WATER QUALITY DESIGN STORMS FOR STORMWATER HYDRODYNAMIC SEPARATORS

Transcription

1 WATER QUALITY DESIGN STORMS FOR STORMWATER HYDRODYNAMIC SEPARATORS By VICTORIA JULIA FERNANDEZ MARTINEZ A thesis submitted to the Graduate School New Brunswick Rutgers, The State University of New Jersey In partial fulfillment of the requirements For the degree of Master of Science Graduate Program in Civil and Environmental Engineering Written under the direction of Professor Qizhong Guo And approved by New Brunswick, New Jersey May, 2008

2 ABSTRACT OF THE THESIS Water Quality Design Storms for Stormwater Hydrodynamic Separators By Victoria Julia Fernandez Martinez Thesis Director: Professor Qizhong Guo Stormwater runoff has become an important source of non-point pollution for surface waters. Management practices such as the installation of hydrodynamic separators are implemented in order to treat the stormwater runoff. In New Jersey, the efficiency of these devices is verified and certified by NJCAT and NJDEP, respectively. When the measure of the device performance is based on laboratory data, NJCAT calculates the average annual removal efficiency using weighting factors. The weighting factors were developed by NJDEP based on the average annual distribution of runoff volumes and the assumed similarity with the distribution of runoff peak flow rates. In this study, 10 years of precipitation records were used to evaluate the frequency distributions of the runoff volumes as well as the runoff peak flow rates. USEPA s SWMM model was used to simulate the runoff events produced by the given precipitation events. From the model results, three sets of weighting factors were determined. The first was based on the runoff volume frequency distribution expressed as percentage of the volume produced by the water quality design storm (uniform or non-uniform). The second was based on the runoff peak flow rates frequency distribution expressed as percentage of the peak flow rate generated by the uniformly distributed water quality design ii

3 storm. The third was also based on the runoff peak flow rates frequency distribution, but expressed as percentage of the peak flow generated by the non-uniformly distributed water quality design storm. The results indicate that the weighting factors, based on the peak runoff rates generated from the uniform design storm, are the closest to the NJDEP weighting factors. The impact of two different water quality design storms, uniform vs. nonuniform distribution, on the sizing of a certified hydrodynamic separator was also evaluated. One sets the design flow rate of the device equal to the peak flow rate generated by the uniform design storm. The other sets the design flow rate of the device equal to the peak flow rate generated by the non-uniform design storm. USEPA s SWMM model was used to continuously simulate solids loading to the treatment device. The lab-measured relationship between flow rate and removal efficiency was used to specify the removal rate of solids within the storm event. The results indicate that sizing with the uniform design storm would achieve a removal efficiency close to the one verified but that sizing with the non-uniform storm would achieve a removal efficiency significantly higher than the one verified. It is concluded that setting design flow rate equal to the peak flow rate generated by the uniform water quality design storm is more consistent with use of the existing weight factors in calculating the average annual solids removal efficiency. Use of the non-uniform water quality design storm is too conservative. iii

4 Acknowledgements I would like to acknowledge and express my sincere gratitude to my research advisor Dr. Qizhong Guo for his support, guidance and instruction not only throughout the course of this research, but also through my academic journey as Masters student at Rutgers University. I am also grateful to the other members of my review committee: Professor Steven Medlar and Professor Yook-Kong Yong. I thank the Fulbright Program for supporting and financing my studies in the United States; and for offering me a valuable experience that not only extended my academic knowledge but also broadened my professional, intercultural and life perspectives. I would like to thank Mrs. Connie Dellamura for her support and kind advices through my academic experience in Rutgers. I am very grateful to my friends Andres Quiroz, Steffen Kahle, Raul Hernandez, Denitsa Tilkidjeva, Jim Jeffers, Litza Vera and Laura Silva for their constant support, advice, encouragement, help, good wishes and friendship. I thank Gerardo Bombin for his love, understanding and patience. I would like to extend special thanks to my parents who have always been my mentors and my support, regardless of the far distance. iv

5 Dedication To my parents: my mentors, my support To Gerardo: my friend, my love v

6 Table of Contents Abstract... ii Acknowledgements... iv Dedication... v List of Tables... ix List of Figures... xii 1. Introduction NJCAT Verification Program and NJDEP Certification Program Testing and Monitoring Variables NJDEP Interim Certification of the Manufactured Treatment Devices TSS Removal Efficiency New Jersey Stormwater Quality Design Storm New Jersey MTD Sizing Methodology Objectives NJCAT Verification Protocols General Verification Protocol NJDEP Total Suspended Solids Laboratory Test Procedure The FloGard R Dual-Vortex Hydrodynamic Separator System Description Technical Performance Stormwater Runoff Simulation Models Storm Water Management Model Modeling Capabilities vi

7 Typical Applications of SWMM Source Loading and Management Model (SLAMM) Simulation Model Selection Frequency Distributions of Runoff Volumes and Peak Flow Rates for 10 Years Records Precipitation Data Collection Stormwater Runoff Simulation Runoff Volumes Distribution Runoff Peak Flows Distribution Modification of the Subcatchment Properties Weighting Factors Stormwater Runoff Depth Stormwater Peak Runoff Peak Rates FloGard DVS-48 Weighted Efficiency Discussion Assessing the Impacts of Water Quality Design Storms on Modeled TSS Removal Efficiencies Sizing Criteria Subcatchment Area Definition Subcatchment Subcatchment Water Quality Simulation Pollutant Buildup Pollutant Washoff The First Flush Effect Water Quality Simulation Cases Removal Efficiency Simulation vii

8 Continuous Simulation Results Subcatchment Subcatchment Discussion Conclusions Appendix Appendix A. Estimation of TSS Removal Efficiency in Sedimentation Devices A.1. Introduction A.2. Stormvault TM A.2.1. System Description A.2.2. Technical Performance Claim A.3. Detention Basin A.3.1. Removal Efficiency Performance Methodology A.3.2. Results A.4. Discussion viii

9 List of Tables 1.1. NJDEP 1.25-Inch/2-Hour Stormwater Quality Design Storm NJDEP Particle Size Distribution (Sandy Loam) NJDEP Weight Factor for Different Percentages of Treatment Operating Rates FloGard Dual-Vortex Hydrodynamic Separator Models and Dimensions Summary of Test Results and Calculated NJDEP Weighted SSC and TSS Removal Efficiencies Annual Precipitation for Trenton, New Jersey ( ) Subcatchment Input Information Simulation Options Input Stormwater Runoff Volume Statistic Report (100 % impervous area) Stormwater Peak Runoff Flow Statistic Report (100 % impervous area) Runoff Peak Flow Rates Expressed as Percentage of the Design Storm Peak Flow Rates Subcatchment Input Information (Trial B) SWMM Infiltration Input Information (Horton Method) Subcatchment Input Information (Trial C) Subcatchment Input Information (Trial D) Subcatchment Input Information (Trial E) Hydrologic Summary. (Trial E) Cumulative Frequency Distribution of Runoff Depths Frequency Distribution of Runoff Depths Runoff Depth Based Weighting Factors ix

10 5.16. Cumulative Frequency Distribution of Peak Runoff Flow Rates Frequency Distribution of Peak Runoff Flow Rates Peak Runoff Flow Based Weighting Factors Weighted Removal Efficiency (Runoff Depths) Weighted Removal Efficiency (Uniform Intensity Design Storm) Weighted Removal Efficiency (Non-Uniform Intensity Design Storm) Water Quality Design Storm (Non-uniform Intensity Distribution) Subcatchment 2 Input Information Input Parameters for Buildup Simulation Tried Exponent-Coefficient Combinations and Percentages of Runoff Volume that Transport 80% of the Mass Distribution of Runoff Volumes and Solids Masses for January 20, 1990 Storm Event Study Cases of Pollutant Washoff Distributions of Influent and Effluent Masses for January 2, 1981 Storm Event Modeled Total Influent and Effluent Mass for First Flush Case 1 (Subcatchment 1) Modeled Total Influent and Effluent Mass for First Flush Case 2 (Subcatchment 1) Modeled Total Influent and Effluent Mass for First Flush Case 3 (Subcatchment 1) Modeled Total Influent and Effluent Mass of No First Flush (Subcatchment 1) Modeled Total Influent and Effluent Mass for First Flush Case 1 (Subcatchment 2) Modeled Total Influent and Effluent Mass for First Flush Case 2 (Subcatchment 2) x

11 6.14. Modeled Total Influent and Effluent Mass for First Flush Case 3 (Subcatchment 2) Modeled Total Influent and Effluent Mass of No First Flush (Subcatchment 2) Mass Balance for Pollutant Buildup (Subcatchments 1 and 2).. 66 xi

12 List of Figures 1.1. NJDEP 1.25-Inch/2-Hour Stormwater Quality Design Storm FloGard Dual-Vortex Hydrodynamic Separator SSC Removal Efficiency Curve Frequency Distribution of Runoff Depths and Peak Flows (10 Years Records) Frequency Distribution of Runoff Depths and Peak Flows. (Trial B) Frequency Distribution of Runoff Depths and Peak Flows. (Trial C) Frequency Distribution of Runoff Depths and Peak Flows. (Trial D) Frequency Distribution of Runoff Depths and Peak Flows (Trial E) Comparison of Weighting Factors Concentration Based First Flush Effect Mass Based First Flush Effect Mass Based First Flush Effects for Three Study Cases. (January 20, 1990 Storm) A.1. StormVault TM A.2. TSS Removal Rates for Wet Ponds A.3. Inflow and Outflow Hydrographs for Stormvault Model VS68x2 under Water Quality Design Storm xii

