Modeling the dispersion of viable and total Escherichia coli cells in the artificial semi-enclosed bathing area of Santa Marinella (Latium, Italy).

Marine Pollution Bulletin xxx (2015) xxx–xxx

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Marine Pollution Bulletin journal homepage: www.elsevier.com/locate/marpolbul

Modeling the dispersion of viable and total Escherichia coli cells in the artificial semi-enclosed bathing area of Santa Marinella (Latium, Italy) S. Bonamano a,⇑, A. Madonia a, C. Borsellino a, C. Stefanì a, G. Caruso b, F. De Pasquale b, V. Piermattei a, G. Zappalà b, M. Marcelli a a b

Laboratory of Experimental Oceanology and Marine Ecology, DEB, University of Tuscia, 00053 Civitavecchia, Italy Institute for Coastal Marine Environment (IAMC), National Research Council (CNR), 98122 Messina, Italy

a r t i c l e

i n f o

Article history: Available online xxxx Keywords: Coastal monitoring Bathing waters Escherichia coli Mathematical models Flushing time

a b s t r a c t Coastal areas are strongly affected by episodes of fecal contamination due to polluted water inflows from inadequately treated sewages. The present study aims to investigate the dispersion of Escherichia coli in the artificial semi-enclosed bathing area of Santa Marinella (Latium, Italy) through in situ samplings carried out in summer 2012 and the application of a dynamic model. Collected samples were analyzed by the Culture-Based technique and the Fluorescent Antibody method in order to estimate both the viable culturable cells and the total E. coli population, respectively. The in situ datasets were used to test the proposed modeling approach and simulate the behavior of bacteria as particles subjected, or not, to decay. Next, the flushing time and the computation of the Microbiological Potential Risk Area allowed the evaluation of the contribution of physical and biological processes to coliform dispersion and the related potential risk for bathers. Ó 2015 Elsevier Ltd. All rights reserved.

1. Introduction In the last few decades the impact of human population on coastal areas has increased significantly, since about 60% of people lives within 100 km from the shoreline (Vitousek et al., 1997). In the summer period, an additional increase of population density is observed due to tourism and those recreational activities involved with the use of coastal waters (swimming, diving, water skiing, etc.). In Italy, urban development on coastal areas is not often followed by an improvement of sewage treatment plants, resulting in frequent emergency discharge of wastewaters. This can spread a great amount of organic, inorganic and biological pollutants into seawaters. Moreover, the construction of artificial barriers aimed at protecting the coasts from wave erosion reduces the degree of water circulation and exchange. All these factors contribute to lower seawater quality levels and increase the risk of exposure to potential microbial pathogens in bathing areas. This may cause several health problems ranging from simple skin infections or mucosal irritations to gastric-intestinal diseases (Cabelli et al., 1982; Cheung et al., 1990; Calderon et al., 1991; McBride et al., 1998; Haile et al., 1999; Colford et al., 2007). ⇑ Corresponding author. Tel./fax: +39 0766 366538.

Knowledge of the dynamics of spreading and transport is of great interest to assess seawater quality and possible risks to human health related to contamination by potentially infectious microorganisms in near-shore waters. The methods commonly used to analyze the presence of enteric bacteria in seawater rely on the ability of bacteria to grow on culture media (Sartory and Watkins, 1999). However, bacteria can also enter a state of dormancy under adverse conditions, or they can constitutively produce spores to face periods of nutrient deprivation; in this state, they become viable but not culturable cells which cannot be detected with the standard culture methods (Roszack and Colwell, 1987). The survival and the dispersion of fecal microorganisms in coastal waters depend on two main factors: physicochemical conditions (solar radiation, salinity, temperature, pH and nutrient availability) and physical dilution due to hydrodynamics (Carlucci and Pramer, 1959; Gourmelon et al., 2010). In this context, a great interest has been addressed by the scientific community to develop mathematical models able to simulate the behavior of enteric bacteria in seawater. The mathematical models for the study of bacterial contamination of coastal waters have integrated, over time, various parameters to increase their reliability, such as the importance of the run-off of wastewater during heavy rain events (Riou et al., 2007) and the effect of

E-mail address: [email protected] (S. Bonamano). http://dx.doi.org/10.1016/j.marpolbul.2015.04.030 0025-326X/Ó 2015 Elsevier Ltd. All rights reserved.

Please cite this article in press as: Bonamano, S., et al. Modeling the dispersion of viable and total Escherichia coli cells in the artificial semi-enclosed bathing area of Santa Marinella (Latium, Italy). Mar. Pollut. Bull. (2015), http://dx.doi.org/10.1016/j.marpolbul.2015.04.030

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S. Bonamano et al. / Marine Pollution Bulletin xxx (2015) xxx–xxx

Nomenclature c Cx, Cy Ck, C0 C0 C(t) DH, DV et h I f Fx, Fy g k kb kI ks L m Mx, My N p P Px, Py Q

concentration of conservative or no-conservative substance propagation velocities components along x and y directions propagation velocities components of the wave energy density spectrum E(k, #) initial bacterial concentration bacterial concentration at time t horizontal and vertical diffusion coefficients light extinction coefficient water depth solar radiation Coriolis parameter horizontal turbulent shear terms acceleration due to gravity turbulent kinetic energy basic decay rate solar radiation dependent decay rate salinity dependent decay rate mixing length measured value sources or sink of momentum action density spectrum predicted value hydrostatic water pressure horizontal pressure terms source or sink terms per unit area

meteo-marine conditions on the coliforms’ behavior in seawater (De Brauwere et al., 2011). However, in previous studies performed on bacterial dispersion at sea (Fiandrino et al., 2003; Kashefipour et al., 2006), the available microbiological dataset concerned only culturable enteric bacteria obtained with standard procedures. For this reason, in the present work two different modeling approaches were used to explore the dynamics of bacterial load in the Santa Marinella (Mediterranean Sea, Italy) bathing area, in terms of both viable culturable (VC) and total (T) cells, which also include the viable but non-culturable cells (VNC) and the dead cells (D). In addition to the standard Culture-Based method (CB) dataset, used to simulate the dispersion of VC, the rapid Fluorescent Antibody technique (FA) (Caruso et al., 2002, 2004), was employed to analyze the spatio-temporal distribution of the T cell population. The study of the mean retention time of a particle inside an area is particularly important when the dispersion of a substance pathogenic for human health must be explored within a bathing area bounded by submerged and emerged barriers (Jouon et al., 2006). Many authors (Takeoka, 1984; Sanford et al., 1992; Cucco and Umgiesser, 2006; Cucco et al., 2009) propose the use of a flushing time parameter, considered as the time required for a conservative tracer within the water body to be reduced to a factor of 1/e. In addition, to better assess the potential risk for bathers related to the dispersion of enterobacteria in Santa Marinella bathing area, the Microbiological Potential Risk Area (MPRA), defined as the marine area delimited by the 1% value of the bacterial concentration measured at the discharge point, was computed. For a more precise calculation of flushing time and MPRA we took into account the complexity of morphological and hydrodynamical features of the semi-enclosed coastal area, using the simulation results obtained by high resolution numerical models.

