Environmental Pollution 288 (2021) 117779

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Environmental Pollution
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Transport and dilution of fluvial antibiotic in the Upper Gulf of Thailand☆
Aobo Wang a, Xinyu Guo b, *, Akihiko Morimoto b, Kana Maetani a, Rumi Tanoue b,
Siraporn Tong-U-Dom c, Anukul Buranapratheprat caGraduate School of Science and Engineering, Ehime University, Matsuyama, Japan
Center for Marine Environmental Studies (CMES), Ehime University, Japanc
Department of Aquatic Science, Faculty of Science, Burapha University, Thailandb

A R T I C L E I N F O

A B S T R A C T

Keywords:
Sulfamethoxazole
Dilution ratio
Mass balance
Fluvial pollutant
Prediction function

A three-dimensional hydrodynamic-antibiotic model is developed to investigate the transport and dilution of
sulfamethoxazole (SMX) in the Upper Gulf of Thailand (UGoT). The simulation produced a spatially averaged
annual mean SMX concentration of 0.58 μg m− 3 , which varied slightly between seasons assuming a temporally
constant river SMX loading observed in August. In contrast, the horizontal distribution of SMX concentrations
strongly varied with season because of the changing residual currents. In addition, SMX is diluted to concen­
trations lower than 10% of those in river waters a short distance offshore of the estuaries. To better understandthis behavior, we examined the relationship between salinity and SMX concentrations in the UGoT. The annual
budget demonstrates that 98% of SMX in the UGoT is removed by natural decomposition. As the concentrationsof fluvial pollutants in the UGoT depend on their river loading and decomposition rates, functions were derived
to predict pollutant concentrations and flushing times based on the river input flux and half-life.

1. Introduction
Antibiotics are emerging organic pollutants widely used in humantherapy, veterinarians, agriculture, and aquaculture because of their
medicinal properties, growth-promoting properties, and breeding ap­
plications (Tamtam et al., 2008; Jiang et al., 2011; Kafaei et al., 2018).
The increasing use of antibiotics and the resulting accumulation of
antibiotic residues in the environment present serious problems. This
includes harm to biological communities by destroying the ecologicalbalance and the emergence of antibiotic resistance genes (Isidori et al.,2005; Martínez, 2008; Kummerer, 2009; Zhang et al., 2018).
The ocean is an important sink for many terrestrial pollutants (Sab­ourin et al., 2012; Zhang et al., 2013b; Ko et al., 2014; Downs et al.,2016; Ju et al., 2016). Some studies have indicated that the annual
global consumption of antibiotics is 100,000 to 200,000 tons, which is
linked to antibiotic pollution in a variety of environmental media,
including ocean water (Zuccato et al., 2010; Kafaei et al., 2018). Indeed,
several kinds of antibiotics, including sulfonamides, macrolides, fluo­roquinolone, trimethoprim, cephalexin, amoxicillin, carbamazepine,and orphenadrine, have been detected at concentrations of μg ​ L− 1 inseawater including in the Bohai Sea, South China Sea, Baltic Sea, and thecoastal areas of Iran (Zhang et al., 2013a; Björlenius et al., 2018; Kafaei
et al., 2018; Zhang et al., 2018).
Previous studies have shown that large amounts of antibiotics aredischarged into coastal waters from rivers, after which they are trans­
ported by ocean currents and ultimately decomposed by organisms andlight (Jia et al., 2011; Li et al., 2011; Zou et al., 2011). Antibiotics fromrivers can be diluted in seawater, and their concentrations are inversely
proportional to the salinity, showing high dispersion (Du et al., 2017).
The partitioning of antibiotics to aerosols is weak because they aremostly polar or ionic, indicating that atmospheric deposition of antibi­
otics is insignificant (Zhang et al., 2013a).
The observation of antibiotics in seawater helps determine pollution
levels and evaluate their environmental effects and fate (Zhang et al.,2015). However, the field data on antibiotics are usually not continuousin time and space due to natural, economic, and practical constraints(Zhang et al., 2015; Zhu et al., 2019). For example, a bad weather
usually does not allow a survey over sea. Meanwhile, the field obser­
vation cannot quantify the flux of each process contributing to thevariation of concentration of antibiotics.
Ocean models are useful tools to solve the problems mentioned
above by simulating the continuously spatial and temporal variations ofThis paper has been recommended for acceptance by Maria Cristina Fossi.
* Corresponding author.
E-mail address: guoxinyu@sci.ehime-u.ac.jp (X. Guo).

