Advanced Monitoring and Numerical Analysis of Coastal Water and Urban Air Environment pp 33-69 | Cite as

# Numerical Simulation of Urban Coastal Zones

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## Keywords

River Discharge Particulate Organic Carbon Tidal Current Storage Tank Fecal Coliform## 3.1 Numerical Modeling in Urban Coastal Zones

Numerical modeling is an essential technique for the understanding and management of water quality in urban coastal zones. This may be because urban coastal zones are characterized by an extremely wide variety of environments with complicated geographical features, such as urban areas that intersect with outer oceans, and hence are affected by both. The phenomena in this area are not only physical, but also biological or chemical, and they interfere with each other. Therefore, the ecosystem and the water quality of urban coastal zones are highly complicated.

The quality of water has long been deteriorating at many of the world's urban coastal zones (Walker 1990). Damage caused by events such as red tides, harmful algal bloom, and decreased amounts of dissolved oxygen in bottom water occur frequently. Such phenomena induce the degradation of aquatic ecosystems and the loss of aquatic resources such as sea grass beds, as well as fish and shellfish. These phenomena — caused by an excessive inflow of nutrients that has been accelerated by urban populations, resulting in the increase of concentrations called eutrophication (Caperon et al. 1971) — have been causing many problems for fisheries and recreation areas in urban coastal zones. However, these events are also influenced by weather conditions, ocean currents, household and industrial wastes, nutrients coming from agricultural lands, sewage treatment plants, acid rain and other such phenomena (Ærtebjerg et al. 2003). Furthermore, the interactions between all of these factors are quite complicated. Actually, distinguishing between natural and anthropogenic effects is not easy (Jϕgensen and Richardson 1996). This makes the improvement of water quality and the management of resources in this area even more difficult.

Numerical modeling that deals quantitatively with these complex systems has been in development since the 1960s, much like digital computers. The use of modeling also has important practical applications, such as the prediction and management of water quality and ecosystems in urban coastal zone.

This chapter describes how water quality and pathogens are modeled in urban coastal areas. Sect. 3.1.2 explains the basic structure of these models. Physical and ecosystem modeling techniques for enclosed bays are described in Sect. 3.1.3. Sect. 3.1.4 shows the application of ecosystem modeling as a tool for integrated management.

Next, Sect. 3.2 describes pathogen modeling in urban coastal areas.

### 3.1.1 Introduction to Physical Numerical Modeling in Urban Coastal Areas

Water quality is directly and indirectly influenced by a variety of flows. For example, since phytoplankton drifts passively according to water flows, it is strongly influenced by the distribution of the flow of the bay (Lucas et al. 1999). Bay water motions also exert a strong influence on other water quality parameters. These kinds of influences are direct and obvious. The degree of influence naturally increases with the strength of the currents.

In contrast, even if the flow is very small, it has some spatial patterns in common with outer bay directions. In this case, a nutrient load that is discharged at the head of the bay is transported to distant locations and, probably, the water exchange rate of the bay also increases. The nutrient loading that is permissible — i.e. which the bay can accept — will then increase due to the increase in the water exchange rate. For water retention rates, the strength of currents is not as important as spatial patterns. For example, tidal currents are dominant in urban coastal areas. However they are oscillatory. Water particles in tidal currents move to the head of the bay during flood tides, but move back to the mouth of the bay during ebb tides. As a result, tidal currents do not substantially transport water particles. In contrast, density currents are clearly weaker than tidal currents, but they flow in one direction continuously and transport water particles more efficiently. As a result, they substantially effect water retention times and ecosystem characteristics.

A water quality model for bays must therefore consist of a three-dimensional circulation model and an ecosystem model that describes pelagic and benthic aspect of nutrients cycling. This section focuses on the physical modeling and the next section deals with water quality modeling. The final section discusses the application of these models to Tokyo Bay.