rms S SI t T Tf u,v,w U, V

root mean square salinity scatter index flushing time water temperature 1/e Eulerian velocity components in Cartesian coordinates Generalized Lagrangian Mean (GLM) velocity components given by: U ¼ u þ uS V ¼ v þ v s where uS and vS are the Stocks’ drift components U; V depth-averaged GLM velocity components W source of energy density %ERROR average percentage error e dissipation in transport equation for turbulent kinetic energy f water surface elevation above reference datum # wave direction (the direction normal to the wave crest of each spectral component) #T temperature correction factor k relative frequency (as observed in a frame of reference moving with the current velocity) tV, tH kinematic viscosity q local fluid density q0 reference density of water r vertical ‘‘sigma’’ coordinate rc Prandtl–Schmidt number

2. Materials and methods 2.1. Study area Located along the north-east Tyrrhenian coast, the study area lies on the shoreline of Santa Marinella, a little town between Civitavecchia and Santa Severa (Fig. 1a). Known as the Tyrrhenian Pearl, Santa Marinella attracts a lot of tourists from all parts of the world so that in summer the population almost doubles, thanks also to the charm of numerous hotels and bathing facilities. In the last 20 years the population has grown by 47.25% (ISTAT data). Over the past 50 years, to protect the shoreline from coastal erosion, four emerged and three submerged barriers were built (Fig. 1b), creating a semi-enclosed basin which reduces the water masses circulation and renewal. The basin is about 800 m long and 150 m wide and lies along a longitudinal axis slightly rotated from north to south with an average depth of 2 m: the eastern area, bounded by the emerged barriers, is characterized by the highest number of bathers and numerous beach establishments; the western one presents fewer recreational facilities (outlined by the number of umbrellas). In addition, an emergency discharge point (in correspondence to the star) connected to the wastewater pipeline (dashed line) is located between the two areas, leading untreated waters into the bathing area in case of an overflow emergency. In the past, numerous fecal contamination episodes have caused diseases such as skin irritation and gastroenteritis in bathers (personal communication). 2.2. In situ data collection 2.2.1. Sampling strategy Water samplings for fecal pollution monitoring were performed in summer 2012 during three surveys, on July 4th, August 20th and September 12th, respectively. Surface seawater samples were



3

Fig. 1. The location of the study area along the western coast of Italy; a detail of the coastline between Civitavecchia and S. Severa towns; the symbols indicate the moored station (rhombus) used to validate the hydrodynamic model and the WAM grid point data (triangle) used to force the wave model (a); the artificial semi-enclosed bathing area of Santa Marinella considered for flushing time calculation (b). In both the figures the curvilinear grid adopted by numerical models is shown.

collected every three hours from 7:00 a.m. to 7:00 p.m. GMT + 2 (omitted henceforth in the text). Sampling efforts regarded the SM1_T station, located on the discharge point, and SM2_T station as controls within the semi-enclosed basin. According to the direction of the marine currents, three more stations were monitored: SM1A_T and SM1B_T in the west and SM2A_T in the east (Fig. 1b). The choice of the four time intervals (7:00 a.m. to 10:00 a.m., 10:00 a.m. to 1:00 p.m., 1:00 p.m. to 4:00 p.m., 4:00 p.m. to 7:00 p.m.) is based on beach use. 2.2.2. Meteo-climatic conditions The meteo-climatic conditions during the sampling periods were monitored using data collected by the weather station of Civitavecchia Port Authority. This station is a part of the Civitavecchia observational monitoring network which allows the acquisition of physical, chemical and biological data integrating

fixed stations, in situ samplings and satellite observations (Zappalà et al., 2013). The weather station measures the values of air temperature and pressure, relative humidity, solar radiation, rain fall, wind speed and direction with a time interval of 10 min, the data being transmitted twice a day via FTP using a cellular modem. In Fig. 2 the mean daily variations of solar radiation, wind speed and direction and precipitation are reported. In addition, wave height and direction data calculated with WAM model and available from Skiron Forecasting System (AM&WFG, University of Athens) (Galanis et al., 2012), have been used at a point nearest to the study area (Lat. 42.00 N and Long. 11.85 E, Fig. 1a, WAM). Furthermore, a moored station was positioned off to Capo Linaro (Lat. 42.0348°N, Long 11.8115°E), at a 30 m depth and a distance of about 4 km to the north of the Santa Marinella bathing area (Fig. 1a, MOOR). The platform was equipped with a single point current meter (Sensor Data 6000) located in the sub surface layer


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Fig. 2. Meteo-marine conditions (solar radiation, precipitation, wind speed and direction, wave height and direction) time series in summer 2012.

providing current speed and direction data every 30 min, from 1st to 11th August 2012. Three different meteo-marine conditions characterized the sampling days: July 4th showed the highest mean solar radiation (285.4 W/m2) with moderate meteo-marine conditions (3.62 m/s of wind speed coming from 312.5°N and 0.28 m of wave height from 256°N); during August 20th the mean radiation was 256.7 W/m2 while the lowest wind speed (1.44 m/s coming from 263.7°N) and wave height (0.08 m from 283°N) were recorded. On September 12th, high values of wind speed (4.4 m/s from 189.6°N) and wave height (0.42 m from 220°N) were observed; the solar radiation was 171.92 W/m2. The latter was also the only sampling day during which it rained (0.7 mm). Fig. 3 shows the daily variation of solar radiation, wind speed, wave height and their directions recorded on the three sampling days. The July 4th and September 12th sampling days were characterized by a decrease in solar radiation during the afternoon, due to the increasing presence of clouds. On September 12th an increase was observed in wind speed and wave height, which in the evening reached 6 m/s and 1 m, respectively. During July 4th wind speed varied from 1 to 5.5 m/s while wave height ranged between 0.2 and 0.6 m. August 20th was characterized by a low wind speed and wave height and the absence of cloud cover for the entire day.

2.2.3. Microbiological analysis Samples were stored in sterile containers at a temperature of 4 °C in the dark prior to laboratory analysis within 24 h. The CB and the FA methods were used to discriminate the fraction of VC within T cell population, which includes VC, VNC and D cells (Caruso et al., 2002). The CB method relied on the filtration of 100 mL water samples through 0.45 lm membranes and on their successive incubation on ECD-MUG agar plates (Biolife) at 35 °C for 24 h. The FA method was applied using the indirect procedure based on the use of polyclonal immune sera (Murex Escherichia coli agglutinating sera, mix of polyclonal 2 + 3 + 4, working dilution: 1:80 in phosphate-buffered saline), followed by labeling with goat anti-rabbit IgG (whole molecule) conjugated to FITC (fluorescein isothiocyanate), from Sigma–Aldrich Co, working dilution: 1:320 in phosphate-buffered saline). Culture and microscopic counts were expressed as colony-forming units (CFU) and cells per 100 mL of water, respectively. 2.3. Mathematical models The dispersion of E. coli within the Santa Marinella bathing area was studied using DELFT3D. This is a modeling system that, thanks to its several modules, can simulate flows, waves, sediment transports, morphological changes and ecological processes (Roelvink and Van Banning, 1994; Lesser et al., 2004). In this work the



5

Fig. 3. Hourly solar radiation, wind speed and direction, wave height and direction in the three sampling days.

