A three-dimensional numerical assessmenton the influences of treated sewage effluent
on an adjacent seaweed farm with different
discharge operations
Cite as: AIP Conference Proceedings 2157, 020031 (2019); https://doi.org/10.1063/1.5126566
Published Online: 18 September 2019
Xu Zhang, Yusuke Uchiyama, and Akihiko Nakayama

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AIP Conference Proceedings 2157, 020031 (2019); https://doi.org/10.1063/1.5126566© 2019 Author(s).

2157, 020031

A Three-dimensional Numerical Assessment on the
Influences of Treated Sewage Effluent on an Adjacent
Seaweed Farm with Different Discharge Operations
Xu Zhang1, Yusuke Uchiyama1, a) and Akihiko Nakayama 2
1

2

Department of Civil Engineering, Kobe University, Japan
Department of Environmental Engineering, Universiti Tunku Abdul Rahman, Malaysiaa)Corresponding author: uchiyama@harbor.kobe-u.ac.jp

Abstract. Coastal dispersal of sewage effluent from the Tarumi Sewage Treatment Plant located in Osaka Bay, Japan, wasinvestigated with a quadruple-nested high-resolution ROMS model coupled with a 3-D passive tracer model. The study
area is quite famous for seaweed farming in the nation, which has been claimed considerably affected by the effluent fromthe plant, particularly in fall when the seaweed spores are most vulnerable to contamination that counteracts their favorable
growth and productivity. We applied the model to three different discharge scenarios to explore possible optimal operations
that could reduce the influences. If the discharged volume rate is simply decreased at ~16.7% relative to the baseline casethat represents the actual operation, the effluent accumulating in the farm is reduced at ~25.4% showing a significant
nonlinearity. A tracer flux analysis demonstrated that the transient component accounting for fluctuating tides and eddies
dominates over the time-averaged linear contribution to the effluent accumulation in the cross-shore effluent transport tothe farm. By contrast, in the along-shore direction, the linear component leads to increased influx near the surface at thewestern transect of the farm, while the transient component is more essential to the overall reduction of the incoming tracer
flux to the farm. On the other hand, density adjustment of the effluent for suppressing surfacing of buoyant effluent plumereleased from the bottom-mounted diffusers does not work well as expected, because pronounced far-field dilution takes
over the near-source dilution affected by the density adjustment.

INTRODUCTION
The Seto Inland Sea (SIS) is the largest estuary in Japan, where complex tidal and estuarine circulations form
because of complicated coastline with more than 3000 islands. Osaka Bay is located in its northeastern area,
encompassing densely populated Osaka and Kobe areas. Historically, the number of “red tide” has increased
significantly due to the nutrient loading, resulting in serious water quality deteriorations that damage the aquaculture
and marine ecosystem (e.g., Imai et al., 2006 [1]; Terawaki et al., 2003 [2]). A series of environmental preservation
policies and laws have worked adequately in improving the water quality in recent years in the SIS (Tomita et al.,2016 [3]). However, Osaka Bay still suffers from severe water pollution. The Tarumi Sewage Treatment Plant (TSTP)
is a major wastewater treatment plant in Osaka Bay, resulting in a possible nutrient source to the adjacent seaweed
farm. To reduce possible negative influences of the TSTP on the local aquacultures, the local government, the City ofKobe, has constructed an alternative western outfall (Figure 1b; indicated by “W”) 500 m away from the original
central outfall (Figure 1b, “C”) in the TSTP.
Our previous study (Uchiyama et al., 2018 [4]; hereafter we call U18) reported that the western outfall
successfully decreases the accumulation of the TSTP effluent in the adjacent farm by ~28% on average, while theinstantaneous effluent concentration lowers at ~50%. Although the diversion outfall works efficiently to reduce theeffluent accumulation in the seaweed farm, the further reduction of the influence of the TSTP is still requested by theInternational Symposium on Green and Sustainable Technology (ISGST2019)
AIP Conf. Proc. 2157, 020031-1–020031-7; https://doi.org/10.1063/1.5126566
Published by AIP Publishing. 978-0-7354-1902-5/$30.00

020031-1

FIGURE 1. Quadruple nested ROMS model domains and bathymetry (color: m). (a) The ROMS-L1-L4 (black boxes)
embedded in the JCOPE2 domain (outside of the perimeter of the L1). The gray contours are the isobaths with intervals of
1,000 m, while the red contours indicate the approximate Kuroshio region with the surface velocity magnitude greater than 0.5
m s-1 at intervals of 0.2 m s-1. (b) The L4 domain with isobaths at intervals of 20 m. The red star shows the location of the
Tarumi Sewage Treatment Plant (TSTP); the red dots are the two outfalls (W: western alternative outfall, C: central original
outfall); the blue box is the approximate seaweed farm area; TFP is the Tarumi Fishery Port area.

