numerical modelling of heavy metal dynamics in a river

Research ArticleNumerical Modelling of Heavy Metal Dynamics ina River-Lagoon System

F Torres-Bejarano1 C Couder-Castantildeeda 2 H Ram-rez-Leoacuten3

J J Hernaacutendez-Goacutemez 2 C Rodr-guez-Cuevas 4 I E Herrera-D-az5

and H Barrios-Pintildea6

1Departamento de Ingenierıa Ambiental Universidad de Cordoba Monterıa Colombia2Centro de Desarrollo Aeroespacial Instituto Politecnico Nacional Mexico3PIMAS Proyectos de Ingenierıa y Medio Ambiente SC Ciudad de Mexico Mexico4Facultad de Ingenierıa Universidad Autonoma de San Luis Potosı San Luis Potosı Mexico5Departmento de Ingenierıa Agroindustrial Universidad de Guanajuato Campus Celaya-Salvatierra Celaya Guanajuato Mexico6Tecnologico de Monterrey Campus Guadalajara 45138 Zapopan Jalisco Mexico

Correspondence should be addressed to C Couder-Castaneda ccouderipnmx

Received 10 January 2019 Revised 1 April 2019 Accepted 18 April 2019 Published 6 May 2019

Academic Editor Luis Cea

Copyright copy 2019 F Torres-Bejarano et al This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

This paper describes the development of a two-dimensional water quality model that solves hydrodynamic equations tied totransport equations with reactions mechanisms inherent in the processes This enables us to perform an accurate assessment ofthe pollution in a coastal ecosystem The model was developed with data drawn from the ecosystem found in Mexicorsquos southeaststate of Tabasco The coastal ecosystem consists of the interaction of El Yucateco lagoon with Chicozapote and Tonala rivers thatconnect the lagoon with the Gulf of Mexico The results of pollutants transport simulation in the coastal ecosystem are presentedfocusing on toxic parameters for two hydrodynamic scenarios wet and dry seasons As it is of interest in the zone the transportof four metals is studied Cadmium Chromium Nickel and Lead In order to address these objectives a self-posed mathematicalproblem is solved numerically which is based on the measured data The performed simulations show how to characterise metalstransport with an acceptable accuracy agreeing well with measured data in total concentrations in four control points along thewater body Although for the accurate implementation of the hydrodynamic-based water quality model herein presented boundary(geometry tides wind etc) and initial (concentrations measurements) conditions are required it poses an excellent option whenthe distribution of solutes with high accuracy is required easing environmental economic and social management of coastalecosystems It ought to be remarked that this constitutes a robust differential equation-based water quality model for the transportof heavy metals Models with these characteristics are not common to be found elsewhere

1 Introduction

The concern for water environmental pollution by heavymetals has recently increased due to the negative effectsit might have in human beings [1 2] Some heavy metalsas Cadmium (Cd) Chromium (Cr) and Lead (Pb) maytransform into persistent metallic compounds with hightoxicity [3] Due to their damaging effects on the ecologicalenvironment and on human health [4 5] it is necessary tostudy heavy metal contamination in aquatic ecosystems [6]

Metals are naturally present in small concentrations ortraces in earthrsquos crust many of them are essential for thegrowth and development of plants animals and humanbeings The geo-available origin of these metals occursfrom the mother rock to the soils after being released byweathering In contrast the presence of high concentra-tions of metals with respect to the ecological norms isan indicator of anthropogenic activities such as hazardouswastes derived from industrial activities mining and agri-culture

HindawiMathematical Problems in EngineeringVolume 2019 Article ID 8485031 24 pageshttpsdoiorg10115520198485031

2 Mathematical Problems in Engineering

As rivers serve as amedium for transport of dissolved andparticulate matter from continents to the ocean nowadaysinterest in the pollution of rivers by metals has increasedalong with the exponential increment of industrialisationurbanisation and agriculturisation of coastal areas This hassubstantially increased the concern and level of awareness inthis problem [7] For this reason heavy metal concentrationsin waters have been analysed worldwide particularly byproposing new numerical approaches [8]

In coastal waters heavy metals are distributed throughthe water column (particulate and dissolved) and the bottomsediments This occurs during the mixing of fresh andmarine water which causes flocculation and sedimentationof organic matter nutrients and trace elements from riversActually dissolvedmetals come into the particulate phase dueto processes as flocculation water pH sediment mineralogyand others during estuarine mixing [9] Thus heavy metalsget bound to these elements and precipitate to the bottom

In this work it is assumed that the partition coefficientdoes not depend on the concentration of the sorbing solidsaccording to Thomann and Mueller [10] in which thehypothesis is that the partition coefficient of metal in wateris different from the partition coefficient of that metal inthe bottom sediment and it is assumed that the decay inthe sediment is approximately zero This analysis applies torivers where solids are not suffering a net resuspension inthe water column thus this model was used to evaluate theconcentrations of Cd Cr Pb and Ni in the water column Onthe other hand according to Shimazu et al [11] the sediment-water partition of the chemical mainly depends on sorptionto sediment organic matter sediment inorganic matter andreaction group

Flocculation plays a key role in the dynamics of estuarineand coastal environments controlling the transport of fine-grained cohesive sediments and particulate contaminantsthroughout these systems [12ndash14] (usually characterised bymuddy bottoms [15]) Nevertheless it should be pointed outthat during natural estuarine mixing flocculation processmay not occur actually salinity plays an important rolein the process depending on the reaction mechanism of aparticular metal For instance flocculation starts at 10 ofsalinity during estuarine mixing for Cd [16 17] Moreoverother metals are known for their nutrient-like behaviour[18]Thus flocculation process constitutes an arduous task tomodel [19] which is not the aim of this work

For the above reasons strategies and tools to mitigate thepollution of heavy metals are required [20] A huge numberof mathematical models that intend to predict the transportof heavy metals in flows exist for example the statisticalmodels based on exponential functions (analytic models)which allow the achievement of a simulation in a relativelyuncomplicated way This is the case of the use of sigmoidfunctions to determine metal concentrations in rivers thatcan yield to average concentrations in a section [21] Despitethe fact that the power of CPU processors is nowmuch betterthan decades ago simulations continue to be carried outthrough analytic models which allow using a minimum ofexperimental measurements [22]

Most common methods to evaluate heavy metals pollu-tion in water bodies are based on quality indexes which gen-erally use correlation or fuzzy methods for their estimation[2 23 24] these works model pollutant distribution in thearea under study through GIS system modules which applyinterpolation methods Nevertheless the obtained spatialdistribution through a hydrodynamics module along withtransport equation and considering reaction mechanismsproduces much more accurate results [25] (although itrequires a greater effort to be implemented)

Water quality models (WQM) have increased in numberand have improved in recent years focusing on the study ofthe water quality as well as pollutant transport in shallowwater ecosystems The behaviour and transport of toxicsubstances such as metals andor hydrocarbons have beendeeply studied on shallow aquatic systems during this century[26 27] In this case the complexity of estuarine coastalsystems must be understood in order to clearly pose thesolution of the equations representing water hydrodynam-ics (mass conservation and momentum equations) as wellas mass transport of pollutants (advection diffusion andreaction equation) considering even the highly nonlinearinteractions typical of these regions Thomann and Mueller[10] Thomann [28] Lun et al [29] Ji et al [30] Shimazuet al [11] and Bhavsar et al [31] show different approachesof the toxic substances behaviour problem in water columnsas well as their interaction with sediments and air includingmathematical models and solution methods

The most popular numerical WQM are AQUATOXBranched Lagrangian Transport Model (BLTM) One-Di-mensional Riverine Hydrodynamic Water Quality Model(EPD-RIV1) QUAL2Kw Water Quality Analysis SimulationProgram (WASP) Water Quality for River-Reservoir Sys-tems (WQRRS) ROMS-ICS [32] MIKE EcolabABM andIberWQ[33]Neverthelessmost of themare based on solvingmass balanceadvective diffusion equation but oriented tonutrients (as DO BOD PH4 and Phosphorus) or pathogens(such as coliforms like the Escherichia coli [34]) Suchmodelsdo not contemplate metals transport [35] A review regardingcomputational models of water quality can be found in thearticle by Wang et al [36] who pointed out that most modelssuch as MIKE models EFDC and Delft 3D model havebeen applied to simulate water environmental quality inmostcases of environmental impact assessment However littleinformation is available on the differences in model resultsfrom different models and the suitability and parametersensitivity of these models

In recent decades there has been a boom in the devel-opment of hydrodynamic models with coupled water qualitymodels here we can also mention the POM model MIKE3COHERENS ROMS and MOHID model

The MOHID three-dimensional model has the ability tosimulate complex estuarine and coastal flows in numerousapplications dealing with mesomalar coastal lagoons tidalchannels and estuarine systems [37] It also solves thetransport equation for salinity and temperature but thefree version is not quite user-friendly and neither is itscommercial version

Mathematical Problems in Engineering 3

MIKE 3 model is a general three-dimensional hydro-dynamic model for flow simulation in estuaries bays andcoastal and oceanic areas [38] Although it is a fairly robustand completemodel only the commercial version is available

ROMS model (Regional Ocean Modeling System) hasbeen used to simulate the water circulation in differentregions of the worldrsquos oceans at different scales (local andbasin) [39] Similarly POMmodel (Princeton Ocean Model)is a three-dimensional hydrodynamic model based on semi-implicit finite differences [40]

COHERENSmodel is a multipurpose three-dimensionalmodel based on finite differences This model allows thecoupling with different submodels that simulate physical andbiological processes as well as the transport and transforma-tion of sediments and pollutants [41]

Most of these last models are commonly used today butmost of them do not focus their strength on the solutionof toxic substances transport such as heavy metals theyare more focused on hydrodynamic ocean domain thaninteraction of coastal-lotic-lentic water bodies

Although there are available WQM as discussed aboveit is common to find models developed by particularresearchers that include effects or processes specific to theircase of study such as the MINEQL [42] which has beenused tomodel the concentrations of metals in rivers since the1980s

Despite the existing WQM the aim of this work is todevelop a water quality module which can be coupled to ahydrodynamic numerical model applicable to the study ofhydrodynamics and water quality in coastal ecosystems Thehydrodynamic model adopted in this work is self-developedfor research purposes and has been previously used indifferent research applications including the hydrodynamic-hydrological modelling in flood zones [43] the modelling offlows through vegetation [44] the modelling of the thermaldischarges [45 46] and the modelling of fresh water plumesin river-sea interaction [47] On the other side in order toaccurately simulate turbulent viscosity the turbulence modeldiscussed in detail in the paper by Rodriguez-Cuevas et al[48] is implemented

The water quality module developed herein takes intoaccountmany parameters including the advection-diffusion-reaction mechanism The required parameters cannot befound in a specific existing WQM These parameters wereadopted from Ambrose Jr et al [49] and Kannel et al [50]Four heavy metals Cd Cr Pb and Ni were selected for thewater quality module because they are required by local insti-tutions as well as because they have strong toxicity to humans[51] According to the previous knowledge of the authors theydid not find an ad hocmodule for the transport of these met-als so they developed one that contemplates the greatest pos-sible number of parameters in the reaction mechanisms Thismodule can be applied as any WQM (ROMS-ICS WASP orMIKE) with the advantage that the reaction mechanisms canbe modified by calibrating each of the parameters throughthe equations The case of study is the ecosystem composedby El Yucateco lagoon Chicozapote river and Tonala riverdischarge which has suffered a serious deterioration due topollution originated by oil industry activities [52 53]

In this area several studies have been carried out inthe last decade related to the study of the dynamics of theriver-lagoon-sea interaction [54 55] On the other handthe Institute of Marine Sciences and Limnology (ICMyL)from Universidad Nacional Autonoma de Mexico (UNAM)has conducted studies related to the monitoring of heavymetals and toxic substances associated with oil activity foreight continuous years [52 56] Based on these previousstudies and heavy metal monitoring databases we made theconfiguration and development of the proposedmodel in thiswork

This paper begins by a detailed presentation of the coastalecosystem in Section 2 It then goes to exposition of themethodology applied herein including the measurementprotocol and the design of the hydrodynamic and waterquality modules as well as the numerical strategies fortheir solution (Section 3) Thereafter Section 4 presents theapplication of this WQM to the case of study as well asthe detailed results of the simulations for the transport ofheavy metals along with two metrics that allow assessingthe accuracy of the WQM developed herein with respect tomeasured data Finally in Section 6 the conclusions of thisstudy are presented as well as some final interesting remarks

2 Case of Study

21 Location of the Region under Study Thecoastal ecosystemunder study is located at the east of Tabasco State in thesoutheast of Mexico The lagoon is located between LAT 18∘10rsquo and 18∘ 12rsquo N and between LONG 94∘ 02rsquo and 94∘ 00rsquoW (Figure 1) El Yucateco lagoon interacts with Chicozapoteand Tonala rivers before discharging to the Gulf of MexicoNowadays the lagoon is one of the most important waterbodies of the region

22 Evolution of the Region For hundreds of years the mainactivities in the zone have been agriculture cattle farmingand fishing Nevertheless oil field Cinco Presidentes wasestablished in 1963 in the vicinity of this region with ElYucateco lagoon being the closest water body to the oil facilityLater a network of artificial channels (approx 33 Km) wasbuilt by an oil company during such decade with an areaof about 130 ha These channels drain a considerable volumeof fresh water to the lagoon product of the drainage of thefloodplains (marshes) that surround the area

In the last 20 years important changes in hydrologyand water quality have occurred changing the productivityand reducing the number of endemic species These changeshave had social economical and commercial impacts fornative population This area is currently under industrialdevelopment where two kinds of activities stand out oil(extraction and production) and livestock farming whichtogether account for almost 90 of the productive sectors ofTabasco State [57] More environmental information aboutthis ecosystem is available in [56]

23 Climate The predominant climate in the region iswarm and humid with abundant rainfall through the whole


Figure 1 Location of study zone and control points (CP) in the coastal ecosystem The centre of lagoon is located at W 94∘ 01rsquo 12rdquo N 18∘ 11rsquo6rdquo

year particularly during autumn Its annual thermal regimeoscillates between 258∘C and 278∘C the highest averagetemperature occurs in May with 294∘C while the minimumis registered in January with 231∘C SeptemberOctober isthe most rainy period with an average precipitation of 400mm while June features the minimum precipitation with anaverage value of 433 mm [58]

24 Hydrodynamics For El Yucateco lagoon themain hydro-dynamic driven forces are winds and tides The tide andChicozapote river permanently renew and refreshwater in thelagoon (see Section 35 for further details) For dry season(see Table 2) current flows head predominantly to north dur-ing mornings while mainly to northeast in afternoon hoursin both cases current flows out of the lagoon with velocitiesgreater than 20 cms For rainy (wet) season (Table 2) currentis variable with different directions along the system headingto northeast in the south part of the lagoon and to the northclose to the mouth and to the connection with Chicozapoteriver Typical flow velocities about 10 cms are observedwhich favour the formation of vortices within the lagoon

In this zone of the Gulf of Mexico tide presents a mixedtypewith diurnal influence whose oscillations are not greaterthan 30-60 cm The surge is moderate in E-W directionwith a maximum height of 2 m in normal meteorologicalconditions

3 Methodology

TheWQMdeveloped herein consists essentially of two partsa hydrodynamic module and a water quality module Thelater one is in charge of transporting heavy metals throughthe water body by using the hydrodynamic module resultswhich has been calibrated and tested previously [44 45 4859 60] Later in Sections 33 and 34 these modules areappropriately defined In what follows the protocol regardingmeasurement and data analysis is presented

31 Water Quality Measurement Protocol In measurementcampaigns water and sediment samples were collectedfollowing the guidelines of the Standard Methods for theExamination of Water ampWastewaters methodology [61]

For heavy metals in water 500 ml of sample was takenat each point using a van Dorn water sampler The sampleswere filtered (045 120583m) and the pH was adjusted to 2using HNO3 stored in amber glass bottles and refrigeratedat 40∘C for transportation [61] Filters for the analysis ofparticulate metals were conserved in polyethylene bottleswith HNO3 2M while those used to quantify the suspendedmaterial were kept in petri dishes where they were dried andweighed later The filtered particles were analysed in order toobtain concentration of metals in particulate phase while thewater obtained from this process was analysed to obtain thecorresponding dissolved metals in water


For heavy metals in sediments samples of 100 g werecollected from the surface layer of the bottom sediments(max 5 cm) with an Ekman dredger These samples werestored in sterile polyethylene bags and kept at 40∘C fortransport Particulate and dissolved concentrations of metalswere also obtained in sediments

All the samples were collected in duplicate to deter-mine the precision of tests and sample handling In orderto minimise the effect of hydrological input from riversthe sediment and water sampling were performed duringmornings from 800 to 1200 hours in low tide conditionswhen there is little penetration from the sea to Chicozapoteriver and El Yucateco lagoon

The chemical analyses of heavy metals in water andsediments were performed using atomic absorption spec-trophotometry with spectrophotometer (Shimadzu ModAA 6800)

According to previous in situ studies [56] several metalswere initially sampled Those whose parameters exceededwater andor sediment quality criteria [62 63] were selectedfor diagnosis Cd Cr Pb and Ni

