Review of Stormwater Quality, Quantity and Treatment Methods Part 2: Stormwater: Quality Modelling

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  • ABSTRACT

    In this paper, review of stormwater quality and quantity in the urban environment is presented. The review is presented in three parts. This second part reviews the mathematical techiques used in the stromwater quality modelling and has been undertaken by examining a number of models that are in current use. The important features of models are discussed.


  • KEYWORD

    Stormwater , Quality and quantity , Mathematical models , Analytical technique

  • 1. Introduction

    A review of stormwater quality and quantity in the urban environment is presented. The review is presented in three parts. The first part reviewed the mathematical methods used in stromwater quantity modelling. This second part reviews the mathematical techiques used in stromwater quality modelling and has been undertaken by examining a number of models that are in current use.

    2. Urban Runoff Quantity Problems and Models

       2.1. Pollutant Build-up and Wash-off Model

    2.1.1. Regression Model

    Tasker and Driver (1988) developed simple regression model on the basis of long term urban runoff data and made it applicable for the unmonitored watershed based on some physical (drainage area, impervious percentage, percentage residential or/and industrial) and climatological data (total rainfall, storm duration, mean annual rainfall).1) The model uses the following generalized regression formula for calculating loads:

    image

    where, L = pollutant load, Xn = physical, land use or climatic characteristics, βn ? = regression coefficients, and BCF = Bias Correction Factor

    The model parameters are estimated by a generalized-leastsquare regression method that accounts for cross correlation and differences in reliability of sample estimates between sites. The regression models account for 20 to 65 percent of the total variation in observed loads.

    2.1.2. Simple Empirical Model

    Schueler (1987) introduced an easy empirical equation based model known as Simplified Urban Nutrient Output Model (SUNOM) for urban pollutants load prediction based on five years data collected by United States Environmental Protection Agency (USEPA). The method uses the flow-weighted mean concentration. 2) The generalized equation is as follows:

    image

    where Lp = pollutant load, Hr = total rainfall (mm), Pj = percent of rainfall contributes to runoff (equals to 1 for individual storm events), Rv = runoff coefficients estimated as 0.05+0.009* (impervious percentage), C = flow-weighted pollutant mean concentration (mg/L), A = area (ha)

    According to Schueler, the simple method does not consider base flow runoff and associated pollutant loads, and is better used at small watersheds. The model is rarely appered in the literature. Recently, the model was applied by Flint (2004) to estimate water quality an ultra urban area in Maryland, US.3)

    2.1.3. Sartor and Boyd Model

    James Sartor and Gail Boyd first introduced this model in 1972 (Sartor and Boyd, 1972).4) This model provides the knowledge of pollutants transport and their quantification. The model shows the dislodging of the particles during a rainfall event is dependent on the street characteristics, rainfall intensity and the particle sizes where the wash off can be described by the following equation:

    image
    image

    where, P(t) is the amount of the pollutants washed off in time t, Po is the initial loading, k = wash-off coefficient, and I = rainfall intensity and t is the time and Q = rainfall volume

    Many models such as PSRM-QUAL are based on equation 26 (PSRM-QUAL Users Manual, 1996) and kinematic wave equations.5) Once the particle is dislodged the shear forces generated by the runoff cause its movement when the runoff is above the critical velocity (velocity at which drage force and resistance forces are equal). Critical velocity is given by

    image

    where Vcr is the critical velocity, Cd is drag coefficient, Cs is static coefficient of friction, g is gravitational acceleration constant, r is the average sediment radius, and SG is sediment specific gravity. United State Environmental Protection Agency (USEPA, 1979) estimated the pollutant load using the following equation.6)

    image

    where, M(t) is the pollutant washoff for time period t (kg), L(t - Δt) is the pollutant accumulation per unit area at the previous time period t - Δt (kg ha-1), A is the drainage area (ha), and Kw is the watershed washoff coefficient (mm-1) which is a function of imperviousness of the watershed and the type of simulation, i.e., single event or continuous.

