Marine Pollution Bulletin 141 (2019) 91–103

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Marine Pollution Bulletinjournal homepage: www.elsevier.com/locate/marpolbul

Dilution characteristics of riverine input contaminants in the Seto Inland Sea
Junying Zhu
a
b
c

a,b,c

, Xinyu Guo

a,b,⁎

a,c

T

a,c

, Jie Shi , Huiwang Gao

Key laboratory of Marine Environment and Ecology, Ocean University of China, Ministry of Education, 238 Songling Road, Qingdao 266100, China
Center for Marine Environmental Studies, Ehime University, 2-5 Bunkyo-Cho, Matsuyama 790-8577, Japan
Laboratory for Marine Ecology and Environmental Sciences, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China

A R T I C LE I N FO

A B S T R A C T

Keywords:
Dilution
Riverine pollution
The Seto Inland Sea
Hydrodynamic model
Residual currents

Riverine input is an important source of contaminants in the marine environments. Based on a hydrodynamic
model, the dilution characteristics of riverine contaminants in the Seto Inland Sea and their controlling factors
were studied. Results showed that contaminant concentration was high in summer and low in winter.
Contaminant concentration decreased with the reduction of its half-life period, and the relationship between
them followed power functions. Sensitivity experiments suggested that the horizontal current and verticalstratiﬁcation associated with air-sea heat ﬂux controlled the seasonal cycle of contaminant concentration in thewater column; however, surface wind velocity was the dominant factor aﬀecting the surface contaminantconcentration. In addition, contaminant concentration in a sub-region was likely controlled by the variations inriver discharges close to the sub-region. These results are helpful for predicting contaminant concentrations inthe sea and are expected to contribute to assessing the potential ecological risks to aquatic organisms.

1. Introduction

populations, increasing the emergence of antibiotic resistant strains(Boon and Cattanach, 1999; Vivekanandhan et al., 2002; Baquero et al.,2008) and severely damaging ﬁsheries production (Karunasagar et al.,
1994; Majtán et al., 2012). Previous studies have concluded that mostantibiotics pose a relatively high ecological risk to relevant aquaticorganisms in marine environments (Lee et al., 2008; Zhao et al., 2010;
Zhang et al., 2012). Moreover, the degree of the eﬀect of these contaminants on aquatic organisms mainly depends on their concentrationin the water.
Riverine input is a major source of contaminants in marine environment and a high concentration of contaminants in rivers has been
observed in many countries (Xu et al., 2007; Minh et al., 2009; Zhao
et al., 2010; da Silva et al., 2011; Zou et al., 2011; Zhang et al., 2012;
Grill et al., 2016). However, the contaminant concentrations in rivers
and sea water are extremely diﬀerent due to diﬀerent hydrodynamic
environments. When contaminants enter coastal seas from rivers, twomajor questions are raised. The ﬁrst is whether the ocean dilutes thecontaminant levels to a lower concentration (i.e., to a nontoxic degree)
without considerable consequences to aquatic organisms. The second iswhat are the key factors controlling contaminant concentration in thesea.
To answer these questions, many ﬁxed-point observations of thecontaminant concentrations and risk assessments based on contaminantconcentrations have been conducted in some coastal seas (Gómez-With the rapid development of human life, more and more terrestrial chemical substances, such as pharmaceuticals (Hirsch et al., 1999;
Managaki et al., 2007), polychlorinated biphenyls (Burreau et al.,2006), polycyclic aromatic hydrocarbons (Montuori and Triassi, 2012)and organochlorine pesticides (Hu et al., 2009), enter the natural water
environment and are detected in coastal seas around the world. The
adverse ecological risks of these contaminants to aquatic organisms area major concern due to their potential toxic eﬀects and biomagniﬁcation in marine mammals (Halling-Sørensen et al., 1998; Burreau et al.,2006).
Among these contaminants, persistent organic pollutants (POPs)
have been of concern over the last decade (Iwata et al., 1993; Lohmann
et al., 2006) because they have a long half-life period and remain inwater environments for years. On the other hand, some contaminants,
such as antibiotics, have a short half-life period of a few days and are
relatively easy to remove from water environments. However, due tothe potential threat to ecosystem balance and risk to health of humans
and animals, antibiotics in aquatic environments have generated
growing interest among researchers in recent years (Marti et al., 2014;
Chow et al., 2015; Grill et al., 2016; Liu et al., 2017; Liu et al., 2018).
Antibiotics that enter water environments exert constant selection
pressure on both bacterial ﬁsh pathogens and marine microbial⁎

Corresponding author at: Center for Marine Environmental Studies, Ehime University, 2-5 Bunkyo-Cho, Matsuyama 790-8577, Japan.
E-mail address: guoxinyu@sci.ehime-u.ac.jp (X. Guo).

https://doi.org/10.1016/j.marpolbul.2019.02.029
Received 14 November 2018; Received in revised form 13 February 2019; Accepted 13 February 20190025-326X/ © 2019 Elsevier Ltd. All rights reserved.

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Fig. 1. (a) Location of the Seto Inland Sea (SIS). (b) Bathymetry (m) of the SIS and model domain. The solid black lines in (b) indicate the Bungo Channel and Kii
Channel, which are the southern boundaries of the hydrodynamic model. The red solid line running in a northwest direction in (b) is the Kanmon Strait, which is
closed in the model. The blue solid circles with lower case letters a to u along the coast in (b) are the locations of 21 major rivers (Table 1), and the green solid circles
are the locations of the ﬁve secondary rivers. (c) Monthly observation stations in the SIS. The hollow blue circles and solid red circles in (c) are the monthly samplingstations. The solid red circles and lines with numbers from 1 to 50 denote selected stations to validate the model results in the vertical direction used in Fig. 4. The
dashed black rectangles in (c) represent selected sub-regions for the purpose to show the seasonal variation of mean contaminant concentration in the sub-region.
(For interpretation of the references to colour in this ﬁgure legend, the reader is referred to the web version of this article.)of riverine contaminants in the entire SIS and their inﬂuencing factors
by using a hydrodynamic model. We ﬁrst explored the spatial andtemporal variations of concentrations of conservative contaminant andnon-conservative contaminants (using antibiotics as an example), andthen investigated the mechanisms controlling contaminant concentration in the SIS.

