Modeling fecal coliform contamination in a tidal Danshuei River estuarine system

Science of the Total Environment 502 (2015) 632–640

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Modeling fecal coliform contamination in a tidal Danshuei River estuarine system Wen-Cheng Liu a,b,⁎, Wen-Ting Chan a, Chih-Chieh Young c a b c

Department of Civil and Disaster Prevention Engineering, National United University, Miao-Li 36003, Taiwan Taiwan Typhoon and Flood Research Institute, National Applied Research Laboratories, Taipei 10093, Taiwan Hydrotech Research Institute, National Taiwan University, Taipei 10617, Taiwan

H I G H L I G H T S • • • •

A three-dimensional hydrodynamic and fecal coliform model was developed. The contamination of fecal coliform in a tidal estuarine system was investigated. Freshwater discharge plays an important role on fecal coliform distribution in an estuarine system. Loading reduction is an effective strategy to reduce fecal coliform concentration.

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Article history: Received 10 June 2014 Received in revised form 18 September 2014 Accepted 20 September 2014 Available online xxxx Editor: Simon Pollard Keywords: Fecal coliform contamination Numerical modeling Freshwater discharge Salinity Tidal estuarine system SELFE-FC

a b s t r a c t A three-dimensional fecal coliform transport model was developed and incorporated into a hydrodynamic model to obtain a better understanding of local microbiological water quality in the tidal Danshuei River estuarine system of northern Taiwan. The model was firstly validated with the salinity and fecal coliform data measured in 2010. The concentration comparison showed quantitatively good agreement between the simulation and measurement results. Further, the model was applied to investigate the effects of upstream freshwater discharge variation and fecal coliform loading reduction on the contamination distributions in the tidal estuarine system. The qualitative and quantitative analyses clearly revealed that low freshwater discharge resulted in higher fecal coliform concentration. The fecal coliform loading reduction considerably decreased the contamination along the Danshuei River–Tahan Stream, the Hsintien Stream, and the Keelung River. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Polluted estuarine waters which mainly result from the human and warm-blood animal release flowing directly or indirectly into the aquatic environments contain a large amount of pathogenic micro-organisms. For risk reduction of waterborne disease and strategic planning of sanitation efforts, it is important and necessary to obtain a better understanding of microbiological distribution in the rivers and tidal estuaries. To monitor water quality conditions, fecal coliform is one of the most commonly used bacteria indicators among numerous viruses, bacteria, and protozoa in the polluted waters (Servais et al., 2007a; ⁎ Corresponding author at: Department of Civil and Disaster Prevention Engineering, National United University, Miao-Li 36003, Taiwan. Tel.: +886 37 382357; fax: +886 37 382367. E-mail address: [email protected] (W.-C. Liu).

http://dx.doi.org/10.1016/j.scitotenv.2014.09.065 0048-9697/© 2014 Elsevier B.V. All rights reserved.

Ouattara et al., 2013; de Brauwere et al., 2014). Besides, several microbiology modeling approaches have been successfully developed and applied for water quality management (Manache et al., 2007; Yang et al., 2008; Zhu et al., 2011; Sokolova et al., 2013). In general, the transport and fate of fecal coliform are modeled using a water quality model which solves the advection–dispersion equation with extra terms for its die-off or disappearance. Typically, the net concentration reduction in space and time is addressed by the first order decay coefficients. In the last decade, a lot of efforts have been paid to develop various kinds of one-dimensional or horizontally/vertically twodimensional hydrodynamic and water quality models for predicting the bacterial conditions in different water bodies (e.g. Kashefipour et al., 2002; Liu et al., 2006; Manache et al., 2007; Schnauder et al., 2007; Gao et al., 2011; de Brauwere et al., 2011, 2014; Romeiro et al., 2011; Liu and Huang, 2012). Overall, these previous works suggested that accuracy of the solution depends on the adequacy of biological