    Haiping and Yamada (1996) applied Sartor and Boyd equation with refinement by adding some constants such as i) maximum amount of constituents on impervious areas (k1) and ii) removal due to wind and vehicles as well as biological and chemical decay (k2) besides wash-off coefficient.7) The amount of pollutant accumulation on impervious surface is given by

    image

    where, PoR is the residual amount of constituents on impervious surface after street sweeping or storm runoff in grams. Residual amount of constituents on impervious areas (PR) after wash-off in storm is given by;

    image

    where, k3is the wash-off coefficient in mm-1. Q is the total runoff volume (mm). The equation reflects both effects of accumulation in dry weather (Q=0) and wash-off in wet weathers (Q>;0). This can be used as a tool for continuous simulation of urban non point source pollution for a long term prediction.

    Furumai et al., (2003) applied Sartor and Boyd model with some modification in urban catchment in Japan. They assumed that the runoff from road and roof are different so that the washoff behavior as follows.8) They provided two different runoff coefficients for road and roof.

    image

    where, P(t) is the amount of the pollutants washed off in time t, k = wash-off coefficient, and I = rainfall intensity at time t, i = 1 and 2 the roof and road respectively.

    The above model (Furumai et al., 2003) was applied by Murakami et al., (2004) to predict the wash-off behavior of particlebound PAHs from road and roof and stated that the model could explain suspended solids and particle-bound PAHs runoff well except during and after heavy rainfall (>;10mm/hr).

    Aryal (2003) applied Sartor and Boyd model to predict the pollutants wash-off behavior in highway runoff. As this equation states that the quantity of the constituents available for washoff decreases exponentially with runoff volume during the event, the model could not be applied to the events where the two or more pollutants loading pattern were observed due to the change in rainfall intensity (intermittent rainfall) during wet weather period.9) The Sartor and Boyd model found not suitable to the events where two or more SS loading patterns observed. This indicated difficulty in applying the model in those runoff events where the rapid fluctuation of concentration occurred. The following equation establishes the relationship between concentration and the Sartor and Boyd model.

    image
    image

    where, C(t) = concentration, quantity/volume F(t) = A*R(t) = Flow rate (L/sec) A = Area (ha) R(t) = Runoff rate (mm/hr)

    This equation (11) shows that the primary difficulty of the Sartor and Boyd equation is that it always produces decreasing concentrations as a function of time regardless of the time distribution of runoff. This is counter-intuitive, since it is expected that high runoff rates during the middle of the storm might produce higher concentration than those proceeding. Aryal (2003) descretized the storm event and applied the model which he finally summed up to calculate the pollutant load.

    Egodawatta et al., (2007) also applied the modified version of Sartor and Boyd model by introducing the capacity factor (CF). They reported that a storm event has the capacity to washoff only a fraction of pollutants available and this fraction varies primarily with rainfall intensity, kinetic energy of rainfall and characteristics of the pollutants. They then modified the Sartor and Boyd equation in order to incorporate the wash-off capacity of rainfall by introducing the ‘capacity factor’ CF. According to them, the fraction wash-off can be written as

    image

    where, CF is the value ranging from 0 to 1 depending on the rainfall intensity. Other factors such as road surface condition, characteristics of the available pollutants and slope of the road may also have influence on CF.

    Chen and Adams (2007) also applied the Sartor and Boyd wash-off with refinement by introducing the pollutants accumulation rate based on Osuch-Pajdzinksa and Zawilski (1998) that can accommodate the dry weather period also.10,11) The rate of pollutant accumulation is:

    image

    where Mb is amount of pollutant per unit area on catchment surface, h is the fraction of the impervious area of the catchment, md is a constant rate of pollutant deposition (dust fall), mw is the quantity of street sweeping effectiveness parameter, η is the street sweeping effectiveness parameter, kb is a constant pollutant removal rate, b is the time elapsed since the last rainfall, β1  is the conversion of the mass of particulate matter into a parameter of a given type of pollutant and  β2 describe the conversion of mass of sweeping into a parameter of a given type of pollutant. Integrating the above equation, Mb is:

    image

    where, Mo is residual pollutant mass not washed off by the previous runoff event.