Measured data and the chemical analyses of these param-eters served to specify boundary and initial conditions tothe numerical model developed herein They also aided invalidating the model for the field site

32 Numerical Modeling The numerical model used in thiswork is self-developed strongly based on the proposal ofCasulli and Cheng [59] In this case 2 modules were appliedthe hydrodynamic module and the water quality module(WQM) Figure 2 schematizes the simulation process ofthe hydrodynamic model to compute the distribution ofthe velocity components Once the velocity field (119880119881) isobtained it constitutes the input for the WQM which isfirst executed and then validated with measured data Thenthe WQM is executed iteratively for calibrating some of thecoefficients that appear in the equations until calculationsmatchmeasured data within an expected error (see Figure 3)Thus the WQM developed herein is of high computationalburden even though the time step (Δ119905) used in the WQM ismuch greater than that used in the hydrodynamic module

33 HydrodynamicModule Thehydrodynamic module usedin this work is based on the two-dimensional shallow waterequations which can be derived from the Reynolds-averagedNavier-Stokes equations [48] These equations are

120597119880120597119905 + 119880120597119880120597119909 + 119881120597119880120597119910 = minus119892120597120578120597119909 + ]119879119867(12059721198801205971199092 + 12059721198801205971199102 )

minus 120591119887119909119867 + 120591119908119909119867 (1)

120597119881120597119905 + 119880120597119881120597119909 + 119881120597119881120597119910 = minus119892120597120578120597119910 + ]119879119867(12059721198811205971199092 + 12059721198811205971199102 )

minus 120591119887119910119867 + 120591119908119910119867 (2)

Initial Data

Mesh Setup

InitialParameters

BoundaryConditions

Starts time cycle

Hydrodynamicsolver

Turbulence

Advection

SurfaceElevation

Diffusion

Store U V

End cycle END

Figure 2 Simulation process flowchart for hydrodynamic module

where 119880 and 119881 are the depth-averaged velocity componentsin 119909 and 119910 directions respectively (ms) 119892 is the accelerationdue to gravity (ms2) 120578 is the free surface elevation (m) ]119879119867is the horizontal eddy viscosity (m2s) 119867 is the water depth(m) 120591119908119909 and 120591119908119910 are the wind shear stress terms in 119909 and 119910directions respectively and 120591119887119909 and 120591119887119910 are the bottom shearstress terms in 119909 and 119910 directions respectively It is notedthat the units of the wind and bottom shear stress terms arem2s2

The equation to calculate the free surface elevation (120578)is obtained by integrating the continuity equation over thewater depth and by applying a kinematic condition at the freesurface yielding

120597120578120597119905 = minus120597119867119880120597119909 minus 120597119867119881120597119910 (3)

As mentioned above in order to achieve more real-istic hydrodynamics mechanical dispersion phenomenonis also considered through the introduction of a modelof turbulence Due to the nature of the water bodytreated herein the following mixing-length model is used[48] which contributes to the horizontal eddy viscosityas

]119879119867 = radic1198974ℎ [2(120597119880120597119909 )2 + 2(120597119881120597119910 )

2 + (120597119881120597119909 + 120597119880120597119910 )2] (4)


Initial concentration Starts time cycle

Load U V

Water QualityModule Diffusion

Advection

ReactionEnd cycle

The results meet theexperimental data END

YesNo

Figure 3 Simulation process flowchart for water quality module

where 119897ℎ = 120573119897V is the horizontal mixing length (m) 120573 is adimensionless constant and 119897V (m) is defined as

119897V = 120581 (120578 minus 119911119887) if (120578 minus 119911119887) lt 120572120582119911119887 if (120578 minus 119911119887) gt 120572 (5)

where 119911119887 is the bathymetry (m) 120572 = 120582120578120581 and 120582 and 120581are dimensionless constants (the latter so called von Karmanconstant) This hydrodynamic module has been calibratedaccording to Casulli and Cheng [59] and has been testedin the works of Leon et al [60] Ramırez-Leon et al [45]Barrios-Pina et al [44] and Rodriguez-Cuevas et al [48]

The wind shear stress terms in 119909 and 119910 directions arecalculated with

120591119908119909 = 119862119908120588119908120596119909 10038161003816100381610038161205961199091003816100381610038161003816 (6)

120591119908119910 = 119862119908120588119908120596119910 1003816100381610038161003816100381612059611991010038161003816100381610038161003816 (7)

where 120588119908 is the air density (kgm3) 120596119909 and 120596119910 are the windmagnitude components measured 10 m above the groundlevel in 119909 and 119910 directions respectively and 119862119908 is the winddrag coefficient obtained from the Garrat formulation 119862119908 =(075 + 006712059610)1000 where 12059610 is the magnitude of thewind velocity vector 10m above the ground level (ms) Thebottom shear stress terms in 119909 and 119910 directions are calculatedas follows

120591119887119909 = 119892radic1198802 + 11988121198621199112 119880 (8)

120591119887119910 = 119892radic1198802 + 11988121198621199112 119881 (9)

where 119862119911 is the Chezy coefficient

The numerical solution scheme of the hydrodynamicmodule is based on a second-order finite difference formu-lation in both time and space The solution method is anadaptation of the semi-implicit Eulerian-Lagrangian schemeproposed by Casulli and Cheng [59] This method treats theadvection and diffusion terms differently The solution ofthe nonlinear advection terms of (1) and (2) is given by aLagrangian formulation through the characteristics methodand the solution of the diffusion terms is given by an Eulerianformulation through the Adams-Bashforth scheme

A 2D mesh is used for the numerical simulations basedon a staggered cell arrangement as shown in Figures 4 and5 where dot at the center of the cell represents the locationof any scalar value (free surface elevation water properties orpollutants 120593119894119895) and circles on faces indicate the location ofthe velocity components 119880 and 119881

In Figure 5 119867119894+12119895 is the water depth at point 119894 + 12 119895where 119880 the is velocity component and 119867119894119895+12 is the waterdepth at point 119894 119895 + 12 where the velocity component 119881 isevaluated

The solution of the system of (1) (2) and (3) is giventhrough linear systems as follows

119880119899+1119894+12119895 = 119865119880119899119894+12119895 minus 119892 Δ119905Δ119909 (120578119899+1119894+1119895 minus 120578119899+1119894119895 )+ Δ119905119867119899119894+12119895

120591120596119909 minus Δ119905119867119899119894+12119895

120591119887119909 (10)

119881119899+1119894119895+12 = 119865119881119899119894119895+12 minus 119892 Δ119905Δ119910 (120578119899+1119894119895+1 minus 120578119899+1119894119895 )+ Δ119905119867119899119894119895+12

120591120596119910 minus Δ119905119867119899119894119895+12

120591119887119910(11)


Vijminus 1

2

Vij+ 1

2

Uiminus 1

2 jU

i+ 12 j

Δy

Δx

y

x

ij

Figure 4 Discretization cell Top view

Hij+ 1

2

Hij+ 1

2

Uiminus 1

2 jU

i+ 12 j

Ui+ 1

2 j

Δy

Δx

Δx

y

x

x

ij

waterzero level

water column

bottom

free surfaceelevation

ijV ij

+12

Hi+ 1

2 j

Figure 5 Vertical mesh arrangement

120578119899+1119894119895 = 120578119899119894119895 minus Δ119905Δ119909 (119867119894+12119895119880119899+1119894+12119895 minus 119867119894minus12119895119880119899+1119894minus12119895)minus Δ119905Δ119910 (119867119894119895+12119880119899+1119894119895+12 minus 119867119894119895minus12119880119899+1119894119895minus12)

(12)

where the operators 119865119880 and 119865119881 join the advective terms andthe turbulent diffusion terms as

119865119880119899119894+12119895 = 119880119899119894+12119895+ ]119879119867Δ119905(119880

119899119894+32119895 minus 2119880119899119894+12119895 + 119880119899119894minus12119895Δ1199092

+ 119880119899119894+12119895+1 minus 2119880119899119894+12119895 + 119880119899119894+12119895minus1Δ1199102 ) (13)

119865119881119899119894119895+12 = 119881119899119894119895+12+ ]119879119867Δ119905(119881

119899119894+1119895+12 minus 2119881119899119894119895+12 + 119881119899119894minus1119895+12Δ1199092

+ 119881119899119894119895+32 minus 2119881119899119894119895+12 + 119881119899119894119895minus12Δ1199102 ) (14)

where 119880119899 and 119881119899 are the explicit velocity componentscalculated at time 119899 using the characteristics method as

119880119899119894+12119895 = 119880119899119894+12minus119886119895minus119887= (1 minus 119901) [(1 minus 119902)119880119899119894+12minus119897119895minus119898 + 119902119880119899119894+12minus119897119895minus119898minus1]+ 119901 [(1 minus 119902)119880119899119894+12minus119897minus1119895minus119898 + 119902119880119899119894+12minus119897minus1119895minus119898minus1]

(15)

where 119886 and 119887 are the Courant numbers in 119909 and 119910 directionsrespectively from which 119897 and 119898 are the integer parts and 119901and 119902 are the decimal parts

34 Water Quality Module TheWQM consists of two partsthe one in charge of transport and the one in charge ofthe reaction mechanisms to which the substance is sub-ject Within the quality module the Advection-Diffusion-Reaction equation is solved as

120597119862120597119905⏟⏟⏟⏟⏟⏟⏟Rate of change

+ 119880120597119862120597119909 + 119881120597119862120597119910⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟Net rate of flow (Advection)


1

＋＞

＋＞

＋＋

ＰＭＰＯ

ℎDissolved

metalParticulated

metalWater

column

2 Sedimentactive layer

Figure 6 Depiction of reaction metal model parameters for a completely mixed lake 1 stands for water column and 2 for sediment layer

= 120597120597119909 (119864119909120597119862120597119909 ) + 120597120597119910 (119864119910120597119862120597119910 )⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟⏟Rate of change due to diffusion

+ 119876119904⏟⏟⏟⏟⏟⏟⏟Point sources

+ Γ119862⏟⏟⏟⏟⏟⏟⏟Rate of change due to reaction sources

(16)

where 119862 is the concentration of a substance (mgL) 119864119909 and119864119910 are the horizontal dispersion coefficients (m2s) [48] andΓ119888 is the substance reaction term (mgL) and 119876119904 are the pointsources Discretization of (16) is given in a similar way to thatfor the velocity of (1) and (2)

The WQM is initialized considering no reaction (Γ119862(119905 =0) = 0) and assuming stationary sediment constantkinetic coefficients and suspended solid which is uniformlydistributed in space over the river reach Once the initialconcentration distribution 119862 is calculated through (16) it isreestimated through a model of reaction of toxic substancesThomann and Salas [64] present a complete model that con-siders diffusive exchange decomposition processes volatil-isation settling and resuspension (important processes ina coastal aquatics ecosystem) which is governed by thefollowing ordinary differential equation

Γ119862 = 119870fℎ (119891119889119887119862119887120593119887 minus 119891119889119908119862119908) minus 1198701198891199081198911198891119862119908+ 119870119871ℎ ( 119888119892119867119890 minus 119891119889119908119862119908) minus

V119904ℎ 1198911199011119862119908 + V119906ℎ 1198911199012119862119887(17)

where 119908 and 119887 define water column and sediment bedconditions respectively 119862119908 is the concentration of the toxicsubstance (mgL) (estimated using the transport equation)119862119887 is the concentration of the toxicant in sediments (mgL)119870119891 is the diffusive exchange of dissolved toxicant between thesediment and the water column (md) ℎ is the river depth(m) 119891119889 is the dissolved fraction (1) 119891119901 is the particulatefraction (unitless)1198701198891 is the degradation rate of the dissolvedtoxic substance (dminus1) 119870119871 is the overall volatilisation transferrate (md) 119888119892 is the vapour phase concentration (mgL) 119867119890is Henryrsquos constant V119904 is the settling velocity (md) V119906 is theresuspended velocity (md) and 1205932 is the sediment porosity(unitless) It ought to be mentioned that the concentrations119862119908 and 119862119887 for any metal are the sum of both particulate anddissolved metalsrsquo concentration [30] For further reference onthese terms see Figure 6

In (17) the first term on the right hand is the diffusiveexchange of dissolved toxicant between sediment and water

column The second term is the decomposition processesof the dissolved form due to microbial decay photolysishydrolysis and so forth in the water column (decay ofparticulate form is assumed to be zero) The next term isthe air water exchange of the toxicant due to volatilisationor gaseous input The next term represents the settling of theparticulate toxicant from the water column to the sedimentand the last term is resuspension into the water column ofthe particulate toxicant from the sediment

A description of the processes underlying the parametersin (17) is detailed in what follows

For the decay of the dissolved substances themost impor-tantmechanisms in the degradation rate of the dissolved toxicsubstance (1198701198891) are represented by the equation

1198701198891 = 119870119901 + 119870119867 + 119870119861 (18)

where119870119901 is the photolysis rate (dminus1)119870119867 is the hydrolysis rate(dminus1) and 119870119861 is the microbial degradation rate (dminus1)

The overall exchange rate (119870119871) estimates the importanceof losses due to volatilisation According to Mackay [65]the application of the two film theory yields an overallvolatilisation transfer parameter given by

1119870119871 =1119896119897 +

1119896119892119867119890 (19)

where 119896119897 is the liquid film coefficient (md) and 119896119892 is thegas film coefficient (md) As seen from (19) 119870119871 depends onchemical properties as well as on characteristics of the waterbody such as water velocity (affecting 119896119897) and wind velocityover water surface (affecting both 119896119897 and 119896119892)

Henryrsquos (119867119890) constant present in (17) and (19) takes intoaccount the water-atmosphere interaction and it representspartitioning of the toxicant between water and atmosphericphases Its dimensionless form is given by

119867119890 = 119888119892119888119908 (20)

where 119888119908 is the water solubility (mgL) and 119888119892 is the vapourphase concentration (mgL)

The sediment diffusion rate119870119891 (md) reflects the fact thatgradients can occur between interstitial sediments and theadjacent water column [66] An effective model for 119870119891 canbe written according to Manheim [67] and OrsquoConnor [68]as

119870119891 = 191205932 (119872119882)minus23 (21)


DRY North

West

South

East

(ms)

25 20 15 10 5 0

gt=75

6-75

45-6

3-45

15-3

0-15

Figure 7 Wind rose used in the hydrodynamic module for dry season

where 119872119882 is the molecular weight (gmol) and 1205932 is thesediment porosity

To determine both settling and resuspended velocities aparticle characterisation in the regionunder study is requiredIn this work such characterisation was made with sedimentsamples taken from field campaigns where organic matterparticle sizes porosity humidity and so forth were quantifiedas follows the organic matter was obtained by the wetoxidation technique using exothermic heating and oxidationof organic carbon of the sediment sample [69] Humidity wasdetermined by drying method [70] and the percentages ofsand silt and clay were determined by the Bouyucos method[71]

In coastal ecosystems a good approximation to thesettling velocity V119904 is given by Hawley [72] who provides thefollowing empirical relation

V119904 = 02571 (119889)1138 (22)

where 119889 is the particle diameter (m)For the resuspended velocity V119906 Thomann and Di Toro

[73] have proposed the next equation

V119906 = V119889 ( V119904V119899 minus 1) (23)

where V119889 is the net rate loss of solids (md) and V119899 is thesettling net rate of the water solids (md)

In estuarine and coastal environments usually charac-terised by muddy bottoms flocculation plays a key role

[15] nevertheless the water body under study has lowcirculation because there is no important water interchangewith Chicozapote river With this being a quasi-static waterbody with very small velocities the sediment transport inthe muddy environment at the bottom of the lagoon can beactually neglected

35 Implementation of the Model to the Case Study Thehydrodynamic characteristics of water bodies such as coastallagoons are governed by a slight balance between tidalforces currents flow wind stresses and density which inducepressure and friction forces at the bottom [74 75] in additionto other factors such as the geometry and flow which ispredominantly turbulent with a horizontal length scale muchgreater than the vertical one [76]

The forcing boundary conditions of the sea-Chicozapoteriver-El Yucateco lagoon system were set by considering theastronomical tide winds and river discharges The inputinformation into the model as boundary conditions is dataobtained from measurements for each season andor gen-erated by official institutions in the study area [56] Windforcing was implemented with data measured directly overEl Yucateco lagoon during the field measurements campaignsin both dry and wet seasons (see Figures 7 and 8) Windforcing was considered spatially invariant along the modeldomain Tidal boundary condition was imposed on thenorthwest edge of the study domain at the river dischargezone bymeans of the measured variation of water height withrespect to the mean sea level (see Figures 9(a) and 9(c)) The


WET North

West

South

East

(ms)

25 20 15 10 5300

gt=49

398-49

306-398

214-306

122-214

3-122

Figure 8 Wind rose used in the hydrodynamic module for wet season

minus08

minus04

0

04

08

12

(m)

1 5 10 15 20 25 30(days)

35 40 45 50

(a)

1 5 10 15 20 25 30 35 40 45 500

50

100

150

200

250

Q (Ｇ

3s

)

(days)

(b)

minus08

minus04

0

04

08

12

(m)

1 5 10 15 20 25 30(days)

(c)

0

50

100

150

200

250

1 5 10 15 20 25 30

Q (Ｇ

3s

)

