A system model for water quality simulation of the MogiGuau river

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A system model for water quality simulation of
the Mogi-Guaçu river
State University of Campinas Food Engineering
Department

• Marlei Roling Scariot and Dr. Enrique Ortega

September/2006
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Introduction
The water quality of a river provides

• Health security for population
• Basis for regional economic development
• Diminish conflicts between users domestic,
  industrial, commercial, agricultural and
  environmental.

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Introduction
Scientific research
Alternatives of actions
Decisions makers
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Main Water Quality Models
QUAL2E Stream Water Quality Model SWAT
Soil and Water Assessment Tool AGNPS
AGricultural Non-Point Source BASINS
Better Assessment Science Integrating Point
Nonpoint Sources
DESERT DEcision Support system for Evaluating
River basin sTrategies
QUASAR Quality Simulation Along River System
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Mogi-Guaçu River
Main problems

• Erosion
• Silting
• Floading
• Low water quality.

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Area under Study
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Main Polluted Cities of the Watershed

• Mogi-Guaçu
• Mogi-Mirim
• Porto Ferreira
• S. João da Boa Vista
• Jaboticabal
• Sertãozinho

The lower WQI (Water Quality Index) monitored by
CETESB was located near to the region of
Mogi-Guaçu/Mogi-Mirim (Brigante, 2003).
80 of the domestic sewage are not treated
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Methodology
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Modeling
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Physical ModelsWell Mixing Reactor in Series
ModelLongitudinal Variation Model
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Physical Longitudinal Model
Mathematical Model

• The mathematical model is a mass conservation
  balance described by Brown and Barnwell (1987)
  and Chapra (2006) as

• Physical Model of a Completely Mixed Reactors in
  Series

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Completely Mixed Reactors in Series Model

• Tributaries
• Multiples
• - Punctual pollution
• Non-punctual pollution

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Differential Equations Solution

• Numeric integration method (1a order
  Runge-Kutta) was used in this work.
• The simulation process was worked out in
  Microsoft Excel spreadsheet.
• The simulation operates on an hour time step.

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Systemic Model
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Diagram of the River System
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Main Stores

• Dissolved Oxygen
• Nitrogen - Ammonium
• Nitrogen - Nitrate
• Total Phosphorus
• Biomass

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Main Processes

• Production
• Respiration
• Nitrification
• Denitrification
• Reaeration
• Sedimentation
• Resuspention

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Production

• Prod. BiomkmáxFI ?FP, FN ?min

Where the smallest between FP and FN will govern
the algal growing kinetic.
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Solar Energy

• d(Im)/d(t) JR k0 JR X
• where
• Im solar energy available to be used by the
  primary production (KJ day-1m-2)
• JR solar energy not used by the system
  (Jday-1m-2)
• X nutrient limiting concentration (mgl-1)
• k0 solar energy transference coefficient (day-1)

The diary and seasonal variation can be
calculate by Im R1cos(t2?/24)R2cos(t2?/8
760) I JR e(-kz) where z is the water
column depth (m) and k is the coefficient of
light extinction.
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Respiration

• While the photosynthesis does the work of
  integrating simple nutrients to form more complex
  components, the respiration degrades them to
  simpler ones as described in the follow Equation
• Resp. kresp. Biom. O2
• Once the dissolved oxygen is not available, the
  respiration continues in anoxic conditions. The
  Equation can be rewritten as
• Resp. kresp. Biom

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Dissolved Oxygen Subsystem
d (DO) / d(t) O2_iki Inflow -
O2ko Outflow (TR) Reaeration balance -
knit(N_NH4)(O2) Nitrification consumption
(N_NO3)kdnit Inlet by denitrification
(Prod.) Production inlet - (Resp.) Respiration
consumption - kdboO2 DBO consumption
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Nitrogen-Ammonium Subsystem
d (N_NH4) / d(t) NH4_iki Inflow
-N_NH4ko Outflow -N_NH4kair Lost to the
atmosphere -knit(N_NH4)(O2) Nitrification
consumption - Prod. Consumption by
biomass Resp. Respiration production
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Nitrogen Nitrate Subsystem
d (N_NO3) / d(t) N_NO3_iki Inflow -
N_NO3ko Outflow - N_NO3ksed Sedimentation
N_Sedkresus Resuspension -kdnit(N_NO3)
Denitrification consumption N_NO3(O2_col)knit
Nitrification production Resp. Respiration
production
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Biomass Subsystem
d (Biom) / d(t) Biom_iki Inflow -Biom_Nko
Outflow -Biom_Nksed Sedimentation outlet
Prod. Biomass production -Resp
Respiration consumption
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Total Phosphorus Subsystem
d(TP) / dt P_ikpi Inflow - P_colkpo
Outflow P_colkpresus Resuspension income
P_Sedkpsed Lost by sedimentation (Prod.)
Consumption by biomass (Resp.) Respiration
income
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Transference rates