### 3.1.2 Three-Dimensional Hydrodynamic Model

Many three-dimensional hydrodynamic models have been developed in the last decades, including POM (Princeton Ocean Model; Blumberg and Mellor 1987), CH3D (Curvilinear Hydrodynamics in 3 Dimensions; Johnson et al. 1993) and ROMS (Regional Ocean Modeling System; MacCready et al. 2002 ; Li et al. 2005). These models solve the Navier-Stokes equation with the forcing (the wind stress, Coriolis force and buoyancy force) under adequate approximations that are called the hydrostatic and Boussinesq approximations.

Hydrostatic approximation assumes that there is a perfect balance between pressure gradients and gravity: in other words, no acceleration occurs in a vertical direction. This is justified because the aspect ratio of urban coastal areas is extremely small, and hence the vertical motions are considered to be small and also to be further inhibited by gravitational forces under stable density stratification. This means that vertical acceleration is negligible and the fluid behaves as though it were under static equilibrium as far as vertical motion is concerned (Prudman 1953).

_{0}everywhere except in terms involving gravitational acceleration constant g. Under such approximations, the governing equations are transformed as follows:

Here, *t* stands for time. *u, v* and *w* are the velocity components in *x, y* and *z* directions. The symbol ρ′ is the reference density and it is defined as ρ = ρ_{0} + ρ′. The symbols *A* x*A* _{y} and *A* _{z} are the eddy viscosities in *x*, *y* and *z* directions. The symbol *g* is the acceleration due to gravity.

#### 3.1.2.1 Grid Systems of Urban Costal Model

All currents including tides, wind-driven currents, and density currents in urban coastal zones are strongly influenced by geometry and bathymetry, whereas these areas are rarely regular in shape. In particular, a coastline near an urban coastal area is more complex, due to reclamations and the constructions of harbors, than a natural one. In addition, the uniformity of the bathymetry further lessened as a result of dredging for vessel transport. A computational grid is required to accurately represent such complex geometry and bathymetry. For this reason, the selection of which grid system to use has varied along with the progress of modeling, although the governing equations are not basically different.

*z*-coordinate vertical grid) and sigma-coordinate grids have been widely used. A Cartesian grid is easy to understand and shows the correspondence between program codes and governing equations. It is sometimes more accurate than sigma-coordinate grids, especially if the bathymetry of the bay is simple and mild. The sigma-coordinate system tends to have an error featuring the presence of steep-bottom topography. However, unless an excessively large number of vertical levels are employed, the Cartesian grid fails to represent the bottom topography with satisfied accuracy.

The sigma-coordinate system is convenient in the sense that it can essentially introduce a “flattening out” mechanism for variable bottoms at z = −h(x, y). The flow near the seabed is also calculated well. Moreover, the sigma-coordinate system is easy to program since the number of vertical grids can be the same, and the setting of boundary conditions will be simple. The sigma-coordinate system has long been widely used in both meteorology and oceanography (Phillips 1957 ; Freeman et al. 1972).

where, *t* stands for time. *u, v* and *s* are the velocity components in *x*, *y* and *z* directions in the *s* coordinate system. *h* is change in surface elevation, *h* is initial water depth, and *H* is the total water depth (*H* = *h* + *h*).*f* is the Coriolis coefficient and *P* stands for pressure. ρ and ρ′ are the constant reference density and the deviation from it, and ρ = ρ_{0} + ρ′*A* _{ H } and *A* _{ V } are the horizontal and vertical eddy viscosity coefficients, respectively. *g* is acceleration due to gravity.

Recently, the stretched grid system (S-grid system) has also been popular. This grid system is an extension of sigma-coordinate system. Generally, a sigma-coordinate grid divides the vertical coordinate into an equal number of points. The S-grid system has a higher resolution near the surface and bottom (Haidvogel et al. 2000).

Wind-stress and bottom friction are considered at surface and bottom boundary conditions, respectively. Settings in boundary conditions are easy in sigma-coordinate systems since they use the same number of vertical grids. At lateral boundaries, normal velocities are set at zero, and a free slip condition is applied to the friction terms. At open boundary, the velocity gradient is set at zero.

This is extended to a nested grid system in which finer grids are used in regions to yield detailed information.

