and k w =-0.19 ft/d for the Norwalk WDS, N=80. 40

52 r ² % interval 1.4 Modeled (mg/l) Figure Measured (mg/l) Cl 2 residual measured versus modeled concentration based on estimated values k b =-1.38 d -1 and k w = ft/d for the Barberton WDS, N=14. The overall THM formation yield was µg/mg as presented in the previous section. This indicates that an estimated µg are formed per unit mg of chlorine consumed. From the calibrated chlorine decay model, the specific amounts of chlorine demanded in the bulk phase and along the pipe wall can be determined at any location in the distribution system. This data was input into WinBUGS to estimate a THM yield specific to the bulk and wall chlorine decay and the results were presented in Table 4.8. The results show that a significantly higher THM yield occurs along the pipe wall than within the bulk water. This finding may explain why previous researchers have found a higher overall THM formation in a distribution system versus bottle tests under the same conditions (Rossman, Brown, Singer, & Nuckols, 2001). Figures 4.13 through 4.16 show 41

53 the proportion of bulk and wall reactions for chlorine decay and THM formation. The variation in chlorine decay in the bulk phase is mainly due to the increase in modeled water age; however, the variation in wall decay is due mainly to the pipe characteristics in the flow path to each sampling location. The flow path diameter fractions for each sample location are presented in Table The main source of variation shown on Figure 4.16 is the amount of THM formation due to reactions along the pipe wall. In this figure, the amount of THM formation due to bulk reactions shows little variation. This is because the bulk formation shown at each sampling location includes a constant 48.4 µg/l for the THM concentration measured at the point of entry. 42

54 Chlorine Consumed (mg/l) Bulk Cl 2 Consumption Wall Cl 2 Consumption 0.0 J-3850 J-2460 J-550 J-4550 J-790 J-3180 J-7300 J-4200 J-7070 J-4820 J-7750 Figure 4.13 THM Formation (µg/l) 100 Sample Location (Specific Flow Path) Cl 2 bulk and wall consumption based on estimated values k b =-0.31 d -1 and k w =-0.19 ft/d for the Norwalk WDS, sorted by lowest to highest water age J-3850 J-2460 J-550 J-4550 J-790 J-3180 J-7300 J-4200 J-7070 J-4820 J-7750 Bulk THM Formation Wall THM Formation [THM] POE = Sample Location (Specific Flow Path) Figure 4.14 THM bulk and wall formation based on the estimated yields of Y b =5.7 µg/mg and Y w =32.32 µg/mg for the Norwalk WDS. 43

55 Chlorine Consumption (%) Bulk Consumption Wall Consumption 0 Sample Location (Specific Flow Path) Cl 2 bulk and wall consumption percentage based on estimated values k b =-0.31 d-1 and k w =-0.19 ft/d for the Norwalk WDS Bulk THM Formation Wall THM Formation J-3850 J-2460 J-550 J-4550 J-790 J-3180 J-7300 J-4200 J-7070 J-4820 J-7750 J-3850 J-2460 J-550 J-4550 J-790 J-3180 J-7300 J-4200 J-7070 J-4820 J-7750 Figure 4.15 THM Formation (%) Sample Location (Specific Flow Path) Figure 4.16 THM bulk and wall formation percentage based on the estimated yields of Y b =5.7 µg/mg and Y w =32.32 µg/mg for the Norwalk WDS. 44

56 Table 4.10 Flow path diameter fractions and a wall decay rate converted to inverse time for each sampling location within the Norwalk WDS. Sample k w Flow Path Diameter Fractions Location (d -1 ) f 6 f 8 f 12 f 16 f 18 Shaker Village (J-7300) Sanitation Building (J-7750) Eagle Creek (J-4820) AAA (J-550) Eschen Residence (J-790) Austin's Sunoco (J-4550) Co. Highway Garage (J-4200) Wal-Mart (J-7070) WWTP (J-3180) Van Dresser Bldg (J-2460) Fire Department (J-3850) As a final analysis of results, a simplistic sensitivity analysis was performed on the Norwalk WDS chlorine decay estimated parameters. This was performed by first maintaining a constant wall rate coefficient and running EPANET scenarios using the lower (2.5%) and upper (97.5%) confidence interval values for the bulk rate coefficient as previously presented in Table 4.6. The bulk rate coefficient was then maintained constant at the mean value and multiple scenarios were run in EPANET using the lower and upper confidence interval values for the wall rate coefficient. The results are presented in Table The results indicate a greater variation in modeled chlorine residual with adjustment to the bulk reaction rate coefficient than with the wall reaction rate coefficient. 45