☆

https://doi.org/10.1016/j.envpol.2021.117779
Received 22 October 2020; Received in revised form 1 July 2021; Accepted 10 July 2021
Available online 13 July 2021
0269-7491/© 2021 Elsevier Ltd. All rights reserved.

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Environmental Pollution 288 (2021) 117779

pollutants and calculating the pollutant flux of each biogeochemical
processes contributing to these variations. For example, ocean models
for suspended sediment, nutrients, and persistent organic pollutants(POPs) have been used to investigate their physical and biogeochemical
processes (Bian et al., 2013; Wang et al., 2019a; Wang et al., 2019b;
Zhang et al., 2019). However, to date, few simulations have been per­formed for antibiotics in seawater. One recent study found that theconcentrations of antibiotics in the Seto Inland Sea decreased with areduction in half-life, deriving a power function relationship between
them (Zhu et al., 2019).
The Upper Gulf of Thailand (UGoT) is a semi-closed shallow sea
located in the northern area of the Gulf of Thailand, which is located in
the tropical area on the western side of the South China Sea and issurrounded by Thailand, Vietnam, Cambodia, and Malaysia (Fig. 1)(Boonyatumanond et al., 2007; Yu et al., 2018). The UGoT is affected bywet southwesterly monsoon conditions from May to September and drynortheasterly monsoon conditions from November to January (Yu et al.,2018). The area is bordered by several large cities in Thailand, including
Bangkok, and is affected by significant human activity (Burnett et al.,2007). There are four rivers around the UGoT, namely the Bangpakong,
Chaopraya, Thachin, and Maeklong Rivers, which flow through several
cities and deliver large amounts of urban discharge to the UGoT (Yuet al., 2018). The combined annual mean river discharge of these four
rivers is 960 m3 s− 1 . Among the four rivers, the Chaopraya River has amaximum annual mean runoff of 365 m3 s− 1 (Buranapratheprat et al.,2008; Yu et al., 2018).
Sulfamethoxazole (SMX), a widely used sulfonamide, is generally
used to treat urinary infections in humans as well as in veterinary,aquaculture, and livestock breeding to treat disease and promote growth(Drillia et al., 2005; Xu et al., 2011). SMX is widely detected ingroundwater, rivers, and seawater worldwide (Watkinson et al., 2009;
Tewari et al., 2013; Zhang et al., 2013a; Du et al., 2017; Chen et al.,2018; Li et al., 2018) and it is integrated into biogeochemical cycle via
hydrolysis, biodegradation, and adsorption to sediment (Li, 2014). The
half-life of SMX is reported to be 14.9 days in the sediment-seawater
system (Xu et al., 2009) but 3, 9, 16, 49, 85, and >100 days in otherexperimental conditions, as shown in Table S1 (Benotti and Brownawell,2009; Baumgarten et al., 2011). Among those values, we chose 14.9 days
(bolded in Table S1) as a default half-life of SMX in the UGoT because
experiment conditions used by Xu et al. (2009) were similar to themarine environment in the UGoT (details of the experiment conditions
are shown in Appendix A of the supplementary material).
Previous work indicates that the canals and river systems of
economically productive cities around the UGoT are threatened by
antibiotic residues of various origins (Tewari et al., 2013; Mrozik et al.,2019). However, there is little information regarding the occurrence andconcentrations of antibiotics in seawater in the UGoT. In addition, riverdischarge and ocean currents in the UGoT show strong seasonality due
to the monsoon system and rainfall, which can affect pollutant con­centrations. Therefore, there is a need to determine the extent and de­gree of antibiotic pollution in the UGoT, and to examine the effects ofenvironmental factors on spatial and temporal variations of pollution.
Here, SMX was chosen as the target pollutant, having high ecologicalrisk in some coastal waters (Zhang et al., 2013a, 2013b; Du et al., 2017).
We use a three-dimensional (3D) model to simulate the temporal andspatial variations of SMX in the UGoT, to calculate its dilution ratio andbudget in seawater, and to examine the relationship between salinity
and SMX concentrations. Furthermore, we develop a prediction function
for the concentration of pollutant in the UGoT based on its half-life andriver input load.
2. Field sampling and model description
2.1. Field sampling and SMX analysis
River and seawater surface water samples were collected in August
2019 including 12 samples from the UGoT and four samples from the
Bangpakong River, Chaopraya River, Thachin River, and Maeklong
River estuaries (Fig. 1b; Table S2). The samples in the sea were collectedon the ship deck while those in estuaries were collected along the shore
side. The samples of river water and seawater were all collected for thesurface water using a stainless steel bucket. After three times of washing
the amber polypropylene bottles using the collected surface water, thesurface water was saved in the bottles. After adding 1.0 g L− 1 ascorbic

Fig. 1. (a) Location of the Upper Gulf of Thailand; (b) Model domain and bathymetry (color shading for depth and contours are in meters). Black triangles are theestuaries of the Bangpokong, Chaopraya, Thachin, and Maeklong Rivers. Black dots represent the SMX water sampling stations sampled in August 2019. The dashedline indicates the section shown in Fig. S2. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)2

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Environmental Pollution 288 (2021) 117779

acid to it, we stored the amber polypropylene bottles in darkness at− 20 ◦ C prior to analysis. The details of SMX analysis are shown in Ap­
pendix B.