#### 3.1.2.2 Density Effect Modeling for Urban Coastal Waters

Estuarine circulation also plays an important role in the nutrient cycles of stratified bays. Organic matters are deposited on the seabed after phytoplankton blooms or river runoffs. They are decomposed by bacteria in the seabed. These nutrients are supplied from the seabed under anoxic condition in summer.

In order to include density effects in the numerical model, conservation equations for temperature and salinity are also included. Then, to obtain a realistic prediction for vertical stratification, a turbulent closure model is employed (Mellor and Yamada 1982). Consequently, flows driven by various mechanisms—e.g. the gravitational, wind-driven, and topographically induced flows—can be reproduced within physical numerical models.

Here, *T* and *S* stand for temperature and salinity, respectively. Heat balance and moisture balance at surface are considered as surface boundary condition for temperature and salinity, respectively. *C* _{ P } and *Q* stands for specific heat coefficient and net surface heat flux at the surface, respectively. *R* stands for river discharge. *k* _{H} and *k* _{V} are the horizontal and vertical eddy diffusion coefficients, respectively.

#### 3.1.2.3 Turbulence Closure

*V*

_{A}and

*V*

_{K}are obtained by appealing to the second order turbulent closure scheme of Mellor and Yamada (1982), which characterizes turbulence by equations for the kinetic energy of turbulence,

*q*

^{2}, and turbulence macro scale

*l*, according to:

*W*̄ is defined as follows:

The stability functions *S* _{ M } *S* _{ H }, and *S* _{q} are analytically derived from alge-braic relations. They are functionally dependent on \(\frac{{\partial u}}
{{\partial z}},\frac{{\partial v}}
{{\partial z}},g\rho _0 \frac{{\partial \rho }}
{{\partial z}}\), *q* and *l* These relations are derived from closure hypotheses described by Mellor (1973) and later summarized by Mellor and Yamada (1982).

#### 3.1.2.4 Numerical Scheme

A semi-implicit finite difference scheme has been adopted where equations are discretized explicitly in the horizontal direction and implicitly in the vertical direction. An Arakawa C staggered grid has been used with the first order upwind scheme. The tri-diagonal formation of the momentum equation is utilized and in combination with the mass conservation equation, an algebraic equation is obtained where the only unknown variable is the surface elevation η in implicit form. This algebraic equation is solved through the successive over relaxation (SOR) method.

### 3.1.3 Ecosystem Modeling of Coastal Regions

The phenomena in urban coastal zone are not only physical but also biological or chemical, each of which relates to the other. The ecosystems and water quality of urban coastal zones are highly complicated. To deal with these complex systems, a water quality model is composed of a three-dimensional physical circulation model and an ecosystem model that describes pelagic and benthic aspect of nutrients cycling. The pelagic and benthic systems also have interactions with each other.

In ecosystem models, each water quality variable is often called a compartment. Various kinds of models are also proposed for ecosystem models (Kremer and Nixon 1978 ; Fasham et al. 1990 ; Chai et al. 2002 ; Kishi et al. 2007). Some models, such as CE-QUAL-ICM (Cerco and cole 1995), Delft3D-WAQ (delft Hydraulics 2003), MIKE3_WQ [Danish Hydraulic Institute (DHI) 2005], RCA&ECCOM (Hydroqual 2004), etc., have been widely used to simulate water quality in estuaries and in the ocean.

Each model is developed with basic aquatic compartments such as phytoplankton, zooplankton, and nutrients. Some differences in the modeling of sediment, detritus, and the detailed modeling of phytoplankton exist, depending on the target ecosystems and the objectives of the study. As a result, no fully adaptive model applicable for all water bodies exists. If we were to make a model that could be adapted for all areas, its results would be too complex to discuss. It would not be so different from observing the real world. For example, ocean ecosystem models tend to focus only on pelagic systems. They tend to ignore benthic modeling, since the open ocean is deep enough to prevent the return of detritus to the seabed. Meanwhile, ocean ecosystem models generally deal with some metals in order to represent the limiting factor of phytoplankton. These metals are fully abundant in urban coastal zones. However, they are often depleted during phytoplankton growth in the open ocean. On the other hand the concentration of phytoplankton in coastal areas is highly variable both spatially and temporally as compared to the open sea. Subsequent sedimentation of this bloom also constitutes a major input to benthic ecology (Waite et al. 1992 ; Matsukawa 1990 ; Yamaguchi et al. 1991). To represent these phenomena, ecosystem models of coastal zones usually cover benthic systems.