57 Table 4.11 Results of parameter variation and the average absolute difference between measured and modeled values for each scenario. Average Difference Scenario (mg/l) Mean % k b % k b % k w % k w

58 CHAPTER V SUMMARY AND RECOMMENDATIONS 5.1 Summary The overall objective of this research was to develop and calibrate a model of the water distribution systems for the Cities of Norwalk and Barberton in Northern Ohio as a tool for predicting free chlorine decay and THM formation throughout the distribution system. As previously stated, this overall objective was completed through the following tasks: (1) Develop, plan, and conduct an IDSE for the Stage 2 D/DBP Rule compliance for each system; (2) conduct an extensive field study and further calibrate the extended period simulation hydraulic model; and (3) develop a protocol for the estimation of global bulk and wall reaction rate coefficients based on results from field studies conducted with this research and by application of Bayesian inference Using Gibbs Sampling (BUGS). As a result of this research the following findings and conclusions are developed for each distribution system model: 1. An extended period simulation was developed and updated to the system conditions and parameters during the time of calibration. This hydraulic model was calibrated to exceed the minimum calibration requirements required by the USEPA for use in the IDSE SSS. A Form 4: Modeling Study Plan was submitted and approved for each distribution system by the USEPA. 47

59 2. A field study was planned and conducted to gain additional data and insight on system operations during summer The EPS model was further calibrated based on the results of the field study to maintain an up-to-date and accurate model for distribution system analysis. The updated model was utilized to select Stage 2 D/DBP future monitoring locations and will be submitted to the USEPA for approval by January 1, 2010 by each system. A draft Form 5: ISDE Report for a Modeling SSS was completed for each system. 3. An overall first-order reaction expression for chlorine decay was expanded upon to more accurately estimate the specific bulk and wall chlorine consumption. The expression was developed and can be analyzed with standard distribution system chlorine sampling data. There is no requirement for additional water quality characteristics to be analyzed that are typically required with other first-order and second-order chlorine decay models. 4. An overall first-order THM formation model based on a yield parameter and chlorine consumption was expanded to estimate the specific bulk and wall THM formation. The model was developed using THM concentrations obtained during the field study and is dependent on the chlorine consumption, therefore as minor changes are made to the distribution system, calibration with standard chlorine residual sampling is utilized and no additional samples are required to be collected and analyzed. 5. Flow path diameter fractions were determined for each sampling location within each distribution system. These flow path diameter fractions account 48

60 for samples within a distribution system that have the same water age but different concentrations due to changes in the pipe wall surface area to bulk volume ratio through each pipe. 6. Water age model output from the well calibrated model was combined with free chlorine residual concentrations and THM concentrations obtained during the field study. This information, along with the flow path diameter fractions, was input into WinBUGS software to provide a probabilistic estimate of reaction rate coefficients for the bulk and wall reactions for both chlorine decay and THM formation. 7. The estimated reaction rate coefficients were input into EPANET modeling software. Good correlation was found between measured chlorine residuals and modeled values for both distribution systems. Flow path specific expressions better predicted actual data from the field study. The wall THM yield was estimated to be 3 to 10 times greater than the bulk yield. 8. A sensitivity analysis found that changes to the bulk reaction rate coefficient have a greater impact on modeled sample results than changes to the wall reaction rate coefficient. 5.2 Recommendations The safety and reliability of quality drinking water on tap, along with necessary consumer confidence, is a continual process. As more stringent regulations are promulgated and as new DBPs are discovered in distribution systems that may have adverse human health effects, distribution systems will be faced with the continuous and 49

61 ever-changing challenge of improving drinking water quality. Based on the outcomes and conclusions of this research, the following recommendations are offered: 1. Continue to update and calibrate the distribution system models as necessary for use as a tool in water infrastructure decisions. 2. Continue to apply the flow-path specific approach to additional DBPs and other contaminants as they become known. Further develop the simplistic approach for modeling THMs and expand to HAAs. As more complex reaction kinetics are found, develop multiple simplistic flow-path specific models for chemical fate. 3. Develop an algorithm for use in distribution system modeling software such as EPANET or WaterGEMS to determine a general flow path from the point of entry into the distribution system to any specific junction. From the flow path, calculate the total length of pipe and each flow path diameter fraction for use in the expressions developed herein. Based on the analysis efforts completed with this research, the following water quality model calibration protocol is recommended: 1. Utilize calibrated hydraulic model to predict water age (hours) at each sample location/time and DBP data collected during the IDSE sampling event. 2. Obtain historical chlorine residual information from existing sampling requirements to estimate the chlorine residual at each IDSE sample location during DBP sampling. 3. Analyze model to determine flow path to each sample location and the flow path diameter fractions 50

