Table 3 3.2. Boundary conditions and grid resolution
Related Figures (60)
Fig. 1.1 combined (left) and separated (right) type of sewerage pipe (Onozawa, 2005) Some of the wards have large lowland areas posing a high risk of flooding, and in the development of this vast sewer system great emphasis was laid on flood protection measures in concern with wastewater disposal. In view of this, the combined sewer system, which collects both wastewater and storm water, was adopted. As a result, most of the sewer system now consists of combined sewers, while the separate sewer, which is reputedly superior to the combined sewer in terms of pollution prevention, accounts for only 18% (Sugita et. al., 2003). Fig.1.3. CSO event due to heavy rain and beach of Odaiba, Tokyo Bay (Onozawa, 2005) Though the sewerage system of Tokyo metropolitan is well established, water quality degradation of Tokyo Bay is still taking place. The reason behind this is the overflowed sewer during storms (Fig.1.3). This overflowed sewage contains pathogens that are unhygienic for beach swimmers. To decrease the risk from introduced pathogens, monitoring that is both well designed and routine is essential. The conservation of mass can be expressed through the continuity equation An expansion into three equations can be made to obtain Navier-Stokes equation fplane, upon which the present model is based which are as follows: Equation (2.9) becomes: Conversion into sigma coordinate system: For the numerical solution of the above set of equations, traditionally two simplifying approximations are used. mplementation of these two approximations and conversion into sigma coordinate Bottom friction is considered at bottom boundary condition: Bottom friction coefficient y, is taken as 0.0026 (Sasaki and Isobe, 1996). Boundary conditions: Wind-stress is considered at surface boundary condition: For conservation of temperature, sea surface boundary condition: The governing equations for temperature and salinity in sigma coordinate system: Governing equations for Temperature and Salinity: Net sea surface heat flux penetrating into the water column, Qs is expressed as follows; Fig. 2.1 A typical CSO flowchart (Fordiani, 2006) Fig.2.2. Preventing overflow by increasing interceptor sewer capacity (Fordiani, 2006) This system involves adjusting the flow controls within existing pipe systems to maximize the carrying capacity of interceptors (Fig. 2.2) or to take advantage of unused large pipe storage during wet weather. This system also includes flow redirection (Fig 2.3), diverting and reduce the flow to the system (Fig. 2.4). Inflow Reduction can be done by making detention ponds, inflow reduction by roof leader disconnection (Fig. 2.5), infiltration reduction, repair existing pipe and rehabilitate manholes. Fig.2.3. Preventing overflow by flow redirection (Fordiani, 2006) Fig.2.5. Roof leader disconnection to reduce flow to the system (Fordiani, 2006) Fig.2.6. Sewer separation to reduce flow to the treatment plant (Fordiani, 2006) sewer separation system. It is a method of minimizing the amount of street runoff that mixes with sanitary sewage. However, complete separation is difficult and prohibitively expensive to achieve. Existing combined sewer systems are likely more than 100 years old and under capacity for current development. It causes infrastructure replacement and community disruptions. Even separation done, but future treatment of storm water may be required. Now a day, this practice within combined-sewered areas is not practiced on a large scale. Separation is still practiced to solve pollution problems within small portions of combined sewer areas connected to separated systems or to solve flooding problems within combined systems where there is inadequate flow capacity. Figure 2.6 shows sewer separation system. Fig. 2.7. Design of Storage treatment unit to store and treat CSOs (Fordiani, 2006) Constructed parallel to the existing system utilizing tanks, conduits, or underground tunnels having facilities for either draining by gravity or pumping flow to and from storage. Yonstructed parallel to the existing system utilizing tanks, conduits, or underground Fig. 3.1. Study site and sampling locations (Odaiba area) of Tokyo bay, 2007 Fig. 3.2. Sampling locations (Odaiba area) of Tokyo Bay, 2004 Field observation was also conducted by Mr. Onozawa during his bachelor study in 2004 in the same site of Odaiba. He sampled in the 5 stations in Odaiba (see Fig. 3.2). Station 3, 2 and 1 is the same place of the present study stations of 3, 4 and 5. As observation stations 3, 2 and 1 in 2004 coincide with the observation stations 3, 4 and 5 of the present study, the data from those particular stations was used as a secondary data to validate the numerical modeling. Table 3.1 Instruments for field measurement Bacterial and viral (E. coli, T. coli, AdV) data were kindly provided by Furumai-lab, Department of Urban Engineering (Hongo campus), University of Tokyo. All the pathogen data are presented as colony-forming units (CFU) per 100 ml of water. Fig. 3.4 Sampling and Laboratory analysis Fig. 3.5. Nutrients Analysis AACS III (BRAN+LUBBEE) Fig. 3.6 Two nested computational domains. Left domain 1, whole bay area and right domain 2 with pumping station distributions around the Odaiba area Table 3.3. Volume of discharge pollutants from the pumping stations The concentration below 4mgL" was found to be happened frequently, where the lowest value was recorded from the station 6 (2.26 mgL’). This indicates poor water quality of the Bay. The mean DO value was recoded as 4.17mgL". Fig. 4.2. Observed mean temperature from all the stations surface water Water temperature