)(
where Friver (C) (μg s− 1 ) is the river input load, Q m3 s− 1 is the river()
runoff, and Cr μg m− 3 is the concentration of SMX in the river water.
When Friver (C) is to the grid point of the estuary, it is diluted by theseawater corresponding to that grid point, that is, S(C) = (Friver (C) +C0 × V0 )/(Q + V0 ), where C0 is the SMX concentration at the estuary
grid point and V0 is the volume of seawater at the grid point.
The SMX decomposition term in Eq. (1) is described as follows:

2.2. Model description
A 3D hydrodynamic-antibiotic model was developed to simulate thetransport and dilution of SMX from rivers in the UGoT. The model in­cludes two parts, a hydrodynamic module and an antibiotic module.
Using this model, we simulated the distribution of SMX from different
rivers and examined the effects of the half-life on their spatial andtemporal variations in the UGoT.

Dec(C) = − ln 2 × α− 1 × C

where Dec(C) (μg m− 3 s− 1 ) is the SMX decomposition flux and α (s) is the
SMX half-life (Table S1). We assumed a SMX half-life 14.9 days, as thiswas experimentally derived in seawater (Xu et al., 2009). Eqs. (1)–(3)can be applied to SMX from each river (C1 , C2 , C3 , and C4 ), referred to as
Cases 1 to 4. As Eq. (1) represents a linear system, the total SMX con­centrations in the sea were determined as the sum of C1 , C2 , C3 , and C4 .
The initial concentration of SMX in the model domain was set to 0 in
the four cases. During the calculation, the SMX concentration gradientacross the open boundary was set to zero. The SMX concentrations in theriver waters in August 2019 are shown in Table S3, and mean riverdischarge for August 2019 was used to obtain the SMX flux from eachriver to the UGoT. Based on the assumption that the usage of SMX in thecities around the UGoT varies little throughout a year, the SMX flux fromeach river was held at the August 2019 level throughout the calculation.
To avoid the influence of the initial conditions, we run the model for athree-year period, using the results for the third year in our analysis.

2.3. Hydrodynamic module
The hydrodynamic module is the same as that used to study theseasonal variations of low-salinity water in the UGoT (Yu et al., 2018),
based on the Princeton Ocean Model (Mellor et al., 2002). The model
domain extended from 99.9500 ◦ E to 100.9750 ◦ E and from 12.6666 ◦ N
to 13.5083 ◦ N (Fig. 1b) with horizontal resolution of ~1 km and a
vertical resolution of 21 sigma layers (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,− 1.000).
The forcing data of the hydrodynamic model include monthly heat
flux, wind stress, river discharge, water temperature and salinity at the
open boundary, and tide height at the open boundary datasets obtained
from Buranapratheprat et al. (2008, 2009). Given the small size of the
UGoT (approximately 100 km × 100 km, Yu et al., 2018), the surface
forcing data were assumed to be horizontally homogeneous in themodel. For the tides, four tide constituents (M2 , S2 , K1 , and O1 ) wereadded to the elevation at the open boundary (Yu et al., 2018). These
forcing conditions are for a simulation of the climatological state in the
UGoT.