(days)

(d)

Figure 9 Forcing boundary conditions used in the study domain for dry season (a) tide condition (b) hydrological flow and for wet season(c) tide condition (d) hydrological flow The gauging stations where the hydrological flow and the tide condition were taken are shown inFigure 10(a)


tide

hydrological flow

000-025-050-075-100-125-150-175-200-225-250-275-300

(m)

(a) Bathymetry of the study zone measured relative to the mean sea level in metres (m) Arrows indicate gaugingstations of hydrological flow and tide boundary conditions

(b) Mesh configuration for the study zone

Figure 10 Bathymetry and mesh of the domain

hydrological flow of Tonala river was imposed according tothe data shown in Figures 9(b) and 9(d) The points of appli-cation of boundary conditions are shown by the arrows inFigure 10(a)

The bathymetry shown in Figure 10(a) was acquiredthrough an echo sounder and GPS Several planned transectswere carried out to obtain latitude longitude and depth inorder to fully characterise the complete bathymetry of thewater body Subsequently these data were interpolated tomatch the bathymetry with the central points of each cell ofthe numerical mesh

The area under studywas discretised through a structuredmesh A variable-spacing grid was used to make the modelmore efficient in zones of interest with Δ119883max = 102 m andΔ119883min = 20m while Δ119884max = 73m and Δ119884min = 15m Thegrid has 213 elements in the 119909-direction and 79 elements inthe119910-direction for a total of 16827 elements of which 3720 areactive elements (see Figure 10(b)) For a staggered cell freesurface elevation and metals concentrations are discretisedusing central differences while velocity vector componentsare considered at the faces of the cell Also the time step Δ119905is related to the Courant number and so to the resolutionof the mesh This guarantees stability and convergence ofthe numerical solution while minimising the computationalburden

36 Validation Criterion of the Numerical Solution In orderto assess the quality of the numerical solution with respect tofield measured data the Root Square Mean Error

RMSE = radicsum (120593119900119887119904 minus 120593119904119894119898)2119873 (24)

and the Nash-Sutcliffe efficiency coefficient (NSE) are pre-sented with the last given by Horritt [77]

NSE = 1 minus sum (120593119900119887119904 minus 120593119904119894119898)2sum (120593119900119887119904 minus 120593)2 (25)

where 120593119900119887119904 is an observedmeasured datum and 120593119904119894119898 is therespective datum obtained from numerical simulation (sameplace and time) The parameter 120593 is the measured datamean Clearly if 119877 is close to 1 it is possible to considerthat numerical simulation data is quite approximate to fieldmeasured data

A value of RMSE = 0 indicates a perfect fit Somesuggested values for decision-making related to the dataproduced by the RMSE are presented in Table 1 [78]

For NSE coefficient the range [minusinfin 1] is considered asan acceptable performance with 1 being the optimal valuewhereas values lt 00 indicate that the mean observed value


Table 1 Criteria for the qualitative evaluation of goodness of fit forRMSE

RMSE Fitgt 070 Not satisfactory060-070 Satisfactory030-060 Good000-030 Very good

Table 2 Considered time spans for wet and dry seasons

Season Initial date Duration (days)Dry June 1st 50Wet September 1st 30

is a better predictor than the simulated value which indicatesunacceptable performance [78]

4 Results and Discussion

41 Field Measurements Tabasco State is the region of Mex-ico where the climate is very rainy so seasons of the yearare not highly differentiated with abundant precipitationsthroughout the whole year Thus it is important to differ-entiate between the typical characteristic climate periods inthe study area such as summertime and wintertime becausethe dynamics can significantly change [79] for instance theyhave influence on parameters that define the transport ofsolutes such as salinity [80] or sediments [31 81 82] For thepurpose of yielding to results which can be compared withstationary measurements in situ wet and dry seasons for thisstudy are selected as detailed in Table 2 [52 56] The timespans selected for wet and dry seasons simulations guaranteethat weather remains essentially constant through them Inother words those periods are highly representative of theirrespective season [83]

In this work metals concentrations were measured infour control points (CP) Two sampling points are locatedwithin the lagoon (CP 1 and 2) and two in the river (CP3 and 4) (see Figure 1) For field measurement purposes themeasured data was performed in campaigns each 5 days forthe four referred heavymetals according to the methodologydefined in Section 31

42 Validation of WQM The validation process of theWQMconsists in finding the values involved in the consideredreaction mechanism (see Section 34 and particularly (17))which yield to the most accurate simulated results withrespect to field measured data

In order to start the validation process initial conditionsare required Such values are introduced to the computationaldomain on each control point at the beginning of thesimulation The initial parameters were imposed based onEPA recommendations Then a numerical simulation carriedout forcing with measured concentrations in CP 1 CP 2and CP 3 of Figure 1 After convergence calculated con-centrations in CP 4 were compared against measurements

Table 3 WQM parameters for the considered heavy metals thatresulted from the validation process

Variable ValueCd Cr Ni Pb

119862 (mgL) Initialised from measured data in CPs1198701198891 (dminus1) 0011198911198891 (120583g) 02119870119871 (md) 185 190 190 185ℎ (m) From hydrodynamic module11987011989112 (md) 00074 00124 00114 000491198911198892 (120583g) 011198621198792 (mgL) By Control PointV119904 (md) 151198911199011 (120583g) 081198911199012 (120583g) 09V119906 (md) 000035 000015 000015 000035119867119890 (1) 00042

Table 4 Calibrated parameters of the hydrodynamic module [4445 48 60]

Variable Valueg (ms) 981120582 (unitless) 009120581 (unitless) 041120573 (unitless) 06

in the same location The measurements considered forcomparisons were taken at the end of the simulation timeperiod Then a trial-and-error procedure was performedup till reaching an error lower than 5 by adjusting theinvolved parameters (in Table 3) in each simulation In asecond step a new numerical simulation carried out forcingwith measured concentrations in CP 1 CP 2 and CP 4After convergence calculated concentrations in CP 3 werecompared against measurements in the same location Thetrial-and-error procedure was performed up till reachingan error lower than 5 by adjusting again the referredparameters The same procedure with control points CP 1and CP 2 was effectuated to complete one calibration cycleThis cycle was repeated iteratively until the best correlationbetween calculations and measurements was obtained Theparameters that resulted from this validation process areshown in Table 3 and are the ones used for the simulationspresent in Section 44

It ought to be noted that the values of the systemcoefficients lie within the typical values reported for them inthe literature [64 66 73]

43 Hydrodynamic Simulations In order to perform thehydrodynamic simulation the hydrodynamic module hasalready been calibratedwith the parameters shown inTable 4as mentioned in Section 33

The hydrodynamic simulations were performed using atime step of Δ119905 = 20 s requiring 2160000 and 1296000


V 14011008005002000300008 0003 0001(ms)

0510152025303540(a)

(b)

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

Figure 11 (a) Dry season day 50 snapshot (b) Wet season day 30 snapshot

Cd 0013300093000930007800072000660006300061 C P 1

C P 2C P 3

C P 4

(mgL)

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

Figure 12 Simulated distribution of Cadmium at the end of the dry season (day 50th snapshot)

iterations for dry and wet seasons respectively Figure 11shows the results obtained at the end of simulation

According to results obtained from field measurementsit is possible to observe that for dry season there is anintense recirculation in the lagoon due to the influence ofsea Nevertheless for wet season river flow is higher tidepenetration almost does not exist and lagoonrsquos behaviourpresents lack of recirculation

44 Simulation of Metals Transport

441 Cadmium Transport Figures 12 and 14 show the finalsnapshots of the Cadmium transport simulation for dryand wet seasons respectively Figures 13 and 15 depictthe behaviour of Cadmium at viewers (control points seeFigure 1) Tables 5 and 6 show RSME and Nash-Sutcliffeefficiency coefficient (see (25)) to evaluate the quality of thesimulation against the discrete measured data and the fittedmeasured data at every control point for dry and wet seasonsrespectively

Table 5 Root Mean Square Error (RMSE) and Nash-Sutcliffeefficiency coefficient (see (25)) obtained as quality control ofthe simulation for Cadmium transport in dry season Values areobtained from measured data versusWQM results

Error CP 1 CP 2 CP 3 CP 4RSME 00011 00006 00005 00019NSE 03699 06818 04036 02820

Table 6 Root Mean Square Error (RMSE) and Nash-Sutcliffeefficiency coefficient (see (25)) obtained as quality control ofthe simulation for Cadmium transport in wet season Values areobtained from measured data versusWQM results

Error CP 1 CP 2 CP 3 CP 4RSME 00011 00011 00017 00032N-S 03699 08224 08230 02718

442 Chromium Transport Figures 16 and 18 show the finalsnapshots of the Chromium transport simulation for dry


0004000600080010

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)

CP 1

0007000700080008

CP 2

0006000700080009

CP 3

5 10 15 20 25 30 35 40 45 50

0002000400060008

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

Figure 13 Behaviour of Cadmium concentration119862 at control points during dry seasonThe continuous lines are predicted by theWQM the(orange) asterisks are field measured data

Cd 00440041004000390034002500240018001000070000

(Km)

0510152025303540

108 215 323 431 538 646 754 862 969 1077 1185 1292 1400

(mgL)

C P 4

C P 3C P 2

C P 1

Figure 14 Simulated distribution of Cadmium at the end of wet season (day 30 snapshot)

and wet seasons respectively Figures 17 and 19 depict thebehaviour of Chromium at the control points Tables 7 and8 show RSME and Nash-Sutcliffe efficiency coefficient (see(25)) to evaluate the quality of the simulation against thediscrete measured data and the fitted measured data at everycontrol point for dry and wet seasons respectively

443 Lead Transport Figures 20 and 22 show the finalsnapshots of the Lead transport simulation for dry and wetseasons respectively Figures 21 and 23 depict the behaviourof the Lead at the control points Tables 9 and 10 show RSME

Table 7 Root Mean Square Error (RMSE) and Nash-Sutcliffeefficiency coefficient (see (25)) obtained as quality control of thesimulation for Chromium transport in dry season Values areobtained from measured data versusWQM results


and Nash-Sutcliffe efficiency coefficient (see (25)) to evaluatethe quality of the simulation against the discrete measured


CP 1

CP 2

CP 3

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

0032003400360038

00340036003800400042

0010

0015

001500200025

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)

Figure 15 Behaviour of Cadmium concentration119862 at control points during wet seasonThe continuous lines are predicted by theWQM the(orange) asterisks are field measured data

Cr 00000 00210 00367 00551 00639 00718 0731

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4

Figure 16 Simulated distribution of Chromium at the end of dry season (day 50 snapshot)

Table 8 Root Mean Square Error (RMSE) and Nash-Sutcliffeefficiency coefficient (see (25)) obtained as quality control of thesimulation for Chromium transport in wet season Values areobtained from measured data versusWQM results


data and the fitted measured data at every control point fordry and wet seasons respectively

Table 9 Root Mean Square Error (RMSE) and Nash-Sutcliffeefficiency coefficient (see (25)) obtained as quality control of thesimulation for Lead transport in dry season Values are obtainedfrom measured data versusWQM results


444 Nickel Transport Figures 24 and 26 show the finalsnapshots of Nickel transport simulation for dry and wet


CP 1

CP 2

CP 3

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

0060

0065

007000720074

000000200040

0040006000800100

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)

Figure 17 Behaviour of Chromium concentration 119862 at control points during dry season The continuous lines are predicted by the WQMthe (orange) asterisks are field measured data

Cr 0000 0019 0048 0205 0249 0273 0279 0296

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4

Figure 18 Simulated distribution of Chromium at the end of wet season (day 30 snapshot)

Table 10 Root Mean Square Error (RMSE) and Nash-Sutcliffeefficiency coefficient (see (25)) obtained as quality control of thesimulation for Lead transport in wet season Values are obtainedfrommeasured data versusWQM results


seasons respectively Figures 25 and 27 depict the behaviourof Nickel at the control points Tables 11 and 12 show RSME

and Nash-Sutcliffe efficiency coefficient (see (25)) to evaluatethe quality of the simulation against the discrete measureddata and the fitted measured data at every control point fordry and wet seasons respectively

5 Discussion

The numerical simulations presented above were performedfor wet and dry seasonsThe hydrodynamic results show thatfor dry season tidal reversing currents occur affecting the


0200022002400260

CP 1

024002600280

CP 2

0010

0015

0020

CP 3

5 10 15 20 25 30

001500200025

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)

Figure 19 Behaviour of Chromium concentration 119862 at control points during wet season The continuous lines are predicted by the WQMthe (orange) asterisks are field measured data

Pb000 024018017016015012010009008006

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4

Figure 20 Simulated distribution of Lead at the end of dry season (day 50 snapshot)

Table 11 Root Mean Square Error (RMSE) and Nash-Sutcliffeefficiency coefficient (see (25)) obtained as quality control of thesimulation for Nickel transport in dry season Values are obtainedfrommeasured data versusWQM results


whole ecosystem favouring the formation of vortices withinEl Yucateco lagoon However for wet season tidal effects

Table 12 Root Mean Square Error (RMSE) and Nash-Sutcliffeefficiency coefficient (see (25)) obtained as quality control of thesimulation for Nickel transport in wet season Values are obtainedfrom measured data versusWQM results


decrease and river flows dominate causing the weakening ofthe recirculating vortex


01400150

0160

CP 1

016501700175

CP 2

0000

0100

0200

CP 3

5 10 15 20 25 30 35 40 45 50

0100015002000250

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)

Figure 21 Behaviour of Lead concentration 119862 at control points during dry season The continuous lines are predicted by the WQM the(orange) asterisks are field measured data

Pb 007 011 012 013 015 019 023 025 026

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4

Figure 22 Simulated distribution of Lead at the end of wet season (day 30 snapshot)

The metals transport simulations show that for bothseason scenarios dry and wet the concentration of metalsmaintains a stable behaviour which is perturbed by oscilla-tions due to hydrodynamics The oscillations are greater fordry season where the hydrodynamics are driven by tidesFor the wet scenario metals concentrations show very slightdisturbances maintaining an almost constant behaviourduring the simulation period In general the concentrationsare higher at control points located in El Yucateco lagoon andTonala river discharge (CPs 1 2 and 4) than the one located

near the confluence of Tonala and Chicozapote rivers (CP3) On the other hand for the wet season the concentrationsof the control points located in the lagoon maintain highervalues (CPs 1 and 2) due to the small currents that the lagoonpossess

On the other hand as it can be observed from the struc-ture of the model presented herein it is of great importanceto possess enough information to feed the numerical modelwith the necessary data in order to validate it and to obtain theresults that better reproduce the pollutant transport In other


022002300240

CP 1

023002400250

CP 2

0100

0120

CP 3

5 10 15 20 25 300200030004000500

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)

Figure 23 Behaviour of Lead concentration 119862 at control points during wet season The continuous lines are predicted by the WQM the(orange) asterisks are field measured data

Ni 00101 00198 00369 00534 00596 00711 00723

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)

C P 4

C P 3

Figure 24 Simulated distribution of Nickel at the end of dry season (day 50 snapshot)

words the use of numerical modelling does not exempt thein-depth acquaintance of the dynamics of the system understudy This implies several numerical simulations to achievethe best agreement with field measured data

This in-depth acquaintance means to know besidesthe initial concentrations of the toxicants to be simulatedintrinsic facts of the water body as its detailed bathymetrycontours and boundary conditions as wind forcing tidesflow discharges to the water body and so forth in order toaccurately determine the transport of toxic substances In thissense the required field work to accomplish the study with

this model is enormous but it provides excellent and moreaccurate results than most of models

The procedure adopted to validate the water qualitymodule coupled to the hydrodynamic module is based ona trial-and-error procedure with the aim of adjusting thecoefficients of the reaction equation terms for each metalConcentration field measurements were used to validate themodel by adjusting its parameters until acceptable simula-tion was achieved Although the validation procedure wasdesigned specifically for the present case of study it canbe straightforwardly extended to other cases Although this


0050

0060

0070

CP 1

007200740076

CP 2

0000002000400060

CP 3

5 10 15 20 25 30 35 40 45 50

004000600080

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)

Figure 25 Behaviour of Nickel concentration 119862 at control points during dry season The continuous lines are predicted by the WQM the(red) asterisks are field measured data

19131835171312330327010900820030Ni 0013

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4

Figure 26 Simulated distribution of Nickel at the end of wet season (day 30 snapshot)

constitutes a robust method to validate the model that yieldsaccurate results it requires high computational burden andexecution times

6 Conclusions

In this paper a hydrodynamics-based WQM for the specificevaluation of heavy metals is developed and tested Themodel was set for a specific ecosystem located at the eastof Tabasco State Mexico Chicozapote and Tonala rivers and

El Yucateco lagoon are the parts of this ecosystem wheremeasurements of metals concentrations were obtained fromfield measurements

A heavy metal water quality module based on a lat-erally averaged two-dimensional hydrodynamics and sedi-ment transport model was developed and applied to thetidal Chicozapote and Tonala rivers estuary The modelwas validated with measured data including time-seriesdata and the spatial distribution of the suspended-sedimentconcentration The calculated distribution of total Cadmium