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parameters and the dimensions of governing equations for reflecting the actual physical system. The Danshuei River estuarine system located in northern Taiwan is formed by the confluence of the Tahan Stream, the Hsintien Stream, and the Keelung River (see Fig. 1). As the largest estuarine system in Taiwan, the drainage area of the Danshuei River system encompasses 2,728 km2. In the Danshuei River estuarine system, the barotropic flow is the major forcing mechanisms determined by the upstream discharge and astronomical tide at the river mouth with occasional storm surges induced by typhoon events during the summer seasons. The mean river discharges are 38.99 m3/s, 69.72 m3/s, and 25.02 m3/s in the Tahan Stream, the Hsintien Stream, and the Keelung River, respectively. Semi-diurnal tides are the principal tidal constituents with a mean tidal range of 2.22 m, a neap tide range of 0.85 m, and a spring tidal range of 3.1 m (Hsu et al., 1999), falling within the mesotidal classification (Davies, 1964). Another important transport mechanism in this system is the baroclinic flow forced by seawater intrusion (Liu et al., 2007). The Danshuei River flows through the metropolitan area of the capital city Taipei, where the population approximately reaches 6 million. A large amount of treated and untreated domestic sewage was discharged into the river system, leading to high nutrient concentrations and low dissolved oxygen. Based upon the 2008–2012 water quality monitoring data from the Taiwan Environmental Protection Administration (TEPA), the analysis of river pollution index (PRI) indicated that more than 80% of the Danshuei River estuary system is moderately (or heavily) polluted and the high pollutant level remains over the whole year. Particularly during low-flow periods, the dissolved oxygen concentration of the bottom-layer waters can decline nearly to zero and anoxia has occurred in some portions of the river (Chen et al., 2011). Besides, the field measured data from 1997 to 2013 showed the fecal coliform spatial distribution. Over the measurement period, the mean concentration (in 104 colony forming unit per 100 ml, 104 CFU/100 ml) increased from 6 at the River mouth to 1104 at the Hsin-Hai Bridge along the Danshuei River–Tahan Stream while the mean concentration reduced from 167 at the Hwa-Jiang Bridge to 58 at the Hsiu-Lang Bridge and from 104 at the Bai-Ling Bridge to 58 at the Na-Hu Bridge along the Hsintien Stream and Keelung River, respectively. These monitoring stations can be found in Fig. 2.

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2. Objective of the present study Recently, advanced three-dimensional hydrodynamic and fecal bacteria transport models have been used to evaluate fecal contamination in tidal estuaries. For example, Garcia-Armisen et al. (2006) developed the dynamics of fecal coliforms coupling with a three-dimensional hydrodynamic model for the Seine estuary. The model correctly reproduced the impact of main river flow rates on contamination level and suggested the priority for sanitation efforts in different scenarios. Besides, Rodrigues et al. (2011) applied a three-dimensional hydrodynamic and fecal contamination coupled model to the Aljezur coastal stream with careful validation against the field surveys covering physical, chemical, and microbiological parameters. Regarding management of water safety in the estuary system, their results indicated a direct relation between the tidal propagation limit and the concentration reduction of fecal bacteria along the stream. The Danshuei River estuarine system was subject to serious pollution, especially high concentration of fecal coliform in the main stream and tributaries. The development of high-resolution fecal coliform transport model is urgently needed for water quality prediction and management. In this study, a three-dimensional fecal coliform transport module was developed and coupled with a hydrodynamics model (i.e. SELFE-FC model) to simulate the contamination distribution in the Danshuei River estuarine system of northern Taiwan. The model's capability was revealed by the good comparison between the predicted and measured results. The validated model was then applied to investigate the effects of freshwater discharge on the dynamics of fecal coliform and the influence of point source reduction on fecal contamination in the tidal estuarine system. 3. Materials and methods 3.1. Hydrodynamic model The multi-scale ocean circulation modeling from ocean basins to estuaries has become a mature field nowadays. These models typically describe the free-surface elevation, velocities, and salinity of various water bodies by solving the Reynolds averaged Navier–Stokes equations which represent conservation of mass, momentum, and salt subject to

Fig. 1. Map of the tidal Danshuei River estuarine system including main Danshuei River, Tahan Stream, Hsintien Stream, and Keelung River.

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Fig. 2. The topography of Danshuei River estuarine system and its adjacent coastal and unstructured grid for computational domain.