3. Results
3.1. Seasonal variations and spatial distribution of SMX in the UGoT
SMX transport from each of the four study rivers was simulated by
the model. As shown in Fig. 2, the calculated concentrations in thesecond and third years of the simulation are almost the same, indicatingthat the calculation reached a stationary state after the second year. In
the following analysis, we use the results in the third year to examine thetemporal and spatial variations in the SMX in the UGoT. The notablesmall oscillations in Fig. 2 have a period of approximately 15 days,
reflecting the cycle of SMX exchange with the open sea driven by springand neap tides.
Although we used a temporally constant river input flux of SMX, theconcentrations of it varied in the UGoT across the year (Fig. 2). A
maximum concentration of 0.68 μg m− 3 occurs in February and a min­imum of 0.45 μg m− 3 occurs in November (Fig. 2). Such seasonal vari­ations are attributed to a weak residual current in February, which is notfavorable for SMX transport out from the UGoT. Among the four rivers,
the concentrations of SMX delivered from the Chaopraya River are
highest, having a dominant control on the seasonal variations in the
UGoT, which is due to the highest input flux of SMX from Chaopraya
River (Table S3).
February, August, October, and November were chosen to examine
the spatial distributions of monthly averaged SMX concentrations(Fig. 3). These months were selected as the northeastward wind andsouthwestward wind prevailed in August and November, respectively,
and the transition of wind occurred in October. Furthermore, the highest
mean concentration of SMX occurred in February (Fig. 2).
As shown in Fig. 3, the model results for August are generally
consistent with the spatial variation of surface SMX concentrations
based on the field data, being high in nearshore areas and low in offshore
areas. The concentrations of SMX at stations S1 S3–S5, S9, and S11–S12
match well with the field data; however, the concentrations at stations
S7 and S10 are higher than the field data, while those at stations S2, S6
and S8 are lower. Another difference is the patches of high or low
concentrations in the field data but only a smoothly spatial change in the
SMX concentrations of model results. This likely reflects the field data

2.4. Antibiotic module
SMX was considered in the model in its dissolved state, with con­centrations expressed as C1 , C2 , C3 , C4 , for the Bangpakong River,
Chaopraya River, Thachin River, and Maeklong River contributions,
respectively. As well as advection and diffusion, SMX was assumed todecompose in the seawater (Fig. S1). The concentration of dissolved
SMX was calculated using the following equation.
(
)
∂C
∂C
∂C
∂C ∂
∂C
+u +v +w =Kh
+ D(C) + S(C) + Dec(C)
(1)
∂t
∂x
∂y
∂z ∂z
∂z
In the equation, C (μg m− 3 ) is the concentration of SMX in seawater
driven by one of the four rivers (C1 , C2 , C3 , C4 ); t is time (s); x, y, and z
are the zonal, meridional, and vertical coordinates (m); u, v, and w are
the velocity components in the x, y, and z directions, respectively(m s− 1 ); Kh is the vertical eddy diffusion coefficient (m2 s− 1 ); D(C) is thehorizontal diffusion term; S(C) is the source term; and Dec(C) is thedecomposition term. Some research reported back-transformation of
SMX from degradation products (Nödler et al., 2012; Bonvin et al.,2013). However, we did not include this process in the model because its
small flux. A detailed explanation is given in Appendix C. Equation (1) is
calculated in two steps: an explicit scheme for horizontal and verticaladvection, horizontal diffusion, and the source and decomposition
terms, and an implicit scheme for vertical diffusion (Mellor et al., 2002).
We assumed that SMX in the UGoT could only be sourced from the()
four rivers. Therefore, S(C) μg m− 3 s− 1 is equaled to the SMX loads ofthe rivers at the grid points corresponding to the estuaries but was set tozero at the other grid points. The SMX load from each river was calcu­
lated as follows:
Friver (C) = Q × Cr

(3)

(2)
3

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Environmental Pollution 288 (2021) 117779

Fig. 2. Time series of mean SMX concentrations over the entire model domain during the three-year calculation period. ‘All’ represents SMX from all four rivers, and‘B’, ‘C’, ‘T’, ‘M’ represent SMX from the Bangpakong, Chaopraya, Thachin, and Maeklong Rivers, respectively. The black, blue, and green lines refer to the left-hand yaxis and the magenta and red lines refer to the right-hand y-axis. (For interpretation of the references to color in this figure legend, the reader is referred to the Web
version of this article.)

Fig. 3. Horizontal distribution of monthly averaged surface SMX concentrations and residual currents in (a) February, (b) August, (c) October, and (d) November.
The black arrows represent residual currents and the red arrows represent wind stress. The filled circles in (b) are the SMX field data in August 2019. The color scale isthe same for the model results and field data. The “surface layer” in the model occupies 0.2% of the water column depth that is the thickness of first sigma coordinate.
The “surface” for field data means the sampling of surface water that is around the layer of 0–0.1 m. Because of the presence of surface mixed layer that is at an orderof meters even in summer, the “surface” for model and field sampling have not essentially difference for the SMX concentration. (For interpretation of the references
to color in this figure legend, the reader is referred to the Web version of this article.)4