Ecosystem models solve conservation equations for relevant components with appropriate source and sink terms. This is the same as temperature and salinity modeling in physical models, as explained in Sect. 3.1.4.

where *C* denotes concentration of the water quality variable and *t* is time. Fluxes into and out of the target control volume are calculated by using physical model results. *S* (*x,y*, σ, *t*) represents sources or sinks of the water quality variable due to internal production and the removal of the biogeo-chemical effect. It also represents the kinetic interactions of each compartment. *W* (*x,y*,σ,*t*) represents the external inputs of the variable *c*.

For example, phytoplankton constitutes the first level in the food chain of the pelagic ecosystem of bays. Phytoplankton photosynthesizes by using sunlight and increases. At this time, the source term *S* of phytoplankton is increased depending on the amount of photosynthesis that takes place. Phytoplankton is then decreased by the grazing of zooplankton. The source term S of zooplankton is increased along with this grazing, and the source term of phytoplankton is decreased. Ecosystem models basically express the relationship of each compartment through mathematical expressions. These models provide a quantitative description of the influences of physical circulation on the biological and chemical processes of urban coastal zones.

#### 3.1.3.1 Outline of an Ecosystem Model Description

Since the basic structure of the model follows the widely applied CE-QUAL-ICM (Cerco and Cole 1993, 1995), this section mainly focuses on our modifications of the CE-QUAL-ICM model in the following description.

#### 3.1.3.2 Phytoplankton and Zooplankton Modeling

This model deals with four phytoplankton groups. Phyd1 is based on *Skeletonema costatum*, which is a dominant phytoplankton species in Tokyo Bay. Phyd2 represent a winter diatom group (such as *Eucampia*). Phyr is a mixed summer assemblage consisting primarily *of Heterosigma akashiwo* and *Thalassosira*. Phyz denotes the dinoflagellates. These four phytoplankton assemblages have different optimal levels of light for photosynthesis, maximum growth rates, optimal temperatures for growth, and half saturation constants for nutrient uptake. Diatoms only use silica during growth.

*x*=

*d1, d2, r, z*, denote each phytoplankton assemblage. The phytoplankton growth rate μ depends on temperature

*T*, on photosynthetically available radiation

*I*, and on the nutrient concentration of nitrogen, phosphorus, and silica :

_{max}(T) is the growth rate at ambient temperature, which relates μ

_{max}, the maximum growth rate μ

_{max}= μ

_{0}1.066

^{T}(Eppey 1972),

*T*

_{ opt }the optimal temperature of each plankton assemblage, and β

_{1}and β

_{1}are shaping coefficients,

*k*

_{ No3 },

*k*

_{ No4 },

*k*

_{ po4 }and

*k*

_{ S }, which is the Michaelis of the half-saturation constant for each nutrient.

*I*is exponentially decreasing with water depth z according to:

where *I* _{ 0 } is shortwave radiation, and *par* is the fraction of light that is available for photosynthesis. *K* _{ w } *K* _{Chl}, and *K* _{ Sal } are the light attenuation coef-ficients for water, chlorophyll, and depth average salinity, respectively.

*L*(

*I*) represents the photosynthesis-light relationship (Evans and Parslow 1985),

*g*, which is a function of an ambient temperature:

where *k* _{ grz } is the predation rate at 20°C. Other phytoplankton loss terms are mortality, represented by the linear rate *m* _{ p }, where *W* _{ px } is the constant vertical sinking velocity for each phytoplankton.

Here β is the assimilation efficiency of phytoplankton by zooplankton, and *l* _{ BM } and *l* _{ E } denote excretion due to basal metabolism and ingestion, while the remaining fraction is transferred to the detritus. *M* _{ z }, is the loss coefficient of zooplankton mortality.