Gutiérrez et al., 2007; Zhao et al., 2010). Two related factors were
proposed. Dilution factors, deﬁned as the concentration ratio of contaminants in a river (or eﬄuent) and sea water near the outfalls, havebeen used to represent the dilution levels of sea water (Minh et al.,2009; Zhang et al., 2012). The other is the contaminant ﬂux from rivers,
which is the product of contaminant concentrations and river discharge, and it is often used to estimate the contribution of rivers tocontaminant concentration in sea water (Wolschke et al., 2011; Zouet al., 2011; Montuori and Triassi, 2012). However, the key factors
responsible for contaminant concentration in the ocean were still unclear, and it should be explored in a speciﬁc estuary or coastal sea.
Due to limitation in observation, there is insuﬃcient data to show
continuous spatio-temporal variation of contaminant concentration in
sea water. In addition, previous dilution studies about contaminants
have mostly focused on the areas around the mouths of rivers; therefore, there is a lack of information on dilution characteristics in oﬀshoreand shelf areas. Modeling is a powerful tool for coupling processes
under diﬀerent disciplines and spatio-temporal scales. To address theseissues, a hydrodynamic model is required.
The Seto Inland Sea (SIS) region (Fig. 1) is one of the most industrialized areas in Japan. Murata et al. (2011) reported the results ofa nationwide survey of commonly used human and veterinary antibiotics in 37 Japanese rivers. They found that concentrations of the sumof 12 target antibiotics in 8 rivers, which directly emptied into the SIS,
ranged from low ng/L to 60 ng/L. The SIS is a major ﬁshing ground(Nagai, 2003) that includes aquacultures sites for ﬁsh, bivalves, andseaweed; therefore, it is necessary to evaluate the dilution levels ofantibiotics in the whole SIS.
The aim of this study was to investigate the dilution characteristics

2. Model conﬁguration and validation
2.1. Simulation and validation of temperature and salinity
The SIS is a shallow semi-enclosed shelf sea located in westernJapan, with a surface area of 23,000 km2 and an average depth of 38 m
(Fig. 1a). It opens to the Paciﬁc Ocean via the Bungo Channel and Kii
Channel, through which tidal waves enter the SIS. It is surrounded by
the islands of Honshu, Shikoku, and Kyushu, which are rapidly developing, urbanized regions in Japan. There are 21 ﬁrst-order rivers and643 secondary order rivers emptying into the SIS. It has a complicatedgeographical character, including a series of broad basins (called“nada” in Japanese), such as Suo-Nada, Iyo-Nada, Hiuchi-Nada, andHarima-Nada, which are connected to each other through narrowstraits. The predominant tidal constituent in the SIS is the lunar semidiurnal M2 (Yanagi, 1981). Tidal currents are strong in the narrowstraits (> 1 m/s) and weak in the broad basins (approximately 0.1 m/s)
(Takeoka, 2002).
Appendix A describes the details of hydrodynamic model. Model
domain ranges from 130.98°E to 135.5°E and from 32.8°N to 34.8°N(Fig. 1b). The model includes 21 major rivers and 5 secondary rivers
whose locations along the coastline are shown in Fig. 1b. Multi-year92

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J. Zhu, et al.

averaged river discharges are used (discharges of the 21 major rivers
were shown in Table 1). Compared with the major rivers, the dischargesof the ﬁve secondary rivers are much smaller with a total annual meandischarge of 61 m3 s−1. In terms of the seasonal variation, the dischargeof 26 major and secondary rivers reaches its maximum (2868 m3 s−1) in
July and minimum (490 m3 s−1) in January, with a mean value of1010 m3 s−1 (Fig. 2d).
In addition to river discharge, the model was also driven by climatological atmospheric forcing (wind stresses and air-sea heat ﬂux,
Fig. 2). It started with an initial condition in January and run for three
years to ﬁnish its spin-up. The results in the fourth year were used in thepresent study. The temperature, salinity, and current velocity were
saved at 1-hour interval, and the impacts of tides were removed byapplying a tide ﬁlter (Hanawa and Mitsudera, 1985) to the hourly saved
results. Regular hydrographic observations at monthly intervals havebeen carried out over the past 40 years by prefectural ﬁshery research
centers around the SIS and the locations of the observation stations areshown in Fig. 1c. The observed temperature and salinity data from 1972
to 2000 at standard levels were obtained and used to validate model
results. February and July are used to represent winter and summer,
respectively.
In winter, warm and saline waters ﬂowed into the SIS through the
Bungo Channel and Kii Channel. Consistent with the observations, thesea surface temperature reached 16 °C and 14 °C in the Bungo Channel
and Kii Channel (Fig. 3a, b), respectively. The surface salinity in the
Bungo Channel was 34–34.5, which was larger than that of around 33.5
in the Kii Channel (Fig. 3e, f). Both temperature and salinity became
lower toward the middle of the SIS. In terms of vertical distributions(Fig. 4), both observation and model results showed that the watercolumn was well-mixed throughout the entire SIS (Fig. 4a-b, e-f).
Temperature was low around stations 25–40 with values of around
8–9 °C. Low salinity water occurred around station 45 in Osaka Bay
because of the large discharge from the Yoda River (Table 1).
In summer, sea surface temperature was as high as 21–25 °C. Low
temperature waters of about 21 °C occurred around the Hayasui Strait
and Aki-Nada (Fig. 3c, d). The salinity showed low values (< 30)around the mouths of river and high values in the Bungo Channel(33–34) and Kii Channel (31.5–32.5) (Fig. 3g, h). Both temperature andsalinity exhibited a well-mixed pattern around the straits (stations
19–20 and 30–32) and a well-stratiﬁed in the broad basins (stations
10–15, 22–28, 35–40) (Fig. 4c–d, g–h). Similar to the observations,warm surface pools and cold bottom water were established in IyoNada (stations 10–15) and Harima-Nada (stations 35–40), although thebottom water temperature in Harima-Nada was 3 °C lower in the model
than in the observation (Fig. 3c, d). Low salinity waters existed in Osaka
Bay (stations 40–50), forming a front structure with high salinity waterin the Kii Channel (Fig. 4g, h).
The simulated temperature and salinity were climatological patterns
since the hydrodynamic model was driven by the multi-year mean
forcings (wind stresses, river discharge and heat ﬂux). On the other
hand, the observations were carried out at ﬁxed-stations on a speciﬁed
day and their monthly mean was the averaged value of snapshot observations in the same month over many years. Considering the nonlinear system of ocean and the diﬀerence in sample numbers for averaging between observations and model results, it is diﬃcult to expect a
perfect agreement between observation and model results. The correlation coeﬃcients of temperature and salinity between observation andmodel results were 0.98 and 0.79 (p < 0.01) throughout a year. In
general, the seasonal variations in temperature and salinity are well
reproduced by the model (Figs. 3 and 4), which in some senses demonstrates that the model can give generally accurate current ﬁelds.
Therefore, the hydrodynamic model can be used to simulate the distribution of contaminant.