is very important as it exerts a major influence on the biological activity and growth of aquatic organisms. It has a great influence on water chemistry. The rate of chemical reactions generally increases at higher temperature, which in turn affects biological activity. The enzymes of most organisms work best at temperatures of 20- 40°C. An important example of the effects of temperature on water chemistry is its impact on oxygen. Warm water holds less oxygen than cool water, so it may be saturated with oxygen but still not contain enough for survival of aquatic life. Some compounds are also more toxic to aquatic life at higher temperatures. Un-ionized ammonia increments with temperature, becoming toxic to both plants and animals (McCorquodale, et. al., 2005). Fig. 4.3. Salinity and EC observed in different station during sampling The mean Conductivity and Specific Conductivities values were low at the stations 1, 6 and 7. This can be explained due to the proximity of these Stations to the mouth of the river where freshwater enters the lake and land locked position. The highest mean values for Conductivity were at station 2, 3, 4 and 5, which were located in the inner part of the Bay. This indicates that the Bay water that is least influenced by fresh water contained more dissolved salts, which are good conductors. Salinity and EC result was found very consistent and correlated (Fig. 4.3). Fig. 4.4. Observed water pH at different stations The mean pH values for the station near the mouth of the river, was the lowest observed (pH 6.1). It indicates little bit acidic condition of river water. The pH values increased as one moved out and away from the mouth (Fig. 4.4). The mean surface water pH was found 6.75. F ig. 4.5. Turbidity and SS observed in different stations during sampling Figure 4.2, 4.3 and 4.5 shows Temperature, Salinity, Turbidity and Suspended Solids at the water surface obtained from the direct measurement. Water near the river outlet was of low-saline, high temperature and high SS and highly turbid. The reason for the high temperature was that river water was a little warmer than seawater in the season. Salinity gradually increased toward the open sea. There is a good positive correlation exists between observed Turbidity and SS (Fig. 4.6). and resuspension also occur near the bottom. Higher turbidity in the bottom layers of the stations (fig. 4.7) supports the above phenomenon. Fig. 4.8. Correlations between Salinity and Turbidity in the surface layer Figure 4.8 shows the correlation between surface salinity and turbidity for all data obtained from the continuous measurement. This linear relation suggests that the process of turbidity decay along the bay axis was mainly caused by “dilution” of the dirty river water with the clean seawater, not by particle settling to a deep layer. It also suggests that the salinity distribution in the bay can be estimated by using turbidity as a tracer. In all the cases surface nutrient have higher concentration (Fig. 4.11). Surface water less dilutes with sea water may be the cause. It was found to have inverse correlations among salinity and nutrients (Fig. 4.12). In all the cases surface nutrient have higher concentration (Fig. 4.11). Surface water less Fig. 4.15 Comparison between modeled and measured temperature at surface and bottom (depth was 12 m) near downstream of Odaiba, Tokyo Bay (2004) The hydrodynamics in the Bay-wide model (domain 1, large grid) were first simulated and the results of temperature, salinity and water surface level elevation were used as an open boundary to simulate domain 2 (fine grid). The model was simulated and validated by comparison with the observed data for a period of two and half months from August 1* to October is" 2004. The output result was presented in an hourly basis. The simulation and observation results of temperature and salinity patterns in the Bay-wide scale grid are shown in the Fig. 4.15 and Fig. 4.16 respectively. The simulation results show stratification, mixing, and an upwelling phenomenon. Fig. 4.16. Comparison between modeled and measured Salinity at surface and bottom (depth was 12 m) near downstream of Odaiba, Tokyo Bay (2004) A comparison between observation and a calculation results are shown in figure 4.17 for temperature and figure 4.18 for salinity in a fine grid scale (domain 2 in Fig. 3.5). Variations between the simulated and observed values were generally less than 2.5°C and 2 psu through the water column. The timing and periods of upwelling events were captured accurately. After rain fall, river discharge was increased remarkably. Model results adequately represent precipitation variation events and their effects. Fig. 4.17. Comparison between modeled and measured Temperature at surface water (2004) Fig. 4.21 Comparison between with and without death rate result of modeled E. coli conc Fig. 4.22. Comparison between with and without wind action result of modeled E. coli conc Fig. 4.23. Comparison between with and without death rate (up) and with and without wind action (down) result of modeled AdV concentration at St. 3 (2004) Fig. 5.1. Relationship between simulated E. coli andAdV The modeled result (Fig. 5.1) shows that occurrence of F. coli did not correlate well with that of AdV. It may be due to the difference in discharge input and survival rate in water column. This is also consistent with the findings of Jiang et. al. (2007). They found in their study that occurrence of human entroviruses did not correlate with that of Fecal Indicator Bacteria. Fong et. al. (2004) also pointed out that Coliform standard often fail to predict the occurrence of many waterborne human pathogens.