1400

1600

CP 1

1600

1800

CP 2

0000020004000600

CP 3

5 10 15 20 25 30

0500

1000

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)

Figure 27 Behaviour of Nickel concentration 119862 at control points during wet season The continuous lines are predicted by the WQM the(orange) asterisks are field measured data

Chromium Lead and Nickel concentrations along the riverand lagoon generally agrees with the field-measured dataThus this module shows its potential in the estimationof toxic substances for water quality assessment in aquaticcoastal systems Albeit the application of the model for otherecosystems is limited it ought to be examined carefully beforebeing applied

In this way this model constitutes an excellent optionwhen a distribution of the solutes with a high accuracy isrequired Nevertheless the most challenging fact on carryingout metal transport numerical simulations is the lack of datafor validation as well as the lack of information about thevalues of the reaction equation terms coefficients

Finally it ought to be remarked that the necessity ofdeveloping this model arose from the fact that it was moreconvenient to solve and program the differential equationsand be able to access and adjust all the model parametrisationto simulate in a better way the hydrodynamics and metalstransport Thus beside the important assessment of metalstransport in the considered estuary the main contribution ofthis work is to provide a highly accurate water quality modelable to deal with the dynamics of these toxicants in the waterbody

Data Availability

The data measured to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

The authors declare that they have no conflicts of interest

References

[1] P Kavcar A Sofuoglu and S C Sofuoglu ldquoA health risk assess-ment for exposure to trace metals via drinking water ingestionpathwayrdquo International Journal of Hygiene and EnvironmentalHealth vol 212 no 2 pp 216ndash227 2009

[2] M K Mahato P K Singh A K Tiwari and A K SinghldquoRisk assessment due to intake of metals in groundwater of eastbokaro coalfield Jharkhand Indiardquo Exposure and Health vol 8no 2 pp 265ndash275 2016

[3] S Cao X Duan X Zhao et al ldquoHealth risks of childrenrsquoscumulative and aggregative exposure to metals and metalloidsin a typical urban environment inChinardquoChemosphere vol 147pp 404ndash411 2016


[4] G M Gadd and A J Griffiths ldquoMicroorganisms and heavymetal toxicityrdquoMicrobial Ecology vol 4 no 4 pp 303ndash317 1977

[5] G M Gadd ldquoHeavy metal accumulation by bacteria and othermicroorganismsrdquo Experientia vol 46 no 8 pp 834ndash840 1990

[6] L Zhang Y-W Qin Y-Q Ma Y-M Zhao and Y Shi ldquoSpatialdistribution and pollution assessment of heavy metals in thetidal reach and its adjacent sea estuary of daliaohe area ChinardquoHuanjing Kexue vol 35 no 9 pp 3336ndash3345 2014

[7] A R Jafarabadi A R Bakhtiyari A S Toosi and C JadotldquoSpatial distribution ecological and health risk assessment ofheavymetals in marine surface sediments and coastal seawatersof fringing coral reefs of the Persian Gulf Iranrdquo Chemospherevol 185 pp 1090ndash1111 2017

[8] M Klavins A Briede V Rodinov I Kokorite E Parele and IKlavina ldquoHeavy metals in rivers of Latviardquo Science of the TotalEnvironment vol 262 no 1-2 pp 175ndash184 2000

[9] A Tessier P G C Campbell and M Bisson ldquoEvaluation ofthe APDC-MIBK extraction method for the atomic absorptionanalysis of trace metals in river waterrdquo International Journal ofEnvironmental Analytical Chemistry vol 7 no 1 pp 41ndash54 1979

[10] R V Thomann and J A Mueller Principles of Surface WaterQuality Modeling and Control Harper amp Row New York NYUSA 1st edition 1987

[11] H Shimazu E Ohnishi N Ozaki T Fukushima and ONakasugi ldquoA model for predicting sediment-water partition oftoxic chemicals in aquatic environmentsrdquo Water Science andTechnology vol 46 no 11-12 pp 437ndash442 2002

[12] A J Manning W J Langston and P J C Jonas ldquoA reviewof sediment dynamics in the Severn Estuary Influence offlocculationrdquoMarine Pollution Bulletin vol 61 no 1 pp 37ndash512010

[13] L H Erikson S A Wright E Elias D M Hanes D HSchoellhamer and J Largier ldquoThe use of modeling and sus-pended sediment concentration measurements for quantifyingnet suspended sediment transport through a large tidallydominated inletrdquoMarine Geology vol 345 pp 96ndash112 2013

[14] B S Caruso ldquoSimulation of metals total maximum daily loadsand remediation in a mining-impacted streamrdquo Journal ofEnvironmental Engineering vol 131 no 5 pp 777ndash789 2005

[15] J C Winterwerp ldquoOn the flocculation and settling velocity ofestuarine mudrdquo Continental Shelf Research vol 22 no 9 pp1339ndash1360 2002

[16] E R Sholkovitz ldquoThe flocculation of dissolved Fe Mn Al CuNi Co and Cd during estuarine mixingrdquo Earth and PlanetaryScience Letters vol 41 no 1 pp 77ndash86 1978

[17] M A Helali W Oueslati N Zaaboub A Added and L AleyaldquoChemical speciation of Fe Mn Pb Zn Cd Cu Co Ni and Crin the suspended particulatematter off the Mejerda River Delta(Gulf of Tunis Tunisia)rdquo Journal of African Earth Sciences vol118 pp 35ndash44 2016

[18] N Tribovillard T J Algeo T Lyons and A Riboulleau ldquoTracemetals as paleoredox and paleoproductivity proxies an updaterdquoChemical Geology vol 232 no 1-2 pp 12ndash32 2006

[19] A R Karbassi and S Nadjafpour ldquoFlocculation of dissolved PbCu Zn and Mn during estuarine mixing of river water with theCaspian Seardquo Environmental Pollution vol 93 no 3 pp 257ndash260 1996

[20] I D James ldquoModelling pollution dispersion the ecosystemand water quality in coastal waters a reviewrdquo EnvironmentalModeling and Software vol 17 no 4 pp 363ndash385 2002

[21] J Sun Z Wang R Li D Ma and B Liu ldquoSimulation of metalcontents through correlated optimal monitoring metals of daguriver sedimentsrdquo Procedia Environmental Sciences vol 8 pp161ndash165 2011

[22] S Pintilie L Branza C Betianu L V Pavel F Ungureanuand M Gavrilescu ldquoModelling and simulation of heavy metalstransport in water and sedimentsrdquo Environmental Engineeringand Management Journal (EEMJ) vol 6 no 2 2007

[23] T T H Nguyen W Zhang Z Li et al ldquoAssessment of heavymetal pollution in Red River surface sediments VietnamrdquoMarine Pollution Bulletin vol 113 no 1-2 pp 513ndash519 2016

[24] Y Zhang C Chu T Li S Xu L Liu andM Ju ldquoAwater qualitymanagement strategy for regionally protected water throughhealth risk assessment and spatial distribution of heavy metalpollution in 3marine reservesrdquo Science of the Total Environmentvol 599 pp 721ndash731 2017

[25] N R W Blessing and S Benedict ldquoComputing principles informulating water quality informatics and indexing methodsAn ample reviewrdquo Journal of Computational and TheoreticalNanoscience vol 14 no 4 pp 1671ndash1681 2017

[26] KWChau ldquoSelection and calibration of numericalmodeling inflow and water qualityrdquo Environmental Modeling amp Assessmentvol 9 no 3 pp 169ndash178 2004

[27] C D W Canham J Cole and W K Lauenroth Models inEcosystem Science Princeton University Press 2003

[28] R V Thomann ldquoThe future ldquogolden agerdquo of predictive modelsfor surface water quality and ecosystem managementrdquo Journalof Environmental Engineering vol 124 no 2 pp 94ndash103 1998

[29] R Lun K Lee L De Marco C Nalewajko and D MackayldquoA model of the fate of polycyclic aromatic hydrocarbons inthe Saguenay Fjord Canadardquo Environmental Toxicology andChemistry vol 17 no 2 pp 333ndash341 1998

[30] Z-G Ji J H Hamrick and J Pagenkopf ldquoSediment andmetals modeling in shallow riverrdquo Journal of EnvironmentalEngineering vol 128 no 2 pp 105ndash119 2002

[31] S P Bhavsar M L Diamond L J Evans N Gandhi J Nilsenand P Antunes ldquoDevelopment of a coupled metal speciation-fate model for surface aquatic systemsrdquo Environmental Toxicol-ogy and Chemistry vol 23 no 6 pp 1376ndash1385 2004

[32] C Kim H Lim and C Cerco ldquoThree-dimensional waterquality modelling for tidal lake and coastal waters with ROMS-ICMrdquo Journal of Coastal Research vol SI no 64 pp 1068ndash10722011

[33] LCeaMBermudez J Puertas et al ldquoIberWQNewsimulationtool for 2D water quality modelling in rivers and shallowestuariesrdquo Journal of Hydroinformatics vol 18 no 5 pp 816ndash830 2016

[34] L Cea M Bermudez and J Puertas ldquoUncertainty and sensi-tivity analysis of a depth-averaged water quality model for eval-uation of Escherichia Coli concentration in shallow estuariesrdquoEnvironmental Modelling amp Software vol 26 no 12 pp 1526ndash1539 2011

[35] D Sharma andA Kansal ldquoAssessment of river quality models areviewrdquo Reviews in Environmental Science and BioTechnologyvol 12 no 3 pp 285ndash311 2013

[36] Q Wang S Li P Jia C Qi and F Ding ldquoA review of surfacewater quality modelsrdquo The Scientific World Journal vol 2013Article ID 231768 7 pages 2013

[37] F Braunschweig P C Leitao L Fernandes P Pina and R JJ Neves ldquoThe object-oriented design of the integrated watermodelling systemMOHIDrdquoDevelopments inWater Science vol55 no 2 pp 1079ndash1090 2004


[38] Q Y Zhang ldquoComparison of two three-dimensional hydro-dynamic modeling systems for coastal tidal motionrdquo OceanEngineering vol 33 no 2 pp 137ndash151 2006

[39] M Grifoll A Fontan L Ferrer J Mader M Gonzalez andM Espino ldquo3D hydrodynamic characterisation of a meso-tidal harbour The case of Bilbao (northern Spain)rdquo CoastalEngineering Journal vol 56 no 9 pp 907ndash918 2009

[40] WHuang ldquoHydrodynamic modeling of flushing time in a smallestuary ofNorth Bay FloridaUSArdquoEstuarine Coastal and ShelfScience vol 74 no 4 pp 722ndash731 2007

[41] D Marinov A Norro and J-M Zaldıvar ldquoApplication ofCOHERENS model for hydrodynamic investigation of Saccadi Goro coastal lagoon (Italian Adriatic Sea shore)rdquo EcologicalModelling vol 193 no 1-2 pp 52ndash68 2006

[42] B M Chapman ldquoNumerical simulation of the transport andspeciation of nonconservative chemical reactants in riversrdquoWater Resources Research vol 18 no 1 pp 155ndash167 1982

[43] I Herrera-Dıaz C Rodrıguez-Cuevas C Couder-Castanedaand J Gasca-Tirado ldquoNumerical hydrodynamic-hydrologicalmodeling in flood zones containing infrastructurerdquo Tecnologiay Ciencias del Agua vol 6 pp 139ndash152 2015

[44] H Barrios-Pina H Ramırez-Leon C Rodrıguez-Cuevas andC Couder-Castaneda ldquoMultilayer numericalmodeling of flowsthrough vegetation using a mixing-length turbulence modelrdquoWater (Switzerland) vol 6 no 7 pp 2084ndash2103 2014

[45] H Ramırez-Leon C Couder-Castaneda I E Herrera-Dıazand H A Barrios-Pina ldquoNumerical modeling of the ther-mal discharge of the Laguna Verde power stationrdquo RevistaInternacional de Metodos Numericos para Calculo y Diseno enIngenierıa vol 29 no 2 pp 114ndash121 2013

[46] H Ramırez-Leon H Barrios-Pina F Torres-Bejarano ACuevas-Otero and C Rodrıguez-Cuevas ldquoNumerical mod-elling of the laguna verde nuclear power station thermalplume discharge to the seardquo Communications in Computer andInformation Science vol 595 pp 495ndash507 2016

[47] H Leon H A Pina C Cuevas and C Castaeda ldquoBaroclinicmathematicalmodeling of fresh water plumes in the interactionriver-seardquo International Journal of Numerical Analysis amp Mod-eling vol 2 pp 1ndash14 2005

[48] C Rodriguez-Cuevas C Couder-Castaneda E Flores-MendezI E Herrera-Dıaz and R Cisneros-Almazan ldquoModelling shal-low water wakes using a hybrid turbulence modelrdquo Journal ofAppliedMathematics vol 2014 Article ID 714031 10 pages 2014

[49] R B Ambrose Jr T A Wool and T O Barnwell JrldquoDevelopment of water quality modeling in the United StatesrdquoEnvironmental Engineering Research vol 14 no 4 pp 200ndash2102009

[50] P R Kannel S R Kanel S Lee Y-S Lee and T Y Gan ldquoAreview of public domain water quality models for simulatingdissolved oxygen in rivers and streamsrdquo Environmental Mod-eling amp Assessment vol 16 no 2 pp 183ndash204 2011

[51] P G Campbell and J Gailer ldquoEffects of non-essential metalreleases on the environment and human healthrdquo in MetalSustainability Global Challenges Consequences and Prospectsvol 221 John Wiley amp Sons 2016

[52] F Villanueva and A V Botello ldquoVigilancia y presencia demetales toxicos en la laguna el yucateco tabasco mexicordquoGolfode Mexico Contaminacion e Impacto Ambiental Diagnostico yTendencias pp 407ndash430 2005

[53] F E C Casanova ldquoEfecto de la contaminacin por metalespesados en los ecosistemas costeros del sureste de mxicordquoKuxulkab vol 19 no 37 pp 65ndash68 2013

[54] C Couder-Castaeda L H Ramırez and D H E IsraelldquoNumeric optimization of the hydrodynamicmodel yaxum3drdquoin Numerical modeling of coupled phenomena in science andengineering M S Arriaga J Bundschuh and F Dominguez-Mota Eds chapter 34 Taylor amp Francis Group 1st edition2009

[55] H A Barrios-Pina H Ramırez-Leon and F Torres-BejaranoDinmica de la interaccin ro-laguna-mar el caso de la lagunael yucateco tabasco mxico chapter 19 Instituto PolitcnicoNacional 2012

[56] A V Botello ldquoMonitoreo ambiental integral de los impactos dela actividad petrolera en la laguna El Yucateco Tabasco MxicordquoSegundo Informe tcnico 12 ICMyL UNAM Mexico 2004

[57] A Yanez-Arancibia and JW Day ldquoThe gulf of mexico towardsan integration of coastal managementwith largemarine ecosys-temmanagementrdquoOceanampCoastalManagement vol 47 no 11pp 537ndash563 2004

[58] Servicio Meteorolgico Nacional (SMN) Clicom Daily ClimateData Government Mexican Agency National MetereologicalService (SMN) 2017

[59] V Casulli and R T Cheng ldquoSemi-implicit finite differencemethods for three dimentional shallow water flowrdquo Interna-tional Journal for Numerical Methods in Fluids vol 15 pp 629ndash648 1992

[60] H R Leon C R Cuevas and E H Daz ldquoMultilayer hydro-dynamic models and their application to sediment transport inestuariesrdquo in Current Trends in High Performance Computingand Its Applications pp 59ndash70 Springer 2005

[61] A Eaton M Franson A P H Association A W WAssociation and W E Federation Standard Methods for theExamination of Water and Wastewater Standard Methodsfor the Examination of Water and Wastewater AmericanPublic Health Association 2005 httpsbooksgooglecommxbooksid=buTn1rmfSI4C

[62] C D H Congreso de la Unin ldquoDOF (Diario Oficial de laFederacin)rdquo Federal Rights Law of Mexico December 2005

[63] Canadian Council of Ministers of the Environment ldquoCanadiansediment quality guidelines for the protection of aquatic liferdquoinCanadian Environmental Quality Guidelines vol 1299 Cana-dian Council of Ministers of the Environment 2002

[64] R Thomann and H Salas ldquoModelos del destino de sustanciastxicasrdquo inManual de evaluacin y manejo de sustancias txicas enaguas superficiales CEPIS-OMS 1988

[65] D Mackay Environmental and Laboratory Rates of Volatiliza-tion of Toxic Chemicals from Water vol 1 Hazard Assessmentof Chemicals Current Developments 1981

[66] D M Di Toro D J OrsquoConnor R V Thomann and J P StJohn ldquoAnalysis of fate of chemicals in receiving waters phase1rdquo in Analysis of fate of chemicals in receiving waters p 260HydroQual Inc Mahwah NJ USA 1st edition 1981

[67] F T Manheim ldquoThe diffusion of ions in unconsolidatedsedimentsrdquo Earth and Planetary Science Letters vol 9 no 4 pp307ndash309 1970

[68] D OrsquoConnor ldquoldquoModeling frameworks toxic substances notesrdquoin Manhattan College Summer Institute in Water PollutionControl Manhattan College Bronx NY USA 1st edition 1985