62 4. Install freely available WinBUGS software and run the model using model syntax and input data formats developed herein. 5. Run 10,000 simulations, verify appearance of convergence, run an additional 20,000 simulations, obtain posterior statistics from simulation 20,000 to 30, Multiply mean rate coefficients by 24 and input into distribution system modeling software 51

63 BIBLIOGRAPHY Alexander, M. T., Boccelli, D. L., & Kupferle, M. J. (2009). Field-Scale Assessment of a Multispecies Water Quality System: A Chloramination Study. Department of Civil and Environmental Engineering, University of Cincinnatti. Baribeau, H., Boulos, L., Haileselassie, H., Crozes, G., Singer, P. C., Nichols, C., et al. (2006). Formation and Decay of Disinfection By-Products in the Distribution System. Denver: AWWA Research Foundation & USEPA. Baribeau, H., Boulos, L., Haileselassie, H., Singer, P., Nichols, C., Schlesinger, S., et al. (2004). Formation and Decay of THMs and HAAs in Five Full-Scale Distribution Systems. AWWA Water Quality Technology Conference, (pp. 1-16). San Antonio, Texas. Bicik, J., Morley, M. S., Keedwell, E. C., & Savic, D. A. (2009, February 7). University of Exeter Centre for Water Systems. Retrieved July 2, 2009, from GANetXL: Boccelli, D. L., Tryby, M. E., Uber, J. G., & Summers, R. S. (2003). A reactive species model for chlorine decay and THM formation under rechlorination conditions. Water Research, 37, Brooks, S. B. (1998). Markov chain Monte Carlo method and its application. The Statistician, 47 (1), Chen, W. J., & Weisel, C. P. (1998). Halogenated DBP concentrations in a distribution system. Journal AWWA, 90 (4), Chowdhury, S., Champagne, P., & McLellan, P. J. (2009). Models for predicting disinfection byproduct (DBP) formation in drinking waters: A chronological review. Science of the Total Environment, 407, Chowdhury, Z. K., Passantino, L., Summers, R. S., Work, L., Smith, N., Rossman, L., et al. (2006). Assessment of Chloramine and Chlorine Residual Decay in the Distribution System. Denver: AWWA Research Foundation. Chun, C. L., Hozalski, R. M., & Arnold, W. A. (2007). Degradation of Disinfection Byproducts by Carbonate Green Rust. Environmental Science and Technology, 41 (5),

64 Chun, C. L., Hozalski, R. M., & Arnold, W. A. (2005). Degradation of Drinking Water Disinfection Byproducts by Synthetic Goethite and Magnetite. Environmental Science and Technology, 39 (21), Chun, C. L., Penn, R. L., & Arnold, W. A. (2006). Kinetic and Microscopic Studies of Reductive Transformations of Organic Contaminants on Goethite. Environmental Science and Technology, 40 (10), Clark, R. M. (1998, January). Chlorine Demand and TTHM Formation Kinetics: A Second-Order Model. Journal of Environmental Engineering, Cooper, J. P. (2008). City of Norwalk, Ohio Hydraulic and Water Quality Model. Unpublished Undergraduate Research, The University of Akron. Cooper, J. P. (2009). QUAL2K Modeling of Dissolved Organic Carbon within the Upper Wolf Creek Watershed. Unpublished Coursework, The University of Akron, 4300: Escobar, I. C., Randall, A. A., & Taylor, J. S. (2001). Bacterial Growth in Distribution Systems: Effect of Assimilable Organic Carbon and Biodegradable Dissolved Organic Carbon. Environmental Science and Technology, 35 (17), Hua, G., & Reckhow, D. A. (2008). DBP formation during chlorination and chloramination: Effect of reaction time, ph, dosage, and temperature. Journal AWWA, 100 (8), Huang, J. J., & McBean, E. A. (2008). Using Bayesian Statistics to Estimate Chlorine Wall Decay Coefficients for Water Supply System. ASCE Journal of Water Resources Planning and Management, Lee, J.-Y., Pearson, C. R., Hozalski, R. M., & Arnold, W. A. (2008). Degradation of trichloronitromethane by iron water main corrosion products. Water Research, 42, Lunn, D. J., Thomas, A., Best, N., & Spiegelhalter, D. (2000). WinBUGS -- a Bayesian modelling framework: concepts, structure, and extensibility. Statistics and Computing, 10, McClellan, J. N., Reckhow, D. A., Tobiason, J. E., Edzwald, J. K., & Smith, D. B. (2000). A Comprehensive Kinetic Model for Chlorine Decay and Chlorination By- Product Formation. In S. E. Barrett, S. W. Krasner, & G. L. Amy, Natural Organic Matter and Disinfection Byproducts: Characterization and Control in Drinking Water. Washington, DC: American Chemical Society. Mulligan, A. E., & Brown, L. C. (1998). Genetic Algorithms for Calibrating Water Quality Models. ASCE Journal of Environmental Engineering, 124 (3),