13 1 Chapter 1 Introduction Human development has generated significant adverse impacts in the quality of the stormwater runoff which has become one of the most important sources of nonpoint pollution and degradation of surface waters. Management strategies have been developed in order to mitigate such impacts before the stormwater runoff reaches its final destination in different bodies of water. One of this management strategies involves the development of innovative environmental technologies that include pre-fabricated stormwater treatment structures known also as manufactured treatment devices (MTDs). Such devices include hydrodynamic separators, filtration systems and underground detention vaults. The State of New Jersey verifies and certifies these technologies according to the guidelines established in the Energy and Environmental Technology Verification (EETV) Act at N.J.S.A. 13:1D-134. This act identifies the New Jersey Corporation of Advance Technology (NJCAT) as the entity responsible for the verification process. After this process the New Jersey Department of Environmental Protection (NJDEP) proceeds with the certification review. The certification will be given only after determining that the technology satisfies regulatory requirements and provides, qualitatively and quantitatively, a net beneficial effect to human health and the environment. 1.1 NJCAT Verification Program and NJDEP Certification Program NJDEP and NJCAT have established a Performance Partnership Agreement whereby NJCAT performs the technology verification review and NJDEP certifies the technology. NJCAT is a not-for profit corporation to promote in New Jersey the retention

14 2 and growth of technology-based businesses in emerging fields such as environmental and energy technologies. Through the verification program, teams of academic and business professionals are formed to implement a comprehensive evaluation of vendor specific performance claims. MTDs performance is determined through the quantification of the percentage of suspended solids that the device is capable of removing from the stormwater runoff. The verification and certification process involves not only the aspect of efficiency performance,but also evaluates methodologies for design, sizing and maintenance. Participants of the verification program are required to follow the Protocol for Stormwater Best Management Practice Demonstration for final certification and the Laboratory Testing Protocols for interim certification. These protocols provide uniform methods for proving the performance of manufactured treatment devices and for developing test quality assurance plans for verification and certification of their performance claims (TARP 2003, NJDEP 2003b). The protocols establish, for instance, criteria for the selection of the particle size distribution of the sediments used in laboratory testing and the influent concentration at the sites for field testing. 1.2 Testing and Monitoring Variables In despite of the development of uniform methods for the evaluation of MTDs, some major variables in both laboratory testing and field monitoring still affect the determination of the device removal efficiency performance and the sizing and designing methodology of the devices (Guo 2007). Some of these variables are listed below: TSS vs SSC: Total Suspended Solids (TSS) and Suspended Sediment Concentration (SSC) are methods for measuring the amount of suspended sediment present in a fluid. The two methods yield different results of removal efficiency performance. Recent studies have determined a linear relation

15 3 between the results of TSS and SSC (Guo 2006). Flow rate: A relationship between the operating flow rate and the removal efficiency should be determined so that the removal efficiency can be assigned for untested flow rates. Scaling: There is not a standard method for assigning removal efficiency to units with sizes different than the one evaluated. Particle size, density and distribution: The exact effect (reduction or increase) that particle size and density have over the removal efficiency is also unknown. Influent concentration: It has been shown that in field monitoring higher influent concentration leads to higher removal efficiency. However, a quantitative relationship has not been developed. Sizing method/design storm: New Jersey has defined a non-uniform design storm that represents a significant increase of the peak flow rate, in small watersheds, in comparison with the former uniform design storm of the same volume. This change of regulation affects the sizing of the device. Relations to adjust the effects that these variables produce are necessary in order to achieve fair assignations of removal efficiencies and sizing methodologies that allow users and project engineers to compare and select a specific technology. 1.3 NJDEP Interim Certification of the Manufactured Treatment Devices TSS Removal Efficiency Through the described Manufactured Treatment Device Verification Program, NJCAT determines whether a new stormwater treatment technology achieves the removal efficiency claimed by the manufacturer. For that purpose the applicant provides to NJCAT, laboratory and/or field data, which is obtained following the

16 4 Protocol for Stormwater Best Management Practice Demonstration and Laboratory Testing Protocols. This data is then evaluated by a Verification Team which will issue a Verification Report. If the vendor presents a claim based on laboratory data and NJCAT verifies it and NJDEP issues a conditional interim certification which provides vendors with the ability of marketing their products in the State of New Jersey while the MTD is tested in the field and evaluated for final verification and certification. In the process leading to interim certification, the treatment performance of the device is measured at five different flow rates specified in the Laboratory Protocol and each treatment flow rate yields different values of removal efficiency. Each removal efficiency is then multiplied by a weight factor that corresponds to the test flow rate. The overall removal efficiency is the total sum of the five weighted removal efficiencies. This result is meant to represent the average annual removal efficiency of the device. According to the Total Suspended Solids Laboratory Testing Procedure (NJDEP 2003b), the weight factors were obtained assuming that the average annual distribution of the peak stormwater runoff rates was similar to the the average annual distribution of runoff volumes in New Jersey. The runoff volume distribution was developed based on accepted computational methods for small storm hydrology and on 52 years of daily rainfall data at 92 rainfall gages. 1.4 New Jersey Stormwater Quality Design Storm The Stormwater Management Rules (NJAC 7:8), section 5.5 requires that Stormwater Best Management Practices (BMPs), for major new developments, achieve an annual average of Total Suspended Solids (TSS) removal efficiency equal or greater than 80 %. Since the regulation refers to an annual average, the treatment devices do not have to be designed to achieve 80 percent of TSS reduction for every individual storm event that occurs in a year.

17 5 The rules specify also, in the same section, the water quality design storm (see Table 1.1) which consists of 1.25 inches of rainfall falling non-uniforminly over two hours (see Figure 1.1). This design storm substituted the one specified in NJAC 5: and 7:13-2.8(b), defined as 1.25 inches of rainfall falling in two hours with a uniform intensity of in/hour. Table 1.1: NJDEP 1.25-Inch/2-Hour Stormwater Quality Design Storm (NJDEP 2004) Time Cumulative Rainfall Incremental Rainfall (minutes) (inches) (inches) According to the proponents of the non-uniform quality storm, although the previous one provided sound basis for the design of stormwater quality treatment facilities that require an estimate of the total runoff volume that will flow to the facility (NJDEP 2003a), it did not provide information for the design of those facilities that required an accurate estimate of the runoff peak flow rate that would flow through them. The non-uniform water quality storm provides estimates of total runoff volume as well as peak runoff rate for the same storm event.

18 6 Figure 1.1: NJDEP 1.25-Inch/2-Hour Stormwater Quality Design Storm (NJDEP 2004) 1.5 New Jersey MTD Sizing Methodology The assumed similarity between the annual frequency distributions of the runoff volume and of the runoff peak flow rates, has lead to the use of the runoff peak flow rate generated by the design storm, with non-uniform intensity, as the design operating rate of the device. For instance, Chapter 9.6 section B of the New Jersey Stormwater BMP Manual specifies the following about the design of flow-through MTDs: Devices that convey inflow with little or no storage and provide pollutant removal only through such techniques as vortex flow, filtration, and/or absorption must be based on the peak rate of stormwater quality design storm runoff. Different sizes and models of MTDs have been certified, by NJDEP, with different treatment flow rates. Therefore under Chapter 9.6 specification, project designers will size the devices to achieve the certified removal efficiency, when

19 7 the treatment flow rate equals the runoff peak flow rate, from the drainage area, generated by the quality design storm, with non-uniform intensity distribution. This study intends to determine the frequency distribution of the runoff volumes in New Jersey and compare it with the distribution of their peak runoff flow rates. For that purpose a statistical analysis was developed through the simulation of the stormwater runoff produced by 10 years of precipitation over a specific subcatchment. The Storm Water Management Model Version 5.0 was used to simulate the stormwater runoff. Based on the obtained results different size criteria were applied to a certified hydrodynamic separator. Their outcomes were compared through the simulation of the removal efficiencies of the device which were continuously modeled for individual storm events. Recommendations for the application of different simplified sizing methodologies are presented. 1.6 Objectives In order to asses and recommend water quality design storms this study aims to accomplish the following: Collect 10 year precipitation data from one meteorological station in the State of New Jersey Select a certified model of hydrodynamic separator Select an appropriate modeling software capable of producing continuous hydrographs and pollutographs with small time increments Define a typical small watershed Simulate, using the selected model and the defined watershed, the runoff flow rates and volumes produced by the 10 year records of rainfall Determine the probability distribution of the runoff depths generated by the model from the collected rainfall

20 8 Determine the probability distribution of the peak flow rates generated by the model from collected rainfall Generate weighting factors and evaluate their effect on the calculation of the weighted solids removal efficiency Evaluate the similarity between the average distribution of runoff volumes and the distribution of the runoff peak flow rates Simulate, using the selected model and the defined watershed, the pollutants concentration distribution (including the first flush effect) that the collected rainfall would produce Simulate the annual removal efficiency that the selected device would achieve when sized with the quality design storm of uniform intensity and with the quality storm of non-uniform intensity Assess and recommend the use of different quality design storms for the design and performance evaluation of hydrodynamic separators The removal efficiency assignation of other treatment devices was also evaluated during this research (see appendix A).