#### 3.1.3.3 Nutrients and Detritus Modeling

For example, certain labile compounds that are rapidly degraded, such as the sugars and amino acids in the particulate organic matter deposited on the sediment surface, decompose readily; others, such as cellulose, are more refractory, or resistant to decomposition.

Divide ratio of detritus

Detritus type | Basal metabolism | Predation | Mortality of zooplankton | ||
---|---|---|---|---|---|

Carbon | | 1 | 0.1 | 0.2 | |

| 0 | 0.45 | |||

| 0 | 0.35 | |||

Nitrogen | | 1 | 0.1 | 0.1 | |

| 0 | 0 | 0.45 | ||

| 0 | 0 | 0.35 | ||

| 0 | 0 | 0.1 | ||

Phosphorus | | 1 | 0.3 | 0.2 | |

| 0 | 0 | 0.35 | ||

| 0 | 0 | 0.15 | ||

| 0 | 0 | 0.3 |

where, *r* _{ dop } is the decomposition rate for DOP, *r* _{ DOP-min } is the minimum constant of DOP decomposition(day ^{−1}), *k* _{ po4 } is a half saturation constant of phosphate uptake, and *r* _{ DOP-di } is the acceleration effect of DOP decomposi-tion by diatoms.

The kinetics of the silica is fundamentally the same as the kinetics of the phosphorus. Only diatoms utilize silica during growth. Silica is returned to the unavailable silica pool during respiration and predation.

#### 3.1.3.4 Sediment Processes

The sediment system is zoned in two layers (see Fig. 3.6), an aerobic and an anoxic layer. Organic carbon concentrations in the sediment are controlled by detritus burial velocity, the speed of labile and refrigerate organic carbon decomposition, and the rate constant for the diagenesis of particulate organic carbon. The thickness of the aerobic layer is calculated by oxygen diffusion when the amount of oxygen at the bottom layer of the pelagic system isn't zero. The nutrient model, which is a simplified version of the model, treats the nutrients ammonium, nitrate, phosphate, and silica and their exchanges with the pelagic system. Silicate-dependent diatoms and non-silicate-dependent algae are distinguished.

#### 3.1.3.5 Dissolved Oxygen

*K*

_{ OC }is the oxygen to carbon ratio.

*K*

_{ ON }is the oxygen to nitrogen ratio.

*K*

_{ a }and \(\theta _a^{T - 20} \) denote the reaeration rate at 20°C and the temperature coefficient for reaeration at the sea surface, respectively.

*K*

_{ Nh 4}and \(\theta _{nh4}^{T - 20} \) are the ammonia oxidation rate at 20°C and the temperature coefficient.

*K*

_{ nit }is the half saturation constant of ammonia oxidation.

*K*

_{ RDOC }and \(\theta _{Ldoc}^{T - 20} \) are the RDOC mineralization rate at 20°C and the temperature coefficient for RDOC mineralization.

*K*

_{ LDOC }and \(\theta _{Ldoc}^{T - 20} \) mark the LDOC mineralization rate at 20°C and the temperature coefficient for LDOC mineralization.

*K*

_{ mLDOC }is the half saturation constant for LDOC mineralization. The concentration of DO saturation is proportional to temperature and salinity. Oxygen saturation value is calculated by using the following equation:

where *T* is temperature and *S* is salinity.

### 3.1.4 Applications of Models to Nutrients Budget Quantifications

In the last decade, a variety of water quality observation equipment has been developed. This has made it easier to measure water quality than in the past. However, even with advanced technology, measuring the flux of nutrients is not easy. To quantify the nutrients budget, we applied our numerical model to Tokyo Bay.

The computational domain was divided into 1km horizontal grids with 20 vertical layers. Computation was carried out from April 1, 1999 to March 31, 2000, with time increments of 300s provided by the Japan Meteorological Agency giving hourly meteorological data that included surface wind stress, precipitation, and solar radiation. At the open boundary, an observed tide level was obtained which can be downloaded from the Japan Oceanographic Data Center (JODC) of the Japan Coastal Guard. DON and DOP was obtained at 30% of TN, TP based on the observation results of Suzumura and Ogawa (2001) at the open boundary.