the hydrostatic and Boussinesq approximations. The equation of state describes water density as a function of salinity and water temperature. In this study, the three-dimensional, semi-implicit Eulerian–Lagrangian finite-element model (SELFE, Zhang and Baptista, 2008) was implemented to simulate the Danshuei River estuarine system and its adjacent coastal sea. The SELFE model solves the governing equations using a semiimplicit finite-element/volume scheme with a key step of decoupling continuity and momentum equations via the bottom boundary. In SELFE, the horizontal domain is discretized with unstructured triangular grids. Along the vertical direction, a hybrid coordinate system consisting of the S-coordinate and Z-coordinate respectively in the upper and deeper parts of the water column can effectively prevent the so-called hydrostatic inconsistency issue (Haney, 1991). The model handles the advection terms in the momentum equation with an Eulerian–Lagrangian method, allowing larger time steps without compromising computational stability and accuracy. A 120-second time step free of numerical instability was used for simulations. For the advection terms in the transport equations (i.e. salinity and temperature), the Eulerian– Lagrangian scheme is applied. When the Eulerian–Lagrangian scheme is used, the transport equations can be efficiently solved by a finiteelement method. To calculate turbulent mixing processes, SELFE uses the generic length scale (GLS) turbulence closure of Umlauf and Buchard (2003), which has the advantage of encompassing most of the 2.5-equation closure (K − ψ) model. 3.2. Fecal coliform transport model A fecal coliform transport module was incorporated into the threedimensional hydrodynamic model in this study. Advection, dispersion and the first-order decay of bacteria due to mortality and settling are the major processes in fecal coliform fate and transport. The sediment resuspension which may also affect the concentrations of fecal coliform in the water (Steets and Holden, 2003; Bai and Lung, 2005; Gao et al.,

2011) is neglected due to limited information and parameterization uncertainty. The temporal and spatial variations of the fecal coliform concentrations can be written as.


 ∂C ∂C ∂C ∂C ∂ ∂C ∂ ∂C ∂ ∂C þ þ þu þv þw ¼ Kh Kh Kv ∂t ∂x ∂z ∂z ∂x ∂x ∂y ∂y ∂z ∂z ð1Þ þF c −KC þ WC where C is the concentration of fecal coliform; u, v, and w are the water velocity components corresponding to a Cartesian coordinate system (x, y, z); Kh is the horizontal eddy diffusivity; Kv is the vertical eddy diffusivity; K is the overall decay rate; and WC is the external loading of fecal coliform. The numerical solution of fecal coliform transport equation in the advection and diffusion terms is same with salinity transport module. There are several point sources imposed along the Danshuei River estuarine system shown in Fig. 1. The external loadings of fecal coliform were estimated from the report by MWH (2011). Different formulations have been proposed to represent the overall decay rate (K). For example, Manache et al. (2007) considered the effects of water temperature, solar radiation, salinity, and sedimentation upon the fecal coliform decay rate. Bedri et al. (2011) used constant bacteria decay rate in their three-dimensional model for Escherichia coli simulation in the Dublin Bay. In the present study, the effects of water temperature, mortality, and sedimentation (Servais et al., 2007b) are taken into account. Mortality and settling are both modeled by firstorder reaction terms (Liu et al., 2006):  v  ðT−20Þ K ¼ kmort þ f p s θ H

ð2Þ

where kmort is the mortality rate; fp is the fraction of fecal coliform attached to the suspended sediment; H is the total water depth; vs is the settling velocity; T is the water temperature; and θ is a temperature correction factor, usually set to be 1.07 (Thomann and Mueller, 1987).