[69] H E Gaudette W R Flight L Toner and D W Folger ldquoAninexpensive titration method for the determination of organiccarbon in recent sedimentsrdquo Journal of Sedimentary Researchvol 44 no 1 pp 249ndash253 1974


[70] ASTM ldquoStandard test methods for moisture ash and organicmatter or peat and other organic soilsrdquo inAnnualBook of ASTMStandard vol 2974 pp 31ndash33 American Society for Testing andMaterials West Conshohocken PA USA 2000

[71] ASTM ldquoStandard test method for particle-size analysis of soilsrdquoin Annual Book of ASTM Standard vol 42 pp 1ndash8 AmericanSociety for Testing and Materials West Conshohocken PAUSA 1998

[72] N Hawley ldquoSettling velocity distribution of natural aggregatesrdquoJournal of Geophysical Research Oceans vol 87 no 12 pp 9489ndash9498 1982

[73] R V Thomann and D M Di Toro ldquoPhysico-chemical modelof toxic substances in the great lakesrdquo Journal of Great LakesResearch vol 9 no 4 pp 474ndash496 1983

[74] J Imberger and G dl Silvio ldquoMixing processes in a shallowlagoonrdquo in Proceedings of the 23rd Coastal Engineering ResearchCouncil of the COPRI (Coasts Oceans Ports Rivers Institute)of the American Society of Civil Engineers Coastal Engineering1992 pp 1867-1868 Venice Italy 1993

[75] L Cea and M E Vazquez-Cendon ldquoUnstructured finite vol-ume discretisation of bed friction and convective flux in solutetransportmodels linked to the shallowwater equationsrdquo Journalof Computational Physics vol 231 no 8 pp 3317ndash3339 2012

[76] I K Tsanis J Wu H Shen and C Valeo ldquoEnvironmentalhydraulics hydrodynamic and pollutant transport models oflakes and coastal watersrdquo inDevelopments inWater Science vol56 Elsevier 2007

[77] M S Horritt ldquoParameterisation validation and uncertaintyanalysis of CFD models of fluvial and flood hydraulics in thenatural environmentrdquoComputational Fluid Dynamics Applica-tions in Environmental Hydraulics pp 193ndash213 2005

[78] D N Moriasi J G Arnold M W Van Liew R L Bingner RD Harmel and T L Veith ldquoModel evaluation guidelines forsystematic quantification of accuracy inwatershed simulationsrdquoTransactions of the ASABE vol 50 no 3 pp 885ndash900 2007

[79] M BrocchiniM Postacchini C Lorenzoni et al ldquoComparisonbetween the wintertime and summertime dynamics of theMisaRiver estuaryrdquoMarine Geology vol 385 pp 27ndash40 2017

[80] L Dong J Su L Wong Z Cao and J-C Chen ldquoSeasonalvariation and dynamics of the pearl river plumerdquo ContinentalShelf Research vol 24 no 16 pp 1761ndash1777 2004

[81] I Herrera-Dıaz F Torres-Bejarano J Moreno-Martınez CRodriguez-Cuevas and C Couder-Castaneda ldquoLight particletracking model for simulating bed sediment transport load inriver areasrdquo Mathematical Problems in Engineering vol 2017Article ID 1679257 15 pages 2017

[82] D S Van Maren and P Hoekstra ldquoSeasonal variation of hydro-dynamics and sediment dynamics in a shallow subtropicalestuaryTheBa Lat River VietnamrdquoEstuarine Coastal and ShelfScience vol 60 no 3 pp 529ndash540 2004

[83] A Yanez-Arancibia and J W Day ldquoEnvironmental sub-regionsin the Gulf of Mexico coastal zone The ecosystem approach asan integratedmanagement toolrdquoOceanampCoastalManagementvol 47 no 11 pp 727ndash757 2005

Hindawiwwwhindawicom Volume 2018

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Numerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisNumerical AnalysisAdvances inAdvances in Discrete Dynamics in

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Dierential EquationsInternational Journal of

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Submit your manuscripts atwwwhindawicom


As rivers serve as amedium for transport of dissolved andparticulate matter from continents to the ocean nowadaysinterest in the pollution of rivers by metals has increasedalong with the exponential increment of industrialisationurbanisation and agriculturisation of coastal areas This hassubstantially increased the concern and level of awareness inthis problem [7] For this reason heavy metal concentrationsin waters have been analysed worldwide particularly byproposing new numerical approaches [8]

In coastal waters heavy metals are distributed throughthe water column (particulate and dissolved) and the bottomsediments This occurs during the mixing of fresh andmarine water which causes flocculation and sedimentationof organic matter nutrients and trace elements from riversActually dissolvedmetals come into the particulate phase dueto processes as flocculation water pH sediment mineralogyand others during estuarine mixing [9] Thus heavy metalsget bound to these elements and precipitate to the bottom

In this work it is assumed that the partition coefficientdoes not depend on the concentration of the sorbing solidsaccording to Thomann and Mueller [10] in which thehypothesis is that the partition coefficient of metal in wateris different from the partition coefficient of that metal inthe bottom sediment and it is assumed that the decay inthe sediment is approximately zero This analysis applies torivers where solids are not suffering a net resuspension inthe water column thus this model was used to evaluate theconcentrations of Cd Cr Pb and Ni in the water column Onthe other hand according to Shimazu et al [11] the sediment-water partition of the chemical mainly depends on sorptionto sediment organic matter sediment inorganic matter andreaction group

Flocculation plays a key role in the dynamics of estuarineand coastal environments controlling the transport of fine-grained cohesive sediments and particulate contaminantsthroughout these systems [12ndash14] (usually characterised bymuddy bottoms [15]) Nevertheless it should be pointed outthat during natural estuarine mixing flocculation processmay not occur actually salinity plays an important rolein the process depending on the reaction mechanism of aparticular metal For instance flocculation starts at 10 ofsalinity during estuarine mixing for Cd [16 17] Moreoverother metals are known for their nutrient-like behaviour[18]Thus flocculation process constitutes an arduous task tomodel [19] which is not the aim of this work

For the above reasons strategies and tools to mitigate thepollution of heavy metals are required [20] A huge numberof mathematical models that intend to predict the transportof heavy metals in flows exist for example the statisticalmodels based on exponential functions (analytic models)which allow the achievement of a simulation in a relativelyuncomplicated way This is the case of the use of sigmoidfunctions to determine metal concentrations in rivers thatcan yield to average concentrations in a section [21] Despitethe fact that the power of CPU processors is nowmuch betterthan decades ago simulations continue to be carried outthrough analytic models which allow using a minimum ofexperimental measurements [22]

Most common methods to evaluate heavy metals pollu-tion in water bodies are based on quality indexes which gen-erally use correlation or fuzzy methods for their estimation[2 23 24] these works model pollutant distribution in thearea under study through GIS system modules which applyinterpolation methods Nevertheless the obtained spatialdistribution through a hydrodynamics module along withtransport equation and considering reaction mechanismsproduces much more accurate results [25] (although itrequires a greater effort to be implemented)

Water quality models (WQM) have increased in numberand have improved in recent years focusing on the study ofthe water quality as well as pollutant transport in shallowwater ecosystems The behaviour and transport of toxicsubstances such as metals andor hydrocarbons have beendeeply studied on shallow aquatic systems during this century[26 27] In this case the complexity of estuarine coastalsystems must be understood in order to clearly pose thesolution of the equations representing water hydrodynam-ics (mass conservation and momentum equations) as wellas mass transport of pollutants (advection diffusion andreaction equation) considering even the highly nonlinearinteractions typical of these regions Thomann and Mueller[10] Thomann [28] Lun et al [29] Ji et al [30] Shimazuet al [11] and Bhavsar et al [31] show different approachesof the toxic substances behaviour problem in water columnsas well as their interaction with sediments and air includingmathematical models and solution methods

The most popular numerical WQM are AQUATOXBranched Lagrangian Transport Model (BLTM) One-Di-mensional Riverine Hydrodynamic Water Quality Model(EPD-RIV1) QUAL2Kw Water Quality Analysis SimulationProgram (WASP) Water Quality for River-Reservoir Sys-tems (WQRRS) ROMS-ICS [32] MIKE EcolabABM andIberWQ[33]Neverthelessmost of themare based on solvingmass balanceadvective diffusion equation but oriented tonutrients (as DO BOD PH4 and Phosphorus) or pathogens(such as coliforms like the Escherichia coli [34]) Suchmodelsdo not contemplate metals transport [35] A review regardingcomputational models of water quality can be found in thearticle by Wang et al [36] who pointed out that most modelssuch as MIKE models EFDC and Delft 3D model havebeen applied to simulate water environmental quality inmostcases of environmental impact assessment However littleinformation is available on the differences in model resultsfrom different models and the suitability and parametersensitivity of these models

In recent decades there has been a boom in the devel-opment of hydrodynamic models with coupled water qualitymodels here we can also mention the POM model MIKE3COHERENS ROMS and MOHID model

The MOHID three-dimensional model has the ability tosimulate complex estuarine and coastal flows in numerousapplications dealing with mesomalar coastal lagoons tidalchannels and estuarine systems [37] It also solves thetransport equation for salinity and temperature but thefree version is not quite user-friendly and neither is itscommercial version











2 Case of Study










3 Methodology












120597119880120597119905 + 119880120597119880120597119909 + 119881120597119880120597119910 = minus119892120597120578120597119909 + ]119879119867(12059721198801205971199092 + 12059721198801205971199102 )

minus 120591119887119909119867 + 120591119908119909119867 (1)

120597119881120597119905 + 119880120597119881120597119909 + 119881120597119881120597119910 = minus119892120597120578120597119910 + ]119879119867(12059721198811205971199092 + 12059721198811205971199102 )

minus 120591119887119910119867 + 120591119908119910119867 (2)

Initial Data

Mesh Setup

InitialParameters

BoundaryConditions

Starts time cycle

Hydrodynamicsolver

Turbulence

Advection

SurfaceElevation

Diffusion

Store U V

End cycle END




120597120578120597119905 = minus120597119867119880120597119909 minus 120597119867119881120597119910 (3)


]119879119867 = radic1198974ℎ [2(120597119880120597119909 )2 + 2(120597119881120597119910 )

2 + (120597119881120597119909 + 120597119880120597119910 )2] (4)



Load U V


Advection

ReactionEnd cycle


YesNo






120591119908119909 = 119862119908120588119908120596119909 10038161003816100381610038161205961199091003816100381610038161003816 (6)

120591119908119910 = 119862119908120588119908120596119910 1003816100381610038161003816100381612059611991010038161003816100381610038161003816 (7)


120591119887119909 = 119892radic1198802 + 11988121198621199112 119880 (8)

120591119887119910 = 119892radic1198802 + 11988121198621199112 119881 (9)






119880119899+1119894+12119895 = 119865119880119899119894+12119895 minus 119892 Δ119905Δ119909 (120578119899+1119894+1119895 minus 120578119899+1119894119895 )+ Δ119905119867119899119894+12119895

120591120596119909 minus Δ119905119867119899119894+12119895

120591119887119909 (10)

119881119899+1119894119895+12 = 119865119881119899119894119895+12 minus 119892 Δ119905Δ119910 (120578119899+1119894119895+1 minus 120578119899+1119894119895 )+ Δ119905119867119899119894119895+12

120591120596119910 minus Δ119905119867119899119894119895+12

120591119887119910(11)


Vijminus 1

2

Vij+ 1

2

Uiminus 1

2 jU

i+ 12 j

Δy

Δx

y

x

ij


Hij+ 1

2

Hij+ 1

2

Uiminus 1

2 jU

i+ 12 j

Ui+ 1

2 j

Δy

Δx

Δx

y

x

x

ij

waterzero level

water column

bottom


ijV ij

+12

Hi+ 1

2 j



(12)


119865119880119899119894+12119895 = 119880119899119894+12119895+ ]119879119867Δ119905(119880

119899119894+32119895 minus 2119880119899119894+12119895 + 119880119899119894minus12119895Δ1199092

+ 119880119899119894+12119895+1 minus 2119880119899119894+12119895 + 119880119899119894+12119895minus1Δ1199102 ) (13)

119865119881119899119894119895+12 = 119881119899119894119895+12+ ]119879119867Δ119905(119881

119899119894+1119895+12 minus 2119881119899119894119895+12 + 119881119899119894minus1119895+12Δ1199092

+ 119881119899119894119895+32 minus 2119881119899119894119895+12 + 119881119899119894119895minus12Δ1199102 ) (14)



(15)



120597119862120597119905⏟⏟⏟⏟⏟⏟⏟Rate of change



1

＋＞

＋＞

＋＋

ＰＭＰＯ

ℎDissolved

metalParticulated

metalWater

column




+ 119876119904⏟⏟⏟⏟⏟⏟⏟Point sources


(16)




V119904ℎ 1198911199011119862119908 + V119906ℎ 1198911199012119862119887(17)






1198701198891 = 119870119901 + 119870119867 + 119870119861 (18)



1119870119871 =1119896119897 +

1119896119892119867119890 (19)



119867119890 = 119888119892119888119908 (20)



119870119891 = 191205932 (119872119882)minus23 (21)


DRY North

West

South

East

(ms)

25 20 15 10 5 0

gt=75

6-75

45-6

3-45

15-3

0-15





V119904 = 02571 (119889)1138 (22)



V119906 = V119889 ( V119904V119899 minus 1) (23)







WET North

West

South

East

(ms)

25 20 15 10 5300

gt=49

398-49

306-398

214-306

122-214

3-122


minus08

minus04

0

04

08

12

(m)

1 5 10 15 20 25 30(days)

35 40 45 50

(a)

1 5 10 15 20 25 30 35 40 45 500

50

100

150

200

250

Q (Ｇ

3s

)

(days)

(b)

minus08

minus04

0

04

08

12

(m)

1 5 10 15 20 25 30(days)

(c)

0

50

100

150

200

250

1 5 10 15 20 25 30

Q (Ｇ

3s

)

(days)

(d)



tide

hydrological flow

000-025-050-075-100-125-150-175-200-225-250-275-300

(m)



































V 14011008005002000300008 0003 0001(ms)

0510152025303540(a)

(b)

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540


Cd 0013300093000930007800072000660006300061 C P 1

C P 2C P 3

C P 4

(mgL)

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540












0004000600080010

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)

CP 1

0007000700080008

CP 2

0006000700080009

CP 3

5 10 15 20 25 30 35 40 45 50

0002000400060008

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4


Cd 00440041004000390034002500240018001000070000

(Km)

0510152025303540

108 215 323 431 538 646 754 862 969 1077 1185 1292 1400

(mgL)

C P 4

C P 3C P 2

C P 1








CP 1

CP 2

CP 3

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

0032003400360038

00340036003800400042

0010

0015

001500200025

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Cr 00000 00210 00367 00551 00639 00718 0731

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4









CP 1

CP 2

CP 3

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

0060

0065

007000720074

000000200040

0040006000800100

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Cr 0000 0019 0048 0205 0249 0273 0279 0296

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4






5 Discussion



0200022002400260

CP 1

024002600280

CP 2

0010

0015

0020

CP 3

5 10 15 20 25 30

001500200025

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Pb000 024018017016015012010009008006

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4









01400150

0160

CP 1

016501700175

CP 2

0000

0100

0200

CP 3

5 10 15 20 25 30 35 40 45 50

0100015002000250

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Pb 007 011 012 013 015 019 023 025 026

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4






022002300240

CP 1

023002400250

CP 2

0100

0120

CP 3

5 10 15 20 25 300200030004000500

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Ni 00101 00198 00369 00534 00596 00711 00723

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)

C P 4

C P 3







0050

0060

0070

CP 1

007200740076

CP 2

0000002000400060

CP 3

5 10 15 20 25 30 35 40 45 50

004000600080

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


19131835171312330327010900820030Ni 0013

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4



6 Conclusions





1400

1600

CP 1

1600

1800

CP 2

0000020004000600

CP 3

5 10 15 20 25 30

0500

1000

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)





Data Availability




References






























































































Journal of












Journal of







Volume 2018






Volume 2018


















2 Case of Study










3 Methodology












120597119880120597119905 + 119880120597119880120597119909 + 119881120597119880120597119910 = minus119892120597120578120597119909 + ]119879119867(12059721198801205971199092 + 12059721198801205971199102 )

minus 120591119887119909119867 + 120591119908119909119867 (1)

120597119881120597119905 + 119880120597119881120597119909 + 119881120597119881120597119910 = minus119892120597120578120597119910 + ]119879119867(12059721198811205971199092 + 12059721198811205971199102 )

minus 120591119887119910119867 + 120591119908119910119867 (2)

Initial Data

Mesh Setup

InitialParameters

BoundaryConditions

Starts time cycle

Hydrodynamicsolver

Turbulence

Advection

SurfaceElevation

Diffusion

Store U V

End cycle END




120597120578120597119905 = minus120597119867119880120597119909 minus 120597119867119881120597119910 (3)


]119879119867 = radic1198974ℎ [2(120597119880120597119909 )2 + 2(120597119881120597119910 )

2 + (120597119881120597119909 + 120597119880120597119910 )2] (4)



Load U V


Advection

ReactionEnd cycle


YesNo






120591119908119909 = 119862119908120588119908120596119909 10038161003816100381610038161205961199091003816100381610038161003816 (6)