21 9 Chapter 2 NJCAT Verification Protocols 2.1 General Verification Protocol NJCAT General Verification Protocol establishes the general guidelines that are to be followed by the Verification Team for the evaluation of the data supplied by vendors or a testing agency to determine whether the data verifies the technology performance claim(s) made by the applicant. If inadequate data is provided, the protocol describes procedures for the collection of additional data. The protocol also describes procedures for revising the claim(s) and for reevaluating the data, if the data can be used to verify an alternative performance claim(s). According to the protocol, the verification process consists of four stages: 1. Review of Application for Acceptance into the NJCAT Verification Program 2. Claims development process (a) Identification of specific claim(s) (b) Verification of specific claims(s) 3. Assessment of data quality (QA/QC) 4. Report preparation The verification process evaluates the following aspects: The technology provides an environmental benefit The technology protects the health and safety of workers and the public The technology is designed and manufactured from materials that ensure its reliability

22 10 The performance claim satisfies the criteria for specifying claims. The claim must be specific, measurable and verifiable Adequate documentation and data were provided by the applicant Applicant data prepared or provided by an independent third party The technology is based on sound scientific and technical principles The technology is supported by acceptable peer review, technical literature or references For more details on the scope of the protocol and the verification process please refer to the NJCAT Technology Verification Program Protocol (NJCAT 2003) 2.2 NJDEP Total Suspended Solids Laboratory Test Procedure This protocol specifies the particle size distribution of the material used to evaluate the manufactured treatment devices, the requirements for full scale laboratory testing and the measurement of the treatment efficiency (NJDEP 2003b). It also indicates the weighting factors through which the devices removal efficiency is verified. Particle Size Distribution The following particle size distribution (see Table 2.1) will be utilized to evaluate a manufactured treatment system (a natural/commercial soil representing USDA definition of a sandy loam material). This hypothetical distribution was selected as it represents the various particles that would be associated with typical stormwater runoff from a post construction site. Full Scale Lab Test requirement The protocol states the following for Full Scale Lab Tests 1. At a minimum, complete a total of 15 test runs, including 3 tests each at a constant flow rate of 25, 50, 75, 100, and 125 percent of the treatment

23 11 Table 2.1: NJDEP Particle Size Distribution (Sandy Loam) Particle Size % mass (microns) (coarse 5.0 sand) (medium 5.0 sand) (fine sand) (very fine 15.0 sand) 8-50 (silt) (silt) (clay) 5.0 flow rate. These tests should be operated with initial sediment loading of 50% of the units capture capacity. 2. The 3 tests for each treatment flow rate will be conducted for influent concentrations of 100, 200, and 300 mg/l. 3. For an online system, complete 2 tests at the maximum hydraulic operating rate. Utilizing clean water, the tests will be operated with initial sediment loading at 50% and 100% of the units capture capacity. These tests will be utilized to check the potential for TSS resuspension and washout. 4. The test runs should be conducted at a temperature between degrees Fahrenheit or colder. Measuring Treatment Efficiency The protocol describes the following steps for measuring treatment efficiency. 1. Calculate the individual removal efficiency for the 15 test runs, based on influent and effluent concentrations. 2. Average the three test runs for each operating rate. 3. The average percent removal efficiency will then be multiplied by a specified weight factor (see Table 2.2) for that particular operating rate.

24 12 Table 2.2: NJDEP Weight Factor for Different Percentages of Treatment Operating Rates Treatment Weight factor operating rate 25 % % % % % The results of the 5 numbers will then be summed to obtain the theoretical annual TSS load removal efficiency of the system

25 13 Chapter 3 The FloGard R Dual-Vortex Hydrodynamic Separator By the time of this study the NJCAT verification report most recently available corresponded to the FloGard R Dual-Vortex Hydrodynamic Separator. For this reason it was the device selected to evaluate the MTD s sizing methodology. 3.1 System Description The FloGard R Dual-Vortex Hydrodynamic Separator provides stormwater treatment through enhanced gravitational separation. Particle settling is accelerated by centripetal forces induced by the tangential flow pattern augmented by a highly circuitous flow path (NJCAT 2007a). The system consists of two vortex separators that intersect a central platform to a debris holding reservoir below the platform (NJCAT 2007a). In between the two separators, horizontally, a pipe conveys the runoff water into the system through the inlet extending from the wall of the cylinder to the wall of each of the separators (see Figure 3.1)(NJCAT 2007a). Figure 3.1: FloGard Dual-Vortex Hydrodynamic Separator (NJCAT 2007a) The flow is directed tangentially to the inner wall of the separators to generate

26 14 a vortex type flow. Low flows reach the separator through a closed wall throughpipe. High-flows enter the system through an open top bypass chute. When flows from storm runoff are greater than the capacity of the two separators, the water flows over the high-flow passage way until it reaches the exit of the system into the storm drain. The FloGard has been shown to capture trash and debris, Total Suspended Solids (TSS), sediments and oil and grease (NJCAT 2007a). Table 3.1 shows the different FloGard models available, their dimensions and their treatment flow rates certified by NJDEP for 50 % TSS removal efficiency Table 3.1: FloGard Dual-Vortex Hydrodynamic Separator Models and Dimensions Model Diameter Depth(below Max.inlet Treatment invert) size Flow Rate ft mm ft mm ft mm gpm (cfs) DVS (0.35) DVS (0.63) DVS (0.98) DVS (1.40) DVS (2.50) 3.2 Technical Performance Kristar Enterprises claims the following : The FloGard R Dual-Vortex Hydrodynamic Separator, Model DVS-48 at a flow rate of 280 gpm (0.63 cfs), has been shown to have a 60% total suspended solids (TSS) removal efficiency, measured as suspended solids concentration (SSC) using NJDEP specified material with an average particle size of 70 microns, and average influent concentration of 202 mg/l and 100% initial sediment loading in laboratory studies using simulated stormwater The vendor presented the Flogard Dual-Vortex laboratory performance data to the NJCAT Technology Verification Program. The Verification Report, where the vendor s claim was confirmed was issued in July The DVS-48 is a model of the FloGard R Dual-Vortex with 4 feet of diameter and 8 feet of height. The inlet and outlet of the unit have 12 inches of diameter

27 15 with the inverts located 60.5 inches above the floor. The internal overflow weirs for low and high flow in the units are 8-in and 14-in respectively. The dual hydrodynamic separators are conformed by two 12-inches vertical tubes that connect the upper and lower chamber (see Figure 3.1). The verification testing of the model DVS-48 of the FloGard Dual-Vortex Hydrodynamic Separator, conducted at Alden Research Laboratory, followed the Total Suspended Solids Laboratory Test Procedure (NJCAT 2007a), developed by NJDEP, with the aim to provide the participants on the verification program with a normalized and uniform test procedure. Hydraulic Capacity In order to determine the maximum hydraulic capacity (MHC) of the device, the unit was tested without sediments under 5 different flows as stated by the NJDEP protocol. The flows ranged from 0 to 700 gpm (1.56 cfs). The fluid reached a maximum of 240 gpm (0.53 cfs) before flowing over the first weir and a maximum flow (MHC) of 560 gpm (1.25 cfs) before reaching the bypass weir (NJCAT 2007a). Sediment Removal Efficiency The sediment was introduced in the test system unit and the collection of samples began after a volume equal to 3 times the system volume had passed. As required by the NJDEP guidelines, the unit was tested under 5 different flows ranging from 70 to 350 gpm (0.16 to 0.78 cfs) and influent sediment concentrations of 100, 200 and 300 mg/l. A minimum of 5 influent/effluent pair of samples were collected under each flow. The effluent sample was collected one residence time after the influent sample. The concentration of the collected samples was determined by the laboratory following the Method B, ASTM Designation: D Standard Test Methods for Determining Sediment Concentration in Water Samples. This methodology analyses the entire sample whereas the TSS methodology requires splitting the sample for its analysis.

28 16 The removal efficiency of each test is calculated through the following equation: Removal Efficiency = 100 Influent Concentration Effluent Concentration Influent Concentration Table 3.2 shows the summary of Removal Efficiency Tests as presented in the FloGard Verification Report (NJCAT 2007a). The five test at 125, 100, 75, 50, and 25 % of the operating rate generated removal efficiencies of 38.7, 43.8, 55.6, 67.6 and 74.6 % respectively. After applying the pertinent weighting factor to each removal efficiency and adding the results, NJCAT verified a TSS removal efficiency of 60.5 %. An exponential regression of the removal efficiency versus flow rate yields the following mathematical expression for the model DVS-48: RE=100e.00299Q (3.1) where RE = removal efficiency at a time t (%) Q = influent flow rate at a time t (gpm) Figure 3.2 shows the SSC efficiency curve, obtained from the previous expression for the model DVS-48

29 17 Table 3.2: Summary of Test Results and Calculated NJDEP Weighted SSC and TSS Removal Efficiencies (NJCAT 2007a) % Revised Flow Concentration Unadjusted Data Adjusted Data TSS Data Influent Effluent Efficiency Av Efficiency Influent Effluent Efficiency Av Efficiency Influent Effluent Efficiency Av Efficiency MHC gpm mg/l mg/l mg/l % % mg/l mg/l % % mg/l mg/l % % Weighted Eff Weighted Eff Weighted Eff. 31.1

30 18 Figure 3.2: SSC Removal Efficiency Curve

31 19 Chapter 4 Stormwater Runoff Simulation Models 4.1 Storm Water Management Model The EPA Storm Water Management Model (SWMM) is a dynamic rainfall-runoff simulation model used for single event or continuous simulation of runoff quantity and quality from primarily urban areas (Rossman 2004). The model operates with a collection of subcatchment areas that receive precipitation and generate runoff and pollutant loads. The precipitation received by the subcatchment can be provided by the user as time-series of accumulated volumes over time steps or as intensity distribution over a period of time The surface runoff concept of SWMM treats each subcatchment surface as a nonlinear reservoir. Inflow sources are precipitation and the runoff from any designated upstream subcatchments. Outflows consist of infiltration, evaporation, and surface runoff. The maximum depression storage of the subcatchment is assumed as the capacity of this reservoir. Surface runoff, Q, occurs only when the depth of water d in the reservoir exceeds the maximum depression storage, dp, in which case the outflow is given by Manning s equation. The routing portion of SWMM transports this runoff through a system of pipes, channels, storage/treatment devices, pumps, and regulators. The program keeps track of the quantity and quality of runoff generated within each subcatchment, and the flow rate, flow depth, and quality of water in each pipe and channel during a simulation period comprised of multiple time steps. The current edition, Version 5, is a complete re-write of the previous release. The model runs under Windows and provides an integrated environment for editing study area input data, running hydrologic, hydraulic and water quality simulations, and viewing the results in a variety of formats. These include color-coded