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Environmental Pollution 288 (2021) 117779

only capturing a snapshot of time, while the model results are averaged
by month.
The horizontal distribution of SMX concentrations is strongly
affected by residual currents. In February, the residual current isgenerally weak, when SMX accumulates around the estuary regions anddecreases in concentration from north to south, yielding the steepest
gradient among the four studied months (Fig. 3). In August, SMX con­centrations decrease from northeast to southwest, which is consistent
with the eastward and southeastward residual currents. The SMX con­centrations in October decrease from the coastal area to the open
boundary due to the southward and southwestward currents. In
November, concentrations are higher in the northwest than in thesoutheast, which reflects the westward and southwestward currents.
Residual currents in the UGoT are affected by river runoff, wind, andthe Coriolis force (Yu et al., 2018). The slow currents in February are
induced by weak winds and small amounts of river runoff. Although the
UGoT is not positioned at a very high latitude, the influence of the Co­
riolis force on residual currents can still be observed. For example, thenortheastward monsoon causes residual currents to flow eastward andsoutheastward in August. In October, wind speed is lower and thesouthwestward residual currents are mainly affected by diluted riverwater and the Coriolis force. The residual currents in November are
mainly southwestward, likely resulting from the southward wind andthe Coriolis force.
A section across the UGoT (Fig. 1) was chosen to examine the vertical
distribution of SMX in more detail. As shown in Fig. S2, simulated SMX
concentrations vary vertically in the coastal areas near the estuary while
being almost homogeneous offshore. As low-salinity water flows into thesea from the estuary along with the pollutant, higher SMX concentra­
tions occur at the sea surface. Subsequently, the surface layer is mixed
vertically from the estuary area to offshore area by winds and tidal
currents. Consequently, the model simulates relatively homogenous
vertical SMX concentrations in the offshore area.
3.2. Dilution of SMX from different rivers
The distribution of SMX in the UGoT from each river is affected by its
concentration in river water, the river runoff, the location of estuaries,
and the residual currents in the UGoT (Fig. 4). The concentration of SMX
decreases from the estuary of the Bangpakong River to the west andsouth of the UGoT. As shown in Fig. 4a1–a4, the area with SMX con­centrations higher than 0.001 μg m− 3 is smaller in February and AugustFig. 4. Horizontal distribution of monthly averaged surface concentrations and dilution ratios of SMX from the (a1–a4) Bangpakong, (b1–b4) Chaopraya, (c1–c4)
Thachin, and (d1–d4) Maeklong Rivers. Color shading represents SMX concentrations and contours represent dilution ratios. (For interpretation of the references tocolor in this figure legend, the reader is referred to the Web version of this article.)5

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Environmental Pollution 288 (2021) 117779

than in October and November, which is likely caused by the slow,
eastward residual currents and southwestward residual currents,
respectively. SMX concentrations decrease from the estuary of the
Chaopraya River to the south in February, being low in the southeast due
to the input of water from the open sea. SMX from the Chaopraya Riveris transported to the southeast in August and the southwest in Octoberand November. The transport characteristics of SMX from the Thachin
River are similar to those of the Chaopraya River. For the Maeklong
River, SMX concentrations decrease from its estuary to the south andeast areas of the UGoT. The area with an SMX concentration higher than
0.001 μg m− 3 is smaller in February than in other studied months(Fig. 4d1–d4).
To investigate the general dilution characteristics of SMX from eachriver, dilution ratios were calculated as Dr = CCr × 100%, where Dr is thedecomposition in seawater and transport by ocean currents. To study the
behaviors of SMX quantitatively, the fluxes related to these processes
were calculated as 15-day mean values to filter out tidally driven os­
cillations (Fig. 5). SMX flux from the rivers is assumed constant in themodel while decomposition is dynamic. The poor water exchange withthe open sea causes the accumulation of SMX in the UGoT from February
to September, which leads to the highest decomposition flux occurringin October (Fig. 5). The poor exchange with the open sea is also indi­cated by SMX flux through the open boundary, which is close to 0 fromFebruary to September (Fig. 5) when the northeastward or northward
monsoon drives an eastward or northeastward current. From October tothe following January, SMX is removed from the UGoT through the open
boundary in association with the southward currents.
The annual SMX budget was evaluated by integrating the different
)
(
365
∫
∭ F(x,y,z)dxdydzdt, where F kg m− 3 day− 1
fluxes in Fig. 5 based on

dilution ratio, C (μg m− 3 ) is the monthly averaged concentration of SMX()
in seawater originating from a river, and Cr μg m− 3 and is the monthly
averaged concentration of SMX in the river water. Unlike the SMX
concentration simulated for seawater, the dilution ratios are indepen­
dent of the river water concentration.
The SMX dilution ratios from all four rivers are plotted in Fig. 4,
which extend in the same direction as the SMX concentration as previ­
ously described. The extended range of the dilution ratios is affected by
the residual currents, as controlled by wind stress and river runoff. The
10% dilution ratios of SMX for the four rivers occur at different distancesfrom their respective estuaries; based on the outermost points on the
10% contour from the estuaries, these distances vary between 7 and 25
km for the Bangpakong River in different months, 9–32 km for the
Chaopraya River, and 23–30 km for the Maeklong River. In comparison,
SMX concentrations become less than 10% of those in the Thachin Riverover a very short distance from the estuary.Where field data of riverconcentration are available, dilution ratios can be used to deduce the
SMX concentrations in seawater using Eq. (4):4∑C=