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3.3. Model implementation In the present study, the bathymetry and topography data of the Taiwan Strait and Danshuei River estuarine system were obtained from the Taiwan's Ocean Data Bank and Water Resources Agency, respectively. The deepest point located at the northeast corner of the computational domain (Fig. 2) is around 110 m (below the mean sea level). The mesh for the Danshuei River estuarine system and its adjacent coastal sea consisted of 5119 elements (Fig. 2). To save the computational time, a fine-grid resolution was used locally and a coarse resolution was implemented away from the region of interest. The grid size varied from 6000 m in the Taiwan Strait down to 40 m in the upper reach of the Danshuei River estuary. In the vertical direction, ten zlevels and ten evenly spaced S-levels were specified at each horizontal grid. To drive the hydrodynamic model simulation including tidal elevation, velocity, and salinity, the freshwater discharges in 2010 were used to specify the upstream boundaries at the Fu-Chou Bridge (Tahan Stream), Hsiu-Lang Bridge (Hsintien Stream), and Shehou Bridge (Keelung River) (Fig. 1). The elevation time-series generated by five tidal constituents (i.e. M2, S2, N2, K1, and O1) was employed at the ocean boundaries (Fig. 2). There are 43 open boundaries specified to force the model simulation. The salinities of open boundaries in the coastal sea were set to 35 ppt while salinities at upstream boundaries including the Fu-Chou Bridge (Tahan Stream), the Hsiu-Lang Bridge (Hsintien Stream), and the Shehou Bridge (Keelung River) were set to be 0 ppt. The initial conditions for tidal elevation, velocity, and salinity were set to 1.5 m, 0.5 m/s, and 30 ppt, respectively. The spinning period is 15-day to reach a regime situation. Because the water depth in the Danshuei River estuarine is shallow, water temperature is not included in the model simulation. Monthly data of fecal coliform concentration at the river/ocean boundaries established by the Taiwan Environmental Protection Administration (TEPA) was specified to force the model simulation of fecal coliform. The fecal coliform concentrations at upstream boundaries including the Fu-Chou Bridge (Tahan Stream), the Hsiu-Lang Bridge (Hsintien Stream), and the Shehou Bridge (Keelung River) were 43 × 104 CFU/100 ml (56 × 104 CFU/100 ml), 5.6 × 104 CFU/100 ml (3.9 × 104 CFU/100 ml), and 5.2 × 104 CFU/100 ml (3.5 × 104 CFU/100 ml), respectively, in September (December), 2010. The fecal coliform concentration at open boundaries was 100 CFU/100 ml. The initial condition for fecal coliform concentration was set to be 5 × 104 CFU/100 ml. Because the generic length scale (GLS) turbulence closure model was used to calculate the turbulent mixing processes, no parameter can be tuned for simulating salinity distribution. Many reports documented that die-off rate (or mortality rate) is the most sensitive parameter ranging from 0.05 to 4 day− 1 adopted in the fecal coliform transport model (Kashefipour et al., 2002; Rodrigues et al., 2011). Therefore this parameter would be carefully adjusted according to the observed fecal coliform concentration. The bottom roughness height (zo) is the only parameter to be adjusted in the hydrodynamic model. Based on the model calibration procedure, a constant bottom roughness height (zo = 0.005 m) is adopted in the model simulation. The detailed model calibration of tidal elevation and current has been reported in Liu and Chan (in press). The model calibration of salinity distribution and fecal coliform concentration can be found in the next sections.

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applied for model validation. As shown in Fig. 3, the simulated top/ bottom-layer salinity distributions compared favorably with the field measurement data along the Danshuei River–Tahan Stream during the flood and ebb tides on November 26, 2010. Note that the salinity data was measured at 0.5 m below water surface. Fig. 3a and b are supposed to be self-contained. The modeling performance for the ebb tide is better than that for the flood tide. The results in Fig. 3 also suggest that salinity simulation of the bottom layer gave a better match to measurements which are taken close to the water surface (i.e. 05 below water surface). It may be the reason that the higher horizontal and vertical eddy diffusion coefficients are calculated through 2.5-equation closure model (K − ψ) resulting in salinity diffusion to upstream regions during flood tide and slightly underestimating the simulated salinity at the top layer. The absolute mean error and root mean square error at the top layer are 0.49 ppt and 0.67 ppt (2.71 ppt and 3.72 ppt) during the ebb (flood) tide, respectively. 4.2. Fecal coliform Fecal coliform is the main water quality indicator in this study. Note that validation is generally more challenging for a bacterial water quality model (rather than a hydrodynamic model) due to numerous factors controlling the fate of fecal coliform. Accurate model predictions require an adequate representation for the critical processes including advection, dispersion, and overall decay rate (Gao et al., 2011; Sokolova et al., 2013). Along the Danshuei River–Tahan Stream, Hsintien Stream, and Keelung River, the predicted longitudinal distributions of fecal coliform on September 3, 2010 and December 2, 2010 are shown in Figs. 4 and 5, respectively, for model calibration and verification. Note that the simulation and measurement in the figures represent the vertical average

4. Model validation 4.1. Salinity distribution Salinity distributions can reflect the combined influences of various processes on density circulation and mixing (Hsu et al., 1999). In the present study, the salinity distribution along the Danshuei River– Tahan Stream collected by the Water Resources Agency, Taiwan, was

Fig. 3. The comparison between the measured and simulated salinities along the Danshuei River–Tahan Stream on November 26, 2010 during (a) flood tide and (b) ebb tide.

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Fig. 4. Comparison of simulated fecal coliform concentrations and measured data on September 3, 2010 along the (a) Danshuei River to Tahan Stream, (b) Hsintien Stream, and (c) Keelung River.

Fig. 5. Comparison of simulated fecal coliform concentrations and measured data on December 2, 2010 along the (a) Danshuei River to Tahan Stream, (b) Hsintien Stream, and (c) Keelung River.