Table 1
Annual mean river discharges (m3 s−1) of 21 major rivers in the Seto InlandSea, based on dataset of the Ministry of Land, Infrastructure and Transport. The
locations of the rivers are shown in Fig. 1b.
No.

River

1994

Multi-year average (1993–2016)

2015

a
b
c
d
e
f
g
h
i
j
k
l
m
n
o
p
q
r
s
t
u

Kino
Yamato
Yodo
Kako
Ibo
Yoshii
Asahi
Takahashi
Ashida
Oota
Oze
Saba
Yamakuni
Oita
Oono
BanshoHiji
Sigenobu
Doki
Yoshino
Naka

30.74
13.51
151.44
17.53
17.52
34.2
30
31.19
3.93
13.46
8.42
10.85
7.4
13.58
33.08
4.05
4.96
4.23
1.07
40.7
43.03

48.47
25.62
261.65
57.82
27.20
58.81
52.63
52.17
11.20
57.78
12.63
17.20
21.03
26.21
60.35
12.56
29.11
9.65
2.13
39.90
66.66

35.05
28.52
308.14409.89
36.28
70.11
57.39
8.76
13.56
72.18
13.65
18.3
21.56
25.84
48.43
8.28
42.73
12.54
2.18
35.72
80.13

Fig. 2. Daily variations in multi-year averaged (a) wind speed, (b) wind direction, (c) air-sea heat ﬂux, and (d) river discharges used in case Control. The
wind direction in (b) is clockwise and the direction of southerly wind is 0.
Positive values in (c) indicate that the ocean gains heat, while negative values
indicate the opposite situation.

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Fig. 3. Spatial distributions of simulated and observed (a-d) surface temperature and (e-h) salinity in February and July in the SIS.

concentration was set to 100. Since the contaminant concentration in
the ocean had the same unit as the riverine contaminant concentration,
we did not specify its unit in this study. In fact, it can be the unit of any
concerned contaminant. Contaminants were controlled by the processesof advection, diﬀusion, and attenuation (only non-conservative contaminants) in the model. The attenuation of non-conservative contaminant was described in Appendix B for details.
The number of grids in the model is 362(x) × 238(y) × 21(z),where x is zonal direction, y is meridional direction and z is verticaldirection (see Appendix A). Along with the hydrodynamic model online, the contaminant concentration deﬁned in the center of each grid
point was calculated at a time step of 120 s. The averaged contaminantconcentration within one day was saved at 1-day interval during themodel calculation. Based on the concentration at each grid point, thevolume-averaged concentration (Cv) in the SIS was calculated as follow.

2.2. Simulation of contaminant concentration
In this study, we investigated conservative and non-conservative
contaminants discharged into the SIS from rivers. Conservative contaminants do not decay with time and are used to represent contaminants such as most POPs that have a half-life period of several years
which is longer than the mean residual time (~15 months) of the total
sea water in the SIS (Takeoka, 1984). On the other hand, non-conservative contaminants are considered to decay with a time scale
shorter than the mean residual time (~15 months) of sea water in the
SIS. Antibiotic is a typical non-conservative contaminant. Xu et al.
(2009) suggested that the half-life periods of four commonly used antibiotics, oﬂoxacin (OFL), roxithromycin (RTM), erythromycin (ETM),
and sulfamethoxazole (SMZ) were 3.4 days, 6.7 days, 7.3 days, and14.7 days in the ﬂume seawater system, respectively. Therefore, theconservative contaminants and the four antibiotics were selected astargets of simulation in this study.
The initial concentration of contaminant in the SIS and the concentration at two boundaries with open ocean (the Bungo Channel andKii Channel) were set to 0. Contaminant was released from rivers and its