Cr n × Dr n

1

is the flux of each process; dx, dy, and dz are unit distance (m) in three
directions; and dt is the unit time interval (day). The volume integral wasfor the entire UGoT and the time integral was one year. As shown in
Table 1, the Chaopraya River is the dominant source of SMX (82%) in
the UGoT. Approximately 98% of the SMX from rivers is removed bydecomposition, while only 2% is removed via horizontal advection
through the open boundary. The annually averaged SMX mass in the
UGoT is 37.5 kg.
Subsequently, the flushing time was calculated as the ratio of SMX
mass inside the UGoT to the flux of SMX removed from the system(Monsen et al., 2002), as follows:
tr =M
Fob + Fcwd

(5)where tr is the flushing time, M is the mass of SMX, and Fob and Fcwd are
the SMX fluxes through the open boundary and via decomposition,
respectively. Based on this, a flushing time of SMX in the UGoT isapproximately 21 days. Based on the same method, the flushing time of

(4)

n=1

Table 1
Annually averaged SMX mass balance in the model. ‘B’, ‘C’, ‘T’, and ‘M’ indicate
the Bangpakong, Chaopraya, Thachin, and Maeklong Rivers, respectively.

where C is the SMX concentration in seawater, n is an index for a river,
Cr n is the SMX concentration in the nth river, and Dr n is the SMX dilution
ratio from the nth river, whose value at every grid point is given in
Fig. 4.

B

C

T

M

Open
boundary(kg ​ yr− 1 )

83.2

535.0

11.8

21.3

9.9

River input (kg ​ yr− 1 )3.3. Mass balance of SMX in the UGoTThe SMX in the model domain derives from rivers and undergoes

Decomposition(kg ​ yr− 1 )Annualmean ofSMX mass
(kg)

639.8

37.5

Fig. 5. Time-series of SMX fluxes from rivers (blue line), through the open boundary (green line), and via decomposition (red line). The lines represent mean values
over 15 days. A negative flux through the open boundary indicates a loss of SMX, while a positive flux means a gain. Line colors in the plot indicate the corresponding
axes. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)6

A. Wang et al.

Environmental Pollution 288 (2021) 117779

diluted water from the rivers is much longer at approximately 169 days.

be represented by Eq. (4) (see Section 3.2) and Eq. (6), as follows:()
4∑
S = S0 1 −(6)an

4. Discussion

n=1

4.1. Relationship between salinity and SMX concentrations

where S is the salinity of the seawater, n is the index for a river, S0 is thesalinity in the open sea, and an is the proportion of nth river water in theseawater.
If there is only one river flowing into the UGoT, Eqs. (4) and (6) canbe simplified to Eqs. (7) and (8):The relationships between salinity and contaminant concentrations
are often used in provenance studies in seawater. In general, the con­centration of a pollutant in the sea shows an inverse linear relationshipwith salinity if the pollutant has a sole origin from rivers. However, as
shown in Fig. 6, the modeled relationship can be represented by four
different lines denoting variable SMX concentrations for the same
salinity conditions in the UGoT.
The pollutant concentration and salinity at one point in the UGoT can

C = Cr1 × Dr1(7)

S = S0 (1 − a1 )(8)

Fig. 6. Relationships between monthly averaged salinity and SMX concentrations of the whole water column in the UGoT. The plus signs with different colors
represent model results driven by each corresponding river; the regression lines are deduced in the area with the dilution ratio larger than 10%. (For interpretation of
the references to color in this figure legend, the reader is referred to the Web version of this article.)7

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Environmental Pollution 288 (2021) 117779

where Cr1 is the concentration of pollutant in the target river, Dr1 is thedilution ratio of pollutant from the river, and a1 is the proportion of theriver water in seawater. Dr1 equals a1 at every point when the pollutant
is persistent, and its transport in the sea is the same as that for the
dilution water. As a result, it is deduced from Eqs. (7) and (8) that theconcentration of a pollutant in seawater (C) is linearly related to the