65 Munavalli, G. R., & Mohan Kumar, M. S. (2006). Autocalibration of a water distribution system model for water quality parameters using GA. Journal AWWA, 98 (9), Nieuwenhuijsen, M., Toledano, M., Eaton, N., Fawell, J., & Elliott, P. (2000). Chlorination disinfection byproducts in water and their association with adverse reproductive outcomes: a review. Occupational and Environmental Medicine, 57 (2), Ntzoufras, I. (2009). Bayesian Modeling Using WinBUGS. Hoboken, New Jersey: John Wiley & Sons, Inc. Ntzoufras, I. (2002). Gibbs Variable Selection Using BUGS. Journal of Statistical Software, 7 (7), Pope, P. G., Speitel Jr, G. E., & Collins, M. R. (2006). Kinetics of dihaloacetic acid formation during chloramination. Journal AWWA, 98 (11), Rodriguez, M. J., & Serodes, J. (2005). Laboratory-scale chlorination to estimate the levels of halogenated DBPs in full-scale distribution systems. Environmental Monitoring and Assessment, 110, Rodriguez, M. J., Serodes, J., & Roy, D. (2007). Formation and fate of haloacetic acids (HAAs) within the water treatment plant. Water Research, 41, Rossman, L. A. (2000). EPANET 2 Users Manual. National Risk Management Research Laboratory, Office of Research and Development. Cincinnati: USEPA. Rossman, L. A., Brown, R. A., Singer, P. C., & Nuckols, J. R. (2001). DBP formation kinetics in a simulated distribution system. Water Research, 35 (14), Serodes, J. B., Rodriguez, M. J., Li, H., & Bouchard, C. (2003). Occurrence of THMs and HAAs in experimental chlorinated waters of the Quebec City area (Canada). Chemosphere, 51, Shang, F., Uber, J. G., & Rossman, L. A. (2008). EPANET Multi-Species Extension User's Manual. USEPA, National Risk Managment Research Laboratory, Cincinnati, OH. Shang, F., Uber, J. G., & Rossman, L. A. (2008). Modeling Reaction and Transport of Multiple Species in Water Distribution Systems. Environmental Science and Technology, 42 (3), U.S. Census Bureau. (2009). Barberton, Ohio Fact Sheet. Retrieved September 13, 2009, from American Fact Finder: ocontext=01000us 04000US US

66 U.S. Census Bureau. (2009). Norwalk, Ohio Fact Sheet. Retrieved September 13, 2009, from American Fact Finder: &_street=&_county=norwalk&_citytown=norwalk&_state=04000us39 USEPA. (2006). Initial Distribution System Evaluation Guidance Manual for the Final Stage 2 Disinfectants and Disinfection Byproducts Rule. EPA 815-B USEPA. (1998, December). Stage 1 Disinfectants and Disinfection Byproducts Rule. Fact Sheet. EPA 815-F USEPA. (2005, December). Stage 2 Disinfectants and Disinfection Byproducts Rule. Fact Sheet. EPA 815-F Walski, T. M., DeFrank, N., Voglino, T., Wood, R., & Whitman, B. E. (2006). Determining the accuracy of automated calibration of pipe network models. ASCE 8th Annual International Symposium of Water Distribution Systems Analysis. Cincinnati, Ohio. Westerhoff, P., Reckhow, D., Amy, G., & Chowdhury, Z. (2002). Mechanistic-based Disinfectant and Disinfection By-Product Models. Final Report, USEPA. Wu, Z. Y. (2005). Water Quality Model Calibration by Means of Fast Messy Genetic Algorithm. Proceedings of the World Water & Environmental Congress. Anchorage, Alaska. Wu, Z. Y., & Sage, P. (2006). Water loss detection via genetic algorithm optimizationbased model calibration. ASCE 8th Annual International Symposium on Water Distribution System Analysis. Cincinnati. 55