IM

Cv =

∫1 ∫1

JM

IM

20
∫1 C (i, j, k ) dzdydx

∫1 ∫1

JM

20
∫1 dzdydx

i, j, k are grid indexes along the x, y, z directions, respectively. IM
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Fig. 4. The same as Fig. 3, but for vertical distributions along the solid red line with numbers in Fig. 1c. The numbers of stations correspond to numbers from 1 to 50
in Fig. 1c. (For interpretation of the references to colour in this ﬁgure legend, the reader is referred to the web version of this article.)mean of Cv. Consequently, the dilution factor of conservative contaminants in the entire SIS was 61 for Cv and 25 for Ca. In diﬀerent subregions, the contaminant concentrations showed apparent distinctions(Fig. 5b, c). For Cv, the high value occurred in Harima-Nada, Osaka Bay,and the Bisan Strait with an annual mean of 4.58, 3.87, and 3.84, respectively (Fig. 5b). The annual mean of Cv were 2.18, 1.97, and 1.86 in
Hiuchi-Nada, Suo-Nada, and Hiroshima Bay (Fig. 5b), respectively. Low
value of Cv was found in the Kii Channel, Iyo-Nada, and the Bungo
Channel with an annual mean value of 1.57, 1.11, and 0.42, respectively (Fig. 5b).
River was the only source of contaminant in the SIS and the total
input amount of pollutants were proportional to the river discharges
because we used a constant contaminant concentration in the riverwater. There were two rivers around Osaka Bay: the Yamato River andYodo River (Fig. 1b and Table 1). The river discharges of both werehigh in summer and low in winter, and the highest value occurred inJuly (ﬁgures not shown), which agreed with the seasonal variation ofcontaminant concentration in Osaka Bay (Fig. 5b, c). Besides, the annual mean discharge of Yodo River was 261.65 m3 s−1 while that of
Yamato River was only 25.62 m3 s−1 (Table 1). Therefore, the Yodo
River was responsible for the high concentration values and seasonalvariation in Osaka Bay.
Four rivers (d, e, f, g in Fig. 1b and Table 1) emptied into HarimaNada with a total discharge of 196.46 m3 s−1 (the Kako River and Ibo
River emptied directly into the sub-region of Harima-Nada, while the
Yoshii River and Asahi River did not directly empty into it), which wasless than that of the Yodo River. However, the Cv in Harima-Nada washigher than that in Osaka Bay (Fig. 5b). By calculating water age of
Yodo River's waters using constituent-oriented age and residence timeand JM are grid numbers in x and y directions. Apparently, Cv was anaveraged concentration using weighting factor of volume of each grid
point. Similarly, the area-averaged surface concentration (Ca) in the SIS
was calculated as follow.
IM

Ca =

∫1 ∫1

JM

IM

C (i, j, 1) dydx

∫1 ∫1

JM

dydx

Cv and Ca were ﬁrst calculated from daily saved model results andthen were averaged for a month (=monthly mean) and a year (=annual mean). Several important sub-regions (rectangle in Fig. 1c) wereselected for the purpose to know the seasonal variation of mean contaminant concentration inside the sub-regions where both Ca and Cv
were also calculated.
3. Results
3.1. Spatio-temporal variations of conservative contaminants
This section investigated the temporal variations and spatial distributions of conservative contaminants originating from rivers in the
SIS by case Control listed in Table 2. Fig. 5a showed the temporalvariation of contaminant concentration in the entire SIS. The average
concentration in the fourth year was almost the same as that in the thirdyear. Therefore, we analyzed the results in the fourth year. The same
applied to all numerical experiments listed in Table 2.
The annual mean of Cv in the entire SIS was 1.64, showing a highvalue of 2.03 in July and a low value of 1.39 in January (Fig. 5a). The
annual mean of Ca was 4.03, which was more than twice the annual95

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Table 2
List of numerical experiments.
No.

Case

River discharge

Concentration in river

Half-life period of contaminant (days)

Forcing

1
2
3
4
5
6

Control
OFL
RTM
ETM
SMZ
Constant-river

100
100
100
100
100
100

×
3.4
6.7
7.3
14.7
×

NormalNormalNormalNormalNormalNormal78910No-windConstant-heat
River-1994
River-2015

NormalNormalNormalNormalNormalAnnual mean
(1010 m3 s−1)
NormalNormal
1994
2015

100
100
100
100

×
×
×
×

No wind forcing
Annual mean air-sea heat ﬂux ﬁeld in Supplementary Fig. 2
NormalNormalNote: × indicates that the contaminant is conservative.

2006). This indicated a weak dynamic exchange ability in the Bisan
Strait that allowed the contaminants to be trapped there. The seasonalvariation of contaminant concentration in the Bisan Strait was not
likely related to river discharge, but rather the dynamic exchange
ability with surrounding regions.
Ca showed a similar seasonal cycle as Cv over the entire SIS (Fig. 5a),which was consistent with seasonal variation in river discharges(Fig. 2d). For sub-regions, Ca in Osaka Bay was the largest with anannual mean of 15.38. The next was Hiroshima Bay and Harima-Nada
with an annual mean of 7.82 and 6.96, respectively (Fig. 5c). The
maximum value of Ca is larger in the eastern SIS than in the western SIS(Fig. 6a). It was over 30 in Osaka Bay, and over 15 in Hiroshima Bay,northern Harima-Nada, western Suo-Nada, and other river mouths(Fig. 6a). The maximum value of Ca occurred mostly in July (Fig. 6b).
However, the occurrence times in the Bungo Channel and Kii Channel
were close to August and September (Figs. 5c and 6b). The time lag of
1–2 months in the Bungo Channel and Kii Channel was caused by thecontaminant transport from river mouths to the open ocean.
The vertical distributions of contaminant concentrations in four
seasons were shown in Fig. 7. The concentration exhibited a vertically
mixed pattern in winter and a vertically varying pattern in summer,
which was similar to those of salinity (Fig. 4e–h). High contaminantconcentrations (> 3) occurred at stations 30–45 (the Bisan Strait,
Harima-Nada, and Osaka Bay) throughout the year. A vertically varying

Fig. 5. Time series of (a) 15-day running mean of Cv and Ca over the entire SIS
of case Control in four computational years. (b) and (c) are 7-day running meanof Cv and Ca in selected sub-regions in the fourth year, respectively.

