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Numerical modeling to define remediation actions forwater quality in streamsA.N. Menendezab, E.A. Lecertúaab, N.D. Badanoab & P.E. Garcíaab

a Hydraulics Laboratory, National Institute for Water (INA), Buenos Aires, Argentinab LaMM, Department of Engineering, University of Buenos Aires, Buenos Aires, ArgentinaPublished online: 18 Aug 2015.

To cite this article: A.N. Menendez, E.A. Lecertúa, N.D. Badano & P.E. García (2015): Numerical modeling todefine remediation actions for water quality in streams, Journal of Applied Water Engineering and Research, DOI:10.1080/23249676.2015.1072850

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Journal of Applied Water Engineering and Research, 2015http://dx.doi.org/10.1080/23249676.2015.1072850

Numerical modeling to define remediation actions for water quality in streams

A.N. Menendeza,b∗, E.A. Lecertúaa,b, N.D. Badanoa,b and P.E. Garcíaa,b

aHydraulics Laboratory, National Institute for Water (INA), Buenos Aires, Argentina; bLaMM, Department of Engineering, Universityof Buenos Aires, Buenos Aires, Argentina

(Received 20 March 2015; accepted 8 July 2015 )

Numerical modeling can play a key role in establishing an adequate strategy to achieve specific goals regarding waterquality in streams. This is illustrated through an application to a big water basin with both rural and urbanized zones, andwith degraded water quality. One-dimensional unsteady hydrodynamic modeling, linked to a hydrological model, is used. Itis shown that the identification of the main pollutant sources, the estimation of their associated loads, and the assessment ofnon-point contributions, constitute a fundamental step in the model buildup. Only the relevant mechanisms are included inthe water quality model. A challenging statistical criterion is proposed in order to calibrate the model. This model constitutedthe backbone for the formulation of the integral strategy adopted to remediate the water quality of the river, including thereduction in biochemical oxygen demand loads from big industries and water treatment plants, and the location and designparameters of aeration stations.

Keywords: water quality modeling; stream pollution; remediation actions; non-point sources

IntroductionThe quantity and quality of data needed to interpret,through statistical analysis, the relationship between waterquality in streams and pollutant sources (Chang 2008)is seldom available. Moreover, projections of changes inwater quality due to interventions on pollutant sourcescannot be accounted for with this methodology. Hence,numerical modeling constitutes often the fundamental toolin order to undertake water quality assessment.

One-dimensional (1D) water quality modeling of riverstretches using quasi-steady-state approaches are presentlya relatively common task, either to produce diagnoses(Palmieri & Carvalho 2006; Park & Lee 2002) or totest management strategies (Kannel et al. 2007; Paliwalet al. 2007). Application of unsteady approaches, linked tohydrological modeling, is scarcer (Krysanova et al. 1998;Tong & Chen 2002; Easton et al. 2008). Unsteady simula-tion is necessary when reaching sea inlets; if well mixed,1D modeling (Fortes Lopes et al. 2008) or two-dimensionalmodeling (Menéndez et al. 2013) is sufficient; in the oppo-site case, fully three-dimensional modeling should be used(Zheng et al. 2004).

Calibration of the water quality model is usually under-taken manually, that is, through a trial-and-error proce-dure. However, different strategies for automatic calibra-tion have been proposed (Whitehead et al. 1981; Sincocket al. 2003; Yuceer et al. 2007; Mannina & Viviani 2010).

*Corresponding author. Email: angel.menendez@speedy.com.ar

Identifiability analysis for selecting the most relevantparameters in the model is a crucial issue (Reichert & Van-rolleghem 2001; Marsili-Libelli & Giusti 2008; Freni et al.2011), since insensitivity to model parameters leads to ahigh level of output uncertainty. In this respect, inclusionof only the relevant mechanisms in the water quality modelis recommended (Vanrolleghem et al. 2001).

A distinction between point and non-point contri-butions to the instream total load is not an easy task(Azzellino et al. 2006). The unsteadiness of non-pointsources poses a further challenge (Lai et al. 2011; Li et al.2011).

Beyond this established knowledge, challenges forpractical applications of numerical modeling remain, espe-cially to circumvent deficiencies in available informationin order to produce the necessary data to undertake a reli-able diagnosis, and test the effects of proposed works andmanagement policies. Some of the main practical issuesare: (i) possibility of analysis at the basin scale and ona time-continuous basis; (ii) identification and representa-tion of a multiplicity of pollutant sources; (iii) estimation ofloads from the pollutant sources; (iv) calibration strategiesand criteria limited by the available data; and (v) designof an adequate strategy to achieve specific goals regard-ing water quality. They are addressed in the applicationpresented in this paper, involving the Matanza-RiachueloRiver Basin (Argentina), in which numerical modeling

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was used to define the type and dimensions of the actionsneeded to achieve a specific level of water quality, takinginto account the whole set of pollutant sources throughoutthe basin. 1D unsteady modeling, linked to a hydrologicalmodel, was applied. Assessment of non-point contributionswas a central issue. Only the relevant mechanisms wereincluded in the water quality model.