    In their study they assumed that the rate of pollutant washoff from the catchment surface is proportional to the amount of pollutant build-up on the catchment surface and is directly related to the volume of runoff.

    image

    where r v is the average runoff rate in mm/hr, kw is the decay or washoff coefficient, in mm-1. Performing integrated yields

    image

       2.2. Advective Diffusion Model (Mass Transport Equation)

    It is the one dimensional conservative advective-diffusion equation, that incorporates the advection and diffusion process is to describe the behaviour of a pollutant in stream;

    image

    where, C is the thermal energy or constituent concentration, t the time, x si the distance, u is the advection velocity, Ax the cross-sectional area, Dx is the diffusion coefficient and S (C, x, t) are all sources and sinks.

    This equation includes the advection of pollutants by the flowing water, diffusion of pollutants in the stream, constituent reactions, interactions and sources and sinks. Assuming that Ax and Dx are constants and using the flow continuity equation then:

    image

    Then

    image

    which is the form of the advective-diffusion equation used in model like HEC-5Q and WQRRS.

    Shaw et al., (2006) proposed a new stochastic physical model that is primarily focused on the rain flow transportation.12) The model was mainly based on Hairsine and Rose (1991) which states that the flow does not exceed the threshold for particles entrainment, mass conservation of suspended particles in the water layer:

    image

    where, e is rate of particles enter the shallow flow by raindrop-induced ejection, h is the rate of particle settle-out of the shallow, Ms is the suspended particle mass (g cm-2), x is the down slope distance, and v is the fluid velocity (cm min-1)

    Particles mass on the surface, Mg (g cm-2), at a distinct spatial position is given by:

    image

    The value e was defined by e = aPMg

    where, a (cm-1) is an experimentally determined “detachability” constant that accounts for mass loss per drop and P is the precipitation rate (cm min-1). Similarly particle settling rate is given by (Equation Ommited) where α adjusts bulk concentration to account for variations near the surface, vset is the particle settling velocity (cm s-1), and D is the depth (cm).

    They also applied water balance at a pint x by using the equation

    image

    where, P is the rain intensity per unit width (mL min-1 cm-1), q is the flow reate per unit width (mL min-1 cm-1) and qo (mL min-1 cm-1) is the constant upslope inflow per unit width.

       2.3. Kinematic Wave Equation Model

    It is another governing one-dimensional equation for pollutant transport on a unit width basis, where solute is injected instantaneously, can be written as

    image

    where, C is solute concentration defined as mass of solute per unit volume of water, w is the mass of solute (M) per unit area A of the plane (M/A), w is the mass of pollutant per unit surface area and δ(t)is the instantaneous unit flux of the solute (I/T). The pollutant discharge Qs is defined as

    Qs = CQ

       2.4. Other Stormwater Quality Models

    2.4.1. SWMM

    SWMM is one of the most successful model produced by United States Environmental Protection Agency (US-EPA). This model is not exclusively designed for urban drainage and single-event or long term (continuous) simulation. The earlier SWMM model used the linear build up formulation. The model provides three options for pollutant build up as follows:

    image

    Among the above equation, exponential and Michaelis-Menton functions clearly define asymptotes or upper limits. Upper limits for linear or power function build-up may be imposed if desired. The wash-off equation as follows: using an exponential washoff equation as follows:

    image

    where, P(t) is the washoff load rate at time t, Kw is the washoff coefficient, R is the runoff rate (mm/hr), n is the power of runoff rate, and P is the amount of pollutant remaining on the catchment.