DELFT3D FLOW hydrodynamic model (Lesser et al., 2004), the surface wave model SWAN (Booij et al., 1999) and the water quality model DELFT3D-WAQ (Van Gils et al., 1993; Los et al., 2004) were applied. 2.3.1. Hydrodynamic models The DELFT3D-FLOW model simulates unsteady flow resulting from meteorological phenomena, solving non-linear shallow water equations in two (depth-averaged) or three dimensions. The equation system consists of the momentum equations, continuity equation, transport equation and a turbulence closure model. In 3D simulations, the vertical grid is defined following the r co-ordinate approach in which the number of layers is constant over the horizontal computational area. The variables description is reported in the ‘Nomenclature’, at the beginning of this manuscript. The horizontal momentum equations are:

@U @U @U w @U þU þV þ fV @t @x @y h @ r 1 1 @ @U tV ¼ Px þ F x þ Mx þ 2 @r q0 h @r @V @V @V w @V 1 1 þU þV þ þ fU ¼ Py þ F y þ My þ 2 @t @x @y h @ r q0 h @ @V tV @r @r @P ¼ qgh @r

ð1Þ

ð2Þ

ð3Þ

The z-component of the momentum equations is approximated to the hydrostatic pressure relation as vertical accelerations are assumed to be small compared to gravitational acceleration and

are not taken into account. In the Eqs. (1) and (2) the horizontal pressure terms, Px and Py, are given by (Boussinesq approximations):

1

q0 1

q0

Px ¼ g

Z 0 @f h @ q @ r0 @ q dr0 þg þ @x q0 r @x @x @ r0

ð4Þ

Py ¼ g

Z 0 @f h @ q @ r0 @ q dr0 þg þ @y q0 r @y @y @ r0

ð5Þ

The horizontal turbulent shear terms, Fx and Fy, are simplified considering the eddy viscosity concept (Rodi, 1993):

F x ¼ tH

@2U @2U þ @x2 @y2

F y ¼ tH

@2V @2V þ @x2 @y2

! ð6Þ

! ð7Þ

The Mx and My represent the contributions due to external sources or sinks of momentum (external forces by hydraulic structures, discharge or withdrawal of water, wave stresses, etc.). The depth-averaged continuity equation is given by:

@n @½hU @½hV þ þ ¼Q @x @t @y

ð8Þ

in which Q represents the contributions per unit area due to the discharge or withdrawal of water, evaporation, and precipitation. The transport of conservative and non-conservative substances in a fluid is commonly described by the advection–dispersion equation in Cartesian coordinates:


6


@½hc @½hUc @½hVc @ðwcÞ þ þ þ @t @x @y @r @ @c @ @c 1 @ @c þ þ þ hQ DH DH ¼h DV @x @x @y @y h @r @r

ð9Þ

In DELFT3D-FLOW, the frictional terms (horizontal and vertical viscosity, tH and tV, and diffusivity DH and DV) are considered to be composed of three parts: a part due to ‘‘2D-turbulence’’, a part due to ‘‘3D-turbulence’’ (Uittenbogaard et al., 1992) and a part due molecular viscosity. To solve the transport equation, the relation between vertical eddy diffusivity and vertical eddy viscosity is used:

DV ¼

tV rc

ð10Þ

where rc is the Prandtl–Schmidt number. In DELFT3D-FLOW, the values of tV and DV are calculated by the following turbulence closure models:

2.3.3. Flushing time and MPRA calculation In this study, the flushing time is used both to define the decay speed of the living bacterial population (DECAY-FT) and to know the time of permanence of the total bacterial population in the study area (NO-DECAY-FT). The flushing time is calculated assuming that: (1) a known amount of non-conservative/conservative substances/particles are spilled into the coastal area, resulting in an initial concentration C0, (2) there are no further inputs the system after the initial time t0 and (3) the flow and volume remain constant over time. So, the concentration within the study area is given by (Thomann and Mueller, 1987):

CðtÞ ¼ C 0 et=T f

constant coefficient; Algebraic Eddy viscosity closure Model (AEM); k–L turbulence closure model; k–e turbulence closure model.

These models differ in their prescription of the turbulent kinetic energy k, the dissipation rate of turbulent kinetic energy e, and/or mixing length L. The wave effects have been included in the DELFT3D-FLOW simulation by running the separate SWAN model that uses the same computational grid. SWAN is a third-generation wave model that computes random, short-crested wind-generated waves in coastal regions and inland waters. In this model the evolution of the wave spectrum is described by spectral action balance equation (Mei, 1983):

@ @ @ @ @ W N þ cx N þ c y N þ c k N þ c h N ¼ @t @x @y @k @h k

hT = temperature correction factor, S = salinity (ppt), T = water temperature (°C), I = solar radiation (W/m2) and et = light extinction coefficient (m1). In the NO-DECAY approach the concentration of total E. coli analyzed by FA was used, considering the cells as conservative tracers.

ð11Þ

The first term in the left side of this Eq. (11) represents the local rate of change of action density in time, the second and the third terms indicate the propagation of action in geographical space. The fourth one means the shifting of the relative frequency due to variation in depths and currents and the fifth term represents depth-induced and current-induced refraction. Finally, the W variable at the right side of the action balance equation is the source of energy density representing the effects of generation, dissipation and non linear wave-wave interaction.

ð13Þ

where t is the flushing time, that period in which a substance achieves 37% of its initial value, and Tf is 1/e. In both cases the concentration of the bacterial population used for the flushing time calculation is that obtained by the model DELFT3D-WAQ in the DECAY and NO-DECAY simulations, respectively. MPRA is defined as the area within which the concentration of the bacterial load C(t) is greater than or equal to 1% of the concentration measured at the discharge point (C0), according to the following equation:

CðtÞ P 0:01 C0

ð14Þ

The MPRA computation arises from the need to follow the spatial dispersion and the temporal dynamics of bacteria discharged inside a bathing area in order to estimate the potential risk for the bathers who make use of the area. As reported for flushing time, also the calculation of MPRA has been carried out from the concentration of E. coli determined by the WAQ model, through DECAY and NO-DECAY simulations. The integration between flushing time and MPRA allows to detect which process has the highest influence on the decrease of E. coli in the study area: the physical transport processes governed by currents or the biological ones related to solar radiation, temperature and salinity conditions. 2.4. Simulations setup

2.3.2. Water quality model The bacterial dispersion was simulated by the DELFT3D-WAQ, which is an Eulerian transport model that solves the equations for transport and physical, (bio)chemical and biological processes. This model is based on the transport Eq. (9) where Q indicates the sources and sinks due to biological, bacteriological, ecological, chemical or other reaction. In this study the WAQ model has been used to simulate the dispersion of both VC and T cells of E. coli by two different approaches, called respectively DECAY and NO-DECAY. The DECAY simulation of the dispersion of living fecal bacteria takes into account the decay rate of bacterial cells in seawaters that depends on the ambient salinity (or chloride concentration), temperature and the intensity of the UV radiation. In general the decay rate is determined using the following equation (Thoe, 2010):

kðzÞ ¼ ðkb þ kS SÞhT20 þ kI Ieet z T

ð12Þ 1

where kb = basic decay rate (d ), ks = salinity dependent decay rate (d1), kI = solar radiation dependent decay rate (d1),