The Matanza-Riachuelo River, about 64 km long, dis-charges toward the Plata River (actually, an estuary),located at approximately 35° S on the eastern coast ofSouth America (Figure 1). Its basin, which includes asignificant part of the city of Buenos Aires, has an area

of about 2240 km2. The population living in the basinamounts to around 5 million inhabitants. In its lowerreach, the hydrologic river discharge (i.e. the one gen-erated only by rain) ranges between 0.1 m3/s (exceeded90% of the time) and 12 m3/s (10%). On top of that thereare significant anthropic discharges (with water importedmostly from the Plata River and the rest from groundwa-ter). Additionally, the tidal wave penetrating from the PlataRiver introduces a large oscillation in the discharge, withamplitudes of the order of 40 m3/s close to the Matanza-Riachuelo River mouth, which attenuates relatively fasttoward the upstream direction.

Figure 1. Matanza-Riachuelo Basin.

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A significant amount of population of the basinlive in homes not connected to the sewage system, forwhich expansion plans have been formulated, but not yetimplemented.

The basin has been the site of industrial activities ofdifferent types for many decades. The industries used to belocated in the urbanized lower basin, but in the last decadesbig factories have been installed in the mostly rural middleand upper basins. The total number of industries generatingliquid effluents is around 4000.

The high pollutant load contribution from both treatedand untreated effluents to the Matanza-Riachuelo River hasled to the breakdown of its water quality, with the associ-ated degradation of the neighborhoods along its banks. In2006, the Supreme Court of Justice, taking into considera-tion a claim from a group of 150 inhabitants of the basin,summoned the national and regional authorities to set up aremediation plan for water, air, and social conditions. Thenumerical modeling study presented in this paper has con-stituted the backbone for the formulation of the integralstrategy adopted to remediate the water quality of the river.

MethodologyStrategy of the study1D hydrodynamic and water quality numerical modelsof the Matanza-Riachuelo River and its main tributarieswere built. The hydrodynamic model is driven by runoff,anthropic discharges, and the tidal wave. In turn, thehydrodynamic model and the loads associated to the pol-lutant sources drive the water quality model. Figure 2schematizes these relationships. The implementation andcalibration of these models are described below.

The numerical models are used to: (i) perform a diag-nosis for the present water quality situation, from whichthe conceptual design of remediation actions are defined,leading to the formulation of a remediation plan and (ii)

Figure 2. Flow chart of the relations among models.

test and eventually adjust the proposed remediation plan,in order to achieve specific quantitative goals.

Hydrodynamic modelThe use of 1D hydrodynamic modeling (based on SaintVenant equations) to simulate the longitudinal distribu-tion of river water levels and currents is well established(Cunge et al. 1980). Two conditions justify the chosen 1Dapproach for the present problem: (i) the high ratio betweenriver length and transversal (width, depth) dimensions and(ii) the objective of the remediation plan, which aims atproducing satisfactory water quality levels at a basin scale.Proprietary software Mike 11, from DHI, was used (DHI2012a).

In addition to the Matanza-Riachuelo River, the hydro-dynamic model includes its six main tributary creeks(Rodriguez, Cañuelas, Chacón, Morales, Aguirre, andOrtega), as indicated in Figure 3. A total of 84 cross sec-tions were used to represent the geometry of the watercourses. Additional cross sections were built through inter-polation, reaching a total of 345 (see Table 1), leading to aspatial step of around 450 m. The spatial step is then aboutfour times smaller than the geometrical scale of resolution;additionally, it is much smaller than the extension of theflood waves (of the order of the basin longitudinal length)and the wavelength of the tidal wave (much higher thanthe basin longitudinal length), so it was considered a pri-ori a sound choice. Some sensitivity tests were performedinitially for higher spatial steps, showing differences belowthe uncertainty range of the recorded data.

Three roughness zones were distinguished: (I) thelower 10 km reach of the Matanza-Riachuelo River, themain siltation zone; (II) the following 16 km stretch,where tidal effects are still significant; and (III) the restof the water courses. The corresponding Manning rough-ness coefficient values were adjusted during calibration(see below).

The contributions from runoff were provided by ahydrologic model (see below). A fraction of the contri-bution from each sub-basin was used as the upstreamboundary condition for the corresponding creek (the frac-tion was the relative area of the drainage basin to this point,highlighted in Figure 3); the rest was distributed as a lateraldischarge toward the creek and/or river stretch. The estima-tion procedure for anthropic contributions from domesticand industrial sources was made together with the one forpollutant loads, described below. The tide was imposedas the downstream boundary condition at the Matanza-Riachuelo mouth, using hourly records provided by theNaval Hydrographic Service of Argentina (SHN).