120591119908119910 = 119862119908120588119908120596119910 1003816100381610038161003816100381612059611991010038161003816100381610038161003816 (7)


120591119887119909 = 119892radic1198802 + 11988121198621199112 119880 (8)

120591119887119910 = 119892radic1198802 + 11988121198621199112 119881 (9)






119880119899+1119894+12119895 = 119865119880119899119894+12119895 minus 119892 Δ119905Δ119909 (120578119899+1119894+1119895 minus 120578119899+1119894119895 )+ Δ119905119867119899119894+12119895

120591120596119909 minus Δ119905119867119899119894+12119895

120591119887119909 (10)

119881119899+1119894119895+12 = 119865119881119899119894119895+12 minus 119892 Δ119905Δ119910 (120578119899+1119894119895+1 minus 120578119899+1119894119895 )+ Δ119905119867119899119894119895+12

120591120596119910 minus Δ119905119867119899119894119895+12

120591119887119910(11)


Vijminus 1

2

Vij+ 1

2

Uiminus 1

2 jU

i+ 12 j

Δy

Δx

y

x

ij


Hij+ 1

2

Hij+ 1

2

Uiminus 1

2 jU

i+ 12 j

Ui+ 1

2 j

Δy

Δx

Δx

y

x

x

ij

waterzero level

water column

bottom


ijV ij

+12

Hi+ 1

2 j



(12)


119865119880119899119894+12119895 = 119880119899119894+12119895+ ]119879119867Δ119905(119880

119899119894+32119895 minus 2119880119899119894+12119895 + 119880119899119894minus12119895Δ1199092

+ 119880119899119894+12119895+1 minus 2119880119899119894+12119895 + 119880119899119894+12119895minus1Δ1199102 ) (13)

119865119881119899119894119895+12 = 119881119899119894119895+12+ ]119879119867Δ119905(119881

119899119894+1119895+12 minus 2119881119899119894119895+12 + 119881119899119894minus1119895+12Δ1199092

+ 119881119899119894119895+32 minus 2119881119899119894119895+12 + 119881119899119894119895minus12Δ1199102 ) (14)



(15)



120597119862120597119905⏟⏟⏟⏟⏟⏟⏟Rate of change



1

＋＞

＋＞

＋＋

ＰＭＰＯ

ℎDissolved

metalParticulated

metalWater

column




+ 119876119904⏟⏟⏟⏟⏟⏟⏟Point sources


(16)




V119904ℎ 1198911199011119862119908 + V119906ℎ 1198911199012119862119887(17)






1198701198891 = 119870119901 + 119870119867 + 119870119861 (18)



1119870119871 =1119896119897 +

1119896119892119867119890 (19)



119867119890 = 119888119892119888119908 (20)



119870119891 = 191205932 (119872119882)minus23 (21)


DRY North

West

South

East

(ms)

25 20 15 10 5 0

gt=75

6-75

45-6

3-45

15-3

0-15





V119904 = 02571 (119889)1138 (22)



V119906 = V119889 ( V119904V119899 minus 1) (23)







WET North

West

South

East

(ms)

25 20 15 10 5300

gt=49

398-49

306-398

214-306

122-214

3-122


minus08

minus04

0

04

08

12

(m)

1 5 10 15 20 25 30(days)

35 40 45 50

(a)

1 5 10 15 20 25 30 35 40 45 500

50

100

150

200

250

Q (Ｇ

3s

)

(days)

(b)

minus08

minus04

0

04

08

12

(m)

1 5 10 15 20 25 30(days)

(c)

0

50

100

150

200

250

1 5 10 15 20 25 30

Q (Ｇ

3s

)

(days)

(d)



tide

hydrological flow

000-025-050-075-100-125-150-175-200-225-250-275-300

(m)



































V 14011008005002000300008 0003 0001(ms)

0510152025303540(a)

(b)

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540


Cd 0013300093000930007800072000660006300061 C P 1

C P 2C P 3

C P 4

(mgL)

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540












0004000600080010

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)

CP 1

0007000700080008

CP 2

0006000700080009

CP 3

5 10 15 20 25 30 35 40 45 50

0002000400060008

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4


Cd 00440041004000390034002500240018001000070000

(Km)

0510152025303540

108 215 323 431 538 646 754 862 969 1077 1185 1292 1400

(mgL)

C P 4

C P 3C P 2

C P 1








CP 1

CP 2

CP 3

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

0032003400360038

00340036003800400042

0010

0015

001500200025

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Cr 00000 00210 00367 00551 00639 00718 0731

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4









CP 1

CP 2

CP 3

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

0060

0065

007000720074

000000200040

0040006000800100

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Cr 0000 0019 0048 0205 0249 0273 0279 0296

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4






5 Discussion



0200022002400260

CP 1

024002600280

CP 2

0010

0015

0020

CP 3

5 10 15 20 25 30

001500200025

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Pb000 024018017016015012010009008006

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4









01400150

0160

CP 1

016501700175

CP 2

0000

0100

0200

CP 3

5 10 15 20 25 30 35 40 45 50

0100015002000250

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Pb 007 011 012 013 015 019 023 025 026

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4






022002300240

CP 1

023002400250

CP 2

0100

0120

CP 3

5 10 15 20 25 300200030004000500

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Ni 00101 00198 00369 00534 00596 00711 00723

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)

C P 4

C P 3







0050

0060

0070

CP 1

007200740076

CP 2

0000002000400060

CP 3

5 10 15 20 25 30 35 40 45 50

004000600080

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


19131835171312330327010900820030Ni 0013

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4



6 Conclusions





1400

1600

CP 1

1600

1800

CP 2

0000020004000600

CP 3

5 10 15 20 25 30

0500

1000

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)





Data Availability




References






























































































Journal of












Journal of







Volume 2018






Volume 2018













3 Methodology












120597119880120597119905 + 119880120597119880120597119909 + 119881120597119880120597119910 = minus119892120597120578120597119909 + ]119879119867(12059721198801205971199092 + 12059721198801205971199102 )

minus 120591119887119909119867 + 120591119908119909119867 (1)

120597119881120597119905 + 119880120597119881120597119909 + 119881120597119881120597119910 = minus119892120597120578120597119910 + ]119879119867(12059721198811205971199092 + 12059721198811205971199102 )

minus 120591119887119910119867 + 120591119908119910119867 (2)

Initial Data

Mesh Setup

InitialParameters

BoundaryConditions

Starts time cycle

Hydrodynamicsolver

Turbulence

Advection

SurfaceElevation

Diffusion

Store U V

End cycle END




120597120578120597119905 = minus120597119867119880120597119909 minus 120597119867119881120597119910 (3)


]119879119867 = radic1198974ℎ [2(120597119880120597119909 )2 + 2(120597119881120597119910 )

2 + (120597119881120597119909 + 120597119880120597119910 )2] (4)



Load U V


Advection

ReactionEnd cycle


YesNo






120591119908119909 = 119862119908120588119908120596119909 10038161003816100381610038161205961199091003816100381610038161003816 (6)

120591119908119910 = 119862119908120588119908120596119910 1003816100381610038161003816100381612059611991010038161003816100381610038161003816 (7)


120591119887119909 = 119892radic1198802 + 11988121198621199112 119880 (8)

120591119887119910 = 119892radic1198802 + 11988121198621199112 119881 (9)






119880119899+1119894+12119895 = 119865119880119899119894+12119895 minus 119892 Δ119905Δ119909 (120578119899+1119894+1119895 minus 120578119899+1119894119895 )+ Δ119905119867119899119894+12119895

120591120596119909 minus Δ119905119867119899119894+12119895

120591119887119909 (10)

119881119899+1119894119895+12 = 119865119881119899119894119895+12 minus 119892 Δ119905Δ119910 (120578119899+1119894119895+1 minus 120578119899+1119894119895 )+ Δ119905119867119899119894119895+12

120591120596119910 minus Δ119905119867119899119894119895+12

120591119887119910(11)


Vijminus 1

2

Vij+ 1

2

Uiminus 1

2 jU

i+ 12 j

Δy

Δx

y

x

ij


Hij+ 1

2

Hij+ 1

2

Uiminus 1

2 jU

i+ 12 j

Ui+ 1

2 j

Δy

Δx

Δx

y

x

x

ij

waterzero level

water column

bottom


ijV ij

+12

Hi+ 1

2 j



(12)


119865119880119899119894+12119895 = 119880119899119894+12119895+ ]119879119867Δ119905(119880

119899119894+32119895 minus 2119880119899119894+12119895 + 119880119899119894minus12119895Δ1199092

+ 119880119899119894+12119895+1 minus 2119880119899119894+12119895 + 119880119899119894+12119895minus1Δ1199102 ) (13)

119865119881119899119894119895+12 = 119881119899119894119895+12+ ]119879119867Δ119905(119881

119899119894+1119895+12 minus 2119881119899119894119895+12 + 119881119899119894minus1119895+12Δ1199092

+ 119881119899119894119895+32 minus 2119881119899119894119895+12 + 119881119899119894119895minus12Δ1199102 ) (14)



(15)



120597119862120597119905⏟⏟⏟⏟⏟⏟⏟Rate of change



1

＋＞

＋＞

＋＋

ＰＭＰＯ

ℎDissolved

metalParticulated

metalWater

column




+ 119876119904⏟⏟⏟⏟⏟⏟⏟Point sources


(16)




V119904ℎ 1198911199011119862119908 + V119906ℎ 1198911199012119862119887(17)






1198701198891 = 119870119901 + 119870119867 + 119870119861 (18)



1119870119871 =1119896119897 +

1119896119892119867119890 (19)



119867119890 = 119888119892119888119908 (20)



119870119891 = 191205932 (119872119882)minus23 (21)


DRY North

West

South

East

(ms)

25 20 15 10 5 0

gt=75

6-75

45-6

3-45

15-3

0-15





V119904 = 02571 (119889)1138 (22)



V119906 = V119889 ( V119904V119899 minus 1) (23)







WET North

West

South

East

(ms)

25 20 15 10 5300

gt=49

398-49

306-398

214-306

122-214

3-122


minus08

minus04

0

04

08

12

(m)

1 5 10 15 20 25 30(days)

35 40 45 50

(a)

1 5 10 15 20 25 30 35 40 45 500

50

100

150

200

250

Q (Ｇ

3s

)

(days)

(b)

minus08

minus04

0

04

08

12

(m)

1 5 10 15 20 25 30(days)

(c)

0

50

100

150

200

250

1 5 10 15 20 25 30

Q (Ｇ

3s

)

(days)

(d)



tide

hydrological flow

000-025-050-075-100-125-150-175-200-225-250-275-300

(m)



































V 14011008005002000300008 0003 0001(ms)

0510152025303540(a)

(b)

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540


Cd 0013300093000930007800072000660006300061 C P 1

C P 2C P 3

C P 4

(mgL)

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540












0004000600080010

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)

CP 1

0007000700080008

CP 2

0006000700080009

CP 3

5 10 15 20 25 30 35 40 45 50

0002000400060008

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4


Cd 00440041004000390034002500240018001000070000

(Km)

0510152025303540

108 215 323 431 538 646 754 862 969 1077 1185 1292 1400

(mgL)

C P 4

C P 3C P 2

C P 1








CP 1

CP 2

CP 3

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

0032003400360038

00340036003800400042

0010

0015

001500200025

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Cr 00000 00210 00367 00551 00639 00718 0731

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4









CP 1

CP 2

CP 3

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

0060

0065

007000720074

000000200040

0040006000800100

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Cr 0000 0019 0048 0205 0249 0273 0279 0296

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4






5 Discussion



0200022002400260

CP 1

024002600280

CP 2

0010

0015

0020

CP 3

5 10 15 20 25 30

001500200025

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Pb000 024018017016015012010009008006

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4









01400150

0160

CP 1

016501700175

CP 2

0000

0100

0200

CP 3

5 10 15 20 25 30 35 40 45 50

0100015002000250

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Pb 007 011 012 013 015 019 023 025 026

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4






022002300240

CP 1

023002400250

CP 2

0100

0120

CP 3

5 10 15 20 25 300200030004000500

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Ni 00101 00198 00369 00534 00596 00711 00723

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)

C P 4

C P 3







0050

0060

0070

CP 1

007200740076

CP 2

0000002000400060

CP 3

5 10 15 20 25 30 35 40 45 50

004000600080

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


19131835171312330327010900820030Ni 0013

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4



6 Conclusions





1400

1600

CP 1

1600

1800

CP 2

0000020004000600

CP 3

5 10 15 20 25 30

0500

1000

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)





Data Availability




References






























































































Journal of












Journal of







Volume 2018






Volume 2018
















120597119880120597119905 + 119880120597119880120597119909 + 119881120597119880120597119910 = minus119892120597120578120597119909 + ]119879119867(12059721198801205971199092 + 12059721198801205971199102 )

minus 120591119887119909119867 + 120591119908119909119867 (1)

120597119881120597119905 + 119880120597119881120597119909 + 119881120597119881120597119910 = minus119892120597120578120597119910 + ]119879119867(12059721198811205971199092 + 12059721198811205971199102 )

minus 120591119887119910119867 + 120591119908119910119867 (2)

Initial Data

Mesh Setup

InitialParameters

BoundaryConditions

Starts time cycle

Hydrodynamicsolver

Turbulence

Advection

SurfaceElevation

Diffusion

Store U V

End cycle END




120597120578120597119905 = minus120597119867119880120597119909 minus 120597119867119881120597119910 (3)


]119879119867 = radic1198974ℎ [2(120597119880120597119909 )2 + 2(120597119881120597119910 )

2 + (120597119881120597119909 + 120597119880120597119910 )2] (4)



Load U V


Advection

ReactionEnd cycle


YesNo






120591119908119909 = 119862119908120588119908120596119909 10038161003816100381610038161205961199091003816100381610038161003816 (6)

120591119908119910 = 119862119908120588119908120596119910 1003816100381610038161003816100381612059611991010038161003816100381610038161003816 (7)


120591119887119909 = 119892radic1198802 + 11988121198621199112 119880 (8)

120591119887119910 = 119892radic1198802 + 11988121198621199112 119881 (9)






119880119899+1119894+12119895 = 119865119880119899119894+12119895 minus 119892 Δ119905Δ119909 (120578119899+1119894+1119895 minus 120578119899+1119894119895 )+ Δ119905119867119899119894+12119895

120591120596119909 minus Δ119905119867119899119894+12119895

120591119887119909 (10)

119881119899+1119894119895+12 = 119865119881119899119894119895+12 minus 119892 Δ119905Δ119910 (120578119899+1119894119895+1 minus 120578119899+1119894119895 )+ Δ119905119867119899119894119895+12

120591120596119910 minus Δ119905119867119899119894119895+12

120591119887119910(11)


Vijminus 1

2

Vij+ 1

2

Uiminus 1

2 jU

i+ 12 j

Δy

Δx

y

x

ij


Hij+ 1

2

Hij+ 1

2

Uiminus 1

2 jU

i+ 12 j

Ui+ 1

2 j

Δy

Δx

Δx

y

x

x

ij

waterzero level

water column

bottom


ijV ij

+12

Hi+ 1

2 j



(12)


119865119880119899119894+12119895 = 119880119899119894+12119895+ ]119879119867Δ119905(119880

119899119894+32119895 minus 2119880119899119894+12119895 + 119880119899119894minus12119895Δ1199092

+ 119880119899119894+12119895+1 minus 2119880119899119894+12119895 + 119880119899119894+12119895minus1Δ1199102 ) (13)

119865119881119899119894119895+12 = 119881119899119894119895+12+ ]119879119867Δ119905(119881

119899119894+1119895+12 minus 2119881119899119894119895+12 + 119881119899119894minus1119895+12Δ1199092

+ 119881119899119894119895+32 minus 2119881119899119894119895+12 + 119881119899119894119895minus12Δ1199102 ) (14)



(15)



120597119862120597119905⏟⏟⏟⏟⏟⏟⏟Rate of change



1

＋＞

＋＞

＋＋

ＰＭＰＯ

ℎDissolved

metalParticulated

metalWater

column




+ 119876119904⏟⏟⏟⏟⏟⏟⏟Point sources


(16)




V119904ℎ 1198911199011119862119908 + V119906ℎ 1198911199012119862119887(17)






1198701198891 = 119870119901 + 119870119867 + 119870119861 (18)



1119870119871 =1119896119897 +

1119896119892119867119890 (19)



119867119890 = 119888119892119888119908 (20)



119870119891 = 191205932 (119872119882)minus23 (21)


DRY North

West

South

East

(ms)

25 20 15 10 5 0

gt=75

6-75

45-6

3-45

15-3

0-15





V119904 = 02571 (119889)1138 (22)



V119906 = V119889 ( V119904V119899 minus 1) (23)







WET North

West

South

East

(ms)

25 20 15 10 5300

gt=49

398-49

306-398

214-306

122-214

3-122


minus08

minus04

0

04

08

12

(m)

1 5 10 15 20 25 30(days)

35 40 45 50

(a)

1 5 10 15 20 25 30 35 40 45 500

50

100

150

200

250

Q (Ｇ

3s

)

(days)

(b)

minus08

minus04

0

04

08

12

(m)