32 20 drainage area and conveyance system maps, time series graphs and tables, profile plots, and statistical frequency analyses Modeling Capabilities SWMM is able to simulate different hydrologic processes such as: rainfall and its variation with time evaporation of standing surface water snow accumulation and melting depression storage rainfall interception infiltration of rainfall into unsaturated soil layers percolation of infiltrated water into groundwater layers interflow between groundwater and the drainage system nonlinear reservoir routing of overland flow In addition to modeling the generation and transport of runoff flows, SWMM can also estimate the production of pollutant loads associated with this runoff. The following processes can be modeled for any number of user-defined water quality constituents: dry-weather pollutant buildup over different land uses pollutant washoff from specific land uses during storm events direct contribution of rainfall deposition reduction in dry-weather buildup due to street cleaning reduction in washoff load due to BMPs entry of dry weather sanitary flows and user-specified external inflows at any point in the drainage system

33 21 routing of water quality constituents through the drainage system reduction in constituent concentration through treatment in storage units or by natural processes in pipes and channels Typical Applications of SWMM Typical applications of the model are the following: design and sizing of drainage system components for flood control sizing of detention facilities and their appurtenances for flood control and water quality protection flood plain mapping of natural channel systems designing control strategies for minimizing combined sewer overflows evaluating the impact of inflow and infiltration on sanitary sewer overflows generating non-point source pollutant loadings for waste load allocation studies evaluating the effectiveness of BMPs for reducing wet weather pollutant loadings For more details about the operation of the model please refer to the SWMM User s Manual (Huber & Dickinson 1988) 4.2 Source Loading and Management Model (SLAMM) The Source Loading and Management Model (SLAMM) was originally developed in an effort to explore the relations between sources of urban runoff pollutants and runoff quality. The program considers a variety of pollutant and flow routings that may occur in urban areas. It routes material from unconnected sources to

34 22 the drainage system directly or to adjacent directly connected or pervious areas which finally drain to the collection system (Pitt 2004). The program is able to predict the urban runoff discharge parameters (total storm runoff flow volume, flow-weighted pollutant concentrations, and total storm pollutant yields) for many individual storms and for a complete study period. A major function of the model is to asses the role of different sources of pollutants. The program is able to produce the following outputs: Runoff volume of each event in the model run Runoff duration Particulate concentration and loading Pollutant concentration (for each pollutant) and pollutant loading Event numbers, the rain start date and time, the Julian start date and time, the rain duration, the rain inter-event period, the rain depth, the runoff coefficient, the average flow, the peak flow, the suspended solids concentration, and the suspended solids mass Continuous hydrographs with six minute time increments Continuous hydrographs with fifteen minute time increments The model has been approved by the State of Wisconsin (Department of Natural Resources) to predict the reduction in the average annual mass load of total suspended solids and to predict the concentration of total suspended solids discharged from a sedimentation device installed to treat runoff from a specific drainage area of defined characteristics (Wisconsin Natural Resources Department 2007). For more details of the program please refer to the user s manual (Pitt 2004).

35 Simulation Model Selection As mentioned in section 1.6, one of the objectives of this study is to determine the frequency distribution of hydrologic processes such as runoff depth and runoff peak flows. For that purpose the generation of hydrographs will be necessary. Both models offer hydrographs with small time steps. While SLAMM hydrographs are limited to time steps of 6 and 15 minutes, SWMM generates hydrographs with time steps as small as one minute. For this study statistic reports and summaries as part of the model run results, will also be necessary. Both models offer probability distribution data. However for this study it is also necessary to conduct a continuous simulation of the pollutant concentration distribution within the stormwater runoff event. Despite that SLAMM is a strong model regarding pollutant sources and total load for an individual storm event, it does not offer pollutographs or continuous simulation of pollutant concentrations. Therefore the model selected for the continuous simulations in this study is SWMM, version 5.0.

36 24 Chapter 5 Frequency Distributions of Runoff Volumes and Peak Flow Rates for 10 Years Records 5.1 Precipitation Data Collection Hydrodynamic separators, on site, receive stormwater runoff under non-uniform flow rates that change in small time steps. Such variations affect the removal efficiency of the device, since, as discussed in section 3.2, the removal performance of the device depends on their influent flow rate. For that reason, precipitation data collected with small time steps is necessary to conduct a proper evaluation of the removal efficiency of an on-site hydrodynamic separator. The National Climatic Data Center reports precipitation readings every 15 min, for 19 stations in New Jersey. By the time of this study, reliable readings with smaller time steps were not available from national or regional climatologic services. New Jersey Rainfall Intensity-Duration-Frequency Curves were developed from Trenton area rainfall data between 1913 and 1975 (NJDEP 2004). For this reason Trenton was the meteorologic station selected to collect 10 years of precipitation data. The available data covered the period between 1977 and The data collected for each year was summed in order to select 10 years in a row that presented few missing events and an annual mean total precipitation close to the long term annual mean precipitation in New Jersey, which is inches, according to the Office of the New Jersey State Climatologist. Table 5.1 shows the selected years and their annual rainfalls. Average annual precipitation during the studied 10 years is inches.

37 25 Table 5.1: Annual Precipitation for Trenton, New Jersey ( ) Year Annual Precipitation (in) Stormwater Runoff Simulation In order to determine the distribution of runoff volumes and peak flow rates generated by the collected precipitation data, a drainage area was defined. In the initial phase of the study the area was defined as an 100 % impervious parking lot, with a 1% slope, no depression storage and no evaporation. The area of the subcatchment was calculated assuming that the runoff peak flow rate, produced by the former New Jersey design storm (uniform intensity of in/hour), was equal to the treatment operating rate of the FloGard DVS-48, e.i. 280 gpm (0.623 cfs). Assuming a runoff coefficient equal to 1 and applying the rational method (see equation 5.1), the area obtained is 1 acre. Q peak =C I A (5.1) therefore A= Q peak C I = =1.00acre (5.2) where A= watershed area (acres) Q peak = peak runoff flow (cfs)

38 26 Table 5.2: Subcatchment Input Information Property Value Area 1 acre Width 208 ft Slope 1% Impervious Area 100 % Pervious Area 0% Manning s number for overland flow over the impervious portion of the subcatchment Depth of depression storage on the 0.05 inches impervious portion of the subcatchment Percent of the impervious area with 100% no depression storage Routing to outlet Percent of runoff routed between 100 % subareas C = runoff coefficient I = design storm peak intensity (in/hours) The stormwater runoff flow rates that the collected precipitation data would generate, over the defined area, were calculated using the Storm Water Management Modeling software version 5.0 (SWMM 5.0). Table 5.2 shows the subcatchment input information provided to SWMM for the simulation. The Manning coefficient for impervious areas was selected from the values calculated by Engman (1986) for concrete or asphalt. Properties related with the pervious subarea of the subcatchment do not have effect over this simulation since the subcatchment is assumed to be 100% impervious. Table 5.3 shows the options selected to run the simulation. The simulation model was run following the dynamic water routing which produces the most theoretically accurate results since it resolves the one-dimension Saint Venant Equations (the basic equations that describe the propagation of a wave in an open channel).

39 27 Table 5.3: Simulation Options Input General Dates Flow Units Routing method Cubic feet Dynamic wave Start Analysis on 01/01/1981 at 0:00 End analysis on 01/01/1991 at 0:00 Antecedent dry days Runoff Volumes Distribution After providing SWMM the collected precipitation data, defining the subcatchment area to study and running SWMM the following steps were performed: 1. A rank ordered report was obtained from SWMM, containing magnitude of the total volume of each runoff event, the starting date, their duration, their exceedance frequency and their return period in years. Table 5.4 shows the report for the 10 greatest calculated events Table 5.4: Stormwater Runoff Volume Statistic Report (100 % impervous area) Rank Start Date Event Duration Event Total Exceedance Return Pe- Volume Frequency riod (hours) (ft 3 ) (%) (years) 1 7/2/ /28/ /15/ /26/ /8/ /3/ /28/ /15/ /19/ /1/ The runoff volume was divided by the area of the subcatchment in order to obtain the stormwater runoff depth 3. The runoff depths were expressed as percentage of that from the design storms. In other words the depth values were divided by the design storms

40 28 depth and multiplied by 100. In this case, 100 % of imperivious area, the runoff depth from the design storm is equal to 1.25 inches. The depths generated by both storms (uniform and non-uniform) are equal. 4. The obtained values were ranked from the lowest to the highest in order to obtain the probability of values being equal to or less than the ranked one. The probability was calculated using the Weibull Plotting Position Formula (equation 5.3): P= m n+1 (5.3) where n= total number of events m= rank of the value 5. Figure 5.1 shows the curve of the runoff depths expressed as percentage versus their probability of no-exceedance. 5.4 Runoff Peak Flows Distribution After providing SWMM the collected precipitation data, defining the subcatchment area to study and running SWMM the following steps were performed: 1. The runoff peak flow rate that the water quality design storm of uniform intensity would generate, over the defined area, was obtained through SWMM 2. The runoff peak flow rate that the water quality design storm of non-uniform intensity would generate, over the defined area, was also obtained 3. The runoff flow events generated by the collected precipitation data, were also simulated by SWMM

41 29 Figure 5.1: Frequency Distribution of Runoff Depths and Peak Flows (10 Years Records)