A spatially lumped, time-continuous hydrologic modelwas built, based on the conceptual model NAM (DHI2012a), incorporated within Mike 11. The basin was sub-divided into 12 sub-basins, as shown in Figure 3. Theirgeometric parameters were determined from a Digital

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Figure 3. Schematization of Matanza Riachuelo Basin; gross blue lines: Matanza-Riachuelo River (including its Drainage Channel, DC).Red lines: tributaries represented in hydrodynamic model; green lines: urban creeks; striped zones: upper area of the creeks sub-basinsconsidered as a point contribution at the head of the creek (red dot).

Table 1. Number of cross sections for the 1D hydrody-namic model.

Number of cross sections

Water course Length (km) Original Interpolated Total

Matanza-Riachuelo

72.1 54 113 167

Rodriguez 9.9 3 18 21Cañuelas 13.7 4 26 30Chacón 11.5 3 22 25Morales 26.2 14 44 58Aguirre 13.9 3 26 29Ortega 6.7 3 12 15

Elevation Model built from the topographic information(contour lines) provided by the National Geographic Insti-tute of Argentina (IGN). Daily time series for precipitationwere available from the National Meteorological Serviceof Argentina (SMN).

In order to feed the hydrodynamic model, the wateryield from each sub-basin (as provided by the hydrologicmodel) was distributed as an input along the corresponding

water course (river stretch or creek), except for the fractioncorresponding to the upper area of the creeks sub-basins,which was considered as a point contribution at the headof the creek, as schematized in Figure 3.

From the ratio of the spatial step and the maximumcelerity of the gravity waves (about 10 m/s, based on amaximum water depth of about 10 m at the river mouth),a time step of the order of 50 s arises as convenient foraccuracy reasons. It was fixed at 30 s.

The calibration of the hydrodynamic model was basedon the comparison of predicted and observed dischargetime series. On the one hand, observations made duringthe 1960s at Riccheri station (see Figure 1), where thecontribution of anthropic discharges and tidal effects wereinsignificant at that time (the drainage channel, DC, wasnot yet built, see Figure 3), were used to calibrate the modelresponse to runoff, as illustrated in Figure 4(a). On theother hand, observations for present-day conditions at sta-tions close to the Matanza-Riachuelo mouth were utilizedto calibrate the model response to tide – adjusting Man-ning coefficient values for the different roughness zonesto (I) 0.04, (II) 0.025, and (III) 0.015 – and to the lower

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(a) (b)

Figure 4. Calibration of hydrodynamic model for: (a) response to runoff; (b) response to tide and lower basin base flow. Red dots:observations; blue line: model.

basin base flow – adjusting some anthropic discharges – asillustrated in Figure 4(b).

Water quality modelThe 1D water quality model includes advection, dif-fusion/dispersion, transformation, and source/sink terms(Jorgensen 1994). It was handled through software Mike11 + ECOLAB, from DHI (2012b). However, for theinclusion of the effects of aeration stations (see below), aspecial application was developed (MaRiOD) which runsin series with the former one.

The aim of the remediation plan is to increase the dis-solved oxygen (DO) concentration of the water column(see below), particularly affected by high BOD (biochem-ical oxygen demand) input. Hence, the balance equationsfor DO and BOD were formulated, including the Nitrogencycle through its two main species: Ammonia (NH4) andNitrate (NO3). The corresponding transformation modelwas the following (parameters symbols denote their con-centrations; in the case of NH4 and NO3 they indicate onlyNitrogen content):

dDBOdt

= −KbDO2

KO2 + DO2 θ(T−20)c DBO,

dNH4dt

= −KnDO2

KN2 + DO2 θ(T−20)c NH4,

dNO3dt

= KnDO2

KN2 + DO2 θ(T−20)c NH4

− Kd(ODa − OD)2

KD2 + (ODa − OD)2

× u(ODa − OD)θ (T−20)c NO3,

dDODdt

= −Kaθ(T−20)c DOD + Kb

DO2

KO2 + DO2 θ(T−20)c DBO

+ KnDO2

KN2 + DO2 θ(T−20)c rN NH4 + SOD

R,

where DOD = DOs − DO is the DO deficit, with DOsbeing the saturation value for DO; K,b, Kn, Kd, and Ka,the biodegradation, nitrification, denitrification and reaera-tion coefficients, respectively; rN the rate of oxygen uptakeper unit of ammonia nitrogen oxidation; KO, KN and KDthe half-saturation constants for Oxygen, Ammonia andNitrate; u( . . . ) the step function ( = 1 if argument ispositive; = 0 if argument is negative); ODa a thresholdvalue for OD below which denitrification takes place; θCis the Arrhenius factor; T is the water temperature; SODis the Sediment Oxygen Demand; and R is the hydraulicradius. Note that first-order reactions were considered forthe decay processes, but corrected with Michaelis–Menten-type factors (though quadratic) to account for the reactionrate reduction due to low DO values.