1 5 10 15 20 25 30(days)

(c)

0

50

100

150

200

250

1 5 10 15 20 25 30

Q (Ｇ

3s

)

(days)

(d)



tide

hydrological flow

000-025-050-075-100-125-150-175-200-225-250-275-300

(m)



































V 14011008005002000300008 0003 0001(ms)

0510152025303540(a)

(b)

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540


Cd 0013300093000930007800072000660006300061 C P 1

C P 2C P 3

C P 4

(mgL)

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540












0004000600080010

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)

CP 1

0007000700080008

CP 2

0006000700080009

CP 3

5 10 15 20 25 30 35 40 45 50

0002000400060008

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4


Cd 00440041004000390034002500240018001000070000

(Km)

0510152025303540

108 215 323 431 538 646 754 862 969 1077 1185 1292 1400

(mgL)

C P 4

C P 3C P 2

C P 1








CP 1

CP 2

CP 3

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

0032003400360038

00340036003800400042

0010

0015

001500200025

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Cr 00000 00210 00367 00551 00639 00718 0731

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4









CP 1

CP 2

CP 3

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

0060

0065

007000720074

000000200040

0040006000800100

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Cr 0000 0019 0048 0205 0249 0273 0279 0296

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4






5 Discussion



0200022002400260

CP 1

024002600280

CP 2

0010

0015

0020

CP 3

5 10 15 20 25 30

001500200025

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Pb000 024018017016015012010009008006

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4









01400150

0160

CP 1

016501700175

CP 2

0000

0100

0200

CP 3

5 10 15 20 25 30 35 40 45 50

0100015002000250

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Pb 007 011 012 013 015 019 023 025 026

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4






022002300240

CP 1

023002400250

CP 2

0100

0120

CP 3

5 10 15 20 25 300200030004000500

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Ni 00101 00198 00369 00534 00596 00711 00723

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)

C P 4

C P 3







0050

0060

0070

CP 1

007200740076

CP 2

0000002000400060

CP 3

5 10 15 20 25 30 35 40 45 50

004000600080

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


19131835171312330327010900820030Ni 0013

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4



6 Conclusions





1400

1600

CP 1

1600

1800

CP 2

0000020004000600

CP 3

5 10 15 20 25 30

0500

1000

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)





Data Availability




References






























































































Journal of












Journal of







Volume 2018






Volume 2018










Load U V


Advection

ReactionEnd cycle


YesNo






120591119908119909 = 119862119908120588119908120596119909 10038161003816100381610038161205961199091003816100381610038161003816 (6)

120591119908119910 = 119862119908120588119908120596119910 1003816100381610038161003816100381612059611991010038161003816100381610038161003816 (7)


120591119887119909 = 119892radic1198802 + 11988121198621199112 119880 (8)

120591119887119910 = 119892radic1198802 + 11988121198621199112 119881 (9)






119880119899+1119894+12119895 = 119865119880119899119894+12119895 minus 119892 Δ119905Δ119909 (120578119899+1119894+1119895 minus 120578119899+1119894119895 )+ Δ119905119867119899119894+12119895

120591120596119909 minus Δ119905119867119899119894+12119895

120591119887119909 (10)

119881119899+1119894119895+12 = 119865119881119899119894119895+12 minus 119892 Δ119905Δ119910 (120578119899+1119894119895+1 minus 120578119899+1119894119895 )+ Δ119905119867119899119894119895+12

120591120596119910 minus Δ119905119867119899119894119895+12

120591119887119910(11)


Vijminus 1

2

Vij+ 1

2

Uiminus 1

2 jU

i+ 12 j

Δy

Δx

y

x

ij


Hij+ 1

2

Hij+ 1

2

Uiminus 1

2 jU

i+ 12 j

Ui+ 1

2 j

Δy

Δx

Δx

y

x

x

ij

waterzero level

water column

bottom


ijV ij

+12

Hi+ 1

2 j



(12)


119865119880119899119894+12119895 = 119880119899119894+12119895+ ]119879119867Δ119905(119880

119899119894+32119895 minus 2119880119899119894+12119895 + 119880119899119894minus12119895Δ1199092

+ 119880119899119894+12119895+1 minus 2119880119899119894+12119895 + 119880119899119894+12119895minus1Δ1199102 ) (13)

119865119881119899119894119895+12 = 119881119899119894119895+12+ ]119879119867Δ119905(119881

119899119894+1119895+12 minus 2119881119899119894119895+12 + 119881119899119894minus1119895+12Δ1199092

+ 119881119899119894119895+32 minus 2119881119899119894119895+12 + 119881119899119894119895minus12Δ1199102 ) (14)



(15)



120597119862120597119905⏟⏟⏟⏟⏟⏟⏟Rate of change



1

＋＞

＋＞

＋＋

ＰＭＰＯ

ℎDissolved

metalParticulated

metalWater

column




+ 119876119904⏟⏟⏟⏟⏟⏟⏟Point sources


(16)




V119904ℎ 1198911199011119862119908 + V119906ℎ 1198911199012119862119887(17)






1198701198891 = 119870119901 + 119870119867 + 119870119861 (18)



1119870119871 =1119896119897 +

1119896119892119867119890 (19)



119867119890 = 119888119892119888119908 (20)



119870119891 = 191205932 (119872119882)minus23 (21)


DRY North

West

South

East

(ms)

25 20 15 10 5 0

gt=75

6-75

45-6

3-45

15-3

0-15





V119904 = 02571 (119889)1138 (22)



V119906 = V119889 ( V119904V119899 minus 1) (23)







WET North

West

South

East

(ms)

25 20 15 10 5300

gt=49

398-49

306-398

214-306

122-214

3-122


minus08

minus04

0

04

08

12

(m)

1 5 10 15 20 25 30(days)

35 40 45 50

(a)

1 5 10 15 20 25 30 35 40 45 500

50

100

150

200

250

Q (Ｇ

3s

)

(days)

(b)

minus08

minus04

0

04

08

12

(m)

1 5 10 15 20 25 30(days)

(c)

0

50

100

150

200

250

1 5 10 15 20 25 30

Q (Ｇ

3s

)

(days)

(d)



tide

hydrological flow

000-025-050-075-100-125-150-175-200-225-250-275-300

(m)



































V 14011008005002000300008 0003 0001(ms)

0510152025303540(a)

(b)

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540


Cd 0013300093000930007800072000660006300061 C P 1

C P 2C P 3

C P 4

(mgL)

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540












0004000600080010

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)

CP 1

0007000700080008

CP 2

0006000700080009

CP 3

5 10 15 20 25 30 35 40 45 50

0002000400060008

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4


Cd 00440041004000390034002500240018001000070000

(Km)

0510152025303540

108 215 323 431 538 646 754 862 969 1077 1185 1292 1400

(mgL)

C P 4

C P 3C P 2

C P 1








CP 1

CP 2

CP 3

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

0032003400360038

00340036003800400042

0010

0015

001500200025

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Cr 00000 00210 00367 00551 00639 00718 0731

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4









CP 1

CP 2

CP 3

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

0060

0065

007000720074

000000200040

0040006000800100

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Cr 0000 0019 0048 0205 0249 0273 0279 0296

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4






5 Discussion



0200022002400260

CP 1

024002600280

CP 2

0010

0015

0020

CP 3

5 10 15 20 25 30

001500200025

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Pb000 024018017016015012010009008006

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4









01400150

0160

CP 1

016501700175

CP 2

0000

0100

0200

CP 3

5 10 15 20 25 30 35 40 45 50

0100015002000250

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Pb 007 011 012 013 015 019 023 025 026

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4






022002300240

CP 1

023002400250

CP 2

0100

0120

CP 3

5 10 15 20 25 300200030004000500

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Ni 00101 00198 00369 00534 00596 00711 00723

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)

C P 4

C P 3







0050

0060

0070

CP 1

007200740076

CP 2

0000002000400060

CP 3

5 10 15 20 25 30 35 40 45 50

004000600080

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


19131835171312330327010900820030Ni 0013

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4



6 Conclusions





1400

1600

CP 1

1600

1800

CP 2

0000020004000600

CP 3

5 10 15 20 25 30

0500

1000

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)





Data Availability




References






























































































Journal of












Journal of







Volume 2018






Volume 2018









Vijminus 1

2

Vij+ 1

2

Uiminus 1

2 jU

i+ 12 j

Δy

Δx

y

x

ij


Hij+ 1

2

Hij+ 1

2

Uiminus 1

2 jU

i+ 12 j

Ui+ 1

2 j

Δy

Δx

Δx

y

x

x

ij

waterzero level

water column

bottom


ijV ij

+12

Hi+ 1

2 j



(12)


119865119880119899119894+12119895 = 119880119899119894+12119895+ ]119879119867Δ119905(119880

119899119894+32119895 minus 2119880119899119894+12119895 + 119880119899119894minus12119895Δ1199092

+ 119880119899119894+12119895+1 minus 2119880119899119894+12119895 + 119880119899119894+12119895minus1Δ1199102 ) (13)

119865119881119899119894119895+12 = 119881119899119894119895+12+ ]119879119867Δ119905(119881

119899119894+1119895+12 minus 2119881119899119894119895+12 + 119881119899119894minus1119895+12Δ1199092

+ 119881119899119894119895+32 minus 2119881119899119894119895+12 + 119881119899119894119895minus12Δ1199102 ) (14)



(15)



120597119862120597119905⏟⏟⏟⏟⏟⏟⏟Rate of change



1

＋＞

＋＞

＋＋

ＰＭＰＯ

ℎDissolved

metalParticulated

metalWater

column




+ 119876119904⏟⏟⏟⏟⏟⏟⏟Point sources


(16)




V119904ℎ 1198911199011119862119908 + V119906ℎ 1198911199012119862119887(17)






1198701198891 = 119870119901 + 119870119867 + 119870119861 (18)



1119870119871 =1119896119897 +

1119896119892119867119890 (19)



119867119890 = 119888119892119888119908 (20)



119870119891 = 191205932 (119872119882)minus23 (21)


DRY North

West

South

East

(ms)

25 20 15 10 5 0

gt=75

6-75

45-6

3-45

15-3

0-15





V119904 = 02571 (119889)1138 (22)



V119906 = V119889 ( V119904V119899 minus 1) (23)







WET North

West

South

East

(ms)

25 20 15 10 5300

gt=49

398-49

306-398

214-306

122-214

3-122


minus08

minus04

0

04

08

12

(m)

1 5 10 15 20 25 30(days)

35 40 45 50

(a)

1 5 10 15 20 25 30 35 40 45 500

50

100

150

200

250

Q (Ｇ

3s

)

(days)

(b)

minus08

minus04

0

04

08

12

(m)

1 5 10 15 20 25 30(days)

(c)

0

50

100

150

200

250

1 5 10 15 20 25 30

Q (Ｇ

3s

)

(days)

(d)



tide

hydrological flow

000-025-050-075-100-125-150-175-200-225-250-275-300

(m)



































V 14011008005002000300008 0003 0001(ms)

0510152025303540(a)

(b)

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540


Cd 0013300093000930007800072000660006300061 C P 1

C P 2C P 3

C P 4

(mgL)

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540












0004000600080010

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)

CP 1

0007000700080008

CP 2

0006000700080009

CP 3

5 10 15 20 25 30 35 40 45 50

0002000400060008

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4


Cd 00440041004000390034002500240018001000070000

(Km)

0510152025303540

108 215 323 431 538 646 754 862 969 1077 1185 1292 1400

(mgL)

C P 4

C P 3C P 2

C P 1








CP 1

CP 2

CP 3

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

0032003400360038

00340036003800400042

0010

0015

001500200025

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Cr 00000 00210 00367 00551 00639 00718 0731

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4









CP 1

CP 2

CP 3

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

0060

0065

007000720074

000000200040

0040006000800100

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Cr 0000 0019 0048 0205 0249 0273 0279 0296

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4






5 Discussion



0200022002400260

CP 1

024002600280

CP 2

0010

0015

0020

CP 3

5 10 15 20 25 30

001500200025

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Pb000 024018017016015012010009008006

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4









01400150

0160

CP 1

016501700175

CP 2

0000

0100

0200

CP 3

5 10 15 20 25 30 35 40 45 50

0100015002000250

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Pb 007 011 012 013 015 019 023 025 026

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4






022002300240

CP 1

023002400250

CP 2

0100

0120

CP 3

5 10 15 20 25 300200030004000500

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Ni 00101 00198 00369 00534 00596 00711 00723

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)

C P 4

C P 3







0050

0060

0070

CP 1

007200740076

CP 2

0000002000400060

CP 3

5 10 15 20 25 30 35 40 45 50

004000600080

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


19131835171312330327010900820030Ni 0013

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4



6 Conclusions





1400

1600

CP 1

1600

1800

CP 2

0000020004000600

CP 3

5 10 15 20 25 30

0500

1000

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)





Data Availability




References






























































































Journal of












Journal of







Volume 2018






Volume 2018









1

＋＞

＋＞

＋＋

ＰＭＰＯ

ℎDissolved

metalParticulated

metalWater

column




+ 119876119904⏟⏟⏟⏟⏟⏟⏟Point sources


(16)




V119904ℎ 1198911199011119862119908 + V119906ℎ 1198911199012119862119887(17)






1198701198891 = 119870119901 + 119870119867 + 119870119861 (18)



1119870119871 =1119896119897 +

1119896119892119867119890 (19)



119867119890 = 119888119892119888119908 (20)



119870119891 = 191205932 (119872119882)minus23 (21)


DRY North

West

South

East

(ms)

25 20 15 10 5 0

gt=75

6-75

45-6

3-45

15-3

0-15





V119904 = 02571 (119889)1138 (22)



V119906 = V119889 ( V119904V119899 minus 1) (23)







WET North

West

South

East

(ms)

25 20 15 10 5300

gt=49

398-49

306-398

214-306

122-214

3-122


minus08

minus04

0

04

08

12

(m)

1 5 10 15 20 25 30(days)

35 40 45 50

(a)

1 5 10 15 20 25 30 35 40 45 500

50

100

150

200

250

Q (Ｇ

3s

)

(days)

(b)

minus08

minus04

0

04

08

12

(m)

1 5 10 15 20 25 30(days)

(c)

0

50

100

150

200

250

1 5 10 15 20 25 30

Q (Ｇ

3s

)

(days)

(d)



tide

hydrological flow

000-025-050-075-100-125-150-175-200-225-250-275-300

(m)



































V 14011008005002000300008 0003 0001(ms)

0510152025303540(a)

(b)

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540


Cd 0013300093000930007800072000660006300061 C P 1

C P 2C P 3

C P 4

(mgL)

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540












0004000600080010

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)

CP 1

0007000700080008

CP 2

0006000700080009

CP 3

5 10 15 20 25 30 35 40 45 50

0002000400060008

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4


Cd 00440041004000390034002500240018001000070000

(Km)

0510152025303540

108 215 323 431 538 646 754 862 969 1077 1185 1292 1400

(mgL)

C P 4

C P 3C P 2

C P 1








CP 1

CP 2

CP 3

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

0032003400360038

00340036003800400042

0010

0015

001500200025

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Cr 00000 00210 00367 00551 00639 00718 0731

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4









CP 1

CP 2

CP 3

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

0060

0065

007000720074

000000200040

0040006000800100

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Cr 0000 0019 0048 0205 0249 0273 0279 0296

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4






5 Discussion



0200022002400260

CP 1

024002600280

CP 2

0010

0015

0020

CP 3

5 10 15 20 25 30

001500200025

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Pb000 024018017016015012010009008006

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4









01400150

0160

CP 1

016501700175

CP 2

0000

0100

0200

CP 3

5 10 15 20 25 30 35 40 45 50

0100015002000250

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Pb 007 011 012 013 015 019 023 025 026

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4






022002300240

CP 1

023002400250

CP 2

0100

0120

CP 3

5 10 15 20 25 300200030004000500

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Ni 00101 00198 00369 00534 00596 00711 00723

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)

C P 4

C P 3







0050

0060

0070

CP 1

007200740076

CP 2

0000002000400060

CP 3

5 10 15 20 25 30 35 40 45 50

004000600080

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


19131835171312330327010900820030Ni 0013

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4



6 Conclusions





1400

1600

CP 1

1600

1800

CP 2

0000020004000600

CP 3

5 10 15 20 25 30

0500

1000

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)





Data Availability




References






























































































Journal of












Journal of







Volume 2018






Volume 2018









DRY North

West

South

East

(ms)

25 20 15 10 5 0

gt=75

6-75

45-6

3-45

15-3

0-15





V119904 = 02571 (119889)1138 (22)



V119906 = V119889 ( V119904V119899 minus 1) (23)







WET North

West

South

East

(ms)

25 20 15 10 5300

gt=49

398-49

306-398

214-306

122-214

3-122


minus08

minus04

0

04

08

12

(m)

1 5 10 15 20 25 30(days)

35 40 45 50

(a)

1 5 10 15 20 25 30 35 40 45 500

50

100

150

200

250

Q (Ｇ

3s

)

(days)

(b)

minus08

minus04

0

04

08

12

(m)

1 5 10 15 20 25 30(days)

(c)

0

50

100

150

200

250

1 5 10 15 20 25 30

Q (Ｇ

3s

)

