## 1. Introduction

[2] Anthropogenic and biogenic particulate matter (PM) transported in urban rainfall-runoff has been identified as a significant contributor to deterioration of surface water in the United States (U.S. Environmental Protection Agency (USEPA), Water quality assessment database 2000 305(b), 2000, available at http://www.epa.gov/305b/2000report/). Rainfall-runoff transports an entrained mixture of colloidal and noncolloidal PM, dissolved and complexed pollutants [*Lee and Bang*, 2000; *Sansalone et al.*, 1998]. The temporal particle size distribution (PSD) and composition of PM delivered by rainfall-runoff varies significantly between events and can vary spatially within the same watershed [*Sansalone*, 2002].

[3] Given the variability of transport in rainfall-runoff, the concept of an event mean concentration (EMC) is recognized as the most common and pragmatic index for water chemistry and PM in storm water management [*Strecker*, 1998]. Many models have been developed to predict source area EMCs with EMCs serving as inputs to transport or treatment tools. *Brezonik and Stadelmann* [2002] developed regression models to predict EMCs from urban catchments. *Chen and Adams* [2006] developed an analytical model to estimate storm water EMCs on the basis of rainfall-runoff transformations and buildup-wash-off models; as an alternative to continuous simulations. *May and Sivakumar* [2008] suggest artificial neural networks can improve traditional statistical models in predicting EMCs. Such advances have allowed quantification and prediction of EMC-based responses for watersheds. However, subject to unsteady flows for which rainfall-runoff and PM transport are coupled phenomena in urban source area watersheds; the question is if a single EMC or flow statistic can reproduce treatment phenomena for a physical unit operation system.

[4] The state-of-the-practice in urban drainage treatment for PM separation tends toward application of wastewater clarification principles, which assume reasonably steady flow and reasonably constant primary influent PM granulometry [*Wilson et al.*, 2007] allowing autosampling. In contrast, the rainfall-runoff process and coupled PM and dissolved constituent delivery are highly variable, and in situ unit operations are subject to such coupled and unsteady phenomena. Urban rainfall-runoff models such as the Storm Water Management Model (SWMM) [*Rossman*, 2007; *Huber and Dickinson*, 1988] use a variety of hydrographs and pollutograph input models. Such models that allow the coupling of complex hydrology and pollutant transport have greatly improved the efficacy of design considering the unsteady and interrelated loading parameters. However, the implementation of parallel unsteady modeling for urban drainage treatment design is only occasional at best. *Huber* [2006] reports that in many application of SWMM for modeling hydrodynamic devices, the functionality of the unit is based on a black box approach that neglects maintenance, malfunctioning, poor sizing and maximum treatment rates. Many times such an approach must rely on empirical formulae based on influent and effluent data for a given unit operation, and extensive calibration is required [*Artina et al.*, 2007]. *Urbonas* [1995] synthesized and categorized a list of parameters for selection and design of unit operations to develop selection and comparison strategies. In addition, *Tsihrintzis and Hamid* [1997] report in their review of storm water management that no single class of unit operations and processes is applicable to every situation and identify the need for careful design based on a set of parameters which include inflow and outflow EMCs for water chemistry constituents.

[5] Hydrodynamic separation (HS) has been suggested as a viable urban drainage physical unit operation [*Rushton*, 2004, 2006; *Brombach*, 1987; *Brombach et al.*, 1993; *Pisano and Brombach*, 1994; *U.S. Environmental Protection Agency*, 1999; *Andoh and Saul*, 2003]. Previous studies of PM separation mechanisms by HS units have ranged from simplified overflow rate theory [*Weib*, 1997] to semiempirical approaches based on assumptions of vortex flow behavior [*Paul et al.*, 1991]. *Fenner and Tyack* [1997] and *Fenner* [1998] report that similitude analysis does not yield a single dimensionless group for HS scale up. Because of the complexity and variability of runoff and PM transport, analyses of HS units have been based on a pragmatic EMC approach. However, such an approach does not capture the PSD and hydrologic coupling as commonly facilitated through the use of automated samplers programmed on a flow-weighted basis [*Rushton*, 2004, 2006].

[6] A recent alternative to steady flow and EMC approaches is computational fluid dynamics (CFD) which utilizes numerical methods to solve the equations of fluid dynamics, the Navier-Stokes equations [*Ferziger and Peric*, 2002]. CFD techniques, while computationally intensive, allow examination of complex flows and PM transport. The application of a CFD to model PM-laden flows is an active research area [*Curtis and van Wachem*, 2004; *van Wachem and Almstedt*, 2003]. *Pathapati and Sansalone* [2007, 2009] developed an experimentally validated CFD approach to accurately describe behavior of a screened HS, across a range of steady flow rates for an influent of constant PSD at a constant concentration (EMC) typically observed in urban rainfall-runoff. This study extends the CFD modeling to unsteady rainfall-runoff events with transient flow rates, and variable but flow-coupled influent PSDs and PM concentrations.