Standard values, within the technical literature-reported ranges (USEPA 1987; Garcia-Ruiz et al. 1998;Stright 1999; Henze et al. 2008), were adopted forthe model parameters, without further refinement: rN =4.57 gO2/gN-NH4 (stoichiometric ratio), KO = 1.4 mg/l,KN = 2.0 mg/l, KD = 0.5 mg/l, and θC = 1.01. Thethreshold value was fixed at ODa = 2 mg/l. The water tem-perature was represented through a time series of monthlyaveraged values taken at the inlet of Central Puerto PowerPlant, located close to the river mouth (and provided byEndesa, the Electricity Company), as no significant tem-perature variations along the water courses were detectedfrom measurements.

For the biodegradationcoefficient the reported rangewas considered (USEPA 1985), but a decreasing spatialdistribution in the downstream direction was assumed dueto the following reasons: (a) when water depth increases,a relatively lower volume comes in contact with the bot-tom biological community, which is the prime oxidationagent and (b) the downstream water contains a rela-tively higher portion of oxidation refractory components(USEPA 1997). Figure 5 shows the adopted distribu-tion along the Matanza-Riachuelo River for the presentsituation, after calibration; the one assumed for the project

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Figure 5. Spatial distributions of: biodegradation coefficient Kb for present situation (blue line) and project scenario (green line);reference SOD for project scenario (yellow line); and depth (red line).

scenario, higher (conservative hypothesis) and modulatedaccording to the water depth, is also shown.

The nitrification coefficient was fixed at 0.04 day−1,the same value as for the Plata River (Menéndez et al.2013). The denitrification coefficient was adjusted to 1.2day−1 during calibration, but the effective denitrificationrate is much lower as it quickly reduces to reportedvalues (USEPA 1985, 1988) when DO increases due to theMichaelis–Menten-type factor.

Re-aeration formulae were applied for Ka: Thyssenet al. (1983) for water depths below 0.2 m (creeks);O’Connors and Dubins (1958) for water depths above 0.5m (ordinary rivers); and an interpolation between the twoformulae for intermediate water depths (in order to providea smooth transition between them).

A linear relationship was assumed between SOD andDO (representative of a diffusion process, but taking intoaccount that DO concentration is very low within thesediment):

SOD = SODrefDO

DOref,

where SODref is a reference value for SOD when DOequals DOref, which was fixed at 8 g/l. Different longitu-dinal SODref distributions were assigned to the present andproject scenarios, both of them decreasing in the upstreamdirection due to decreasing urbanization density (hence,lower loads) and backwater effects (hence, lower siltation).For the former scenario, a value of around 6 g/m2/day forthe lower reach was estimated from in situ measurementsperformed by the water supply company (AySA 2012);however, SOD plays only a marginal role in the presentsituation, due to the persistent low DO values. Figure 4(b)shows the assumed distribution for the project scenario,with a value of 1 g/m2/day at the lower reach, corre-sponding to ‘estuarine mud’ (USEPA 1985); this assumesthat mud stabilization, regarding BOD emission, has beenachieved, as discussed below.

LoadsEstablishing the pollutant loads (which includes the asso-ciated water discharges) is a key issue for water qualitymodeling. This requires a thorough analysis of pollu-tant sources and the development of proper methodolo-gies in order to calculate the loads based on the avail-able data, generally scarce and discontinuous. Pollutantloads from many different sources were identified: (i) dis-charges from waste water treatment plants (which includeboth domestic and properly pre-treated industrial efflu-ents); (ii) non-collected domestic discharges (from zonesnot connected to the sewage system); (iii) dischargesfrom industries not connected to the sewage system; (iv)storm water runoff; (vi) emission from bottom sediments;and (vii) discharges from urban creeks, in the populatedareas of the lower basin, collecting a series of domes-tic and industrial sources not captured by the previousanalyses.

There exist seven waste water plants in the basin, whichtime average loads were known or estimated. They wereconsidered as point sources. Their location and input pointto the water quality model are indicated in Figure 6(a)(some decay was estimated along the non-representedstretches from plant location to the input point). The usualwater discharge and BOD concentration for the major one(‘Sudoeste’ Plant) are about 2 m3/s and 30 mg/l, respec-tively, leading to a BOD load of above 5 ton/day. There isalso a discharge from a small treatment plant for percolat-ing water from a sanitary landfill.