theory, Wang et al. (2019) suggested that about 3/8 of the Yodo River's
waters were brought into Harima-Nada though the Akashi Strait andeventually entered the Kii Channel. These waters were thought to havecontributed to the high concentration values in Harima-Nada.
It was surprising that the Cv was high in the sub-region of the Bisan
Strait (Fig. 5b) because no river directly emptied into this sub-region(Fig. 1b, c). Previous studies have determined that the tidal energy
input from the Bungo Channel moved eastward along the main channels and encountered westward propagative tidal energy input from theKii Channel in the Bisan Strait (Yanagi et al., 1982; Kobayashi et al.,Fig. 6. Distributions of (a) maximum surface contaminant concentration and(b) its occurrence time in case Control.
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Fig. 7. Vertical distributions of contaminant concentrations in case Control along the solid red line with numbers in Fig. 1c in (a) January, (b) April, (c) July and (d)October. The numbers of stations correspond to numbers from 1 to 50 in Fig. 1c. (For interpretation of the references to colour in this ﬁgure legend, the reader isreferred to the web version of this article.)vertically varying structure, especially in summer (ﬁgures not shown).
The ratio of annual mean Ca to Cv might reveal the vertical variation
and a large ratio indicated a strong vertical variation. As shown in
Fig. 8c, in all the cases, the vertical variation of contaminants in the
Bisan Strait was the weakest, while it was strong in Hiroshima Bay,
Osaka Bay, and Hiuchi-Nada. As the half-life period of contaminants
shortened, the ratio of annual mean Ca to Cv increased (Fig. 8c), whichindicated that the vertical variation was enhanced. This meant that,
under the same hydrodynamic conditions, contaminants with shorter
half-life period tended to distribute in the surface layer because morecontaminants decayed in the surface layer rather than entering the deep
water.

structure occurred from spring (April) to autumn (October), andreached its greatest variation in summer (July). Consequently, the Ca in
Osaka Bay and Hiroshima Bay were 31.24 and 15.09 in July (Fig. 5c),which were around 5.0 and 6.5 times the Cv of 6.28 and 2.33 in thesame sub-regions (Fig. 5b), respectively.
3.2. Dilution characteristics of non-conservative contaminants
Most riverine contaminants are non-conservative substances with ahalf-life period, which accelerates the removal eﬃciency of contaminants compared with conservative contaminants. Four experiments
were run with half-life periods of four antibiotics, OFL, RTM, ETM, andSMZ, that represented non-conservative contaminants, and their experiments were named cases OFL, RTM, ETM, and SMZ, respectively(Table 2). Expect for half-life period of contaminant, the model conﬁgurations in the four experiments were the same as those in caseControl. The results of the four antibiotic concentrations (ﬁgures notshown) presented the similar spatial distributions with the conservative
contaminants (Fig. 6 and Fig. 7). However, the annual mean of Cv in theentire SIS were 0.36, 0.22, 0.21, and 0.12, while the annual mean of Ca
were 2.16, 1.84, 1.80, and 1.52 for cases SMZ, ETM, RTM, and OFL,
respectively. All these values were smaller than those of case Control(1.64 and 4.03, respectively).
As shown in Supplementary Fig. 1, no matter what the half-life
period was, the concentrations exhibited coherent seasonal variations
with case Control (Fig. 5). With an increase in the half-life period ofcontaminants, both Cv and Ca exhibited a prominent increase (Fig. 8a,b), which indicated that fewer contaminants decayed and more contaminants remained in the SIS. Furthermore, using a riverine input
contaminant concentration of 100 as a reference, the relationship between concentration and half-life period of non-conservative contaminant could be depicted by the series of power functions listed inTable 3, which were within the 95% conﬁdence interval and had r2
values over 0.98. These functions made it possible to predict the contaminant concentration based on riverine input contaminant concentration and its half-life period, which contributed to assessing therisk to aquatic organisms. However, it should be noted that these relationships between concentration and half-life period were not applicable to conservative contaminants as well as non-conservative
contaminants with half-life periods close to zero. Strictly speaking, thefunctions listed in Table 3 were credible when the half-life period
ranged from 3.4 days to 14.7 days which were used as low limitationand high limitation of half-life period in our numerical experiments.
The vertical distributions of the four antibiotics also showed a

4. Discussion
4.1. Key factors responsible for seasonal dilution of contaminantIn this section, the key factors responsible for seasonal dilution ofcontaminant were explored by performing three sensitivity experiments
named cases Constant-river, No-wind, and Constant-heat (Table 2). The
sea surface wind forcing was not included in case No-wind. The annual
mean river discharge of each river was used in case Constant-river. The
annual mean air-sea heat ﬂux (Supplementary Fig. 2) was usedthroughout a year in case Constant-heat. Except for these speciﬁed
conditions, the other model conﬁgurations in the three sensitivity experiments were the same as case Control. We compared Cv and Ca in thethree sensitivity experiments with those in case Control (Fig. 9) anddiscussed the mechanisms in more detail below.
4.1.1. The role of sea surface wind forcing
When sea surface wind forcing was excluded, seasonal variations ofboth Cv and Ca showed similar patterns as those in case Control (blacklines and green lines in Fig. 9a, c), but the contaminant concentration
values were diﬀerent (black lines and green lines in Fig. 9b, d). Cv incase No-wind was about 5% higher from December to June and 9%
lower from July to November than that in case Control (Fig. 9a, b),while Ca in case No-wind was always higher (around 31%) than that incase Control (Fig. 9c, d).
The seasonal variations of meridional component of sub-tidal surface current velocity over the entire SIS in case Control and case Nowind can be found in Fig. 10. In winter, the surface southward current
was stronger in case Control (4.2 cm/s) than in case No-wind (0.7 cm/s)(black and green dots in Fig. 10), indicating a southward wind-induced
surface current of 3.5 cm/s. The southward surface current induced by97

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Table 3The ﬁtted power functions of Cv and Ca (y) with half-life period of contaminant(x) shown in Fig. 8.

Entire SIS
Bungo Channel
Suo-Nada
Iyo-Nada
Hiroshima Bay
Hiuchi-Nada
Bisan Strait
Harima-Nada
Osaka BayKii Channel

CvCa

y = 0.0519 x0.7222
y = 0.0230 x0.5142
y = 0.0548 x0.7412
y = 0.0175 x0.8679
y = 0.0493 x0.7617
y = 0.0222 x0.9291
y = 0.1832 x0.6532
y = 0.0737 x0.9149
y = 0.3036 x0.5796
y = 0.0663 x0.688

y = 1.141 x0.2384
y = 0.2538x0.2234
y = 0.6432 x0.3262
y = 0.2562 x0.3399
y = 4.134 x0.1308
y = 1.028 x0.1877
y = 1.531 x0.271
y = 1.198 x0.3206
y = 7.013 x0.1918
y = 1.264 x0.2415

The functions were all within the 95% conﬁdence interval and the r2 values
were all over 0.98.