*a*concentration of greater than 50 μ g/l. Four different types of plankton assemblages are defined in this model. This model underestimated the chlorophyll-a concentrations from late September through October.

Phosphate is released from sediment in the same amount as that discharged from the rivers, and it is transported to the head of the bay by estuarine circulation. As a result, the amount of phosphate in the inner bay remains high. In contrast, nitrogen is mainly supplied from the river mouth and transported quickly out of the bay. In conclusion, nitrogen and phosphorus showed important differences in the mechanisms by which they cycle in the bay. The regeneration of nutrients and their release from the sediment is an important source for phytoplankton growth and is equal to the contributions from the rivers. Phosphorus in particular is largely retained within the system through recycling between sediment and water. These results denote the difficulty of improving the eutrophication of bays through the construction of sewage treatment plants alone.

## 3.2 Application for CSO Modeling

### 3.2.1 Introduction

Big cities have long been developed near waterfronts. Even now, naval transport remains one of the most important transportation systems, especially for heavy industries and agriculture. Today, many of the world's largest cities are located on coastal zones, and therefore vast quantities of human waste are discharged into near-shore zones (Walker 1990). Fifty percent of the world's populations live within 100 km of the sea. Many people visit urban coastal zones for recreation and leisure, and we also consume seafood harvested from this area. As a result, effluents released into the water pose a risk of pathogen contamination and human disease. This risk is particularly heightened for waters that receive combined sewer overflows (CSOs) from urban cities where both sanitary and storm waters are conveyed in the same sewer system.

To decrease the risk from introduced pathogens, monitoring that is both well designed and routine is essential. However, even though a surprising number of pathogens have been reported in the sea, measuring these pathogens is difficult and time consuming — not least because such pathogens typically exist in a “viable but non-culturable” (VBNC) state.

In addition, the physical environments of urban coastal zones vary widely depending on time and location. Their complicated geographical features border both inland and outer oceans, and so both inland and outer oceans affect them. For example, tidal currents, which are a dominant phenomenon in this area, oscillate according to diurnal periods. Even if the emitted levels of pathogens were constant and we could monitor the levels of pathogen indicator organisms at the same place, they would fluctuate according to tidal periods. Density stratification also changes with the tides.

Consequently, the frequent measurement of pathogens is needed to discuss the risk pathogens pose in urban coastal zones. However, this kind of frequent monitoring appears to be impossible. To solve this conundrum and achieve an assessment of pathogen risk, we developed a set of numerical models that expand upon the models developed in Sect. 3.1 and that include a pathogens model coupled with a three-dimensional hydrodynamic model.

Section 3.2.2 deals with the distributions of pathogens in urban coastal zones. The pathogens model is explained in greater detail in Sect. 3.2.3. Section 3.2.4 deals with numerical experiments that help to understand the effects of appropriate countermeasures.

### 3.2.2 Distributions of Pathogens in Urban Coastal Zones

In salt-wedge estuaries (Fig. 3-14, top), river water is discharged into a small tidal-range sea. The strength of the tidal currents decreases relative to the river flow. This creates a vertical stratification of density. As a result, river water distributes like a veil over the sea's surface and moves seaward. In contrast, bottom water moves to the river mouth and mixes with the river water. Under such conditions, pathogens move on the surface of the sea, and further mixing with low-density fresh water is restricted by stratification.

In partially mixed estuaries (Fig. 3-14, middle), the tidal force becomes a more effective mixing mechanism. Fresh water and sea water are mixed by turbulent energy. As a result, pathogens that are emitted from sewer treatment plants are more mixed than those in the static salt-wedge estuaries.

In well-mixed estuaries (Fig. 3-14, bottom), the mixing of salt and river waters becomes more complete due to the increased strength of tidal currents relative to river flow. Here, the density difference is developed in a horizontal direction. As a result, pathogens are mixed in the water column and settle down on the sea bed in turn contaminating estuarine waters during the spring tide or contaminating rainfall through re-suspension (Pommepuy et al. 1992).