Non-collected domestic discharges were considered asdiffuse sources. Their time average values were estimated,for each county, through a calculation procedure basedon population census data which distinguishes amongdifferent sanitary facilities (sewer, sceptic tank, latrine,cesspool), hence allowing a partition of loads. Losses dueto infiltration and decay were considered in the calculation.The resulting loads per county were subdivided accordingto county subdivisions based on population density, and

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(a)

(b)

Figure 6. Location of loads; (a) sources (P) and corresponding input points (long arrows) for waste water treatment plants; input points(short arrows) for diffuse domestic discharges (the codes are associated to the county names); (b) sources (points) for big industriesassociated to BOD; zones (painted) and input points (arrows) for diffuse industrial discharges associated to BOD.

assigned to points along the water courses, as shown inFigure 6(a).

For the discharges from industries not connected to thesewage system, a mixed approach was undertaken, namely

big industries were considered as point sources (for whichtime-averaged loads were known or estimated), and the restof them were treated as diffuse sources. For example, inthe case of BOD about 50 facilities (Figure 6(b)) explained

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Figure 7. Longitudinal distribution of parameters for the present situation. Black crosses: observations; red line: 10% exceedance; blueline: 50% exceedance; and green line: 90% exceedance (the opposite for DO).

about 75% of the total load; hence, they were treatedas point sources. On the other hand, the time-averagedcontributions from the diffuse sources were determinedbased on emission factors for the following five indus-try types: (i) metallurgy, electroplating, melting, automo-tive spare parts; (ii) beverages, food and derivatives; (iii)textile, footwear, leather, fur; (iv) chemistry, plastic, phar-macy, rubber, paper; and (v) others. These diffuse sourceswere grouped into zones, for which effective loads (includ-ing losses) were determined and plugged into the waterquality model, as indicated in Figure 6(b).

Storm water runoff was considered as a diffuse source.Pollutant concentrations were assigned to the time series ofthe water discharge arising from the hydrologic model (see

above), according to a land-use zoning for three differentcategories, from the upper to the lower Matanza-Riachuelobasin: rural (1 mg/l for BOD), mildly urbanized (5 mg/l),and strongly urbanized (11 mg/l). Note that this is the onlycontribution for which time-continuous – instead of timeaveraged – inputs were established.

The emission from bottom sediments is considered as alongitudinal distribution for SOD, according to the aboveexplained model.

Discharges from urban creeks (Figure 3) were esti-mated through available (scarce) measurements. Theyinclude Santa Catalina, del Rey and the ones from theCildañez creek and its drainage channel; the latter bringspollutants from the neighboring Maldonado Basin.

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Calibration of water quality modelThe time step used for the water quality model was thesame as for the hydrodynamic model (30 s), small enoughto adequately represent the transport and transformationtime scales.

In order to calibrate the water quality model, a com-parison of the predicted pollutant concentrations withobservations must be performed.

Observations of pollutant concentrations for theMatanza-Riachuelo River correspond to a series of instan-taneous samplings for a set of stations, taken with aseasonal period (about three months). They are associ-ated with different instantaneous hydrologic stages (evenfor the same sampling campaign). Additionally, relativelysignificant inter-annual variations in pollutant loads havetaken place throughout the basin. Taking also into accountthat only time-averaged values are used for the most sig-nificant pollutant loads of the water quality model, it isconcluded that it is not possible to undertake a time-continuous comparison. Then, a statistical approach wasadopted.

Now, the scarcity of concentration data for each sta-tion (and variability of the loads) does not allow buildingreliable statistics for each one of them. Hence, an overallstatistical criterion was employed, according to the follow-ing methodology: (a) the water quality model was run forthe time window 1 January 2010–31 December 2011 (twoyears), as representative of the present basin status; (b)histograms for the calculated concentrations of the waterquality parameters were produced for each spatial node,from which the spatial envelopes for different percentileexceedances were obtained; in particular, for the 10% and90% exceedances, which determine a longitudinal fringe of‘maximum likelihood’ for each parameter; (c) all the con-centration observations for those two years were plotted onthe envelopes graph; (d) adjustments in loads from diffusesources (specially for NH4 and NO3), within their uncer-tainty intervals, were performed in order to achieve the bestpossible match between the total relative quantity of obser-vations lying below the 90% exceedances envelopes andwithin the maximum likely fringes, with their correspond-ing theoretical percentages (10% and 80%, respectively),for all the water quality parameters.

Figure 7 shows the envelopes (including the 50%exceedance) and the maximum likely fringes for DO,BOD, NH4, and NO3 after calibration, together with thecorresponding observations (the axis is the longitudinaldistance measured in the upstream direction from the rivermouth). Note that most of the measured concentration val-ues tend to lie within the maximum likely fringe, indicatingthat the model correctly represents the general trends ofthe measured longitudinal distributions. From the quanti-tative point of view, Table 2 indicates the percentage ofobservations lying below the 90% envelope, and within themaximum likely fringe. The agreement with the theoretical

Table 2. Percentage of observations lying within modelpredictions’ percentile ranges.