The DELFT3D-FLOW model domain is rectangular and covers a 30 km area with the bathing area positioned in the center (Fig. 1a and b). The curvilinear grid used in this study is orthogonal and well-structured and it has got about 40,000 elements. The used mesh provides a higher resolution in the Santa Marinella bathing area (up to 15 15 m) and a lower resolution in the outer parts included in the model (reducing to 200 200 m), so limiting the computational efforts. In the vertical direction, 10 sigma layers with uniform thickness were considered to increase the resolution in the shallow areas such as the zone interested by the bacterial discharge. To let the model determine the correct solution at the boundaries, the Neumann type is used to impose the along-shore water level gradient inside the model domain (Roelvink and Walstra, 2004). Neumann boundaries have been applied on cross-shore boundaries in combination with a water level boundary at the seaward margin. The variables of DELFT3D-FLOW are arranged in a pattern called the Arakawa C-grid (a staggered grid) in which the pressure and water levels are located halfway


7


between the velocity variables favouring several numerical advantages (Stelling, 1984). For the simulations presented in this work an Alternating Direction Implicit (ADI) method is employed to solve the continuity and momentum equations (Leendertse, 1987). For the spatial discretization of the horizontal advection terms, a cyclic method (Stelling and Leendertse, 1991) has been used. The hydrodynamical simulations lasted 24 h using a time step of 60 s. All the above mentioned features led to an efficient computational method that is accurate at least the second order, and stable as the Courant conditions is satisfied. In order to damp out all the noise that was introduced through the initial conditions, the day before the sampling period has been used as a spin up time. The wave effects have been included in the DELFT3D-FLOW simulation by running the separate SWAN model that uses the same computational grid. The hydrodynamic characteristics of the study area are mainly controlled by wind and wave forcing, while tidal forcing has been neglected (the maximum astronomical tide does not exceed 0.40 m during the sampling days, according to Tide Tables published by the Italian Navy). So the hydrodynamic simulations reproduce the wind and wave conditions occurred in the three sampling days using wind speed and direction collected by the weather station of the Port of Civitavecchia and wave parameters (significant wave, mean direction and peak period) available from Skiron Forecasting System (see Section 2.2.2). In order to solve the horizontal moment equations and to obtain the best correlation between modeled results and measured data, horizontal background eddy viscosity (mH) and diffusivity (DH) are set equal to 1 m2/s (Briere et al., 2011). To take into account the turbulence effects and to determine vertical viscosity (mV) and diffusivity (DV), the K–e turbulence model has been used (Launder and Spalding, 1974). The performance of the hydrodynamic model has been evaluated by the Scatter Index (SI) which represents the accuracy of the model. It is given by

SI ¼

rmspm maxðrmsm ; jhmijÞ

ð15Þ

where rms is root mean square and p and m indicate the predicted and measured data, respectively. To avoid strange results for data with small mean and large variability, the relative bias is normalized with the maximum of the rms of measured data and the absolute value of the mean of the data. The hydrodynamic simulations were used to feed the DELFT3DWAQ model in order to analyze the E. coli dispersion in the study area. Two different modeling approaches were set up. In the DECAY one, the fate of the living fraction has been reproduced taking into account the E. coli concentration analyzed by CB and the decay rate as expressed in the Eq. (13). In each simulation, kI, ks, and hT values reported by Thomann and Mueller (1987), a kb of 14.6 d1 as suggested by Kashefipour et al. (2006), the temperature and salinity values measured during the surveys and the solar irradiance acquired from the weather station of Civitavecchia, were used. In particular it was assumed that 45% of the solar spectrum energy corresponds to the UV fraction (Gameson and Gould, 1974). To consider the underwater attenuation of light along the water column, the extinction coefficients (Kd490) from MODIS Aqua dataset was used during the simulated days in proximity of the study area (ranging from 0.06 m1 in July to 0.10 m1 in September). In the second approach, called NO-DECAY, the fate of total E. coli analyzed by FA was simulated, considering the cells as conservative tracers. In both the approaches, the discharge point shown in Fig. 1 has been considered as the unique source of pathogenic bacteria in the study area. A continuous release by the discharge point (from

7:00 a.m. to 7:00 p.m.) has been simulated using the E. coli abundances obtained with CB and FA analytic methods. To evaluate the behavior of living fecal bacteria in the study area the initial conditions in the DECAY simulations were set as 0, while to reproduce the dispersion of the total bacterial population, regardless of their physiological state, FA concentrations at 7:00 a.m. were assumed as the initial conditions for the NO-DECAY runs. For each of the two approaches three simulations were computed, one for each sampling day, to validate the two approaches by the comparison with the in situ data. All the simulations have a duration of 24 h and a time step of 60 s (the same values used for the hydrodynamic simulations). The results predicted by the two different modeling approaches were compared with in situ data, by calculating the average percentage error (% ERROR) according to the formula indicated below:

%ERROR ¼

hjp mji hmi

ð16Þ

To estimate the flushing time within the bathing area of Santa Marinella, the temporal variation of the bacterial concentration was obtained by averaging the values calculated by the modelDELFT3D-WAQ in those grid elements included in the polygon shown in Fig. 1b. The flushing time was estimated as the time elapsed between the maximum value (C0) reached when the release of bacteria at the discharge point is exhausted, and C(t) that corresponds to a concentration equal to 37% of C0. To evaluate the effects of the discharge on bathing area during the day, it was divided into four time intervals of 3 h each, corresponding to the sampling period (Table 1). Then, four simulation scenarios for the three sampling days were computed both in the DECAY/NODECAY approaches, considering a 3 h continuous release at the discharge point set as the mean CB and FA E. coli concentrations recorded during the sampling. In each simulation the hydrodynamic field obtained by the steering between DELFT3D-FLOW and SWAN models and the total decay rate resulting from the validation of DELFT3D-WAQ were used. The time step and the duration of each simulation are the same used for the validation of the model DELFT3D-WAQ. To define the area affected by the spread of fecal bacteria, MPRA was calculated using the flushing time simulations described above. In this context, C0 corresponds to the E. coli concentration discharged in the bathing area while C(t) is the value achieved after 3 h (Eq. (14)). MPRA includes those grid elements where C(t) is greater than or equal to 1% of C0. 3. Results 3.1. Microbiological data The values of E. coli obtained with both CB and FA in the investigated area are shown in Table 2 and Fig. 4. The bacterial distribution undergoes significant spatial variations for both the datasets (ANOVA CB = P < 0.0001; FA = P < 0.005). In fact, the SM1_T station, located near the discharge input, is characterized by the highest E. coli abundances during all surveys, with median values of 370, 2930 and 277 CFU/100 mL (CB counts) and 1375,

Table 1 Time intervals used for the flushing time and MPRA calculation. Time intervals

Name and abbreviations

07:00 a.m.–10:00 a.m. 10:00 a.m.–1:00 p.m. 1:00 p.m.–4:00 p.m. 4:00 p.m.–7:00 p.m.