(days)

(d)



tide

hydrological flow

000-025-050-075-100-125-150-175-200-225-250-275-300

(m)



































V 14011008005002000300008 0003 0001(ms)

0510152025303540(a)

(b)

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540


Cd 0013300093000930007800072000660006300061 C P 1

C P 2C P 3

C P 4

(mgL)

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540












0004000600080010

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)

CP 1

0007000700080008

CP 2

0006000700080009

CP 3

5 10 15 20 25 30 35 40 45 50

0002000400060008

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4


Cd 00440041004000390034002500240018001000070000

(Km)

0510152025303540

108 215 323 431 538 646 754 862 969 1077 1185 1292 1400

(mgL)

C P 4

C P 3C P 2

C P 1








CP 1

CP 2

CP 3

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

0032003400360038

00340036003800400042

0010

0015

001500200025

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Cr 00000 00210 00367 00551 00639 00718 0731

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4









CP 1

CP 2

CP 3

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

0060

0065

007000720074

000000200040

0040006000800100

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Cr 0000 0019 0048 0205 0249 0273 0279 0296

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4






5 Discussion



0200022002400260

CP 1

024002600280

CP 2

0010

0015

0020

CP 3

5 10 15 20 25 30

001500200025

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Pb000 024018017016015012010009008006

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4









01400150

0160

CP 1

016501700175

CP 2

0000

0100

0200

CP 3

5 10 15 20 25 30 35 40 45 50

0100015002000250

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Pb 007 011 012 013 015 019 023 025 026

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4






022002300240

CP 1

023002400250

CP 2

0100

0120

CP 3

5 10 15 20 25 300200030004000500

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Ni 00101 00198 00369 00534 00596 00711 00723

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)

C P 4

C P 3







0050

0060

0070

CP 1

007200740076

CP 2

0000002000400060

CP 3

5 10 15 20 25 30 35 40 45 50

004000600080

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


19131835171312330327010900820030Ni 0013

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4



6 Conclusions





1400

1600

CP 1

1600

1800

CP 2

0000020004000600

CP 3

5 10 15 20 25 30

0500

1000

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)





Data Availability




References






























































































Journal of












Journal of







Volume 2018






Volume 2018









WET North

West

South

East

(ms)

25 20 15 10 5300

gt=49

398-49

306-398

214-306

122-214

3-122


minus08

minus04

0

04

08

12

(m)

1 5 10 15 20 25 30(days)

35 40 45 50

(a)

1 5 10 15 20 25 30 35 40 45 500

50

100

150

200

250

Q (Ｇ

3s

)

(days)

(b)

minus08

minus04

0

04

08

12

(m)

1 5 10 15 20 25 30(days)

(c)

0

50

100

150

200

250

1 5 10 15 20 25 30

Q (Ｇ

3s

)

(days)

(d)



tide

hydrological flow

000-025-050-075-100-125-150-175-200-225-250-275-300

(m)



































V 14011008005002000300008 0003 0001(ms)

0510152025303540(a)

(b)

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540


Cd 0013300093000930007800072000660006300061 C P 1

C P 2C P 3

C P 4

(mgL)

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540












0004000600080010

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)

CP 1

0007000700080008

CP 2

0006000700080009

CP 3

5 10 15 20 25 30 35 40 45 50

0002000400060008

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4


Cd 00440041004000390034002500240018001000070000

(Km)

0510152025303540

108 215 323 431 538 646 754 862 969 1077 1185 1292 1400

(mgL)

C P 4

C P 3C P 2

C P 1








CP 1

CP 2

CP 3

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

0032003400360038

00340036003800400042

0010

0015

001500200025

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Cr 00000 00210 00367 00551 00639 00718 0731

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4









CP 1

CP 2

CP 3

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

0060

0065

007000720074

000000200040

0040006000800100

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Cr 0000 0019 0048 0205 0249 0273 0279 0296

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4






5 Discussion



0200022002400260

CP 1

024002600280

CP 2

0010

0015

0020

CP 3

5 10 15 20 25 30

001500200025

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Pb000 024018017016015012010009008006

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4









01400150

0160

CP 1

016501700175

CP 2

0000

0100

0200

CP 3

5 10 15 20 25 30 35 40 45 50

0100015002000250

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Pb 007 011 012 013 015 019 023 025 026

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4






022002300240

CP 1

023002400250

CP 2

0100

0120

CP 3

5 10 15 20 25 300200030004000500

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Ni 00101 00198 00369 00534 00596 00711 00723

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)

C P 4

C P 3







0050

0060

0070

CP 1

007200740076

CP 2

0000002000400060

CP 3

5 10 15 20 25 30 35 40 45 50

004000600080

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


19131835171312330327010900820030Ni 0013

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4



6 Conclusions





1400

1600

CP 1

1600

1800

CP 2

0000020004000600

CP 3

5 10 15 20 25 30

0500

1000

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)





Data Availability




References






























































































Journal of












Journal of







Volume 2018






Volume 2018









tide

hydrological flow

000-025-050-075-100-125-150-175-200-225-250-275-300

(m)



































V 14011008005002000300008 0003 0001(ms)

0510152025303540(a)

(b)

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540


Cd 0013300093000930007800072000660006300061 C P 1

C P 2C P 3

C P 4

(mgL)

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540












0004000600080010

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)

CP 1

0007000700080008

CP 2

0006000700080009

CP 3

5 10 15 20 25 30 35 40 45 50

0002000400060008

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4


Cd 00440041004000390034002500240018001000070000

(Km)

0510152025303540

108 215 323 431 538 646 754 862 969 1077 1185 1292 1400

(mgL)

C P 4

C P 3C P 2

C P 1








CP 1

CP 2

CP 3

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

0032003400360038

00340036003800400042

0010

0015

001500200025

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Cr 00000 00210 00367 00551 00639 00718 0731

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4









CP 1

CP 2

CP 3

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

0060

0065

007000720074

000000200040

0040006000800100

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Cr 0000 0019 0048 0205 0249 0273 0279 0296

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4






5 Discussion



0200022002400260

CP 1

024002600280

CP 2

0010

0015

0020

CP 3

5 10 15 20 25 30

001500200025

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Pb000 024018017016015012010009008006

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4









01400150

0160

CP 1

016501700175

CP 2

0000

0100

0200

CP 3

5 10 15 20 25 30 35 40 45 50

0100015002000250

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Pb 007 011 012 013 015 019 023 025 026

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4






022002300240

CP 1

023002400250

CP 2

0100

0120

CP 3

5 10 15 20 25 300200030004000500

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Ni 00101 00198 00369 00534 00596 00711 00723

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)

C P 4

C P 3







0050

0060

0070

CP 1

007200740076

CP 2

0000002000400060

CP 3

5 10 15 20 25 30 35 40 45 50

004000600080

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


19131835171312330327010900820030Ni 0013

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4



6 Conclusions





1400

1600

CP 1

1600

1800

CP 2

0000020004000600

CP 3

5 10 15 20 25 30

0500

1000

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)





Data Availability




References






























































































Journal of












Journal of







Volume 2018






Volume 2018





























V 14011008005002000300008 0003 0001(ms)

0510152025303540(a)

(b)

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540


Cd 0013300093000930007800072000660006300061 C P 1

C P 2C P 3

C P 4

(mgL)

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540












0004000600080010

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)

CP 1

0007000700080008

CP 2

0006000700080009

CP 3

5 10 15 20 25 30 35 40 45 50

0002000400060008

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4


Cd 00440041004000390034002500240018001000070000

(Km)

0510152025303540

108 215 323 431 538 646 754 862 969 1077 1185 1292 1400

(mgL)

C P 4

C P 3C P 2

C P 1








CP 1

CP 2

CP 3

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

0032003400360038

00340036003800400042

0010

0015

001500200025

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Cr 00000 00210 00367 00551 00639 00718 0731

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4









CP 1

CP 2

CP 3

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

0060

0065

007000720074

000000200040

0040006000800100

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Cr 0000 0019 0048 0205 0249 0273 0279 0296

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4






5 Discussion



0200022002400260

CP 1

024002600280

CP 2

0010

0015

0020

CP 3

5 10 15 20 25 30

001500200025

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Pb000 024018017016015012010009008006

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4









01400150

0160

CP 1

016501700175

CP 2

0000

0100

0200

CP 3

5 10 15 20 25 30 35 40 45 50

0100015002000250

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Pb 007 011 012 013 015 019 023 025 026

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4






022002300240

CP 1

023002400250

CP 2

0100

0120

CP 3

5 10 15 20 25 300200030004000500

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Ni 00101 00198 00369 00534 00596 00711 00723

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)

C P 4

C P 3







0050

0060

0070

CP 1

007200740076

CP 2

0000002000400060

CP 3

5 10 15 20 25 30 35 40 45 50

004000600080

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


19131835171312330327010900820030Ni 0013

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4



6 Conclusions





1400

1600

CP 1

1600

1800

CP 2

0000020004000600

CP 3

5 10 15 20 25 30

0500

1000

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)





Data Availability




References






























































































Journal of












Journal of







Volume 2018






Volume 2018









0004000600080010

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)

CP 1

0007000700080008

CP 2

0006000700080009

CP 3

5 10 15 20 25 30 35 40 45 50

0002000400060008

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4


Cd 00440041004000390034002500240018001000070000

(Km)

0510152025303540

108 215 323 431 538 646 754 862 969 1077 1185 1292 1400

(mgL)

C P 4

C P 3C P 2

C P 1








CP 1

CP 2

CP 3

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

0032003400360038

00340036003800400042

0010

0015

001500200025

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Cr 00000 00210 00367 00551 00639 00718 0731

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4









CP 1

CP 2

CP 3

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

0060

0065

007000720074

000000200040

0040006000800100

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Cr 0000 0019 0048 0205 0249 0273 0279 0296

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4






5 Discussion



0200022002400260

CP 1

024002600280

CP 2

0010

0015

0020

CP 3

5 10 15 20 25 30

001500200025

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Pb000 024018017016015012010009008006

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4









01400150

0160

CP 1

016501700175

CP 2

0000

0100

0200

CP 3

5 10 15 20 25 30 35 40 45 50

0100015002000250

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Pb 007 011 012 013 015 019 023 025 026

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4






022002300240

CP 1

023002400250

CP 2

0100

0120

CP 3

5 10 15 20 25 300200030004000500

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Ni 00101 00198 00369 00534 00596 00711 00723

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)

C P 4

C P 3







0050

0060

0070

CP 1

007200740076

CP 2

0000002000400060

CP 3

5 10 15 20 25 30 35 40 45 50

004000600080

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


19131835171312330327010900820030Ni 0013

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4



6 Conclusions





1400

1600

CP 1

1600

1800

CP 2

0000020004000600

CP 3

5 10 15 20 25 30

0500

1000

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)





Data Availability




References






























































































Journal of












Journal of







Volume 2018






Volume 2018









CP 1

CP 2

CP 3

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

0032003400360038

00340036003800400042

0010

0015

001500200025

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Cr 00000 00210 00367 00551 00639 00718 0731

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4









CP 1

CP 2

CP 3

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

0060

0065

007000720074

000000200040

0040006000800100

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Cr 0000 0019 0048 0205 0249 0273 0279 0296

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4






5 Discussion



0200022002400260

CP 1

024002600280

CP 2

0010

0015

0020

CP 3

5 10 15 20 25 30

001500200025

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Pb000 024018017016015012010009008006

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4









01400150

0160

CP 1

016501700175

CP 2

0000

0100

0200

CP 3

5 10 15 20 25 30 35 40 45 50

0100015002000250

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Pb 007 011 012 013 015 019 023 025 026

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4






022002300240

CP 1

023002400250

CP 2

0100

0120

CP 3

5 10 15 20 25 300200030004000500

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Ni 00101 00198 00369 00534 00596 00711 00723

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)

C P 4

C P 3







0050

0060

0070

CP 1

007200740076

CP 2

0000002000400060

CP 3

5 10 15 20 25 30 35 40 45 50

004000600080

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


19131835171312330327010900820030Ni 0013

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4



6 Conclusions





1400

1600

CP 1

1600

1800

CP 2

0000020004000600

CP 3

5 10 15 20 25 30

0500

1000

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)





Data Availability




References






























































































Journal of












Journal of







Volume 2018






Volume 2018









CP 1

CP 2

CP 3

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

0060

0065

007000720074

000000200040

0040006000800100

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Cr 0000 0019 0048 0205 0249 0273 0279 0296

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4






5 Discussion



0200022002400260

CP 1

024002600280

CP 2

0010

0015

0020

CP 3

5 10 15 20 25 30

001500200025

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Pb000 024018017016015012010009008006

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4









01400150

0160

CP 1

016501700175

CP 2

0000

0100

0200

CP 3

5 10 15 20 25 30 35 40 45 50

0100015002000250

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Pb 007 011 012 013 015 019 023 025 026

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4






022002300240

CP 1

023002400250

CP 2

0100

0120

CP 3

5 10 15 20 25 300200030004000500

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Ni 00101 00198 00369 00534 00596 00711 00723

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)

C P 4

C P 3







0050

0060

0070

CP 1

007200740076

CP 2

0000002000400060

CP 3

5 10 15 20 25 30 35 40 45 50

004000600080

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


19131835171312330327010900820030Ni 0013

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4



6 Conclusions





1400

1600

CP 1

1600

1800

CP 2

0000020004000600

CP 3

5 10 15 20 25 30

0500

1000

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)





Data Availability




References






























































































Journal of












Journal of







Volume 2018






Volume 2018









0200022002400260

CP 1

024002600280

CP 2

0010

0015

0020

CP 3

5 10 15 20 25 30

001500200025

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Pb000 024018017016015012010009008006

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4









01400150

0160

CP 1

016501700175

CP 2

0000

0100

0200

CP 3

5 10 15 20 25 30 35 40 45 50

0100015002000250

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Pb 007 011 012 013 015 019 023 025 026

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4






022002300240

CP 1

023002400250

CP 2

0100

0120

CP 3

5 10 15 20 25 300200030004000500

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Ni 00101 00198 00369 00534 00596 00711 00723

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)

C P 4

C P 3







0050

0060

0070

CP 1

007200740076

CP 2

0000002000400060

CP 3

5 10 15 20 25 30 35 40 45 50

004000600080

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


19131835171312330327010900820030Ni 0013

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4



6 Conclusions





1400

1600

CP 1

1600

1800

CP 2

0000020004000600

CP 3

5 10 15 20 25 30

0500

1000

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)





Data Availability




References






























































































Journal of












Journal of







Volume 2018






Volume 2018









01400150

0160

CP 1

016501700175

CP 2

0000

0100

0200

CP 3

5 10 15 20 25 30 35 40 45 50

0100015002000250

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Pb 007 011 012 013 015 019 023 025 026

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4






022002300240

CP 1

023002400250

CP 2

0100

0120

CP 3

5 10 15 20 25 300200030004000500

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Ni 00101 00198 00369 00534 00596 00711 00723

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)

C P 4

C P 3







0050

0060

0070

CP 1

007200740076

CP 2

0000002000400060

CP 3

5 10 15 20 25 30 35 40 45 50

004000600080

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


19131835171312330327010900820030Ni 0013

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4



6 Conclusions





1400

1600

CP 1

1600

1800

CP 2

0000020004000600

CP 3

5 10 15 20 25 30

0500

1000

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)





Data Availability




References






























































































Journal of












Journal of







Volume 2018






Volume 2018









022002300240

CP 1

023002400250

CP 2

0100

0120

CP 3

5 10 15 20 25 300200030004000500

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


Ni 00101 00198 00369 00534 00596 00711 00723

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)

C P 4

C P 3







0050

0060

0070

CP 1

007200740076

CP 2

0000002000400060

CP 3

5 10 15 20 25 30 35 40 45 50

004000600080

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


19131835171312330327010900820030Ni 0013

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4



6 Conclusions





1400

1600

CP 1

1600

1800

CP 2

0000020004000600

CP 3

5 10 15 20 25 30

0500

1000

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)





Data Availability




References






























































































Journal of












Journal of







Volume 2018






Volume 2018









0050

0060

0070

CP 1

007200740076

CP 2

0000002000400060

CP 3

5 10 15 20 25 30 35 40 45 50

004000600080

Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

5 10 15 20 25 30 35 40 45 50Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)


19131835171312330327010900820030Ni 0013

108 215 323 431(Km) 538 646 754 862 969 1077 1185 1292 1400

0510152025303540

(mgL)C P 1

C P 2C P 3

C P 4



6 Conclusions





1400

1600

CP 1

1600

1800

CP 2

0000020004000600

CP 3

5 10 15 20 25 30

0500

1000

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)





Data Availability




References






























































































Journal of












Journal of







Volume 2018






Volume 2018









1400

1600

CP 1

1600

1800

CP 2

0000020004000600

CP 3

5 10 15 20 25 30

0500

1000

Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

5 10 15 20 25 30Days

CP 4

C (m

gL)

C (m

gL)

C (m

gL)

C (m

gL)





Data Availability




References






























































































Journal of












Journal of







Volume 2018






Volume 2018


































































































Journal of












Journal of







Volume 2018






Volume 2018








numerical modelling of heavy metal dynamics in a river

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