ParameterBelow 90% (10%)

(%)Within 10%/90%

(80%) (%)

DO 13 83BOD 15 73NH4 13 66NO3 23 60

values (10% and 80%, respectively) is considered quite sat-isfactory for OD, good for BOD and NH4, and fair forNO3. However, due to the relatively low NO3 values andthe fact that this discrepancy has no implications in theDO balance, no further attempt was made to improve theagreement.

Present situationDiagnosisThe calibrated water quality model was used in order toformulate a diagnosis on the present water quality status ofthe Matanza-Riachuelo River.

Figure 7 shows that nearly anoxic conditions are fre-quently reached all along the river, and especially in thelower reach. The increment of DO close to the river mouthis due to mixing with the Plata River waters under tidalaction. If anoxia is conventionally established as corre-sponding to DO < 2 mg/l, it is observed that the river canbe considered as anoxic much more than 50% of the timefor the major part of its extension. Anoxia is associatedto persistent bad smell in the river neighborhood, whichstands – according to inquiries – as one of the main reasonsfor social unwillingness to settle in that area, thus leav-ing the site for low-quality settlements and economicallymarginal activities.

Figure 7 also indicates that the organic matter contentof the water column is extremely high all along the river,which is the main cause of oxygen depletion. DBO con-centrations well above the saturation value for DO make itimpossible for the river to neutralize pollution.

Figure 8(a) shows the BOD, NH4, and NO3 daily loadsto the river, discriminated by source (annual median valueswere considered for storm water runoff). It is observed thatthe major contributions for the former two parameters arethe non-collected domestic sources. Big industries standas the second largest contribution to BOD. Note that thecontributions from the water treatment plants are relativelyhigh for the three parameters; in the case of BOD, thisoccurs in spite of producing a big drop in the concentra-tion of the pre-treated waters, as the total water dischargefrom the plants is very significant relative to the naturalriver discharge.

In Figure 8(b), the longitudinal distribution of time-averaged contributions – per unit length – from the

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(a)

(b)

Figure 8. Present situation: (a) loads per parameter and source;(b) longitudinal distribution of contributions to DO.

different mechanisms to the DO balance, for the lower riverreach, are displayed. It is observed that the two main com-ponents are re-aeration (as a source) and biodegradation(as a sink), tending to compensate each other. The contri-bution of nitrification (sink) is rather small, except closeto the river outlet – where tidal action takes place. SOD ispractically insignificant.

Remediation frameworkFrom the preceding results, it turns out that significantreductions in pollutant loads to the river should be achievedin order to reach an observable improvement in waterquality. The following conceptual actions on the major pol-lutant sources were identified: (i) control of non-collected

domestic discharges (through expansion of the sewage sys-tem, which is also a necessity from the social point ofview); (ii) increase in the BOD removal efficiency of theexisting and future (in view of the sewage system expan-sion) water treatment plants; and (iii) reduction in BODloads from industries (through proper treatment). Take intoaccount that the first two actions are linked, since reductionin non-collected domestic discharges implies an increase inthe discharge from the treatment plants.

Now, in order to properly design the remediationactions, specific goals had to be set for the concentrationof the water quality parameters. In view of the complex-ity of the problem – due to the large basin dimensions andthe variety and ubiquity of the pollutant sources – and tak-ing into consideration the primary social requirement forreduction of the bad smell, it was established as the objec-tive for the first-stage remediation plan to achieve the wateruse standard defined as Passive Recreation. This meansrecreation activities pointing to esthetic enjoyment, suchas landscape appreciation, tracking, biking, etc. (NationalHealth & Medical Research Council 1990; WHO 2003).Water quality standards for this water use (and other fiveuses) were defined by a technical commission coordinatedby the Secretary of the Environment (Menéndez 2012). Inparticular, the limiting values established for the modeledparameters were the following: DO > 2 mg/l, BOD < 15mg/l – both of them to be fulfilled 90% of the time – norestriction for NH4 and NO3.

Project scenarioRemediation master planThe remediation master plan (RMP) formulated by theMatanza-Riachuelo Basin Authority (ACuMaR), in orderto achieve the objective of Passive Recreation for theMatanza-Riachuelo River, has three main components.Two of them deal with pollutant sources, and one with theriver assimilation capacity.

The first main component of the RMP refers to thesewage system, which collects mainly domestic discharges(though it also receives industrial effluents previouslytreated to reduce toxic loads to acceptable levels). Theplan includes the following works (Figure 9), undertakenby the Water Company for the Buenos Aires MetropolitanRegion (AySA): (i) expansion of the sewage system (pro-viding nearly total sewer coverage for the projected 2050population); (ii) expansion of two existing water treatmentplants (‘Sudoeste’ and ‘El Jagüel’); (iii) construction oftwo new water plants (‘Laferrere’ and ‘Fiorito’); (iv) con-struction of a Left-Bank Conduit along the left bank of thelower river reach, in order to intercept dry-time loads, andtransport them to the Central Sewage System (CSS); (v)construction of an Industrial Conduit along the right bankof the lower reach, in order to capture treated loads froma tannery industrial pole, and transport them to the CSS.