    As mentioned above earlier, primary difficulty in this equation is always producing decreasing concentrations as a function of time regardless of the time distribution of runoff (Aryal et al., 2003). This problem is overcome in SWMM by making washoff at each time step, P(t), proportional to runoff rate to a power, Kw :

    image

    where, P(t) is constituent load washed off at time, t, quantity/sec, Po (t + Δt) is quantity of constituent available for wash-off at time, t, (e.g., mg), Kw is wash-off coefficient and R(t) is runoff rate.

    It may be seen that if equation is divided by runoff rate to obtain concentration, then concentration is now proportional to ?rKw-1. Hence, if the increase in runoff rate is sufficient, concentrations can increase during the middle of a storm even if PSHED is diminished.

    From the basic equation (48), the wash-off parameters, washoff coefficient and exponent are determined from a finite difference approximation (Nix, 1994) which produces:13)

    image

    where, Po (t + Δt) is the amount of pollutant wash-off during simulation time step ((t + Δt), Po (t)is the amount of pollutant on land surface during a time step (t), Kw is the wash-off or decay coefficient, Δt is the time step, 0.5[R(t)n + R( t + Δt )n] is average runoff rate over a time step and n is the power function of runoff rate.

    2.4.2. HydroWorks/Infoworks

    HydroWorks/InfoWorks calculates the surface pollutant build up for each subcatchment, during a build up (or dry weather) period, before a rainfall event. The basic hypothesis is one of a time-linear accumulation of pollutant on the ground, which depends on the type of activities present on the catchment/subcatchment or in the vicinity. The build-up equation is based on hypothesis that on a clean surface the rate of pollutants accumulation is linear but as the surface mass increases the accumulation rate decays exponentially. The build-up equation is written as:

    image

    where, M is mass of the deposit per surface unit (kg/ha), P is build-up factor (kg/ha/day), kd decay factor (1/day)

    The software carries out the following process to determine the build up of pollution for each subcatchment: i. Determine the decay factor ii. Determine the build-up factor iii. Determine the mass of deposit at the end of the build-up period:

    image

    where, Mo is the mass of sediment at the end of the build-up period (kg/ha) Md is the initial mass of deposit in kg/ha (from catchment sediment data (.CSD) file)., kd is the decay factor (day-1), Nj is the duration of the dry weather period (days), and Ps is the build-up factor (kgha-1day-1).

    The surface wash-off model is based, as the runoff module, on the single linear reservoir model. The model consists of sediment erosion and its wash-off. First the amount of sediment eroded from the surface and held in suspension in the storm water is calculated. Then the amount of sediment washed into the drainage system is calculated using a single linear reservoir routing method. The amount of sediment washed into the drainage drainage system is calculated as

    image

    where, Me is mass of sediments dissolved or in suspension per unit active surface (kg/ha), M(t) is mass of surface deposit pollutants (kg/ha), Ka is erosion/dissolution coefficient (1/s) and is calculated as

    image

    where, i(t) is effective rainfall and C1, C2 and C3 are coefficients. Sediment wash-off is given by

    image

    where, k is linear reservoir coefficient f(t) is sediment flow Then the sediment flow per subcatchment (kg/s) is calculated (sediment inflow to each node)

    image

    where, C is portion of subcatchment, A is subcatchment area.

    2.4.3. MOUSE Trap

    The MOUSE TRAP model provides several submodules for the simulation of sediment transport and water quality for both urban catchments surfaces and sewer systems. Since pollutants are carried by sediment, the model tries its best to correlate sediment transport process and water quality in sewer systems. Mouse Trap can also model the first flush phenomenon based on temporal and spatial distribution of sediment on the catchment surface and sewer system. Surface Runoff Quality (SRQ) computes the pollutant build-up and transport on catchment surfaces. Two major processes that are involved in SRQ are:

    1. Build-up and wash-off of sediment particles on the catchment.

    2. Surface transport of pollutants attached to the sediment particles.

    2.4.4. MUSIC

    MUSIC is one of the most popular stormwater model used in Australia developed by Cooperative Research Centre for Catchment Hydrology (CRCCH) Australia (CRCCH 2005).14) The model uses simple first order kinetics for the pollutant wash- off from the surface. According to the model the pollutant concentrations in the parcel tend to move by an exponential decay process towards an equilibrium value for that site at that time.

    image

    where, Cout is the output concentration, C* is the equilibrium value or background concentration, Cin is the input concentration, k is the exponential rate constant and q is the hydraulic loading (flow rate per surface area) of the treatment measure.

    2.4.5. ASTROM

    ASTROM model uses the following pollutant build up equation.

    image

    where, Mb is the amount of pollutant per unit area on the catchment surface (kg/m2), ko is constant rate of pollutant deposition (kg/m2.h), kb is constant pollutant removal rate (h-1), and b is inter-event time.

    Integrating above equation yields

    image

    where, Mm (= ko/kb) represents the maximum amount of pollutant buildup (kg/m2) and Mo is residual amount of pollutant after the previous runoff or street sweeping event (kg/m2).

    The pollutant washoff model is defined as

    image

    where, l is mass of pollutant washed off per unit area per rainfall event (kg/m2), vr is runoff event volume (mm) and kw is pollutant wash-off coefficient.

    The model assumes that rainfall event pollutant wash-off load is proportional to, or dependent upon, the accumulated pollutant mass on the catchment surface before the runoff event, and the pollutant wash-off load is a direct function of runoff volume.

    Besides, there are several literatures appeared to describe the wash-off behaviour of pollutants during wet weather period. Here, few are described.

    Kim et al., (2005) introduced new wash-off model for highway stormwater runoff that incorporates many parameters such as antecedent dry weather periods, rainfall intensity and runoff coefficient.15) The equation can be initially expressed as

    image

    where, C(t) is pollutant concentration; Q(t) is runoff flow rate discharged at time t, α is wash-off rate coefficient, C(t) is pollutant concentration at time t ; VTRu is total runoff volume (Equation Ommited) which they solved and rearranged in the form

    image

    where, M(t) is the pollutant mass emission rate at time t, VnRu (t) is the normalized cumulative volume, 0≤VnRu (t)≤1.0

    image

    where, δ is an initial concentration related to antecedent dry weather period. The parameters α and γ* are related total runoff. The β* is related to rainfall, runoff coefficient, and storm duration.

    This model has two different functions. The first is linear, γ *VnRu (t)+δ , and the second takes the form of a gamma type function, β *VnRu (t).Exp[?α .VnRu (t)]. To use this model it is necessary to predict the total runoff volume, which must be based upon weather forecast or other information.

    Kanso et al., (2006) applied simple classical pollutant accumulation followed by the wash-off model to describe the water quality.16) He described two accumulation behaviours. The first equation calculates the accumulation of pollutants assumed to follow an asymptotic behaviour that depends on two parameters: an accumulation rate Da (kg/ha/day) and a dry erosion rate De (day-1).

    image
    image

    where, Ma(t) (kg) is the available pollutant’s mass at time t and Si (ha) is the impervious area. The model depends on two parameters: an accumulation coefficient Ka and maximum accumulated mass M1. It is supposes that the accumulation is proportional to the mass still to be accumulated before reaching the maximum M1, which is equivalent to the (Da / De).

    He described the evolution of the available pollutant mass during the stormwater period by applying the following equation.

    image

    (mg/l) is the SS concentration produced by erosion, q(t) is the discharge (m3/s) at the outlet of the watershed at time t, and I(t) is the rainfall intensity (mm/hr).

    3. Conclusion

    This paper reviews mathematical methods used in stomwater quality modelling and has been undertaken by examining a number of models that are in current use. The analytical techniques are presented in this paper. The important feature of models is discussed.

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