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fecal coliform concentration. It can be seen that the fecal coliform concentrations increase from the Danshuei River mouth to the Tahan Stream with a maximum at the Hsin-Hai Bridge. Low fecal coliform at the river mouth is the reason of sea water dilution. Hsin-Hai Bridge is a hot spot of water quality pollution, resulting in maximum fecal coliform concentration. The simulated results overestimated at the Kuan-Du Bridge and Chong-Yang Bridge in the main Danshuei River on September 3, 2010 and December 2, 2010. It may be the reason that the point sources in the main Danshuei River are overestimated. For the Hsintien Stream, the fecal coliform concentrations decrease from the Hsintien Stream mouth to upriver reaches. In the Keelung River, the upstream fecal coliform concentrations are also lower than that at the downstream. Generally, the model simulations well capture the measured data in the Hsintien Stream and somewhat overestimates the observed results at the lower reaches in the Danshuei River estuary. The statistical errors between simulated and measured fecal coliform concentrations are shown in Table 1. Note that the statistical errors include all data points at each stream/river. It can be seen that model performance in the Danshuei River–Tahan Stream is higher than that in other two tributaries because the model overestimates the observed fecal coliform concentration at the Kuan-Du Bridge and Chong-Yang Bridge. The mortality of fecal microorganisms depends cumulatively, synergistically, or antagonistically, on the type and intensities of the occurring stressing factors (Sinton et al., 2002). A few generic formulations have been proposed to explicitly account for some parameters (Alkan et al., 1995; Canteras et al., 1995), but a constant mortality rate is the most commonly used parameterization for the fecal bacteria decay (Steets and Holden, 2003; Servais et al., 2007b). Reports documented that mortality rate, attachment fraction to suspended sediment, and settling velocity ranged 0.05–4 day−1 (Kashefipour et al., 2002; Rodrigues et al., 2011), 0.08–0.34 (Characklis et al., 2005; Fries et al., 2006), and 0.11– 8.64 m/day (Bai and Lung, 2005; Liu et al., 2006). With the calibration and verification procedures, the mortality rate (kmort), attachment fraction to suspended sediment (fp), and settling velocity (vs) were set to 1.2 day−1, 0.1, and 5 m/day for fecal coliform modeling, respectively. 5. Model applications and discussion 5.1. Influence of different freshwater discharges Influence of freshwater discharge changes on fecal coliform distribution in an estuarine system has been reported (Garcia-Armisen et al., 2006). In this study, model simulations with two flow rates, Q75 and Qm, are adopted to represent the low flow and high freshwater discharges, respectively, which are carried out to gain a deeper physical insight. Note that Q75 indicates the occurrence of a freshwater discharge exceeding (or equal to) 75% of the time and Qm is the mean freshwater discharge. The Q75 flow was obtained from flow duration curve. For model setting, the discharges at the tidal limits of the Tahan Stream, Hsintien Stream, and Keelung River are 3.36, 14.23, and 3.33 m3/s (or 38.99, 69.72, and 25.02 m3/s) for the Q75 (or Qm) flow, respectively. Similarly, the ocean boundaries are driven by five tidal constituents (i.e. M2,