42 30 4. A rank ordered report was obtained containing the magnitude of the peak flow rates of each runoff event, the starting date, their duration, their exceedance probability and their return period in years. Table 5.5 shows the report for the 10 greatest calculated events Table 5.5: Stormwater Peak Runoff Flow Statistic Report (100 % impervous area) Rank Start Date Event Duration Event Peak Exceedance Return Pe- Flow Frequency riod (hours) (cfs) (%) (years) 1 3/1/ /11/ /6/ /2/ /19/ /3/ /14/ /8/ /20/ /3/ The runoff peak flow rates were expressed as percentage of the peak flow rates obtained from the simulated design storm events. Therefore two cases were obtained. Table 5.6 shows the percentages obtained for the ten greatest peak flow rates, in each case 6. The obtained values were ranked from the lowest to the highest in order to obtain the probability of values being equal to or less than the ranked one. The probability was calculated as explained in the previous section. 7. Figure 5.1 shows the curves of the runoff peak flow rates, expressed as percentage of the design events peak flow rates, versus their probability of no-exceedance. The two peak flow rate curves in Figure 5.1 indicate that the collected precipitation data did not generate any peak flow rates that represented less than approximately 50% of the peak flow rate produced by the design event of uniform

43 31 Table 5.6: Runoff Peak Flow Rates Expressed as Percentage of the Design Storm Peak Flow Rates Rank Start Date Event Peak Flow (cfs) % Peak Flow (Uniform Intensity 1 ) 1 3/1/ /11/ /6/ /2/ /19/ /3/ /14/ /8/ /20/ /3/ Peak Flow Rate = 0.63 cfs 2. Peak Flow Rate= 3.20 cfs % Peak Flow (Non-Uniform Intensity 2 ) intensity or less than approximately 10 % of the peak flow rated produced by the design event with non-uniform intensity. That is, no storm events produced a peak runoff flow rate of less than cfs (54.60 % of 0.63 cfs and 10.8 % of 3.2 cfs). This outcome was not reasonable. The minimum depth recorded is 0.1 inches, over a 15 minutes interval, which translates to a minimum rainfall intensity of 0.40 in/hr. This precipitation data limitation was amplified by the assumption of no precipitation lost. 5.5 Modification of the Subcatchment Properties For the reasons explained in the previous section, the properties of the subcatchment were modified. Two precipitation loss were included as follow (Trial B): Evaporation with a rate of 0.1 in/day Depression storage of 0.1 in Table 5.7 shows SWMM input information correspondent to trial B.

44 32 Figure 5.2: Frequency Distribution of Runoff Depths and Peak Flows. (Trial B)

45 33 Table 5.7: Subcatchment Input Information (Trial B) Property Value Area 1 acre Width 208 ft Slope 1% Impervious Area 100 % Pervious Area 0% Manning s number for overland flow over the impervious portion of the subcatchment Depth of depression storage on the impervious 0.1 inches portion of the subcatchment Percent of the impervious area with no depression 0 storage Routing to outlet Percent of runoff routed between subareas 100 % Figure 5.2 shows the curves obtained after repeating the steps described in section 5.3 and in section 5.4. It can be observed that for this case the precipitation data generated smaller runoff rates which is closer to what is expected in the field. However the curves still present a steep portion that indicates a significant fraction of runoff flow rates with similar values. For that reason infiltration loss were included by defining the area as 80 % impervious and 20 % pervious (Trial C). The Horton method was used in SWMM, to simulate the infiltration process through the Horton method. Table 5.8 shows the infiltration input information and Table 5.9 shows the SWMM subcatchment input information Figure 5.3 shows the curves obtained after introducing the changes. The shape of the curve is still similar to the one in trial B. However the steep portion of the curve looks smoother indicating that there is a better distribution of the runoff values. This study applies to MTDs that will be installed in urban areas with slopes smaller than 1%. For this reason the study area was redefined with a slope of 0.1 % which is fairly representative, for instance, of the slope common in urban parking lots (Trial D). Table 5.10 shows the SWMM subcatchment input information and

46 34 Figure 5.3: Frequency Distribution of Runoff Depths and Peak Flows. (Trial C)

47 35 Table 5.8: SWMM Infiltration Input Information (Horton Method) Property Maximum infiltration rate on the Horton curve (for loam soils) Minimum infiltration rate on the Horton curve (for sandy loam soils) Infiltration rate decay constant for the Horton curve Time in days for a fully saturated soil to dry completely Maximum infiltration volume possible Value 5 in/hr 0.43 in/hr 4 /hr 7days 0 (not applicable) Figure 5.4 shows the curves obtained after introducing the changes. As mentioned in section 5.2 the area of the subcatchment was obtained assuming that the runoff peak flow rate, produced by the former New Jersey design storm (uniform intensity of in/hour), was equal to the treatment operating rate of the FloGard DVS-48, e.i. 280 gpm (0.623 cfs). Since the initial defined drainage area considered a different slope and land uses, the area of the subcatchment was recalculated considering the different changes. An initial estimation of the value of the area using the rational method (equation 5.1), assuming a runoff coefficient of 0.8, yielded the following: A= Q peak C I = =1.26 acre (5.4) A drainage area of 1.26 acre was inputed in SWMM, resulting in a peak flow of Then a value of 1.25 acre was used, resulting in a peak flow of 0.63 cfs. Table 5.11 shows the final input (Trial E). Figure 5.5 shows the curves after the final changes. Table 5.12 shows the peak runoff rate, lost and runoff coefficient calculated by SWMM for Trial E. The sum of the calculated runoff and precipitation lost equals inches of precipitation. This result gives an error of 3.84 % relative to the inches of precipitation from the collected data. The runoff

48 36 Table 5.9: Subcatchment Input Information (Trial C) Property Value Area 1 acre Width 208 ft Slope 1% Impervious Area 80 % Pervious Area 20 % Manning s number for overland flow over the impervious portion of the subcatchment Manning s number for overland flow over the 0.10 pervious portion of the subcatchment Depth of depression storage on the impervious 0.1 inches portion of the subcatchment Depth of depression storage on the pervious portion 0.2 inches of the subcatchment Percent of the impervious area with no depression 0 storage Routing to outlet Percent of runoff routed between subareas 100 % Table 5.10: Subcatchment Input Information (Trial D) Property Value Area 1 acre Width 208 ft Slope 0.1% Impervious Area 80 % Pervious Area 20 % Manning s number for overland flow over the impervious portion of the subcatchment Manning s number for overland flow over the 0.10 pervious portion of the subcatchment Depth of depression storage on the impervious 0.1 inches portion of the subcatchment Depth of depression storage on the pervious portion 0.2 inches of the subcatchment Percent of the impervious area with no depression 0 storage Routing to outlet Percent of runoff routed between subareas 100 %

49 37 Figure 5.4: Frequency Distribution of Runoff Depths and Peak Flows. (Trial D)

50 38 Figure 5.5: Frequency Distribution of Runoff Depths and Peak Flows (Trial E)

51 39 Table 5.11: Subcatchment Input Information (Trial E) Property Value Area 1.25 acre Width 233 ft Slope 0.1% Impervious Area 80 % Pervious Area 20 % Manning s number for overland flow over the impervious portion of the subcatchment Manning s number for overland flow over the 0.10 pervious portion of the subcatchment Depth of depression storage on the impervious 0.1 inches portion of the subcatchment Depth of depression storage on the pervious portion 0.2 inches of the subcatchment Percent of the impervious area with no depression 0 storage Routing to outlet Percent of runoff routed between subareas 100 % coefficient calculated by SWMM is close to the value assumed to define the surface area of the subcatchment. The runoff depths produced by the uniform and non-uniform design storms (1.25 in) are and inches, respectively. The peak runoff rates that the uniformly and non-uniformly distributed water quality design storms generate over the redefined drainage area are and cfs, respectively. Table 5.12: Hydrologic Summary. (Trial E) Total Precipitation in Total Evaporation in Total Infiltration in Total Runoff in Peak Runoff 9.24 cfs Runoff Coefficient 0.784

52 Weighting Factors Stormwater Runoff Depth Following the weighting factors presented in the NJCAT Laboratory protocol, the obtained values of runoff depth, expressed as percentage of the design storms runoff depth (0.930 inches in average), were divided in the following ranges: 0-25, 25-50, 50-75, and %. Table 5.13 shows, for this ranges, the information extracted from Figure 5.5 related with the distribution of the runoff depth produced by the collected precipitation, expressed as percentage of the runoff depth generated by the design quality events. Table 5.13: Cumulative Frequency Distribution of Runoff Depths % Runoff Depth Probability The probabilities shown in Table 5.13 lead to the calculation of weighting coefficients to be compared with the ones proposed by the Laboratory Protocol. Therefore from the runoff depth distribution, the following coefficients are obtained (see Table 5.14) Table 5.14: Frequency Distribution of Runoff Depths % Runoff Depth Probability of no-exceedance >

53 41 These coefficients result from the difference between the cumulative probabilities shown in Table Each coefficient represents the probability that one value, of the runoff depth, falls in a specific range of magnitude. For instance the probability of one event representing 25 to 50 % of the quality storm runoff depth is However the defined ranges do no consider those values that represent more than 125% of the quality storm runoff depth therefore the total sum of the obtained coefficients is smaller than 1. However, if the portion above 125 % is considered as part of the weight related to the range from 100 to 125 %, the sum equals one. Bigger and less frequent events are considered, leading to conservative factors. An alternative would be to define the following ranges: , 37.5 to 62.5, 62.5 to 87.5, 87.5 to and to This ranges would reduce the amount of not-considered values that are bigger than 125%. However it would give more weight to smaller events. As mentioned above, the removal efficiency, in hydrodyamic separators, increases as the flow decreases. Therefore this ranges would be less conservative than the ones considered previously, since the coefficients are used to assign removal efficiencies of the devices. Table 5.15 shows the obtained coefficients. Table 5.15: Runoff Depth Based Weighting Factors % Runoff Depth Weight Factor