• The transference rates were calculated by
  empirical equations that relate the water body
  characteristics (velocity and depth), by the
  chemical reactions stoichiometric relation and/or
  determined by model calibration.
• The Reaeration rate is given by
• TR krea(Cs - O2_col)
• where Cs is the concentration of saturation of
  the dissolved oxygen in the water body. Cs is
  temperature dependent and is defined by
• Cs C1 C2T C3T2 C4T3
• where T (C) is the water temperature, and
• C1 14,652 C2 0,41022 C3 0,0079910 C4
  0,000077774.

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• The Owens-Gibbs reaeration coefficient is showed
  in the Equation
• (day-1)
• where v (m/s) is the mean velocity of the water
  flow, D (m) is the mean depth, T(C) is the water
  temperature and ? is the temperature coefficient
  of correction.
• The nitrification and denitrification rates were
  calculated by the Equations
• knit 4,57kn(?)(T-20) (day-1)
• where 4,57 is the stoichiometric relation of
  the nitrification reaction considering the
  consumption for the cellular synthesis and kn is
  determined by model calibration.
• kdnit 3,43kdn(?)(T-20) (day-1)
• where 3,43 is the stoichiometric relation of the
  denitrification reaction and kdn is determined by
  model calibration.

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• The production and respiration rates are showed
  by the Equations
• kprod kmáxf(?)(T-20) (day-1)
• where f is the stoichiometric equivalence of the
  production and kmáx is determined by model
  calibration.
• kresp kr f (?)(T-20) (day-1)
• where f is the stoichiometric equivalence of the
  production and kr is determined by model
  calibration.
• The sedimentation rate was calculated by the
  Equation
• ksed vsed/D (day-1)
• where vsed is the sedimentation velocity (m
  day-1) and the D is the mean depth (m).

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• In the calibration process all the rate
  coefficients where adjusted considering a
  variable interval described by Lewis at al.
  (1997), Zheng at al. (2004) and Odum at al.
  (1983) in order to find the small difference
  between the observed data and the model results.
• The systemic model was applied to a Mogi-Guaçu
  river reach located in Brazil - São Paulo State
  among the cities of Mogi-Guaçu and Conchal.

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Scheme of the Simulator
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Initial Conditions and Inlet Data
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Water Temperature
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Solar Energy Along a Year Period
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Solar Energy Along a Day Period
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Flow Rate
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Results
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Dissolved Oxygen
The seasonally variation is clearly showed here,
where the dissolved oxygen concentration increase
during the winter time and decrease during the
summer time.
The observed and predicted concentration pass
down the limit of minimum concentration of
dissolved oxygen permitted by the environmental
Brazilian legislation.
This amplitude reflects the concentration
difference resulted from day and night time.
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Total Phosphorus
The phosphorus dynamic concentration exceeds the
upper limit of concentration along all the year
and consequently governs the production of
biomass.
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Nitrogen - Ammonium
The ammonium form of nitrogen is strongly
affected by the biomass production and
respiration showing a seasonal variation that
increase during the summer and decrease during
the winter and also can be observed a strong day
- night variation.
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Nitrogen - Nitrate
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Adjusted Results
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Longitudinal Model Results
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Conclusions

• This simulation showed clearly the dynamic
  behavior of the water quality parameters along a
  year.
• Permits the watershed planning to evaluate the
  specific self depuration capacity of the
  Mogi-Guaçu River.
• It is also possible to assess the environmental
  impacts or influences of the multiple sources of
  pollution in the water quality variables here
  studied.
• The results showed that its possible to create
  and to apply an adequate simulation tool for the
  Brazilian reality of water quality data base.

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Discussion

• The use of a systemic diagramation in adequacy to
  the physical model allowed the knowledge of the
  dynamic behavior of some parameters used to
  define the river water quality.
• The model presented in this work offers the
  possibility to introduce different scenarios, as
  population growing, economic growing, agriculture
  changes, environmental and social policy changes.