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Additional works are included in the Master Plan for thesewage system (Coastal Conduit, outfalls, etc.), but theypoint to improve the water quality in the coastal zone ofthe Plata River and, in particular, around the water intakes,so they are not relevant for the present study; this issue wasdescribed in Menendez et al. (2013).

The second component of the RMP deals with the bigindustries not connected to the (expanded) CSS. Recon-version Industrial Plans (RIPs) must be formulated fordifferent industries, in order to improve their environmentalperformance. In particular, the RIPs should lead to lower-ing pollutant loads in the effluents, through reduction inliquid residues and improvement of treatment processes.

The third component of the RMP is the construction ofaeration stations of the SEPA (Sidestream Elevated PoolAeration) type (Butts et al. 2000) in the lower reach, inorder to increment the assimilation capacity of the river,pointing to achieve the goal related to DO ( > 2 mg/l for90% of the time).

HypothesesA sediment flux modeling study was undertaken, basedon the approach by Di Toro (2001), in order to establishthe system response once implemented the remediationplan. Details were reported in Menéndez (2012). Themain result was that stabilization of muds, that is, decayto negligible values of SOD, would be achieved afterabout five years. This time scale is of the same orderof the one necessary to build up the works involved inthe RMP, so it was concluded that the present-day sedi-ment pollution level will be reduced during the buildup

Figure 9. Scheme of waste water disposal project.

phase, until reaching a level compatible with the newsituation, categorized as ‘estuarine mud’, as explainedabove.

In order to increase the margin of safety of the pro-posed solutions, the following hypotheses were made forthe project scenario: (a) 10% of the population will stillremain without connection to the sewer system; (b) thepollutant loads from the diffuse industrial sources locatedin areas not covered by the sewage system expansion willnot change (i.e. no improvements in water treatment areconsidered); and (c) the contribution from the urban creekswill remain as in the present situation.

Though the potential establishment of additional bigindustries (or industrial poles) was not explicitly consid-ered for the project scenario, the information provided bythe model for this component – the total maximum admit-ted industrial pollutant loads for different river stretches,to be shared by all the industries settled in each contribut-ing area – could be used as a restriction criterion for futureindustrial settlements.

Model input to RMPThe water quality model was a key tool for the designof the RMP works, and it was responsible for indicat-ing the necessity to implement aeration stations in orderto achieve the established goal. Specifically, the modelprovided the following inputs to the RMP, as necessaryactions: (i) reduction in BOD of the effluents from the mainwater treatment plants (Sudoeste, El Jagüel, Laferrere, andFiorito) from the present standard of 30 mg/l down to 15mg/l; (ii) reduction in BOD of the effluents from the bigindustries – not connected to the sewer system –, from thepresent standard of 50 mg/l down to 30 mg/l; (iii) instal-lation of six SEPA stations, at specific locations and withspecific water treatment capacities (discharges).

Results and discussionFigure 10 shows the envelopes (including the 50%exceedance) and the maximum likely fringes for DO,BOD, NH4, and NO3 corresponding to the project sce-nario, according to the model. In relation to the presentsituation (Figure 7) it is observed that: (i) there is a strongdrop in BOD, due to the cut in loads; in particular, theobjective of maintaining BOD below 15 mg/l during 90%of the time is clearly fulfilled; (ii) there is a significant ele-vation of DO, especially in the upper reach, due to the BODdecrease; in particular, the goal of maintaining DO above 2mg/l during 90% of the time is fulfilled, though marginallyin the lower reach and with the help of the SEPA stations;(iii) there is a small decrease in NH4; in the upper reachthis is due to the reduction in domestic load and in thelower reach it must be due to the increase in DO, whichstimulates nitrification, even though there is a net increasein NH4 load (see below); (iv) there is a decrease in NO3 in

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the upper reach, due to the decrease in domestic load, andan increase in NO3 in the lower reach, due to the increasein NO3 load (see below), the stimulation of nitrification,and the ceasing of denitrification.

Figure 11(a) shows the BOD and NH4 loads to theriver, discriminated by source, for the project scenario.Comparing with the ones corresponding to the presentsituation (Figure 8(a)), it is observed that: (i) there aresignificant increases in loads from the water treatmentplants, which are now the dominant sources for thethree parameters; (ii) the reduction in loads from non-collected domestic sources is also significant for thethree parameters; (iii) urban creeks and diffuse industrialsources lie now second and third in the rank for bothBOD and NH4.