Early Morning (EM) Before Lunch (BL) After Lunch (AL) Late Afternoon (LA)


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Table 2 Minimum, maximum and median values of temperature (T), salinity (S) and E. coli obtained with CB and FA methods during the field surveys. Station

July 04, 2012

August 20, 2012

September 12, 2012

T (°C)

S (mS/ cm)

CB E. coli (CFU/ 100 mL)

FA E. coli (cells/ 100 mL)

T (°C)

S (mS/ cm)



T (°C)

S (mS/ cm)



22.5 27 25.4 22.9 28 26.5 21.8 23.8 22.4

9.6 23.3 17.6 30 31.6 31.1 34 36.7 35.2

420 3780 1375 709 2410 1559.5 845 1220 1035.5 –

24.7 29.9 28.2 24.7 29.9 29.2

13.1 30.6 23.9 32.1 34.8 32.6

592 1620 2930 8 70 30 –

4963 24363 10635 1241 3524 2382.5 –

23.2 25.4 24.5 23.0 25.4 25.1

25.7 29.6 29.8 34.2 35.6 35.8

95 530 277 2 22 6 –

2068 7444 4963 827 2867 1847 –

SM2A_T

220 660 370 8 70 39 1 3 2 –

24.5 31.5 29.9

32.6 35.1 34.9

–

–

–

973 2068 1520.5 –

–

SM1B_T

0 10 5 –

0 4 1.5

1000 4300 265

SM1_T

SM2_T

SM1A_T

24.8 26.9 26

36.1 36.8 37.2

Fig. 4. Bacterial concentrations at the sampling stations obtained by CB (grey columns) and FA (dark columns) methods.

10635 and 4963 cells/100 mL (FA counts) in July, August and September, respectively (Table 2). Moreover, at the SM1_T station the lowest salinity values were measured during the three sampling periods (17.6, 23.9, 29.8 psu, respectively), confirming the risk produced by the direct input of untreated wastewaters from the pipeline in the bathing area. The results obtained with the two analytical methods show that the difference between the median log10 values of FA and CB is 0.95, increasing up to 2.66 at the control stations. Conversely, FA counts show a lower variation between SM1_T and the control stations with a median difference of 0.17 (Fig. 4). Among the control stations, SM2_T shows the highest values of CB abundances in all the surveys. The analysis of variance performed on CB dataset does not show significant changes among the monitored stations (p = 0.516), while this occurs for FA dataset (p = 0.003). These results indicate that the September rain did not affect the distribution of viable bacteria within the study area, confirming that the only point source is given by the station SM1_T. Regarding the daily variations of the bacterial load at SM1_T station no significant trends were detected by one-way ANOVA with

both analytical methods (CB p = 0.87; FA p = 0.9), while substantial differences were observed among the three sampling days (CB = p < 0.01; FA = p < 0.005). This means that the spill of contaminated waters during the day is not constant but depends on the timely achievement of the overflow condition in the pipelines. Conversely, the presence of E. coli in the bathing area shows a direct relationship with the increase of tourism which has its maximum in August, when the highest concentration of fecal bacteria was recorded. Similarly to the results obtained at the discharge station SM1_T, no daily variation was observed at the control stations for both CB and FA analysis (ANOVA CB p = 0.54; FA p = 0.38), highlighting that the discharge point at SM1_T is the main source of untreated waters whose activity affects the level of microbiological contamination of the entire area. Furthermore, while the VC fraction monitored at the control stations does not change significantly in the three days of sampling (ANOVA CB p = 0.29), TC shows a higher significance level (FA p < 0.005). Such discrepancy is due to the rapid decrease of living bacteria compared to the total fraction far from the release point because of the adverse environmental conditions to which the cells undergo when coming into seawater.



9

NO-DECAY simulations show a good correlation between the model data and in situ FA abundances. The overall mean percentage error is estimated to be 1.72% and 8.99% for CB and FA, respectively. The DECAY simulation estimates an increase of the error from 0.31% to 81.35% as the distance increases from the discharge point. In the NO-DECAY simulation the error has a more homogeneous spatial distribution within the study area, with error values rising from 15.6% to 28.7% with increasing distance from the pollution source. 3.3. FT and MPRA computations

Fig. 5. Comparison between model (solid line) and measured (dashed line) current speeds (magnitude and its components) at the mooring station.

3.2. Validation of models In order to test the predictive performance of hydrodynamic (FLOW + SWAN) and water quality (WAQ) models, the in situ measurement and numerical results were compared (Figs. 5 and 6). The 3-D hydrodynamic model was validated with the magnitude and the x–y components of the sea current measured at the moored station between 1st and 11th August 2012. A SI value of 0.62 was calculated for the entire period. The accuracy of the model increases considering only the first part and the last part of the sampling period (SI = 0.35). In the range August 5th7th the predicted marine currents differ from measured marine currents due to a sudden change of the weather conditions. The comparison between field-measured and predicted E. coli abundances at each sampling station with the DECAY set-up model shows that the best fit between model results and CB abundances occurs at the SM1_T and SM2_T stations where higher E. coli concentrations are detected. On the contrary, at SM1A_T station, the model tends to underestimate the measured concentrations except for the July sampling when a good correlation is observed. Also the

Fig. 7 reports a summary diagram of the inputs used to calculate the flushing time parameter for the three days of sampling and the related results, used to analyze the dispersion of living (DECAY-FT) and total (NO-DECAY-FT) bacteria in the study area. On July 4th, the decrease of the decay rate, linked to the increase of cloud coverage (Fig. 3), favors during the day an increase of DECAY-FT which ranges from 75.4 to 85.8 min in the interval EM-LA. An opposite trend is observed for NO-DECAY-FT, as a value of 236.6 min and a minimum peak of 131 are found in EM and LA, respectively, according to the increasing intensity of marine currents up to 0.05 m/s. On August 20th, DECAY-FT and NO-DECAY-FT values show low variability (70.8 ± 7.7 and 288.5 ± 4.1) over the time intervals of each single approach due to the lower intensity of marine currents (0.15 m/s) registered during September 12th determines similar values of DECAYFT and NO-DECAY-FT, in particular during AL and BL intervals, with a difference of 12.75 and 5.54 min, respectively. The highest values were recorded in BL (DECAY-FT = 90.75 min) and EM (NO-DECAYFT = 170.5 min) intervals. Fig. 8 shows MPRAs calculated according to the same set-up conditions used for the evaluation of FT. The greater extent of MPRAs is reached on July 4th in the range LA, both in the approach DECAY (7.921 104 m2) and in the NO-DECAY one (1.076 105 m2). The lowest values were reached during September 12th (AL interval) (DECAY-MPRA = 3.315 104 m2 and NO-DECAYMPRA = 3.96 104 m2) with the lowest mean difference of 1.866 104 m2. On the contrary, on August 20th the highest discrepancy was observed with an average value of 3.33 104 m2. As more clearly occurred on July 4th, the bacterial load is distributed evenly around the discharge point, reaching above all the more frequented sub-area confined by the artificial barriers. On September 12th the distribution of fecal bacteria changes discontinuously between time intervals due to the variability of the weather and sea conditions. 4. Discussion As requested by the European Commission Directives, both the 2000/60/EC (Water Framework Directive, WFD) and the 2008/56/ EC (Marine Strategy Framework Directive, MSFD), the Member States are obliged to reduce the impacts or risks to marine biodiversity, marine ecosystems and human health due to pollution events. The water quality in the Santa Marinella bathing area in summer 2012 is affected by high E. coli concentrations at station SM1_T during all the field surveys (median values of 370, 2930, 277 CFU/100 mL, respectively), exceeding the threshold level reported by the Bathing Water Directive 2006/7/EC of 250 CFU/ 100 mL for excellent water quality in July and September and of 500 CFU/100 mL for good water quality in August. The similar counts recorded by CB and FA at SM1_T station indicate that the


10


Fig. 6. Model (continuous lines) and measured (dots) E. coli abundances obtained from DECAY (a) and NO-DECAY (b) approaches performed on July 4th, August 20th and September 12th surveys.