(Fig. 9a).
Since the wind speed was larger in winter than in summer (Fig. 2a)and the duration of the northerly wind in winter was longer than that of
the southerly wind in summer (Fig. 2b), the increasing concentration
caused by the southerly wind in summer could not contend with thedecreasing concentration caused by the northerly wind in winter, resulting in a lower Ca in case Control than in case No-wind throughout ayear (Fig. 9c, d). On the other hand, wind-induced vertical mixing was
also an important factor that facilitated vertical transport of contaminants and reduced Ca in case Control, especially in winter.
4.1.2. The role of air-sea heat ﬂux
In case Constant-heat, Cv showed a high value in autumn and low
value in spring, which were diﬀerent from those in case Control(Fig. 9a). However, Ca showed a similar seasonal cycle with case Control though the value was a little lower in case Constant-heat than incase Control (Fig. 9c, d). It indicated that the vertical distribution ofcontaminant concentration in the water column, but not the surface
value, was sensitive to seasonal variation in air-sea heat ﬂux.
The annual mean air-sea heat ﬂux used in case Constant-heat was
shown in Supplementary Fig. 2. The waters inside the SIS gained heat,
while the waters of the two boundaries, the Bungo Channel and Kii
Channel, lost heat. In winter, the water temperature in case Constantheat was higher in the middle area of the SIS than in two channels(Fig. 11a), which was opposite to the temperature distribution in caseControl (Fig. 4a). The distributions of salinity were similar in the twocases (Fig. 4e and Fig. 11c). Determined by water temperature, density
in case Constant-heat was low in the middle area and high in the twochannels (Fig. 11e). The diﬀerence of density between middle area andtwo channels in case Constant-heat generated a horizontal pressure
gradient force, inducing a surface density current toward the twochannels and a deep density current toward the middle area, which
could be addressed by the extension of isolines of temperature, salinity,and density (Fig. 11a, c, e).
In case Control, the density distribution was relatively uniform in
winter (Supplementary Fig. 3a) and the horizontal current component
due to density diﬀerence was weak (in principle, it is almost the same as
the southward current of 0.7 cm/s in case No-wind, Fig. 10). The enhanced southward surface current in case Constant-heat (8.5 cm/s)(Fig. 10) brought more contaminants to the open ocean before they
were vertically mixed within the water column. This can be known byvertically non-homogeneous distribution of concentration (Fig. 12a)and low concentration value in case Constant-heat (and Fig. 9a). As aresult, continuous contaminant output to the open ocean from winter tospring (Fig. 10) contributed to the lowest Cv in spring (Figs. 9a and12b).
In summer, there were similar density structures (Fig. 11f and
Supplementary Fig. 3b) and meridional component of surface current
velocity (Fig. 10) between case Control and case Constant-heat.

Fig. 8. The relationships between the half-life periods of contaminants and (a)Cv, (b) Ca, and (c) the ratio of Ca to Cv in several sub-regions of the SIS.
Conservative contaminants are marked with “Inf”. The gray solid lines in (a)and (b) represent the ﬁtted curves according to functions in Table 3. OFL:oﬂoxacin, RTH: roxithromycin, ETM: erythromycin, SMZ: sulfamethoxazole.

northerly wind (Fig. 2b) facilitated the contaminant transport to theopen ocean, decreasing the contaminant concentration (Ca and Cv) incase Control (Fig. 9a). In summer, a southerly wind in case Control(Fig. 2b) induced northward surface current, hindered the contaminanttransport to the open ocean, and increased Cv as found in case Control
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Fig. 9. Time series of 7-day running mean of (a) Cv in the numerical experiments and (b) their change ratios with respect to case Control. (c) and (d) are the same as(a) and (b), but for Ca.

component of sea surface current velocity over the entire SIS (Fig. 10);it mainly aﬀected the amounts of contaminants entering the SIS. The
integrated river water volume from daily river discharge used in caseControl and case Constant-river were shown in Supplementary Fig. 4.
The total river volume reached the same value in case Control andConstant-river in the middle of July, which was close to the time (earlyJuly) when the higher Cv in case Constant-river became lower than thatin case Control (Fig. 9a). However, the inversion of Ca appeared in the
middle of June (Fig. 9c), which was in accordance with the timing ofdrastic changes in river discharge (Fig. 2d). It was obvious that thesurface concentration responded rapidly to the change in river discharge, not only response time but also concentration value indicatedby higher change ratio of Ca than that of Cv between two cases (Fig. 9b,d).
As a summary, for Cv, the air-sea heat ﬂux was the ﬁrst contributor
to its seasonal variability and concentration value, and river discharge
was the secondary factor; for Ca, its seasonal variation was consistent

However, a prominent change, i.e., the weakening of vertical stratiﬁcation, was found in case Constant-heat in terms of the distributions oftemperature (Figs. 4c and 11b), salinity (Figs. 4g and 11d), and density(Supplementary Fig. 3b and Fig. 11f). It was indicated that, in thecondition of weak stratiﬁcation, more contaminants entered the deep
water since the water was vertically mixed more in case Constant-heat
than in case Control (Figs. 7c and 12c). Contaminants in the deep water
were easily accumulated from summer to autumn, resulting in thehighest Cv in autumn (Figs. 9c and 12d).