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S2, N2, K1, and O1). The mean fecal coliform concentration from 1997 to 2013 is used to specify the upstream boundary conditions. The fecal coliform concentrations at upstream boundaries including the Fu-Chou Bridge (Tahan Stream), the Hsiu-Lang Bridge (Hsintien Stream), and the Shehou Bridge (Keelung River) were 45.3 × 104 CFU/100 ml, 3.0 × 104 CFU/100 ml, and 6.3 × 104 CFU/100 ml, respectively. The fecal coliform concentration at open boundaries was 85 CFU/100 ml. Fig. 6 presents the daily and vertically averaged results of the simulated fecal coliform distribution along the Danshuei River–Tahan Stream, Hsintien Stream, and Keelung River. Fig. 7 displays the horizontal view of the fecal coliform concentration in the Danshuei River estuarine system. It also presents the daily and vertically averaged results of the simulated fecal coliform concentration. The low river discharge (Q75 flow) case is compared to the case of mean freshwater discharge (Qm flow). It can be clearly found that the most influenced areas under different flow rates are located at the middle to upstream reaches in the Danshuei River–Tahan Stream and at the downstream reaches in both Hsintien Stream and Keelung River. Further, the higher flow results in much lower concentration between 25 km and 30 km from the Danshuei River mouth, between 0 km and 6 km from the Hsintien Stream mouth, and between 0 km and 15 km from the Keelung River mouth. Fig. 8 presents the vertical distribution of daily average fecal coliform along the Danshuei River–Tahan Stream, Hsintien Stream, and Keelung River under Q75 flow condition. It can be seen that the higher fecal coliform concentration occurs in the Tahan Stream (Fig. 8a) and lower fecal coliform concentration appears at the upper reaches of the Hsintien Stream and Keelung River (Fig. 8b and c). The modeling results implied that the pollutant level of fecal coliform would be more critical during low flow condition. The warning should be taken by the government to avoid the recreational use of waters in the tidal estuarine system 5.2. Influence of fecal coliform loading reduction The influences of wastewater treatment (or loading reduction) on fecal coliform contamination have been investigated using model simulation (Ouattara et al., 2013; de Brauwere et al., 2014). It has been shown that the numerical modeling provides a powerful tool for microbiological water quality management. To examine the present system response to the anthropogenic changes in fecal coliform loadings from the drainage basin, the validated model was used to predict the microbiological water quality under the low flow condition (Q75 flow), because low flow condition is critical for microbiological water quality in tidal estuary. MWH (2011) reported that different projects for nutrient loading and fecal coliform reduction were being executed by the New Taipei City government, including the constructions of a waste water treatment plant, on-site wetland treatment, and waste water interception. Fig. 9 presents the fecal coliform loadings under the present condition and after pollution reduction. It indicates that the reduction rates of the fecal coliform loading are 92.3%, 87.7%, and 1.13% in the Danshuei River–Tahan Stream, Hsintien Stream, and Keelung River, respectively. Fig. 10 shows the predicted fecal coliform distribution for the present condition and after loading reduction under the Q75 low flow

Table 1 Statistical errors between simulated and measured fecal coliform concentrations. Date

Measure of performance

Danshuei River–Tahan Stream

Hsintien Stream

Keelung River

16.77 1.03 4.10 Mean absolute error, MAE (x104 CFU/100 ml) 23.49 1.40 8.16 Root mean square error, RMSE (x104 CFU/100 ml) Relative root mean square error, RelativeRMSE December 2, 2010 Mean absolute error, MAE (x104 CFU/100 ml) 7.18 0.29 3.85 9.71 0.42 6.84 Root mean square error, RMSE (x104 CFU/100 ml) Relative root mean square error, RelativeRMSE sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  2 2 1 N   1 N   1 N

RMSE ∑ C p i −ðC o Þi ; RMS= ∑ ðC o Þi ; RelativeRMSE= ; where N is the total number of data points; Cp is the predicted fecal coNote that MAE= ∑ C p i −ðC o Þi ; RMSE= N i¼1 N i¼1 N i¼1 RMS liform concentration; and Co is the observed fecal coliform concentration. September 3, 2010

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Fig. 6. The effects of different freshwater discharges (Q75 and Qm flows) on the distributions of fecal coliform concentration in the (a) Danshuei River–Tahan Stream, (b) Hsintien Stream, and (c) Keelung River.

along the Danshuei River to Tahan Stream, Hsintien Stream, and Keelung River. It can be seen that the fecal coliform concentrations have a significant decrease in the estuarine system. For a quantitative analysis, the maximum response for the contamination reduction is defined as:     C present −C after  C present

 100%

ð3Þ

where Cpresent is the fecal coliform concentration for the present condition and Cafter is the fecal coliform concentration after loading reduction. Overall, the fecal coliform concentrations were greatly reduced up to 84.8%, 82.0%, and 71.6% in the Danshuei River–Tahan Stream, Hsintien Stream, and Keelung River, respectively. The horizontal views of the fecal coliform concentration in the Danshuei River estuarine system under Q75 low flow condition are presented in Fig. 7a and c for the current condition and after loading

Fig. 7. The horizontal distribution of fecal coliform concentration in the Danshuei River estuarine system under (a) Q75 flow, (b) Qm flow, and (c) Q75 flow for pollution reduction.

reduction. Significant loading reduction effects on the fecal coliform contamination in the Danshuei River tidal estuarine system are revealed. There is no doubt that the investment of infrastructure by the government can significantly improve the degree of pollution.