4.2. Simulation of SMX with different half-lives and pollution predictionin the UGoT
As the decomposition of SMX is affected by light, dissolved oxygen,and other environmental variables, its half-life has a large range (Benotti
and Brownawell, 2009; Xu et al., 2009; Baumgarten et al., 2011). To
investigate how the half-life of SMX affects its behavior in seawater, we
altered the half-life in the model and repeated the calculations. As
shown in Fig. 3, S3, and S4, half-life had little effect on the seasonalvariation and spatial pattern of SMX, although spatially averaged con­centrations increased with increasing half-life.
The area of UGoT is small and river inputs are the main sources of
SMX in the coastal sea, meaning that spatially averaged concentrations
can be predicted based on river input fluxes and SMX half-life. As
inferred from Eq. (1), SMX concentrations are linearly related to river
input, and its decomposition is represented by the first-order model.
Therefore, a prediction function of spatially averaged SMX concentra­tion (Cp ) is given as follows:Csalinity (S) with the slope and intercept − Sr01 and Cr1 , respectively. If thepollutant from a river is decomposable, Dr1 is different from a1 for the
dilution water. Moreover, the relationship between C and S at different
points in the sea is affected by Dr1 and a1 .
Actually, there are four rivers around the UGoT, and the persistenceof pollutants from rivers is not the only requirement for maintaining alinear relationship between the pollutant concentrations and salinity;
the concentration of pollutants in the four study rivers must also be thesame. When these two requirements are true, Dr n in Eq. (4) is equal to an
in Eq. (6), and Cr n in Eq. (4) is a constant. Under such conditions, theconcentration of pollutant (C) driven by the four rivers is linearly related
to salinity (S) in the UGoT. Otherwise, the relationship between C and S
is affected by an , Dr n , and Cr n .
As previously noted, the concentrations of SMX in the waters of thefour rivers are different and SMX is also decomposable in seawater.
Thus, the SMX concentrations are not linearly related to the salinity in
the UGoT. A linear relationship should exist, however, in those areas
controlled by a single river, where SMX can be considered as a passive
tracer given its short residency time after leaving the estuaries and thelow levels of dilution. To confirm this, we defined the area controlled byeach river as the area where the dilution ratio is larger than 10%. As
shown in Fig. 6, the concentrations of SMX in these areas are almost
linearly related to salinity. As previously noted, C is linearly related to S
when Cr n is constant and an equals Dr n , with the slope and intercept
given as − CSr0n and Cr n , respectively. As shown in Fig. 6 and Table S4, theB

Cp = A × r s × e α(9)where rs and α are the river input (kg ​ day− 1 ) of the pollutant and its
half-life (day), respectively; and A and B are constants obtained from themodel results. Using seven different SMX half-lives in the model simu­
lations, we obtained 28 sets of data for each month to determine A and B
values of approximately 2.1 and − 31, respectively (Fig. S5), that remain
relatively constant between months. By substituting A and B into Eq. (9),we obtained concentration values corresponding to a range of river
input fluxes and half-lives (Fig. 7). While we have focused on SMX, it isimportant to note that this approach can be applied to any pollutant witha river input flux and half-life within the modeled range. Based on Fig. 7,
the concentration of a pollutant will increase with an increase in river
input flux and its half-life. Moreover, concentrations increase more
rapidly with river input flux for pollutants with a longer half-life (Fig. 7).
The predicted no-effect concentrations (PNECs) of SMX for resistanceselection and ecotoxicological effects are 16,000 μg m− 3 and 590
μg m− 3 , respectively (Bengtsson-Palme and Larsson, 2016), which are
far higher than those reported here. Furthermore, Eq. (9) can be used toregression lines between SMX concentrations and salinity have slopes
and intercepts close to the analytical values (− CSr0n and Cr n ) deduced
above.

Fig. 7. Predicted concentrations of pollutants in the UGoT in August based on river input flux (kg ​ day− 1 ) and half-life (day). Dots are the model results and lines
represent the predicted results.
8

A. Wang et al.

Environmental Pollution 288 (2021) 117779

calculate the critical river input flux of a pollutant where its half-life and
PNEC are known. For example, in August, the critical river flux of SMX is
61,099 kg ​ day− 1 for Cp to remain lower than the PNEC for resistance

relationship between SMX concentrations and salinity is not linear in the
UGoT. Nevertheless, a linear relationship can be assumed in those areas
mainly affected by a single river, which we defined using the 10%
dilution ratio. Moreover, we established prediction functions for mean
concentrations and flushing times of non-persistent pollutants in the
UGoT based on half-lives and river input flux.
The fundamental simulation scheme for the transport of nonpersistent organic pollutants in the sea is conducted in this work. It isuseful to quantitatively examine the behaviors of pollutants in seawaterand predict pollutant concentrations and spatial variations. The
biogeochemical cycle of antibiotics in the marine environment is com­plex including uptake by phytoplankton as well as bacteria, watersuspended particle partitioning, and adsorption onto sediment (Xuet al., 2009; Li, 2014; Bai and Acharya, 2016; Puckowski et al., 2016; Yu
et al., 2017). In our model, we partially represented these processes byconsidering the flux of SMX via decomposition. However, given theirsignificance for estimating the ecological risk and fate of antibiotics inthe sea, more effort is now required to incorporate them more fully infuture models.