local seaweed farmers. Hence, the local government has proposed two possible alternative operations with thediversion outfall 1) by simply decreasing the sewage discharge volume rate, and 2) by increasing the effluent density
to diminish its surfacing by mixing with the ambient saline seawater with retaining the nutrient total mass.
In the present study, we used the well-validated JCOPE2-ROMS quadruple-nested model coupled with a 3D Eulerian passive tracer model to examine the effluent transport of the TSTP in the coastal area with two newdischarge operations. The present manuscript mainly concentrated on the influence of the two alternative scenarios on
the seaweed farm, and the possible predominant component on the effluent transport.

THE QUADRUPLE-NESTED JCOPE2-ROMS MODEL
We employed a quadruple-nested downscaling model based on Regional Oceanic Modeling System (ROMS;
Shchepetkin and McWilliams, 2005 [5]) model. A 3-D Eulerian passive tracer model (Uchiyama et al., 2014 [6]; U18[4]) was implemented into the innermost ROMS-L4 model to track the coastal dispersal of the sewage effluent. The

020031-2

3

cumulative tracer (m )3cumulative tracer (m )

8

x 10

5

(a) alongshore
2

6

0

4

-2

2

-4

0

case1

-2
6

x 10

4

case2

case3

x 10

-6

1

4

(b) cross-shore

case1

-8

(c) net budget

5

x 10

case2

case3

(d) difference

4

0

2
-1

0

-2
21/Sep

case1
1/Oct

case2
11/Octcase3

21/Oct31/Oct-2
21/Sep

1/Oct

'Q21

'Q31

11/Oct21/Oct31/Oct

FIGURE 2. Time series plot of cumulative tracer fluxes Q (m3) at (a) alongshore and (b) cross-shore directions. (c) Time series
plot of net tracer budget accumulating in the seaweed farm. (d) The temporal net tracer budget changes of difference between
each case. The blue curve is computed by Case2 – Case1, while the red curve is computed by Case3 – Case1. Note that Case 1(black curves) varies quite closely with Case 3 in (a)–(c).

assimilative Japan Coastal Ocean Predictability Experiments (JCOPE2) oceanic reanalysis (Miyazawa et al., 2009 [7])provided the boundary and initial conditions for the outermost ROMS-L1 model. The one-way offline nesting
technique (e.g., Mason et al., 2010 [8]; Kamidaira et al., 2017 [9]) was used for downscaling the parent model results
into the corresponding child models with grid-size refinement (i.e., JCOPE2 at lateral grid resolution of ~10 kmÆ L1
at 2 km Æ L2 at 600 m Æ L3 at 100 mÆ L4 at 20 m). Thus, the innermost ROMS-L4 model resolution ultimately
reached 20 m with vertically stretched 32 s-layers. The ROMS configurations were the same as those in U18 [4]; forinstance, the L4 model was forced by surface wind stress from the Japan Meteorological Agency (JMA) Grid-Point
Values–MesoScale Model (GPV-MSM), the monthly climatological surface heat/freshwater fluxes from theComprehensive Ocean-Atmosphere Data Set (COADS). The principal ten tidal constituents from the TPXO7.0 global
reanalysis provided additional sea surface elevation along the open boundaries of the L2 model, which spontaneously
generated intrinsic barotropic and baroclinic tides through the open boundaries of the embedded L3 and L4 models.
Freshwater discharge from the major rivers was also taken into account whenever needed. The ROMS-L4 model was
run for about 50 days to include October of 2015, when the sewage effluent is most influential to the seaweed growth.

SEWAGE EFFLUENT TRACKING SUBMODEL
The capability of tracking the sewage effluent is implemented into the innermost ROMS-L4 based on the
non-dimensional Eulerian passive tracer concentration c with the near-field dilution submodel (Uchiyama et al., 2014[6]; U18 [4]):డ௖డ௧൅ ‫ ׏‬ή ‫ ܿܝ‬ൌ ࣞ ൅ ࣪,