Figure 11(b) presents the comparison between the BODand NH4 total loads to the river for the project scenarioand the present situation. It is observed that: (i) there is adecrease in total BOD load which amounts to about 40%;(ii) the NH4 and NO3 loads increase, in more than 50% forthe first parameter, and in more than double for the secondone.

The longitudinal distribution of time-averaged contri-butions – per unit length – from the different mechanismsto the DO balance for the lower river reach, correspondingto the project scenario, is displayed in Figure 11(c). Com-pared with the present situation (Figure 8(b)), it is observedthat: (i) the two main components tend to decrease, namelyreaeration – due to DO increase – and biodegradation –due to BOD decrease; (ii) nitrification increases – due to

Figure 10. Longitudinal distribution of parameters for the project scenario. Red line: 10% exceedance; blue line: 50% exceedance; andgreen line: 90% exceedance (the opposite for DO).

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(a)

(c)

(b)

Figure 11. Project scenario: (a) loads per parameter and source; (b) total loads per parameter; (c) longitudinal distribution ofcontributions to DO.

DO increase – reaching a significant contribution closeto the river mouth; and (iii) SOD remains practicallyinsignificant.

ConclusionsNumerical modeling presently plays a key role in establish-ing a diagnosis on the water quality of a water body, and onthe definition of the adequate strategy in order to improveits water quality through remediation actions. In particular,it can be successfully applied to the basin scale and on atime-continuous basis for a long time-interval.

The implementation and calibration of the model is initself a learning process about the absolute and relative rel-evance of the multiplicity of pollutant sources present in abasin on the water quality of the water courses. Addition-ally, the model is the main tool in order to test and adjustspecific remediation actions in order to achieve specificremediation goals.

The identification of the main pollutant sources andthe estimation of their associated loads constitute a fun-damental step in the model buildup. For the particular caseof the Matanza-Riachuelo Basin, the following pollutantsources were identified: waste water treatment plants, non-collected domestic discharges, industries not connected tothe sewage system, storm water runoff, and bottom sedi-ments. Except for storm water runoff, only time-averagedloads were amenable to estimation.

The representation of both domestic and industrialsources into two groups – point and diffuse contributions –was shown to be an efficient way of modeling. Loads frompoint sources arose from measured data, while those fromdiffuse sources were estimated through emission factors,partition of loads, and inclusion of losses. The subdivi-sion of non-connected industrial sources was performed,for each parameter, by considering as point sources the setof largest industries representing about 75% of the totalload.

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SOD was included as a model process. The stabilizationof bottom muds was studied through a separate sedimentflux model, not described in the present paper.

The calibration of the water quality model must beperformed through adjustment of both model parame-ters (especially decay coefficients) and loads from diffusesources. The use of a statistical criterion in order to com-pare with measured values is considered as a sound andchallenging procedure.

After calibration, a relatively low uncertainty remainsregarding the resulting total loads per river stretch, thougha higher uncertainty remains in their subdivision persource.

It was shown that the model allowed the definition ofa strategy in order to achieve the proposed remediationgoal (Passive Recreation). In particular, it established thenecessary quantitative reduction in BOD loads from bigindustries and water treatment plants, and indicated thenecessity of implementing aeration stations, providing thevalues for their design parameters.

Disclosure statementNo potential conflict of interest was reported by theauthors.

Notes on contributorsAngel N. Menéndez, Ph.D. of The University of Iowa(USA) 1983, is a research engineer and consultant onhydraulic, hydrologic, and water-related environmentalproblems. He works on flow, sediment, pollutant, and heatdynamics. He is Head of the Computational HydraulicsProgram at INA (National Institute for Water), and Pro-fessor at the University of Buenos Aires. He is an expertin development and application of numerical models. Hehas to his credit numerous papers in international jour-nals, and has been head of a variety of research andconsulting projects financed by governmental, private, andinternational funds.

Emilio A. Lecertúa, is a Civil Engineer from the Fac-ulty of Engineering of the University of Buenos Aires(FI-UBA, 2010). He is Teaching Assistant of Numeri-cal Modeling and Hydrology at FI-UBA. His areas ofexpertise include numerical simulation of hydrodynam-ics, water quality, environmental impact and hydrologicproblems.

Nicolás D. Badano, is a Civil Engineer from the Facultyof Engineering of the University of Buenos Aires (2010),where he is currently pursuing Ph.D. studies, financed byINA (National Institute for Water, Argentina). His areas ofexpertise include numerical simulation of hydrodynamic,water quality and hydrologic problems, and developmentof computational codes.

Pablo E. García, has graduated in Civil Engineering fromthe Faculty of Engineering of the University of BuenosAires (FI-UBA, 2006), where he is currently pursuingPh.D. studies. Since 2007 he is Teaching Assistant inNumerical Methods for Engineers at FI-UBA. His areasof expertise include numerical simulation of hydrodynam-ics, water quality, environmental impact and hydrologicproblems.

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