Fig. 7. Simulation conditions according to the DECAY-FT (in blue) and NO-DECAYFT (in red) approach obtained for the four time intervals during July 4th (a), August 20th (b) and September 12th (c). Each panel contains the daily variation of (1) the decay rate, (2) the speed of marine currents, (3) the releasing period, (4) the amount of the bacterial load and (5) the corresponding flushing time of the 4 daily intervals. Abbreviations: EM: Early Morning, BL: Before Lunch; AL: After Lunch; LA: Late Afternoon. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

main component of the bacterial population is the living one; conversely, the quantitative discrepancy between the culture and microscopical counts shows an increasing trend as the distance

11

to the discharge point grows, underlining the occurrence of a high percentage of non viable cells, which are detected by FA only, in the seawater closer to the beach as well as at the control stations. With the exception of the most polluted station, the overall level of fecal pollution recorded during the present study has a similar order of magnitude to that observed during a previous summer study of the same area (Zappalà et al., 2012), when fecal coliforms concentrations in the Santa Marinella area ranged from 0 to 150 CFU/100 mL. Variations in the polluting bacterial level within the study area may be related to the environmental parameters, since it is known that bacterial survival in the aquatic environment is strictly related to physical, chemical and biological factors (Troussellier and Legendre, 1989; Pommepuy et al., 1996; Troussellier et al., 1998; Rozen and Belkin, 2001; Noble et al., 2004; Lyons et al., 2010; Bergholz et al., 2011). Salinity above all affects the survival of enteric bacteria in the marine environment, causing them an immediate osmotic shock which dramatically reduces their viability. In the present study, the decrease of the living fraction of E. coli population is found to be strictly related to variations in salinity: at the most polluted SM1_T station, the lowest salinity values are measured during the three sampling periods (17.6, 23.9, 29.8 psu, respectively), indicating the rising of an overflow condition. Moreover, the increase of solar radiation has been reported to favor the die-off rate of E. coli (Noble et al., 2004; Foppen and Schijven, 2006). As expected, during the July period, characterized by the higher values of solar irradiance (285.1 W/m2), the lowest E. coli abundances over all the investigated area are detected. In the present study a major effort was devoted to simulate the behavior of both VC and T bacterial cells – which include the quiescent and dead fraction of the bacterial population – through the use of DECAY and NO-DECAY approaches. The results of model simulations highlight that both the approaches achieve good performances in reproducing the bacterial dispersion. In particular, DECAY simulates the trend of E. coli concentrations better than NO-DECAY (0.31 vs. 15.6% error) near the discharge point where, as previously discussed, most of the bacterial cells are still living. As the distance from the discharge point increases, the DECAY error increases strongly (up to 81.35%) while the NO-DECAY error achieves just 28.7%. Possible diffuse sources (Wang et al., 2010) might explain this difference, affecting the low concentration of living bacteria away from the discharge, and producing an underestimation of the model. Conversely, the abundance of T cells simulated by the NO-DECAY approach is not affected by these small changes, therefore the reliability of the model is maintained on a spatial scale. The calculation of flushing time allows the assessment of the contribution of transport processes and environmental conditions on the dispersion of E. coli with the DECAY and NO-DECAY approaches, as well as the reduction of bacterial dispersion caused by the artificial barriers. The results show that when the difference between DECAY-FT and NO-DECAY-FT is high the bacterial dispersion is more influenced by environmental conditions, conversely a dominance of transport processes determine a similarity between the DECAY-FT and the NO-DECAY-FT. During July 4th, the progressive increase of cloud-cover during the day determined an increase of DECAY-FT in the study area from 75.4 to 85.8 min, meaning that living bacteria remain in the bathing waters for a longer time. On the other hand, the increasing intensity of marine currents favors a sharp decrease of NO-DECAY-FT from 236 to 131 min. The increased hydrodynamism and cloud coverage during the day reduces their difference. The discrepancy of the two approaches is even more pronounced in the August 20th sampling when the stability of weather and environmental conditions (low marine current intensity and absence of cloud cover) maintains constant the values of flushing times calculated with the two methods,


12


Fig. 8. MPRAs calculated with DECAY (left) and NO-DECAY (right) approach during (a) July 4th, (b) August 20th and (c) September 12th. In each panel a graph representing the extension of MPRAs in the four time intervals is reported. Abbreviations: EM: Early Morning, BL: Before Lunch; AL: After Lunch; LA: Late Afternoon.

although they strongly differ (70.6 vs. 276.0 min). During September 12th the increase of marine currents reduces the difference between DECAY-FT and NO-DECAY-FT.

To assess the sanitary risk related to the dispersion of contaminated seawater within the bathing area MPRAs were detected. The results show that July 4th was characterized by the maximum



dispersion of both living and total bacteria even in the recreational area interested by a high number of bathers. On August 20th the prevalence of environmental conditions on transport processes produced a limited distribution of VC fraction compared to T cells. This result indicates the presence in seawater of dormant cells not identified with the DECAY approach, suggesting that the presence of emerged barriers limits their removal from the beach. During the third sampling day the high intensity of the sea currents facilitates the dilution of the bacterial load limiting MPRA to an average of 5.481 104 m2 and 7.347 104 m2 respectively for DECAY-FT and NO-DECAY-FT, suggesting that living bacteria behave as conservative tracers. Using the probability of infection reported in the epidemiological study of Cheung et al. (1990), where 1 of 470 bathers reported highly credible gastrointestinal illness (HCGI) when exposed to a range of bacterial concentration between 69 and 129 CFU/100 mL, the highest potential microbiological risk is found during August 20th. This is confirmed by the maximum concentration of both living and total (and thus potentially pathogenic dormant cells) bacteria, as well as by their accumulation in the most frequented zone, as evidenced by the flushing time and MPRA calculations. As discussed above, because of the low hydrodynamic conditions registered on this day, the impact of the barriers in limiting the spread of bacteria outside the bathing area strongly increases. 5. Conclusions The results obtained in the present study show that Santa Marinella area is characterized by the presence of an anthropic input which produces a surplus of wastewaters when the sewage reaches an overflow condition, especially during the month of August, when the highest values of E. coli concentrations were recorded. This means that in Santa Marinella the impact of overpopulation can be relevant, due to an insufficient depuration capacity of the local sewage treatment plant during the periods when the highest density of tourists is usually recorded. The methodology used in this study was suitable to analyze the dispersion of the VC fraction and T cells of E. coli in the bathing area of Santa Marinella when a spill of untreated waters occurs. This study enables the assessment the potential risk associated with hydrodynamic and environmental conditions, as well as with bacterial cells under different physiological states. However, more information on the fraction of actively metabolizing E. coli cells could be provided by a combination of the analytical protocol used for FA with viability stains like cyanotetrazolium chloride (CTC) (Caruso et al., 2006a,b). Therefore the use of mathematical models is suggested to get integrated responses about the level of contamination in marine ecosystems. Models may offer a useful tool for early warning of pollution phenomena, enabling local authorities to set up suitable protective measures against possible threats. It must be remarked that the combined use of satellite observations, in situ measurements and samplings, integrated with advanced analytical methods to feed mathematical models, will constitute the future approach to environmental knowledge and protection. Finally, to improve the performance of mathematical models there is an increasing need of observational networks (Zappalà et al., 2004; Zappalà, 2009) for real-time synoptic, nowcasting, hindecasting and short-term forecasting of bacterial dispersion in bathing areas as produced by Chan et al. (2013) for Hong Kong beaches. References Bergholz, P.W., Jesse, D.N., Buckley, D.H., 2011. Environmental patterns are imposed on the population structure of Escherichia coli after fecal deposition. Appl. Environ. Microbiol. 77, 211–219.