(1)with the non-dimensional source term,࣪:࣪ሺ‫ݔ‬ǡ ‫ݕ‬ǡ ‫ݖ‬Ǣ ‫ݐ‬ሻ ൌ ܲ௦ ሺ‫ݐ‬ሻ‫ܣ‬ሺ‫ݔ‬ǡ ‫ݕ‬ሻ‫ܪ‬ሺ‫ݖ‬ሻ,(2)where u is the 3-D velocity of the ambient flow, ࣞ is the diffusion term, ‫ ׏‬is the 3-D gradient operator, Ps is the tracer
flux from the outfall, and A and H are the spatial shape functions of plume above the diffusers. The spatial distributionsin the source region are shown in the following integral relations:

020031-3

Q1
Q2
Q3
'Q21
'Q31

TABLE 1. Cumulative non-dimensional tracer fluxes in the seaweed farm, Qfarm (m3)Until Oct. 3
Until Oct. 17
Until Oct. 31
Average
1.84×104
3.67×104
5.49×104
2.01×104
1.60×104
2.78×104
4.15×104
1.50×104
1.85×104
3.66×104
5.22×104
1.98×1044
4
4-0.24×10-0.89×10-1.34×10-0.51×104
4
4
4
0.1×10-0.01×10-0.27×10-0.03×104
Note: 'Q21 = Q2 – Q1, 'Q31 = Q3 – Q1.

Along-shore
direction
Cross-shore
direction

TABLE 2. Mean and transient flow components of tracer flux (m3/h)
Case 1 (baseline)
Case 2632.68572.07ܿ‫ݑ‬௡തതതതത
-2366.80-2018.70ܿҧ‫ݑ‬തതത௡ᇱ ‫ݑ‬ᇱ
2999.482590.77ܿതതതതതത
௡
-576.89-529.82ܿ‫ݑ‬௡തതതതത
104.1689.44
ܿҧ‫ݑ‬തതത௡ᇱᇱതതതതതത
-681.05-619.26ܿ ‫ݑ‬௡
Note: difference: Case 2 – Case1‫ ݕ݀ݔ݀ ܣ ׭‬ൌ ‫ܣ‬௦ , ‫ ݖ݀ ܪ ׬‬ൌ ‫ܪ‬௦ ,Difference-60.61
348.10-408.7147.07-14.7261.79(3)

where As is the horizontal area, Hs is the vertical size, Vs = As Hs is the sewage plume volume. Hence, Ps(t) = Qp(t)/Vs,
where Qp (t) [m3 s-1] is the volume flux. The dimensional pollutant concentrations are retrieved by multiplying Cp,
where Cp is the input pollutant concentration at the outfall.
The Gaussian shape function was used for H based on the following equation‫ܪ‬ሺ‫ݖ‬ሻ ൌ  ቂെሺ௭ି௭ೞ ሻమ
ௗೞమ

ቃ,

(4)

where zs is the plume center height, and ds is the vertical scale of the plume. Because the local depth of 7 m is shallower
than the mixed layer, we defined zs = 0 m and ds = 7 m in our model. The outfall is set by two grid points (viz., afootprint of 20 m u 40 m), where A = 0.5, centered at 40 m offshore from the sea wall of the TSTP, corresponding
approximately to the actual diversion outfall length.
The sewage effluent (tracer) is discharged as a bottom-released freshwater plume at the volume rate of Qp
with the prescribed concentration of c = cs. Hence, the input source flux is Fp = csQp. The released passive tracer is set
to be unity (cs = 1) with temperature of the ambient seawater. For the Case 1 (the baseline case), a discharged volumerate is Qp = 1.8 × 105 m3 d-1 and salinity S = 0. The mean surface temperature and salinity of the seawater near theoutfall are 23.0 qC and 34.4 psu. In such a situation, the resultant mean density is 1023.48 kg m-3, while the freshwater
density is 997.55 kg m-3. The two alternative scenarios were set as the follows. We reduced discharged volume Qp
from 1.8 × 105 m3 d-1 to 1.5 × 105 m3 d-1 in Case 2 (the reduced flux case). And then we increased the sewage density
by mixing with the ambient seawater of 0.8 × 105 m3 d-1 to diminish the surfacing of the buoyant plume in Case 3 (theadjusted buoyancy case). Therefore, the total discharge rate Qp, the density of the mixed effluent U’, and its salinity S’
in Case 3 were modified to Qp = 2.6 × 105 m3 d-1, U’ = 1005.43 kg m-3, and S’ = 10.46 psu, respectively. We further
modified the input source flux cs = (1.8 × 105)/(2.6 × 105) ~ 0.69 to ensure the total tracer load from the TSTP for Case
3 is equalized to that for Case 1.