4.1.3. The role of river discharge
Similar seasonal variations of contaminant concentration wereshown in case Control and case Constant-river. However, the maximum
concentration value became smaller in case Constant-river (red lines inFig. 9a, c), especially for Ca in July that changed about 30% comparedwith case Control.
Change in river discharge had little impact on meridional

Fig. 10. Time series of the meridional component of surface current velocity averaged over the entire SIS in cases Control, Constant-river, No-wind, and Constantheat. Northward current velocity is positive.
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Fig. 11. Vertical distributions of (a–b) temperature (°C), (c–d) salinity, and (e–f) sigma-t (density-1000, kg/m3) along the solid red line with numbers in Fig. 1c in
February and July in case Constant-heat. (For interpretation of the references to colour in this ﬁgure legend, the reader is referred to the web version of this article.)

performed (Table 2) using river discharge in 1994 and 2015 (Table 1
and Supplementary Fig. 5b), which represented a dry river year and a
wet river year, respectively, to investigate the impact of river discharge
on contaminant concentration.
It was showed that the appearing time of maximum Ca were consistent with that of maximum river discharge (Supplementary Fig. 5band Supplementary Fig. 6b). Moreover, Cv was high in March–June of1994 and July of 1995 as a response to high river discharge at the sametime (Supplementary Fig. 5b and Supplementary Fig. 6a). This indicated that high river discharge controlled the maximum contaminantconcentration in both dry year and wet year of river.
When river discharge increased by 1.61 times (from 536 m3 s−1 to1400 m3 s−1), Ca increased by 1.55 times, while Cv increased by 1.33
times (Fig. 13a). It suggested that riverine contaminants tended to
distribute in the surface layer. More speciﬁcally, when river discharge
increased from 536 m3 s−1 (case River-1994) to 1010 m3 s−1 (caseControl), the contaminant concentration increased in all sub-regionswith that of river discharge, however, the sea surface wind velocity was
the dominant factor controlling its value.
4.2. Comparison of dilution characteristics in wet and dry river years
Rivers act as the main source of contaminants in the SIS. With global
climatic change in recent years, river discharge varies obviously andmay have an important eﬀect on contaminant concentration. Therefore,
the responses of contaminant concentrations to river discharge need tobe addressed. Supplementary Fig. 5a showed the long-term variationsin annual mean river discharges of 26 rivers emptying into the SIS
during 1993–2016 with a multi-year averaged value of 1010 m3 s−1
and a standard deviation of 253 m3 s−1. Prominent dry year was observed in 1994 with an annual mean discharge of 536 m3 s−1, whileprominent wet years were shown in 1993, 2004, 2015, and 2016 withan annual mean discharge around 1400 m3 s−1. Combined with caseControl, two experiments named River-1994 and River-2015 were

Fig. 12. The same as Fig. 7, but for case Constant-heat.
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concentration in Osaka Bay and Harima-Nada. Slight increasing in
Hiroshima Bay, Iyo-Nada and Suo-Nada were results of increasing discharges of rivers emptying into these sub-regions (Table 1). As pathways for contaminants ﬂowing into open ocean, the concentrations in
the Bungo Channel and Kii Channel increased as the same as adjacentsub-regions. On the contrary, a slight decreasing was denoted in the
Bisan Strait and Hiuchi-Nada (Fig. 13b), which was caused by the
combination (24.94 m3 s−1 reduction) of a reduction in discharge(43.41 m3 s−1) of Takahashi River and an increase in discharge(18.47 m3 s−1) of other four major rivers (the Yoshii River, Asahi River,
Ashida River and Doki River) near the sub-regions (the discharge of
secondary river in Hiuchi-Nada changed little).
This meant that the average contaminant concentration in the entire
SIS was consistent with total river volume (Supplementary Figs. 4 and
6). However, the response of contaminant concentration in a sub-region
corresponded to the variation of river discharge near this sub-region.
This fact should be considered when evaluating and managing theriverine contaminant levels, such as nutrients, antibiotics, and othersubstances.
5. Conclusion
Based on a hydrodynamic model, this study investigated the dilution features of riverine input contaminants in the SIS. Conservative
contaminant exhibited a vertically mixed pattern with low concentration in winter and a vertically varying pattern with high concentrationin summer. High concentrations occurred in Osaka Bay, Harima-Nada,
and Bisan Strait. With riverine input contaminant concentration of 100,
the annual mean conservative contaminant concentrations were 1.64
and 4.03 for the water column and sea surface, respectively. Non-conservative contaminant showed a similar spatial distribution with theconservative contaminant. As the half-life period of non-conservative
contaminant decreased, the contaminant concentration decreased following power functions. These were useful information for environmental management to set standards for contaminants to protect thehealth of the aquatic ecosystem of the SIS.
The roles played by surface wind velocity, air-sea heat ﬂux and riverdischarge were conﬁrmed with a series of numerical experiments. The
seasonal cycle of air-sea heat ﬂux was responsible for seasonal variations of contaminant concentrations in the whole water column by inﬂuencing horizontal current and vertical stratiﬁcation. The surface
wind velocity, which induced sea surface current, played a vital roleaﬀecting surface contaminant concentration. The appearing time ofhigh contaminant concentration was always consistent with that of highriver discharge. When river discharge increased from a dry river year toa wet river year, the contaminant concentration in the entire SIS increased but the concentrations in diﬀerent sub-regions varied diﬀerently as a response to the change in river discharge near the sub-region.
This should be considered when evaluating the level of contaminantconcentration in diﬀerent river years.
Once introduced into the sea surface water, except for decaying overtime, the contaminants also undergo biodegradation and adsorption tosuspended particles and sediment (Xu et al., 2009; Larcher andYargeau, 2012; Ono et al., 2012). It should be noted that our hydrodynamic model did not include biological processes and adsorption tosuspended particles or sediment that aﬀect the behavior of contaminants. The impact of these processes will be investigated in future
works.

Fig. 13. The relationships between (a) contaminant concentration in the entire
SIS and river discharge; (b) and (c) are the same but for Cv and Ca in several subregions of the SIS, respectively.

(Fig. 13b, c), which was in response to the increase in discharge of eachriver (Table 1).
However, when river discharge increased from 1010 m3 s−1 (caseControl) to 1400 m3 s−1 (case River-2015), the responses of concentration were distinguishing in diﬀerent sub-regions (Fig. 13b, c),which were related to the variations of river discharge (Table 1) near
the sub-regions. A sharp increase in discharge of the Yodo River andKako River (Table 1) caused obvious increasing of contaminantAcknowledgement
This study was supported by the Ministry of Education, Culture,
Sports, Science and Technology, Japan (MEXT) for a project by the
Joint Usage/Research Center–Leading Academia in Marine andEnvironment Pollution Research (LaMer). X. Guo was also supported by
JSPS KAKENHI Grant number JP17H01860. J. Zhu thanks the China
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Scholarship Council (CSC) for supporting her stay in Japan.