selection, and 2253 kg ​ day− 1 for ecotoxicological effects.
We also calculated the flushing time of SMX based on its half-life. As
defined in Section 3.3, this is the ratio of SMX mass to the flux associatedwith its removal. In the model, when the decomposition flux of SMX ismuch larger than its exchange rate with the open sea through the lateralboundary, the flushing time approaches a ratio dependent on its halflife., thus:
M
Fcwd

(10)

M = Cm × V

(11)

ln 2

(12)

tr =

Fcwd =

α× Cm × V

where M (kg) is the annual mean of pollutant mass inside the UGoT, Fcwd
(kg ​ day− 1 ) is the pollution flux from decomposition, Cm (μg m− 3 ) is theannual mean pollution concentration inside the UGoT, V (m3 ) is thewater volume of the UGoT, and α (day) is the half-life. To deduce thetheoretical relationship between flushing time and half-life, Eqs. (11)and (12) assume that SMX concentrations are temporally and spatiallyhomogeneous. From Eqs. (10)–(12), it can be concluded that tr = lnα2(13). Thus, the flushing time of SMX is linearly related to its half-life.
Figure S7a shows the SMX predicted flushing times with half-life as
the dependent variable and this function can be applied to any pollutant
with a half-life within the modeled range. It must be noted that theprediction function is based on the condition that decomposition flux ismuch larger than the exchange rate with the open sea; a value of 8 wasselected as the critical ratio of decomposition flux to the exchange rate
through the lateral boundary to judge their relative magnitudes. Thisratio corresponds to a critical half-life. A full function between thesevariables is shown in Fig. S7b, the derivation of which is detailed inAppendix D. The critical half-life is calculated as 109 days when the ratioof the decomposition flux to lateral boundary flux is equal to 8. In otherwords, the function shown in Fig. S7a can be used to predict the flushingtime of a pollutant from rivers in the UGoT when its half-life is less thanor equal to 109 days.
In Section 3, we presented results assuming a constant river input
flux of SMX throughout a year. Here, we introduce an alternative
assumption that the SMX concentrations in river waters are constantthroughout the year and the detailed results are shown in Appendix E.

Declaration of competing interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Acknowledgment
This study is funded by the Ministry of Education, Culture, Sports,
Science and Technology, Japan (MEXT) to a project on Joint Usage/
Research Center–Leading Academia in Marine and Environment Pollu­
tion Research (LaMer). This work is also supported by Sasakawa fund of
the Japan Science Society (No. 2021–2028).
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.envpol.2021.117779.
Credit author statement
Aobo Wang: Conceptualization; Methodology; Software; Validation;
Formal analysis; Data curation; Writing – original draft. Xinyu Guo:
Conceptualization; Methodology; Validation; Writing – review & edit­
ing; Visualization. Akihiko Morimoto: Validation; Investigation;
Writing – review & editing. Kana Maetani: Validation; Investigation.
Rumi Tanoue: Resources; Writing – review & editing. Siraporn TongU-Dom: Investigation; Resources. Anukul Buranapratheprat: Writing– review & editing.

5. Conclusions
A 3D physical-antibiotic model was developed to investigate thetransport and dilution of SMX from rivers in the UGoT. The simulated
concentrations of SMX generally match the field data and vary slightly
between seasons despite assuming a constant input flux from the rivers.
The horizontal distribution of SMX concentrations varies significantly
between months, which is affected by the residual currents associatedwith the monsoon and river runoff. SMX concentrations also varyvertically in the areas close to estuaries but are vertically homogeneous
offshore. The dilution ratios indicate that SMX concentrations becomeless than 10% of those in river waters over a short distance from theestuaries. The dilution effect does vary between rivers, depending on thelocations of their estuaries and the residual currents in the UGoT.
Overall, input from the Chaopraya River is the main source (82%) of
SMX in the UGoT, the vast majority of which (98%) is naturally
decomposed.
A linear relationship between fluvial pollutant concentrations andseawater salinity occurs only when concentrations are equal between
the rivers and when the pollutant is quasi-persistent. Therefore, theReferences
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