TRACER FLUX ANALYSIS
To quantify the direct impacts of the TSTP on the seaweed farm for the three cases, the tracer flux was
calculated with‫ܨ‬ሺ߬ሻ ൌ ‫׬‬஺ ܿ‫ݑ‬௡ ݀‫ܣ‬௖ ,೎

020031-4(5)

where ‫ݑ‬௡ is the velocity perpendicular to the cross-section, and W is time. Ac is the cross-sectional area at thecorresponding side boundary of the seaweed farm. Because c is non-dimensional concentration, F has the unit of m3
s-1. Then we integrate F over a given time period to evaluate cumulative tracer flux Q (m3) for the duration from W =0 to t as follows,௧ܳሺ‫ݐ‬ሻ ൌ ‫׬‬଴ ‫ܨ‬ሺ߬ሻ ݀߬,(6)
where incoming cumulative tracer fluxes into the farm is defined as positive at the boundaries of the farm. We denote
Qw, Qn, Qe, and Qs as the cumulative flux Q(t) through the western, northern, eastern and southern sections,
respectively.
Because there is an obvious different flow patterns between the alongshore and cross-shore directions (U18[4]), we firstly conduct an effluent flux analysis in the different directions. Qw + Qe and Qn + Qs are the cumulative
tracer fluxes in the alongshore and cross-shore directions. The corresponding temporal variability is shown in Figure
2a and b. There is an obvious effluent accumulation in the alongshore direction and a reduction in the cross-shore
direction. The effluent intrudes into the seaweed farm from the western, northern and eastern sides of the farm, andflow out from the southern transect (U18 [4]). The resultant net accumulation is positive in the alongshore direction
and negative in the cross-shore direction. Meanwhile, these temporal changes in the net fluxes for Case 3 and Case 1
are almost coincident, showing that the adjusted buoyancy configuration does not change the sewage transport
significantly in both zonal and meridional directions.
The sum of Q at the four vertical cross-sections identifies the net tracer (effluent) flux accumulated in theseaweed farm, i.e., Qfarm = Qw + Qn + Qe + Qs. To distinguish Qfarm for the three cases, we will respectively denote thenet flux as Q1, Q2, and Q3 for Qfarm for cases 1, 2, and 3. 'Q21 and 'Q31 represent the differences of Case 2 and Case
3 from Case 1. Figure 2c denotes the time series of the net tracer budget accumulating in the seaweed farm. The Qfarm(Q1, Q2, and Q3) fluctuates periodically at a period of ~14 days, and three peaks appear on Oct. 3, 17, and 21 assummarized in Table 1. This periodic behavior indicates that intrusion of the sewage effluent into the seaweed farmoccurs mainly at a spring–neap tidal cycle. Table 1 illustrates that the peaks in Qfarm occurred on these three days are
1.84×104 m3, 3.67×104 m3 and 5.49×104 m3 for Case 1, 1.60×104 m3, 2.78×104 m3 and 4.15×104 m3 for Case 2, and1.85×104 m3, 3.66×104 m3 and 5.22×104 m3 for Case 3, consistent with the accumulations on average during theanalysis period. The reduction of the released flux of 16.7% (1.8 × 10 5 m3d-1 Æ 1.5 × 105 m3d-1) in Case 2 results inthe 25.4% reduction (2.01 × 104 m3 Æ 1.50 × 104 m3) on average in the accumulation in the farm relative to Case 1.
This result indicates that the reduced flux scenario works more than our expectation for the effluent reduction in thefarm. By contrast, the difference between Case 3 and Case 1 is subtle, indicating that the density adjustment is not avery effective operation in the present configuration.