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Booij, N., Ris, R.C., Holthuijsen, L.H., 1999. A third-generation wave model for coastal regions, Part I: Model description and validation. J. Geophys. Res. 104, 7649– 7666. Briere, C., Giardino, A., van der Werf, J., 2011. Morphological modeling of bar dynamics with DELFT3d: the quest for optimal free parameter settings using an automatic calibration technique. Coast. Eng. Proc.: sediment-60. Cabelli, V.J., Dufour, A.P., McCabe, L.J., Levin, M.A., 1982. Swimming-associated gastroenteritis and water quality. Am. J. Epidemiol. 115, 606–616. Calderon, R.L., Mood, E.W., Dufour, A.P., 1991. Health effects of swimmers and nonpoint sources of contaminated water. Int. J. Environ. Health Res. 1, 21–31. Carlucci, A.F., Pramer, D., 1959. Factors affecting the survival of bacteria in sea water. Appl. Microbiol. 7, 388–392. Caruso, G., Crisafi, E., Mancuso, M., 2002. Immunofluorescence detection of Escherichia coli in seawater: a comparison of various commercial antisera. J. Immunoassays Immunochem. 23, 479–496. Caruso, G., Denaro, R., Genovese, M., Giuliano, L., Mancuso, M., Yakimov, M., 2004. New methodological strategies for detecting bacterial indicators. Chem. Ecol. 20, 167–181. Caruso, G., De Pasquale, F., Mancuso, M., Zampino, D., Crisafi, E., 2006a. Fluorescent antibody-viability staining and b-glucuronidase assay as rapid methods for monitoring Escherichia coli viability in coastal marine waters. J. Immunoassays Immunochem. 27, 1–13. Caruso, G., Zappalà, G., Crisafi, E., 2006b. Assessment of Escherichia coli viability in coastal Sicilian waters by fluorescent antibody and b-glucuronidase activity methods. WIT Trans. Ecol. Environ. 88, 57–66. Chan, S.N., Thoe, W., Lee, J.H.W., 2013. Real-time forecasting of Hong Kong beach water quality by 3D deterministic model. Water Res. 47, 1631–1647. Cheung, W., Chang, K., Hung, R., Kleevens, J., 1990. Health effects of beach water pollution in Hong Kong. Epidemiol. Infect. 105 (1), 139–162. Colford, J.M., Wade, T.J., Schiff, K.C., Wright, C.C., Griffith, J.F., Sandhu, S.K., Burns, S., Sobsey, M., Lovelace, G., Weisberg, S.B., 2007. Water quality indicators and the risk of illness at beaches with nonpoint sources of fecal contamination. Epidemiology 18 (1), 27–35. Cucco, A., Umgiesser, G., 2006. Modeling the Venice lagoon water residence time. Ecol. Model. 193, 34–51. Cucco, A., Umgiesser, G., Ferrarin, C., Perilli, A., Melaku, Canu D., Solidoro, C., 2009. Eulerian and Lagrangian transport time scales of a tidal active coastal basin. Ecol. Model. 220, 913–922. De Brauwere, A., De Brye, B., Servais, P., Passerat, J., Deleersnijder, E., 2011. Modelling Escherichia coli concentrations in the tidal Scheldt river and estuary. Water Res. 45, 2724–2738. European Commission, 2000. Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for Community actions in the field of water policy. Offic. J. Eur. Commun. L327, 1. 22.12.2000. 20. European Commission, 2008. Directive 2008/56/EC of the European Parliament and of the Council of 17 June 2008 establishing a framework for Community actions in the field of marine environmental policy (Marine Strategy Framework Directive). Offic. J. Eur. Commun. L164/19 25.06.2008. Fiandrino, A., Martin, Y., Got, P., Bonnefont, J.L., Troussellier, M., 2003. Bacterial contamination of Mediterranean coastal seawater as affected by riverine inputs: simulation approach applied to a shellfish breeding area (Thau lagoon). Water Res. 37, 1711–1722. Foppen, J.W.A., Schijven, J.F., 2006. Evaluation of data from the literature on the transport and survival of Escherichia coli and thermotolerant coliforms in aquifers under saturated conditions. Water Res. 40, 401–426. Galanis, G., Hayes, D., Zodiatis, G., Chu, C.P., Kuo, Y.-H., Kallos, G., 2012. Wave height characteristics in the Mediterranean Sea by means of numerical modeling, satellite data, statistical and geometrical techniques. Mar. Geophys. Res. 33, 1–15. Gameson, A.L.H., Gould, D.J., 1974. Effects of solar radiation on the mortality of some terrestrial bacteria in sea water. In: International Symposium on Discharge of Sewage from Sea Outfalls. Gourmelon, M., Lazure, P., Hervio-Heath, D., Le Saux, J.C., Caprais, M.P., Le Guyader, F.S., Catherine, M., Pommepuy, M., 2010. Microbial modeling in coastal environments and early warning systems: useful tools to limit shellfish microbial contamination. In: Rees, G., Pond, K., Kay, D., Bartram, J., Santo Domingo, J. (Eds.), Safe Management of Shellfish and Harvest Waters. IWA Publishing: London, pp. 297–318. Haile, R.W., White, J.S., Gold, M.R., Cressey, C., McGee, R.C., Millikan, A., Glasser, N., Harawa, C., Ervin, P., Harmon, J., Harper, J., Dermand, J., Alamillo, K., Barrett, M., Nides, G., Wang, Y., 1999. The health effects of swimming in ocean water contaminated by storm drain runoff. Epidemiology 10, 355–363. ISTAT data: . Jouon, A., Douillet, P., Ouillon, S., Fraunie, P., 2006. Calculations of hydrodynamic time parameters in a semi-opened coastal zone using a 3D hydrodynamic model. Cont. Shelf Res. 26, 1395–1415. Kashefipour, S.M., Lin, B., Falconer, R.A., 2006. Modelling the fate of faecal indicators in a coastal basin. Water Res. 40, 1413–1425. Launder, B.E., Spalding, D.B., 1974. The numerical computation of turbulent flows. Comput. Meth. Appl. Mech. Eng. 3, 269–289. Leendertse, J.J., 1987. A three-dimensional alternating direction implicit model with iterative fourth order dissipative non-linear advection terms. WD-333-NETH, The Netherlands Rijkswaterstaat. Lesser, G.R., Roelvink, J.A., van Kester, J.A.T.M., Stelling, G.S., 2004. Development and validation of a three dimensional morphological model. Coastal Eng. 51, 883– 915.