6. Conclusions A fecal coliform module based on a three-dimensional hydrodynamic model was developed and applied to the Danshuei River tidal estuarine system. The model was validated with the observed salinity data in 2010. The calculated distribution of fecal coliform concentrations along the Danshuei River–Tahan Stream, Hsintien Stream, and Keelung

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Fig. 9. Fecal coliform loadings for present and pollution reduction in the Danshuei River– Tahan Strream (D), Hsintien Stream (H), and Keelung River (K). The data was collected from the report by MWH (2011).

Fig. 8. The vertical distribution of daily average fecal coliform concentration in the Danshuei River estuarine system under Q75 flow condition.

River at different locations was compared with field-measured data on September 3, 2010 and December 2, 2010. The predicted results generally agreed with the data for salinity and fecal coliform concentration in the estuarine system. The validated model was then used to investigate the influences of freshwater discharge and pollutant loading reduction on the fecal coliform contamination in the tidal estuarine system. The model was performed with different freshwater discharges, i.e. Q75 low flow and Qm

Fig. 10. The effects of pollution reduction on the distributions of fecal coliform concentration under Q75 flow in the (a) Danshuei River–Tahan Stream, (b) Hsintien Stream, and (c) Keelung River.

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flow at the upstream boundaries. In comparison to the fecal coliform distribution under Q75 flow condition, the Qm flow condition produces lower fecal coliform concentrations between 25 km and 30 km from the Danshuei River mouth, between 0 km and 6 km from the Hsintien Stream mouth, and between 0 km and 15 km from the Keelung River mouth. The maximum differences of fecal coliform concentration between Q75 and Qm flow conditions are 190.6 × 104 CFU/100 ml, 41.2 × 104 CFU/100 ml, and 12.0 × 104 CFU/100 ml in the Danshuei River– Tahan Stream, Hsintien Stream, and Keelung River. The model was also run for Q75 low flow under the present condition and after fecal coliform loading reduction. A quantitative analysis for the treatment response showed that the fecal coliform concentrations were reduced up to 84.8%, 82.0%, and 71.6% in the Danshuei River–Tahan Stream, Hsintien Stream, and Keelung River, respectively. The reduction of fecal coliform loading has significant impacts on its contamination in the tidal estuarine system. Acknowledgments The project under which this study was conducted was supported by the Ministry of Science and Technology, Taiwan, under grant no. NSC 102-2625-M-239-002. The authors would like to express their appreciation to the Taiwan Water Resources Agency and the Taiwan Environmental Protection Administration for providing the measured data. The authors sincerely thank three anonymous reviewers for their valuable comments. References Alkan U, Elliot DJ, Evison LM. Survival of enteric bacteria in relation to simulated solar radiation and other environmental factors in marine waters. Water Res 1995;29: 2071–81. Bai S, Lung WS. Modeling sediment impact on the transport of fecal bacteria. Water Res 2005;39:5232–40. Bedri Z, Bruen M, Dowley A, Masterson B. A three-dimensional hydro-environmental model of Dublin Bay. Environ Model Assess 2011;16:369–84. Canteras JC, Juanes JA, Perez L, Koev KN. Modelling the coliforms inactivation rates in the Cantabrian Sea (Bay of Biscay) from in situ and laboratory determinations of T90. Water Sci Technol 1995;32:37–44. Characklis GW, Dilts MJ, Simmons OD, Likirdopulos CA, Krometis LAH, Sobsey MD. Microbial partitioning to settleable particles in stormwater. Water Res 2005;39(9): 1773–82. Chen WB, Liu WC, Hsu MH. Water quality modeling in a tidal estuarine system using a three-dimensional model. Environ Eng Sci 2011;28:433–59. Davies JL. A morphogenic approach to world shorelines. Z Geomorphol 1964;8:27–42. de Brauwere A, de Brye B, Servais P, Passerat J, Deleersnijder E. Modelling Escherichia coli concentrations in the tidal Scheldt river and estuary. Water Res 2011;45:2724–38. de Brauwere A, Gourgue O, de Brye B, Servais P, Ouattara NK, Deleersnijder E. Integrated modelling of faecal contamination in a densely populated river-sea continuum (Scheldt River and Esatury). Sci Total Environ 2014;468–469:31–45. Fries JS, Characklis GW, Noble RT. Attachment of fecal indicator bacteria to particles in the Neuse River estuary, N.C. J Environ Eng ASCE 2006;132(10):1338–45.