67 APPENDICES 56

68 APPENDIX A WINBUGS PARAMETER ESTIMATION MODEL The purpose of this appendix is to provide the syntax and content of the model for the chlorine decay and THM formation bulk and wall reaction coefficients estimation. The first model was evaluated to estimate the free chlorine decay coefficients for the Norwalk WDS. The second model was evaluated to estimate the free chlorine decay coefficients for the Barberton WDS. The third model was evaluated to estimate an overall THM formation parameter for the Norwalk WDS. The fourth and final model was evaluated to estimate bulk and wall THM formation parameters for the Norwalk WDS. MODEL # Chlorine decay model using first-order decay for bulk and wall flow-path specific, running with 3 chains for Norwalk WDS model { # model's likelihood for (i in 1:N) { mu[i]<-(co[i]*exp(-kb*t[i])*exp(-kw*(12*f4[i]+8*f6[i]+6*f8[i]+4*f12[i]+3*f16[i]+(8/3)*f18[i])*t[i])); Y[i]~dnorm (mu[i], tau); } # prior distributions kb~dnorm (0.01, 0.01)I(0,); kw~dnorm (0.001, 0.01)I(0,); tau~dgamma(0.001, 0.001); # definition of sigma sigma<-1.0/sqrt(tau); } INITS list(kb=0.015, kw=0.01, tau=0.01) # chain 1 list(kb=0.001, kw=0.005, tau=0.01) # chain 2 list(kb=0.1, kw=0.001, tau=0.01) # chain 3 57

69 DATA # Norwalk Data list(n = 80) t[] Y[] Co[] f4[] f6[] f8[] f12[] f16[] f18[]

70 END MODEL # Chlorine decay model using first-order decay for bulk and wall flow-path specific, running with 3 chains for Barberton WDS model { # model's likelihood for (i in 1:N) { mu[i]<-(co[i]*exp(-kb*t[i])*exp(-kw*(8*f6[i]+6*f8[i]+4*f12[i]+3*f16[i]+2*f24[i])*t[i])); Y[i]~dnorm (mu[i], tau); } # prior distributions kb~dnorm (0.01, 0.01)I(0,); kw~dnorm (0.001, 0.01)I(0,); tau~dgamma(0.001, 0.001); # definition of sigma sigma<-1.0/sqrt(tau); } INITS list(kb=0.015, kw=0.01, tau=0.01) # chain 1 list(kb=0.001, kw=0.005, tau=0.01) # chain 2 59

71 list(kb=0.1, kw=0.001, tau=0.01) # chain 3 DATA # Barberton Data list(n = 14) t[] Y[] Co[] f6[] f8[] f12[] f16[] f24[] END MODEL # THM formation model using sum of Cl2 demand from bulk and wall flow-path specific, running with 3 chains, estimating overall THM formation per unit of Cl2 demand yield parameter model { # model's likelihood for (i in 1:N) { mu[i]<-(ytot*(cl2[i])+poe[i]); Y[i]~dnorm (mu[i], tau); } # prior distributions Ytot~dnorm (35, 0.001)I(0,); tau~dgamma(0.01, 0.01); # definition of sigma sigma<-1.0/sqrt(tau); } INITS list(ytot=25, tau=0.01) # chain 1 list(ytot=10, tau=0.01) # chain 2 list(ytot=50, tau=0.01) # chain 3 DATA # Norwalk Data list(n = 54) Y[] POE[] Cl2[]

72 END MODEL 61

73 # THM formation model using Cl2 decay for bulk and wall flow-path specific, running with 3 chains, estimating bulk and wall THM formation per unit Cl2 bulk and wall demand yield parameter model { #model's likelihood for (i in 1:N) { mu[i]<-(yb*(cl2b[i])+yw*(cl2w[i])+poe[i]); Y[i]~dnorm (mu[i], tau); } #prior distributions Yb~dnorm (10, 0.001)I(0,); Yw~dnorm (10, 0.001)I(0,); tau~dgamma(0.01, 0.01); # definition of sigma sigma<-1.0/sqrt(tau); } INITS list(yb=5, Yw=15, tau=0.01) # chain 1 list(yb=30, Yw=2, tau=0.01) # chain 2 list(yb=10, Yw=10, tau=0.01) # chain 3 DATA # Norwalk Data list(n = 54) Y[] POE[] Cl2b[] Cl2w[]

74 END 63

75 APPENDIX B FORM 4 AND FORM 5 FOR THE NORWALK WDS 64

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122 APPENDIX C FORM 4 AND FORM 5 FOR THE BARBERTON WDS 111

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