14


Los, F.J., Tatman, S., Minns, A.W., 2004. Flyland–A Future Airport in the North Sea? An Integrated Modelling Approach for Marine Ecology. In: Liong, Phoon & Babovic (Eds.), 6th International Conference on Hydroinformatics. World Scientific 2004, ISBN 981-238-787-0. Lyons, M.M., Ward, J.E., Gaff, H., Hicks, R.E., Drake, J.M., Dobbs, F.C., 2010. Theory of island biogeography on a microscopic scale: organic aggregates as islands for aquatic pathogens. Aquat. Microb. Ecol. 60, 1–13. McBride, G., Salmond, C., Bandaranayake, D., Turner, S., Lewis, G., Till, D., 1998. Health effects of marine bathing in New Zealand. Int. J. Environ. Health Res. 8, 173–189. Mei, C.C., 1983. The Applied Dynamics Dynamics of Ocean Surface Waves. Wiley, New York, 740 p. Noble, R.T., Lee, I.M., Schiff, K.C., 2004. Inactivation of indicator micro-organisms from various sources of faecal contamination in seawater and freshwater. J. Appl. Microbiol. 96, 464–472. Pommepuy, M., Fiksdal, L., Gourmelon, M., Melikechi, H., Caprais, M.P., Cormier, M., Colwell, R.R., 1996. Effect of seawater on Escherichia coli b-galactosidase activity. J. Appl. Microbiol. 81, 1365–2672. Riou, P., Le Saux, J.C., Dumas, F., Caprais, M.P., Le Guyader, L.F., Pommepuy, M.P., 2007. Microbial impact of small tributaries on water and shellfish quality in shallow coastal areas. Water Res. 41, 2774–2786. Rodi, W., 1993. Turbulence Models and Their Application in Hydraulics. A State of the Art Review. CRC Press. Roelvink, J.A., van Banning, G.K.F.M., 1994. Design and development of DELFT3D and application to coastal morphodynamics. Proc. Hydroinformatics. Balkema, Rotterdam, pp. 451–456. Roelvink, J.A., Walstra, D.J.R., 2004. Keeping it simple by using complex models. Adv. Hydro-Sci. Eng. 6, 1–11. Roszack, D.B., Colwell, R., 1987. Survival strategies of bacteria in the natural environment. Microbiol. Rev. 51, 365–379. Rozen, Y., Belkin, S., 2001. Survival of enteric bacteria in seawater. FEMS Microbiol. Rev. 25, 513–529. Sanford, L., Boicourt, W., Rives, S., 1992. Model for estimating tidal flushing of small embayments. J. Waterway, Port, Coast. Ocean Eng. 118 (6), 913–935. Sartory, D.P., Watkins, J., 1999. Conventional culture for water quality assessment: is there a future? J. Appl. Microbiol. Symp. Suppl. 85, 225S–233S. Stelling, G.S., 1984. On the construction of computational methods for shallow water problems. Tech. Rep. 35. 94, 219, 288, 292, 293, 294, 296, 299, 302, 313, 391, 392, 528. Stelling, G.S., Leendertse, J.J., 1991. Approximation of convective processes by cyclic AOI methods. In: Spaulding, M.L., Bedford, K., Blumberg, A., Cheng, R., Swanson,

C. (Eds.), Second International Conference on Conference on Estuarine and Coastal Modelling, Tampa, November 13–15, 1991, pp. 771–782. 94, 293, 294, 299, 304. Takeoka, H., 1984. Fundamental concepts of exchange and transport time scales in a coastal sea. Cont. Shelf Res. 3 (3), 311–326. Thoe, W., 2010. A daily forecasting system of marine beach water quality in Hong Kong. Ph.D. Thesis, The University of Hong Kong. Thomann, R.V., Mueller, J.A., 1987. Principles of Surface Water Quality Modelling and Control. Harper & Row Publishers, New York. xvii, 4, 221, 366, 377. Troussellier, M., Legendre, P., 1989. Dynamics of fecal coliform and culturable heterotroph densities in a eutrophic ecosystem: stability of models and evolution of these bacterial groups. Microb. Ecol. 17, 227–235. Troussellier, M., Bonnefont, J.L., Courties, C., Derrien, A., Dupray, E., Gauthier, M., Gourmelon, M., Lebaron, P., Martin, Y., Pommepuy, M., 1998. Response of enteric bacteria to environmental stresses in seawater. Oceanol. Acta 21, 965– 981. Uittenbogaard, R.E., van Kester J.A.T.M., Stelling, G.S., 1992. Implementation of the three turbulence models in 3D-TRISULA for rectangular grid. Tech. Rep. Z81. 204, 208, 228, 230, 232, 309, 401, 613. Van Gils, J.A.G., Ouboter, M.R.L., De Rooij, M.N., 1993. Modelling of water and sediment quality in the Scheldt Estuary. Netherland J. Aquat. Ecol. 27 (2–4), 257–265. Vitousek, P.M., Mooney, H.A., Lubchenco, J., Melillo, J.M., 1997. Human domination of earth’s ecosystems. Science 277, 494–499. Wang, J.D., Solo-Gabriele, H.M., Abdelzaher, A.M., Fleming, L.E., 2010. Estimation of enterococci input from bathers and animals on a recreational beach using camera images. Mar. Poll. Bull. 60, 1270–1278. Zappalà, G., 2009. A versatile software-hardware system for environmental data acquisition and transmission. In: Brebbia, C.A., Carlomagno, G.M. (Eds.), WIT Trans. Model. Simul. 48, 283–294. Zappalà, G., Caruso, G., Crisafi, E., 2004. Coastal pollution monitoring by an automatic multisampler coupled with a fluorescent antibody assay. Environ. Stud. 10, 125–133. Zappalà, G., Bonamano, S., Madonia, A., Caruso, G., Marcelli, M., 2012. Microbiological risk assessment in a coastal marine environment through the use of mathematical models. In: Brebbia, C.A. (Ed.), Water Pollution XI. WIT Press, Southampton, pp. 3–14. Zappalà, G., Caruso, G., Bonamano, S., Madonia, A., Piermattei, V., Di Cicco, A., Martellucci, R., Marcelli, M., 2013. Integrated marine measurements in Civitavecchia (Rome) area. In: Carlomagno, G.M., Brebbia, C.A., Hernandez, S. (Eds.), WIT Trans. Model. Simul. 55, 221–235.


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Hyperspectral proximal sensing of Salix Alba trees in the Sacco river valley (Latium, Italy).

Toxin production and antibiotic resistances in Escherichia coli isolated from bathing areas along the coastline of the Oslo fjord.

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Metabolic characterization of the viable, residually dividing and nondividing cell classes of recombination-deficient strains of Escherichia coli.

Analysis of the protein-kinase activity of Escherichia coli cells.

SynCardia: the total artificial heart.

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