Gao G, Falconer RA, Lin B. Numerical modelling of sediment–bacteria interaction processes in surface waters. Water Res 2011;45:1951–60. Garcia-Armisen T, Thouvenin B, Servais P. Modelling faecal coliforms dynamics in the Seine estuary, France. Water Sci Technol 2006;54:177–84. Haney RL. On the pressure gradient force over steep topography in σ coordinate ocean models. J Phys Oceanogr 1991;21:610–9. Hsu MH, Kuo AY, Kuo JT, Liu WC. Procedure to calibrate and verify numerical models of estuarine hydrodynamics. J Hydraul Eng ASCE 1999;125:166–82. Kashefipour SM, Lin B, Harris E, Falconer RA. Hydro-environmental modelling for bathing water compliance of an estuarine basin. Water Res 2002;36:1854–68. Liu WC, Chan WT. Assessing the influence of nutrient reduction on water quality using a three-dimensional model: case study in a tidal estuarine system. Environ Monit Assess 2014. (in press). Liu WC, Huang WC. Modeling the transport and distribution of fecal coliform in a tidal estuary. Sci Total Environ 2012;431:1–8. Liu L, Phanikumar MS, Molloy SL, Whitman RL, Shively DA, Nevers MB, et al. Modeling the transport and inactivation of E. coli and Enterococci in the near-shore region of Lake Michigan. Environ Sci Technol 2006;40:5022–8. Liu WC, Chen WB, Cheng RT, Hsu MH, Kuo AY. Modelling the influence of river discharge on salt intrusion and residual circulation in Danshui River estuary, Taiwan. Cont Shelf Res 2007;27:900–21. Manache G, Melching CS, Lanyon R. Calibration of a continuous simulation fecal coliform model based on historical data analysis. J Environ Eng ASCE 2007;133: 681–91. Montgomery Watson Harza (MWH). Evaluation of hydrodynamics and water quality monitoring and management in the Danshuei River basin of New Taipei City. Final Report to New Taipei City Government; 2011. Ouattara NK, de Brauwere A, Billen G, Servais P. Modelling faecal contamination in the Scheldt drainage network. J Mar Syst 2013;128:77–88. Rodrigues M, Oliveira A, Guerreiro M, Fortunato AB, Menaia J, David LM, et al. Modeling fecal contamination in the Aljezur coastal stream (Portugal). Ocean Dyn 2011;61: 841–56. Romeiro NML, Castro RGS, Cirilo ER, Natti PL. Local calibration of coliforms parameters of water quality problem at Igapo I Lake, Londrina, Parana, Brazil. Ecol Model 2011;222: 1888–96. Schnauder I, Bockelmann-Evans B, Lin B. Modelling faecal bacterial pathways in receiving waters. Proc Inst Civil Eng Marit Eng 2007;160:143–53. Servais P, Billen G, Goncalves A, Garcia-Armisen T. Modelling microbiological water quality in the Seine river drainage networks: past, present and future situations. Hydrol Earth Syst Sci 2007a;11:1581–92. Servais P, Garcia-Armisen T, George I, Billen G. Fecal bacteria in the rivers of the Seine drainage network (France): sources, fate and modeling. Sci Total Environ 2007b; 375:152–7. Sinton LW, Hall CH, Lynch PA, Davies-Colley RJ. Sunlight inactivation of fecal indicator bacteria and bacteriophages from water stabilization pond effluence in fresh and saline waters. Appl Environ Microbiol 2002;68:1122–31. Sokolova E, Pettersson TJR, Bergstedt O, Hermansson M. Hydrodynamic modelling of the microbial water quality in a drinking water sources as input for risk reduction management. J Hydrol 2013;497:15–23. Steets BM, Holden PA. A mechanistic model of runoff-associated fecal coliform fate and transport through a coastal lagoon. Water Res 2003;37:589–608. Thomann RV, Mueller JA. Principles of surface water quality and control. New York: Harper Collins; 1987. Umlauf L, Buchard H. A generic length-scale equation for geophysical turbulence models. J Mar Res 2003;61:235–65. Yang L, Lin B, Falconer RA. Modelling enteric bacteria levels in coastal and estuarine waters. Proc Inst Civil Eng Eng Comput Mech 2008;161:179–86. Zhang YL, Baptista AM. SELFE: a semi-implicit Eulerian–Lagrangian finite-element model for cross-scale ocean circulation. Ocean Model 2008;21:71–96. Zhu X, Wang JD, Solo-Garbriele HM, Fleming LE. A water quality modeling study of nonpoint sources at recreational marine beaches. Water Res 2011;45:2985–95.