54 Stormwater Peak Runoff Peak Rates As mentioned in the previous section, the obtained values of runoff depth, expressed as percentage of the design storm runoff depth, were divided in the following ranges: 0-25, 25-50, 50-75, and % Table 5.16 shows the information extracted from Figure 5.5 related with the distribution of the peak flow rates produced by the collected precipitation, expressed as percentage of the peak runoff rates generated by the design quality storm events with uniform and non-uniform intensities Table 5.16: Cumulative Frequency Distribution of Peak Runoff Flow Rates % Peak Flow Probability of no-exceedance Uniform Int. Non-Uniform Int The probabilities shown in 5.13 and 5.16 lead to the calculation of weighting coefficients to be compared with the ones proposed by the Laboratory Protocol. Therefore, for the peak runoff flow rates distribution the following coefficients were obtained (see Table 5.17) As explained before, the coefficients are the result from the difference between the cumulative probabilities shown in table Each coefficient represents the Table 5.17: Frequency Distribution of Peak Runoff Flow Rates % Peak Flow Probability Uniform Int. Non-Uniform Int >

55 43 probability that one value of the runoff peak flow, falls in a specific range of magnitude. For instance the probability of one event representing 25 to 50 % of the quality storm peak flow rate is 0.47, for the uniform intensity. However, as occurred with the runoff depth coefficients, the defined ranges do no consider those values that represent more than 125% of the quality storm peak flow. Therefore, the total sum of the obtained coefficients is smaller than 1. As mentioned previously the portion above 125 % was considered as part of the weight related to the range from 100 to 125 %, and the following factors were obtained Table 5.18: Peak Runoff Flow Based Weighting Factors % Peak Flow Weight Factor Uniform Int. Non-Uniform Int FloGard DVS-48 Weighted Efficiency Figure 5.6 shows the frequency distribution of the peak runoff flow rates generated from the weighting factors defined by New Jersey regulations. The frequency distributions generated from the coefficients obtained in this study are also shown. Table 5.19 shows the weighted removal efficiency of the FloGard DVS-48 after the application of the obtained weighting factor for the runoff depths: Table 5.20 shows the weighted removal efficiency of the FloGard DVS-48 after the application of the obtained weighting factor for the peak flow rates expressed as percentage of the design storm with uniform intensity: Table 5.21 shows the weighted removal efficiency of the FloGard DVS-48 after the application of the obtained weighting factor for the peak runoff flow rates expressed as percentage of the design storm with non-uniform intensity:

56 44 Table 5.19: Weighted Removal Efficiency (Runoff Depths) % Operating Rate Av Efficiency (%) Weighting Coefficients Removal Efficiency Total Weighted Efficiency: % Table 5.20: Weighted Removal Efficiency (Uniform Intensity Design Storm) % Operating Rate Av Efficiency (%) Weighting Coefficients Removal Efficiency Total Weighted Efficiency: % 5.8 Discussion Figure 5.6 suggests that the weighting coefficients obtained from the peak flow rates expressed as percentage of the runoff peak flow rate generated by the design storm event, with uniform intensity, are closer to the weighting coefficients defined by NJ Stormwater regulations than the coefficients obtained using the storm event with non-uniform intensity distribution. The same figure also suggest that the distribution of the runoff depth of the events is also closer to the distribution associated with the design event with uniform intensity. This observations is consistent with the weighted removal efficiencies obtained for the FloGard DVS-48. As mentioned above NJCAT verified a weighted removal efficiency of 60.5%. The coefficients generated from the runoff depth distribution yielded a weighted removal efficiency of %, which is close to the verified efficiency. The coefficients generated from the design storm with uniform intensity yielded a removal efficiency of % which is almost equal to the verified

57 45 Table 5.21: Weighted Removal Efficiency (Non-Uniform Intensity Design Storm) % Operating Rate Av Efficiency (%) Weighting Coefficients Removal Efficiency Total Weighted Efficiency: % removal efficiency. The coefficients generated with the design event with nonuniform intensity distribution yielded a removal efficiency of % which has an absolut difference of 12% higher than the verified performance.

58 46 Figure 5.6: Comparison of Weighting Factors

59 47 Chapter 6 Assessing the Impacts of Water Quality Design Storms on Modeled TSS Removal Efficiencies 6.1 Sizing Criteria Two different criteria were applied to select the surface area of the subcatchment that the FloGard DVS-48 would drain. Sizing Criterion 1 The design operating rate of the FloGard DVS-48 is equal to the peak flow rate generated by water quality design storm with a uniform intensity of in/hr Sizing Criterion 2 The design operating rate of the FloGard DVS-48 is equal to the peak flow rate generated by the water quality design storm with a non-uniform intensity distribution. (see Table 1.1) 6.2 Subcatchment Area Definition Subcatchment 1 As shown in section 5.4, the design event of uniform intensity generates, over the subcatchment defined in Table 5.11, a peak flow rate of 0.63 cfs which, as mentioned in the same section, is equal to the design flow rate verified by NJCAT Subcatchment 2 Table 6.1 shows the intensity distribution of the water quality design storm defined by New Jersey Storm Water Rules and shown in Table 1.1. As shown the peak intensity reached by the design event of non-uniform intensity is equal to 3.2 in/h. An initial estimation of the value of the area using

60 48 Table 6.1: Water Quality Design Storm (Non-uniform Intensity Distribution) Time Intensity (in/hr) the Rational Method (Equation 5.1), assuming a runoff coefficient of 0.8, yielded the following: A= Q peak C I = =0.243 acre (6.1) The runoff peak flow rate was calculated in SWMM introducing an area of 0.24 acre, resulting in a peak flow of Then a value of 0.25 acre was introduced, resulting in a peak flow of 0.63 cfs. Table 6.2 shows the SWMM input information that defines the Subcatchment 2.

61 49 Property Table 6.2: Subcatchment 2 Input Information Value Area 0.25 acre Width ft Slope 0.1% Impervious Area 80 % Pervious Area 20 % Manning s number for overland flow over the impervious portion of the subcatchment Manning s number for overland flow over the 0.10 pervious portion of the subcatchment Depth of depression storage on the impervious 0.1 inches portion of the subcatchment Depth of depression storage on the pervious portion 0.2 inches of the subcatchment Percent of the impervious area with no depression 0 storage Routing to outlet Percent of runoff routed between subareas 100 % 6.3 Water Quality Simulation Pollutant Buildup The American Public Works Association reported from its studies on stormwater pollution, in Chicago in 1969, results indicating that the accumulation of street pollutants ( dirt and dust ) was a linear function of time. Barbe (1996), developed also a linear function to model pollutant buildup. However other studies have shown that pollutant buildup can be non-linear (Sartor et al. 1972). For that reason SWMM provides power-linear, exponential and Michael-Menton methods for simulating pollutants buildup processes. SWMM assumes that the pollutants accumulate during dry time steps (runoff less than in/hr). For this study, the power-linear simulation method was chosen to calculate the possible accumulation of pollutants in the subcatchment area, that is: B=C 1 t C 2

62 50 where B = Pollutant Buildup (pounds) C 1 = Buildup rate constant(pounds/day) t = Dry time step (days) C 2 = time exponent The simulation ran under the assumption that an initial buildup, from a period of five days with no rainfall prior to the start of the simulation, was present on the surface of the study area. Table 6.3 shows the input parameters provided to the program for the pollutant buildup simulation. Table 6.3: Input Parameters for Buildup Simulation Property Value Function Power Maximum Buildup 200 lbs/acre per year Rate Constant 0.55 lbs/day Time exponent 1 Normalizer Area Function: Type of pollutant buildup function Maximum Buildup: The maximum buildup that can occur, expressed as pounds of pollutant per unit of the normalizer. This value was obtained using the 200 TSS load per acre per year suggested by the New Jersey Stormwater BMP Manual, for an industrial and Commercial area (NJDEP 2004). Rate Constant : The time constant that governs the rate of pollutant buildup in pounds/day. Its value was determined dividing the TSS load per acre per year, mentioned above, by 365 days. Power: Linear buildup is a variation of power buildup when the power value is

63 51 1 (one) Normalizer: The variable to which the maximum buildup value used is normalized. As shown in Table 6.3 for this study the maximum buildup is normalized with the subcatchment area Pollutant Washoff Pollutants accumulate or buildup on urban areas and are erosioned (washed off) during a rainfall event. They are usually discharged via an outfall to a watercourse (Patel 2005). SWMM uses an exponential formulation, suggested by Sartor and Boyd Sartor, Boyd & B. (1972), where the pollutant washoff rate from a impervious surface in a given time interval is proportional to the runoff rate and the mass of pollutant remaining on the ground surface. SWMM uses the following mathematical expression (Huber & Dickinson 1988): Poff = Rcoef r Washpo Pshed (6.2) where: Poff = constituent load washed off at time t (pounds) Pshed = quantity of constituent still available on the surface at time t (pounds) Rcoef= washoff coefficient r = runoff rate (cfs) Washpo= modeling exponent The parameters of Rcoef and Washpo are determined by the user. Values of Washpo are normally in the range of 1.5 and 2.5. Values of Rcoef are harder to infer since they may vary by almost five orders of magnitude. Normally, the values of Rcoef, however, may be in the range of 1 to 10. Both parameters, Rcoef