EFFECTS OF MEAN AND TRANSIENT COMPONENTS ON TRACER FLUXES
To examine the possible reason why 16.7% reduction of the input flux results in the 25.4% effluent
accumulation decrease in Case 2, we introduce a Reynolds decomposition to the prognostic variables into mean andᇱtransient components, in which ܿ ൌ ܿҧ ൅ ܿ ᇱ and ‫ݑ‬௡ ൌ ‫ݑ‬
തതത
തതതതത
௡ ൅ ‫ݑ‬௡ . Therefore, the time-averaged tracer flux ܿ‫ݑ‬
௡ isdecomposed asᇱ തതത ൅ ܿҧᇱ ‫ ݑ‬ᇱ ൌ ܿҧ‫ݑ‬ᇱ ᇱതതതതതതത
തതതതതതതതതതത
ܿ‫ݑ‬௡ ൌ ܿҧ‫ݑ‬
തതതതത
തതത
‫ݑ‬௡ᇱ ൅ ܿതതതതതതതതത௡ ൅ ܿ ‫ݑ‬௡௡௡ ൅ ܿ ‫ݑ‬௡ ,(7)where the overbar denotes the time-averaging operator, and the prime represents the transient component, including
the influences from eddies and tides. The first term ܿҧ‫ݑ‬തതത௡ represents the linear interaction of the mean tracerᇱ ‫ ݑ‬ᇱ stands for the tracer flux associated with theconcentration ܿҧ and residual currents തതത,‫ݑ‬௡ whereas the second term ܿതതതതതത
௡nonlinear interaction of transient currents ‫ݑ‬௡ᇱ with fluctuating tracer concentration ܿ ᇱ .
ᇱ ᇱതതതതതത
തതതThe time-averaged tracer flux തതതതത,ܿ‫ݑ‬௡ mean flow component ܿҧ‫ݑ‬
௡ and transient component ܿ ‫ݑ‬௡ in thealongshore and cross-shore directions are summarized in Table 2. It should be noted that because the tracer flux
fluctuates pronouncedly at diurnal and semidiurnal tidal frequency (Figure 2), there are slight deviations in the timeaveraged results. However, this table is still able to be utilized to conduct a qualitative analysis. The sewage effluent
accumulates in the seaweed farm in the alongshore direction and reduces in the cross-shore direction. Compared with
Case 1, the effluent reduction occurs in the alongshore direction, whereas the increase appears in the cross-shore
direction. In addition, the mean flow component controls the sewage effluent outflow, while the transient component
results in incoming tracer flux in the along-shore direction. According to more detailed analysis (not shown here), thedifference between two cases indicates that the mean component ܿҧ‫ݑ‬തതത௡ enhances effluent accumulation in the farm

020031-5ᇱ ‫ ݑ‬ᇱ promotes effluent outflow from the farm in Caseparticularly at the surface layer, while the transient component ܿതതതതതത
௡
2. Since the western boundary of the seaweed farm is located nearest the outfall, the flux along this section on the net
tracer budget is much more prominent than that along eastern section. By contrast, in the cross-shore direction, thedecomposed flux results demonstrate a significant predominance of the transient component തതതതതതܿ ᇱ ‫ݑ‬௡ᇱ over the meanᇱᇱതതതതതത
component ܿҧതതത.
‫ݑ‬௡ Moreover, compared with Case 1, the transient component ܿ ‫ݑ‬௡ plays a key role in decreasing theeffluent outflux in the farm in Case 2.

CONCLUSIONS
We utilized the configurations of the validated, quadruple-nested, high-resolution ocean model described in
U18 [4] to track the wastewater effluent transport of two more alternative operations at the TSTP in the coastal region
and examine its influence on an adjacent seaweed farm. In the reduced flux case, discharged wastewater volume
decrease at a rate of 16.7% (1.8 × 105 m3 d-1 Æ 1.5 × 105 m3 d-1) contributed to an overall 25.4% reduction of theeffluent accumulation in the farm on average (2.01×104 m3 Æ 1.50×104 m3). To analyze such a nonlinearity, the timeaveraged tracer flux was decomposed into the mean and transient flow components. The results illustrated that themean flow component led to the sewage effluent outflow, while the transient component provoked incoming tracer
flux in the along-shore direction. However, as compared to the baseline case, the mean component increased the tracer
accumulation in the seaweed farm specially at the surface, while the transient component dominated the tracer
reduction in the reduced flux case. The resultant net effects were an overall reduction of tracer accumulation. By
contrast, in the cross-shore direction, the transient flow component plays a dominant role in decrease of influx in the
reduced flux case. The impact of mean component was much weaker. Concurrently, the transient component wasessential to reduction of the effluent outflux compared with the baseline case. For another operation scenario as theadjusted buoyancy case, we modified the wastewater density by mixing with ambient seawater of 8 × 10 4 m3 d-1 tosuppress surfacing of the effluent plume. The adjusted buoyancy scenario did not lead to an obvious effluent
accumulation in the farm and the net cumulative tracer fluxes in both zonal and meridional directions, indicating thatthe density adjustment did not work well to reduce the impacts of the effluent from the TSTP on the farm.

ACKNOWLEDGMENTS
The present research was financially supported by the City of Kobe and Japan Society for the Promotion of Science(JSPS) Grants-in-Aid for Scientific Research 15KK0207 and 18H03798 at Kobe University. We also appreciate Yota
Suzue (CTI Engineering Co., Ltd.) and Taichi Kosako (Port and Airport Research Institute) for their help in thedevelopment of the quadruple-nested model.

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