### 3.2.3 Applications of Pathogens Models in Urban Coastal Zones

#### 3.2.3.1 Modeling of Escherichia coliform

The modeling of major pathogens of concern (including Adenovirus, Enterovirus, Rotavirus, Norovirus, and Coronavirus) is not usually conducted owing to the difficulty of modeling and the lack of observational data in coastal environments. We modeled *Escherichia coliform (E. coli*)by using experimental data in coastal sea water.

*E. coli*model (Onozawa et al. 2005). The mathematical framework employed in the

*E. coli*model takes the same approach that was explained in Sect. 3.1.3. The mass balance of

*E. coli*is expressed as follows:

where *Coli* denotes concentrations of *E. coli* (CFU/100 ml), and t is time. *Sink* represents the sinking speed of *E. coli.u* _{i} denotes flow speed for the calculation of the advection term. ϵ_{ i } denotes the diffusion coefficients. *sal* denotes the salinity-dependent die-offrate ( ppt/day). Sunlight is generally recognized to be one source by which bacteria are inactivated, due to UV damage to the bacterial cell (Sinton et al. 2002). However, this particular target area has high turbidity that rapidly absorbs UV rays at the sea's surface. As a result, this process has been ignored in this model.

Boundary conditions and grid resolution

Domain 1 | Domain 2 | ||
---|---|---|---|

Computational area | 50 km × 66km | 5 km × 12.7 km | |

Grid size (m) | 2,000 | 100 | |

Number of grid | 25 × 33 × 10hyer = 8,250 | 50 × 127 × 10hyer = 63,500 | |

Total number of grid | 2,920 | 22,740 | |

Computational duration | 2004/April/1 ~October/31 | 2004/August/1 ~October/15 | |

Time step | 10 min | 30 s | |

Total time step | 30,240 step | 218,880 step |

#### 3.2.3.2 Model Validation

*E. coli*at Stn.1. The current standard for acceptably safe beaches for swimming set by of the Ministry of the Environment of Japan is a fecal coliform rate of 1000 coliforms unit per 100 mL (CFU/100 mL). This index of fecal colif-orm includes not only

*E. coli*but also others. However, it is well known that the majority of the fecal coliform in this area comes from

*E. coli*. Therefore, we use a value of 1000 CFU/100 ml

*E. coli*as the standard for the safety of swimming in the sea. From the calculation results, we can see that durations when the standards are exceeded are very limited, and that most of the summer period falls below the standard for swimming. This result also denotes that the increasing rates of

*E. coli*do not agree with levels of precipitation. Even in small precipitations,

*E. coli*significantly increased.

Understanding the effects of physical factors is important to understanding the fate and distributions of pathogens. Such an understanding is in turn highly related to the assessment of sanitary risks in urban coastal zones. Numerical experiments were thus conducted to examine the variations in rates of *E.coli* according to time and space.

### 3.2.4 Numerical Experiments with the Pathogens Model in Urban Coastal Zones

#### 3.2.4.1 Numerical Experiments

Variations in levels of *E. coli* are directly correlated with the discharge from pumping stations, tidal currents, river discharges, and density distributions, as explained in Sect. 3.2.2. As a result, the distributions of CSO differ according to timing, even when the level of discharge is the same. We performed numerical experiments in order to evaluate the contributions of these different discharges and phenomena to CSO distributions. The first numerical experiment was a nowcast simulation that calculated *E. coli* distributions under realistic conditions. The second experiment was a numerical experiment to estimate the effects of a waste-reservoir that was being constructed near the Shibaura area. Numerical experiments were also applied Odaiba area, which is used as a bathing area.