64 52 and Washpo, vary with the calibration of the model to the observed data (Huber & Dickinson 1988) The remaining material on the surface at the end of a time step is determined by the following formulation Pshed(t + Δt) = Pshed(t) e -Rcoef r(t)w ashpo +r(t+δt) W ashpo 2 Δt (6.3) The First Flush Effect The term first flush describes in general a disproportionately high delivery of either concentration or mass of a constituent during the initial portions of a rainfall-runoff event. The term concentration-based first flush is used when disproportionately high concentrations of constituents are observed in the initial portions of a rainfall runoff (see Figure 6.1). The term mass-based first flush effect indicates the washoff of a disproportionately high portion of mass during the initial portion of the stormwater runoff (see Figure 6.2). For this study there was no site-specific water quality calibration data available. For that reason 3 different cases where simulated varying the value of the parameters Rcoeff and Washpo in order to generate concentration distributions that reflect the first flush effect (FF) during the storm event. However there is not a standard quantitative definition of the first flush effect. Different authors propose different mass-based indicators. For this reason 3 pairs of coefficients were selected to generate pollutant washoff distributions that would reflect most common definitions of the mass based first flush effect. Case 1 simulates a FF of 80% of the total pollutant load transported by the first 20% of the total runoff volume (Sansalone & Chad 2004). Case 2 simulates a first flush effect of 80% of the total pollutant load transported by the first 30% of the total runoff volume (Bertrand-K. et al. 1998) volume and case 3 simulates a first flush effect of 80% of the total pollutant load transported by the first 40% of the total

65 53 Figure 6.1: Concentration Based First Flush Effect

66 54 Figure 6.2: Mass Based First Flush Effect

67 55 runoff volume. In order to determine the coefficients that would compose each case, 3 events were selected to evaluate the concentration distribution generated by different exponent-coefficient combinations (see Equation 6.2) until the desired distributions were achieved. These events were selected because their peak flow frequency exceedance was close to 50 %, according to SWMM statistic report, for subcatchment 1 (1.25 acres). Table 6.4 shows the selected events, the different combinations tried and the percentage of runoff volumes that delivered 80 % of the total pollutant mass. This mass-volume relation was an indicator comparable with the mentioned FF definitions. In order to identify the mass based first flush effect it was necessary to relate the portion of the total mass delivered at a time t with the portion of the total runoff volume delivered to the device at the same time t. For that purpose the following methodology was used: Assuming that the stormwater flow rate q t remains constant during a small time step Δt, if the concentration of pollutants C t also remains constant during the same time step, the pollutant load ṁ, delivered in that time step, will be equal to the product of the concentration and the stormwater runoff flow rate, (see Equation 6.4) ṁ=q t C t (6.4) The mass m of pollutants delivered during that time step will be equal to the product of the pollutant load and Δt The mass of pollutants delivered at a time k will be equal to the sum of the mass delivered in each of the previous time steps: m k = k ṁ Δt (6.5) t o

68 56 Table 6.4: Tried Exponent-Coefficient Combinations and Percentages of Runoff Volume that Transport 80% of the Mass Event Rcoeff Washpo % Volume /25/ /20/ /19/ Therefore the fraction M(t) that m k represents of the total mass M total would be given for the following expression: M(t) = m k M total % (6.6) The runoff volume V delivered during a time step will be equal to the product of the flow rate q t and Δt The runoff volume delivered at a time k will be equal to the sum of the runoff volume delivered in each of the previous time steps:

69 57 V k = k q t o t Δt (6.7) Therefore the fraction V (t) that V k represents of the total runoff volume V total would be given for the following expression: V(t) = V k V total % (6.8) To illustrate the explained method Table 6.5 shows the obtained distribution for the event occurred on January 20, 1990 for Washpo and Rcoeff values of 1.6 and 50 respectively, for the first 5 hours of the event Water Quality Simulation Cases As shown in Table 6.4 the event occurred on January 20, 1990 presented the first flush effect with exponent values close to the ranges recommended by SWMM. The SWMM manual recommends a range from 1 to 10 for Rcoeff but it is mentioned also that the values of this parameter are highly variable, therefore values greater than 10 are possible and acceptable. Table 6.6 shows the selected coefficients for the simulation. Figure 6.3 shows the relation between % of influent volume versus % of influent mass, for each case. It can be observed in the curve related with case 1 that 20% of the volume carries 80% of the pollutant mass. In the curve related with case 2 it can be observed that aproximately 30% of the volume carries 80% of the pollutant mass, and for the case 3, 40% of the influent volume, carries 80% of the pollutant mass

70 58 Table 6.5: Distribution of Runoff Volumes and Solids Masses for January 20, 1990 Storm Event Date Time Runoff TSS Mass Load Influent Mass Influent Volume Cumulative Cumulative % Volume % Mass (cfs) (mg/l) (mg/sec) (mg) (l) Volume (l) Mass(mg) 1/20/ : /20/ : /20/ : /20/ : /20/ : /20/ : /20/ : /20/ : /20/ : /20/ : /20/ : /20/ : /20/ : /20/ : /20/ : /20/ : /20/ : /20/ : /20/ : /20/ : /20/ : /20/ : /20/ :

71 59 Figure 6.3: Mass Based First Flush Effects for Three Study Cases. (January 20, 1990 Storm)

72 60 Table 6.6: Study Cases of Pollutant Washoff Case Washpo Rcoeff % Volume at 80 % mass % % % 6.4 Removal Efficiency Simulation Continuous Simulation. As described before, the laboratory test of the FloGard Dual Vortex showed that its removal efficiency depends on the flow rate. Therefore during a stormwater event, on-site, the performance of the device will variate with the runoff flow rate. Assuming that the stormwater flow rate q t remains constant during a small time step Δt, the removal efficiency (RE) of the FloGard Dual Vortex, during that time step, will be equal to 100e.00299qt (see Equation 3.1), unless the flow rate is greater than the hydraulic capacity (560 gpm for the tested model DVS-48). After the hydraulic capacity is exceeded, the removal efficiency is assumed to be zero since the removal efficiency of the treated part of the flow is small and the bypassed part will not receive any treatment. However, the use of positive and zero removal efficiency, may not be appropriate if a significant bottom sediment resuspension occurs. The removal efficiency could be negative if a severe sediment resuspension occurs during the high flow. If the concentration of pollutants C t also remains constant during the same time step, the pollutant load ṁ that enters in the device will be equal to the product of the concentration and the stormwater runoff flow rate, see equation 6.9 ṁ=q t C t (6.9) The mass of pollutant that would enter the device (Mass in ), during the time step, is given by equation 6.10

73 61 Mass in =Δt ṁ (6.10) The mass removed (Mass re ) during the same time step will be given by equation removed mass Mass re = Mass in RE (6.11) At the end of the event the total mass of pollutant that entered the device will equal the sum of the calculated influent mass during each time step of the entire storm. The total mass removed from the storm water runoff will equal the sum of the mass removed during each time step of the entire storm. Table 6.7 illustrates the application of the described methodology, for the event on January 2, 1981 over the subcatchment 1, under case 1. Table 6.7: Distributions of Influent and Effluent Masses for January 2, 1981 Storm Event Date Time Runoff TSS Mass Rate Rem Efficiency Influent Mass Effluent Mass (CFS) (MG/L) (MG/S) (%) (MG) (MG) 1/2/1981 2: /2/1981 2: /2/1981 2: /2/1981 3: /2/1981 3: /2/1981 3: /2/1981 3: /2/1981 4: /2/1981 4: /2/1981 4: /2/1981 4: /2/1981 5: /2/1981 5: /2/1981 5: /2/1981 5: The removal efficiency of the device for the entire event will be obtained dividing of the total mass removed by total mass that entered the device, i.e. RE event = ΣMass re ΣMass in % (6.12)

74 62 For this study, the procedure described in this section was used to calculate the removal efficiency that the FloGard Dual Vortex could have achieved under the runoff pattern simulated by SWMM for the precipitation data collected. The time step selected for this computation was determined by precipitation data collected. As explained in section 5.1 reliable readings of precipitation data were available only in time steps equal to 15 minutes Results Subcatchment 1 Case 1 Table 6.8 summarizes the results on total influent and effluent mass obtained through the mentioned spreadsheet for the case 1. Table 6.8: Modeled Total Influent and Effluent Mass for First Flush Case 1 (Subcatchment 1) (Kg) (lbs) Influent Mass Effluent Mass The annual removal efficiency of the FloGard Dual Vortex Model DVS-48 will be given by equation 6.13 Removal Efficiency = Influent Mass Effluent Mass Influent Mass = =70.58% (6.13) Case 2 Table 6.9 summarizes the results on total influent and effluent mass obtained through the mentioned spreadsheet for the case 2. Table 6.9: Modeled Total Influent and Effluent Mass for First Flush Case 2 (Subcatchment 1) (Kg) (lbs) Influent Mass Effluent Mass The annual removal efficiency of the FloGard Dual Vortex Model DVS-48

75 63 will be given by equation 6.14 Removal Efficiency = Influent Mass Effluent Mass Effluent Mass = =69.66% (6.14) Case 3 Table 6.10 summarizes the results on total influent and effluent mass obtained through the mentioned spreadsheet for the case 3. Table 6.10: Modeled Total Influent and Effluent Mass for First Flush Case 3 (Subcatchment 1) (Kg) (lbs) Influent Mass Effluent Mass The annual removal efficiency of the FloGard Dual Vortex Model DVS-48 will be given by equation 6.15 Removal Efficiency = Influent Mass Effluent Mass Influent Mass = =67.44% (6.15) A simulation of the removal efficiency of the device was also performed for the case when 80 % of the mass is transported by 80 % of the volume, i.e., the runoff mass is uniformly distributed through the event. For that purpose the buildup process was modeled using an exponent equal to 2.5 and a coefficient of 2.00 (see Table 6.4). Table 6.11 summarizes the results on total influent and effluent mass obtained through the mentioned spreadsheet for this case. Table 6.11: Modeled Total Influent and Effluent Mass of No First Flush (Subcatchment 1) (Kg) (lbs) Influent Mass Effluent Mass The annual removal efficiency of the FloGard Dual Vortex Model DVS-48 will