Appendix A. Model description
The hydrodynamic model used in this study is based on the Princeton Ocean Model (Blumberg and Mellor, 1987), which is a three-dimensional
free-surface primitive equation ocean model that has been widely applied to many coastal seas. Chang et al. (2009) used the three-dimensional
numerical model with monthly forcing conditions to simulate the seasonal variation of sub-tidal current in the SIS. The model was updated withdaily forcing conditions and applied in the present study. There were two southern boundaries in the model, Bungo Channel and Kii Channel (solid
black lines in Fig. 1b). In fact, the SIS has another opening to the Tsushima Strait at its northwestern boundary, the Kanmon Strait (Fig. 1b). Since theKanmon Strait is very narrow (approximately 0.6 km) and its inﬂuence on the SIS is small (Takeoka, 2002), it was closed in this model following
previous studies (Takeoka, 1984; Kobayashi et al., 2006). The model had a horizontal resolution of 1/80° in the zonal direction and 1/120° in themeridional direction, and a vertical resolution of 21 sigma levels (0.000, −0.002, −0.004, −0.010, −0.020, −0.040, −0.060, −0.080, −0.100,−0.120, −0.140, −0.170, −0.200, −0.300, −0.400, −0.500, −0.650, −0.800, −0.900, −0.950, and −1.000). The internal mode time step was
120 s, and the external mode time step was 4 s.
The daily surface ﬂuxes used to drive the model included surface ﬂuxes of momentum, heat and fresh water. The daily wind stress was based onhourly averaged results of wind stress, which was calculated by wind velocity from the Grid Point Value of Meso-Scale Model (GPV-MSM) (http://
database.rish.kyoto-u.ac.jp/arch/jmadata/data/gpv/) during 2007–2016 provided by the Japan Meteorological Agency with the resolution of 1/
16° × 1/20°, adopting the drag coeﬃcient of Large and Pond (1981). The northwest wind prevails in winter with an average speed of 4.3 m/s, while
the southwest wind is dominant in summer with an average speed of 1.4 m/s (Fig. 2a, b). The daily shortwave radiation was based on the newly
released of Japanese Ocean Flux Data Sets with Use of Remote Sensing Observation (J-OFURO3) (https://j-ofuro.scc.u-tokai.ac.jp/) with a resolutionof 1/4° × 1/4° and averaged during 2002 to 2013. The daily longwave, sensible heat ﬂux and latent heat ﬂux were calculated and averaged byadopting bulk formula (Gill, 1982) using hourly air temperature, sea surface temperature, relative humidity, cloud cover, and wind velocity from the
GPV-MSM (2007–2016). The SIS obtains around 131 W/m2 of heat in summer and loses about 155 W/m2 in winter (Fig. 2c). Daily evaporation was
obtained by calculating the latent heat ﬂux. The daily precipitation was provided by the GPV-MSM and averaged hourly from 2007 to 2016. Finally,
the values of precipitation and evaporation were adjusted according to the values in Tawara (1986). The daily sea surface temperature was based on
Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) (http://ghrsst-pp.metoﬃce.com/pages/latest_analysis/) with a resolution of 1/
20° × 1/20° and averaged during 2007 to 2016. The sea surface temperature from OSTIA was adjusted by observed multi-year (1971 to 2000)
monthly averaged sea surface temperature which was obtained from prefectural ﬁshery research centers (dots in Fig. 1c).
The initial temperature and salinity ﬁelds in January were produced by merging the Marine Information Research Center dataset in the SIS region
and the model results of Guo et al. (2003). The boundary conditions including de-tided current velocity, temperature, salinity, and surface elevation
were obtained from the diagnostic model of Guo et al. (2004). There were four major tidal constituents, M2, S2, O1 and K1, along the open boundary
provided by Matsumoto et al. (2000). The daily river discharges averaged over 24 years (1993–2016) from the Ministry of Land, Infrastructure andTransport were used in the model. Other detailed information about the model can be found in Chang et al. (2009).
A.1. Attenuation of non-conservative contaminant
The attenuation process of antibiotics is depicted by a ﬁrst-order reaction kinetics model, in which the reaction rate is proportional to the reactant
concentration. It follows that

dx
= −kx
dt
where x is the reactant concentration, t is the reaction time, and k is the reaction rate constant. For a reactant concentration of x0 at initial time t0, thereactant concentration x(t) at time t is

x (t ) = x 0 e−k (t − t0)
In the model, the attenuation process of antibiotics is expressed byx (t + ∆t ) = x (t ) e−k ∙∆twhere x(t) is the antibiotic concentration in the current time step, while x(t + Δt) is the antibiotic concentration in the next time step, and Δt is thestep time.
The half-life period is often used to show the persistence of antibiotics in the environment. In the ﬁrst-order reaction kinetics model, the half-life
period is independent of reactant concentration, but is inversely proportional to the reaction rate. If the half-life period is known as DT50, the reaction
ln

rate constant k is −( 12 ) . The half-life periods of OFL, RTM, ETM, and SMZ suggested by Xu et al. (2009) were used in the model.

DT50

Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.org/10.1016/j.marpolbul.2019.02.029.

Blumberg, A.F., Mellor, G.L., 1987. A description of a three-dimensional coastal ocean
circulation model. In: Three-Dimensional Coastal Ocean Models. 4. pp. 1–16.
https://doi.org/10.1029/CO004p0001.
Boon, P.I., Cattanach, M., 1999. Antibiotic resistance of native and faecal bacteria isolated
from rivers, reservoirs and sewage treatment facilities in Victoria, south-eastern
Australia. Lett. Appl. Microbiol. 28 (3), 164–168. https://doi.org/10.1046/j.1365-

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