#### 3.2.4.2 Nowcast Simulations of E. coli Distributions

*E. coli*concentrations discharged from three different areas. Shibaura and Sunamachi area are located at the upper bay location from the Odaiba area. Morigasaki has the largest area, but is located in the lower bay location from the Odaiba area (see Fig. 3-17). In this spring tide period, tidal ranges can reach 2 m. Small precipitations were measured from August 29th to 30th. River discharge increased with precipitation, reaching 50 m

^{3}/s. The levels of

*E. coli*increased rapidly after the rainfall event, due mainly to discharges from the Sunamachi and Chibaura areas. Near the end of this period of increase, effluent from Morigasaki also reached the Odaiba area. Fig. 3-22 shows the spatial distributions of

*E. coli*from three different times. From this Fig., the

*E. coli*emitted from the Morigasaki area can be seen to have been transported from the lower region of the bay to the Odaiba area. This is because the small amount of river discharge resulted in a thin layer of low-density, highly concentrated

*E. coli*on the surface of the sea, and tended to isolate the

*E. coli*by preventing it from mixing with the water column.

^{3}/s. In this period, only the upper bay's CSOs arrived at the Odaiba area. No contributions from the Morigasaki area took place.

In conclusion, the concentrations of *E. coli* vary widely according to space and time. The density distributions produced by the balance of tides and river discharges have very complex effects. *E. coli* concentrations reached maximum levels after small precipitation events, but did not increase so much under large precipitation events due to mixing. These kinds of results would be impossible to understand only from observation. The model successfully captured complex distributions of *E. coli* and helped our understanding of pathogens contaminations.

#### 3.2.4.3 Improvement of CSO Systems Through the Construction of Storage Tanks

To mitigate CSO pollutions, the construction of storage tanks at three sites in Tokyo has been planned by the Tokyo Metropolitan Government. Shibaura is the target area of this plan around the Odaiba area. Numerical experiments were conducted to evaluate the effects of storage tanks on CSOs. Calculation results were compared by the duration of periods when the amount of CSOs in the Odaiba area exceeded bathing standards.

Numerical simulation was performed with and without the proposed storage tank, which has a capacity of 30,000 m ^{3}. This storage tank can store CSOs after rainfall. To include the effect of continuous rain, we assumed that the CSOs stored in the tank could be purified within 1 day.

Effects of storage tank

Stn.l | Stn.2 | Stn.3 | Stn.4 | Stn.5 | ||
---|---|---|---|---|---|---|

Before | 8.21 | 2.06 | 15.44 | 10.48 | 3.6 | |

After | 6.35 | 1.96 | 11.42 | 8.53 | 3.31 | |

Improvement | 1.86 | 0.1 | 4.02 | 1.95 | 0.29 |

For example, over 10,000 storage tanks have been built in Germany alone, and another 10,000 were planned during the 1980s in Germany. Our plans for dealing with CSOs are not enough to mitigate the effects of CSOs completely. At the same time, these results show us the complexity of pathogens distributions and the importance of numerical modeling for this problem.

### 3.2.5 Summary

Numerical simulation is one of the most important tools for the management of water quality and ecosystems in urban coastal zones. We have developed a water quality model to simulate both nutrient cycles and pathogens distributions, and coupled it with a three-dimensional hydrodynamic model of urban coastal areas. To quantify the nutrients budget, a numerical model should include material cycles with phytoplankton, zooplankton, carbons, nutrients, and oxygen. We applied this model to the Tokyo Bay and simulated water column temperatures, salinity, and nutrient concentrations that were closely linked with field observations. This model successfully captured periods of timing, stratification events, and subsequent changes in bottom water oxygen and nutrients. Our model results also indicated that there were clear differences between the material cycles of nitrogen and phosphorus inside the bay. The regeneration of nutrients and its release from sediment was found to be a source of phytoplankton growth on the same order of importance as contributions from rivers. In particular, phosphorus was found to have been largely retained within the system through recycling between sediment and water.

We also developed a pathogen model that includes *E. coli* and is applied to the simulation of CSO influences in urban coastal zones. These results indicate that, because of stratif ication, concentrations of *E. coli* signif icantly increase after even small precipitation events. From this study, the balance between tidal mixing and river waters can be seen to be significant. However, these are only two case studies; it remains necessary to simulate the structure and characteristics of CSO distributions and their impact on urban coastal zone pollution. Such simulations remain as future works to be undertaken.

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