76 64 be given by equation 6.16 Removal Efficiency = Influent Mass Effluent Mass Influent Mass = =55.58% (6.16) Subcatchment 2 Case 1 Table 6.12 summarizes the results on total influent and effluent mass obtained through the mentioned spreadsheet for the case 3. Table 6.12: Modeled Total Influent and Effluent Mass for First Flush Case 1 (Subcatchment 2) (Kg) (lbs) Influent Mass Effluent Mass The annual removal efficiency of the FloGard Dual Vortex Model DVS-48 will be given by equation 6.17 Removal Efficiency = Influent Mass Effluent Mass Influent Mass = =90.06% (6.17) Case 2 Table 6.13 summarizes the results on total influent and effluent mass obtained through the mentioned spreadsheet for the case 2. Table 6.13: Modeled Total Influent and Effluent Mass for First Flush Case 2 (Subcatchment 2) (Kg) (lbs) Influent Mass Effluent Mass The annual removal efficiency of the FloGard Dual Vortex Model DVS-48

77 65 will be given by equation 6.18 Removal Efficiency = Influent Mass Effluent Mass Effluent Mass = =89.83% (6.18) Case 3 Table 6.14 summarizes the results on total influent and effluent mass obtained through the mentioned spreadsheet for the case 3. Table 6.14: Modeled Total Influent and Effluent Mass for First Flush Case 3 (Subcatchment 2) (Kg) (lbs) Influent Mass Effluent Mass The annual removal efficiency of the FloGard Dual Vortex Model DVS-48 will be given by equation 6.19 Removal Efficiency = Influent Mass Effluent Mass Influent Mass = =89.38% (6.19) As in Subcatchment 1, a simulation of the removal efficiency of the device was performed for the case when 80 % of the mass is transported by 80 % of the volume, i.e., the runoff mass is uniformly distributed through the event. For that purpose the buildup process was modeled using an exponent equal to 2.5 and a coefficient of 2.00 (see Table 6.4). Table 6.15 summarizes the results on total influent and effluent mass obtained through the mentioned spreadsheet for this case. Table 6.15: Modeled Total Influent and Effluent Mass of No First Flush (Subcatchment 2) (Kg) (lbs) Influent Mass Effluent Mass The annual removal efficiency of the FloGard Dual Vortex Model DVS-48 will

78 66 be given by equation 6.20 Removal Efficiency = Influent Mass Effluent Mass Influent Mass = =74.36% (6.20) Table 6.16 shows the total mass balance for the simulated buildup in subcatchment 1 and 2. The values remained constant through the 3 washoff simulation cases. Table 6.16: Mass Balance for Pollutant Buildup (Subcatchments 1 and 2) Mass balance Subcatchment 1 Subcatchment 2 Initial buildup (lbs) Total mass accumulated on the ground (lbs) Total mass washed out (lbs) Mass remaining on the ground (lbs) Discussion The FloGard DVS-48 achieved an average annual removal efficiency of %, in an area of 1.25 acres, after considering the desing operating rate equal to the peak flow rate generated by the design event with uniform intensity. This value is higher than the 60.5 % verified by NJCAT. The same device achieved a removal efficiency of 89.75% over an area 5 times smaller when sized considering the desing operating rate of the device, equal to the peak flow generated by the desing event with non-uniform intensity distribution. This removal efficiency is significantly higher than the verified 60.5 %. The use of uniform rainfall distribution is already a conservative criterion. The use of non-uniform rainfall distribution is even more conservative. For the case where the runoff mass was considered to be uniformly distributed within the storm event, the continuous simulation yielded a removal efficiency of

79 % which is closer to the removal efficiency verified by NJCAT. This result is consistent with the fact that the application of NJDEP weighting factor is also based on the assumption that the runoff mass is uniformly distributed through the event.

80 68 Chapter 7 Conclusions The current NJ stormwater rules define the water quality design storm as 1.25 inches of rainfall non-uniformly distributed over two hours. This is a significant change from the previous definition of uniform rainfall distribution. The current definition leads to a higher peak runoff rate in small watersheds in comparison with the previous definition although the runoff volume remains similar. This study indicates that defining the design flow rate as the peak flow rate generated by the water quality design storm with uniform intensity is more consistent with the NJDEP weighting factors used by NJCAT to quantify the average annual solids removal efficiency. If the design operating rate of the devices is specified as the peak flow rate generated by the water quality design storm with non-uniform distribution, then the average annual solids removal efficiency should be calculated using the weighting factors derived from frequency distribution of the peak runoff rates as a percentage of the peak runoff generated by the non-uniform water quality design storm. The most scientifically appropriate method to predicting the average annual removal efficiency performance of manufactured treatment devices is the continuous simulation. However this method requires calculations of flow rates and pollutant concentrations at small time steps such as ten minutes. Application of such a continuous modeling approach also requires specific technical knowledge on hydrologic computational models. Moreover, the procedure is difficult to standardize and the results are difficult to review by the environmental regulatory agencies. Therefore, alternative of

81 69 using the specified weight factors is expected to continue, and this study has contributed to better understating and quantification of the weight factors.

82 Appendix 70

83 71 Appendix A Estimation of TSS Removal Efficiency in Sedimentation Devices A.1 Introduction The environmental agencies in California, Illinois, Massachusetts, New Jersey and Pennsylvania defined a process for the reciprocal evaluation, acceptance and approval of environmental technologies among the six states. The process enables participating states to consider data, evaluations, verifications, certifications, approvals and permits from another participating state as if they had been produced in their respective states (TARP 2000). The partnership has developed a Protocol for Stormwater BMP demonstrations which specifies the guidelines and requirements that participants manufactures must follow in order to fulfill the Reciprocity Partnership Agreement. The protocol sets requirements for the selection of the sites where field testing will be developed and the requirements for analytical laboratory testing. However particle size and runoff concentration distribution can variate significantly from one region o the other. Therefore the State of New Jersey environmental agencies should adjust the TSS removal efficiency, claimed in other states, to its own characteristics, regulations and needs. For instance, the StormVault system, manufactured by CONTECH Stormwater Solutions Inc., was evaluated in the State of Virginia resulting in a final claim of 86% TSS removal efficiency. The device was presented later in the NJCAT verification program. However, as mentioned before, the effects of the particle size distribution and concentration proper of New Jersey, are unknown and therefore is difficult to obtain an exact estimation of the removal efficiency that the device would achieve in the State.

84 72 The StormVault operates as a sedimentation device with a permanent pool that enhances the removal efficiency process. Wet Ponds operate in a similar fashion and their TSS removal efficiency is declared by the State of New Jersey in the New Jersey Stormwater BMP Manual(NJDEP 2004). This chapter estimates the minimum removal efficiency that the New Jersey State could assign to the StormVault based in its similarity with the wet ponds. A.2 Stormvault TM A.2.1 System Description StormVault is a below grade sedimentation vault that uses a permanent pool. Firgure A.1 shows the configuration of the device and its internal parts. Figure A.1: StormVault TM (NJCAT. 2007b) Energy Baffle: Generates a laminar flow by dissipating inflow energy. Collects

85 73 floatable trash and debris Sediment Baffles: Generates laminar flow inside the vault and prevents the resuspension of the sediments. Provides storage area for settled particles Multiple Accesses: Provides entrance to the device for observation and maintenance Trapezoidal Exit Baffle: Stabilizes velocity and prevents floatables from reaching exit orifice Control Orifice: Controls the drain down time from the device. Large Outlet Screen: Prevents blockage of the orifice Diversion Weir: Bypass flows greater than the hydraulic capacity of the device Permanent Pool: Aids in the sedimentation process A.2.2 Technical Performance Claim After complying the field and laboratory tests required by the New Jersey Certification Process, under the established protocols, Contech Stormwater Solutions claims the followoing: StormVault TM,designed with a permanent pool volume equal to the ASCE/WEF stormwater quality storm runoff volume and with an active storage of 6-hours detention time, has demosntrated a total suspended solids (TSS Standard MEthod 2540D) removal efficiency of 86% with 95 % confidence intervals of 81 % and 91 % for a sandy loam texture sediment in the field using the NJDEP TARP/Tier II Protocol A.3 Detention Basin A wet pond is a excavated stormwater BMP, with a permanent pool, that provides permanent and temporary storage of stormwater runoff. It has an outlet structure

86 74 that detains and attenuates runoff inflows and promotes the settlement of pollutants. This BPM can be designed as multi-stage facilities that provided extended retention time, which enhance their effluent water quality(njdep 2004). A.3.1 Removal Efficiency Performance According to New Jersey Stormwater Regulations (see section 1.4) wet ponds should be designed to treat the runoff volume generated by the stormwater quality design storm (see section 1.4). The ratio of the volume of its permanent pool to the quality storm runoff volume will determine the TSS removal efficiency of the wet pond, depending also on the residence time. Residence time is defined by the New Jersey Stormwater BMP Manual as the period between the moment when the maximum storage volume is achieved until only 10 percent of that volume remains in the facility. parameters Figure A.2 shows the relation between the mentioned Methodology A StormVaul device was sized choosing as criteria, a ratio of permanent pool volume to stormwater quality storm runoff volume equal to 1. A watershed area of 1/3 acre was assumed in order to calculate the stormwater quality storm runoff volume. The storm was routed through the StormVault in order to determine its detention time. The outflow rates and detention time where determined through the storage relationship. See equation A.1 I=O+ ds dt (A.1) where: I= Inflow (cfs) O= Outflow (cfs)

87 75 Figure A.2: TSS Removal Rates for Wet Ponds (NJDEP 2004) ds/dt = Storage variation rate Through equation A.3 and spread sheet the outflow and storage at the end of each time increment Δt were determined In +I n+1 + ( ) 2S n Δt O n = 2S n+1 +O Δt n+1 (A.2) The flow through a free outlet discharge pipe is given by equation A.3: O = CYH 1 2 (A.3) where: Y = the cross-sectional area of the discharge outlet(ft 2 ) H = the head above the free outled elevaton (ft)