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LINNAEUS-PALME ACADEMIC EXCHANGE
PROGRAMME
Chemical P rocesses and
SHORT COURSE
Department of GeologyUniversity of Dhaka
17-20 April, 2011
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Prosun BhattacharyaProfessor
KTH-International Groundwater Arsenic Research GroupDepartment of Land and W ater Resources Engineering
Royal Institute of Technology (KTH)SE-100 44 Stockholm
SWEDENPart 3
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Contents
Geochemical modeling
What is Geochemical Modeling? Considerations in geochemical modeling Purpose Approaches Types of models
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Forward modeling Inverse modeling
Applications of modeling
What is Geochemical Modeling?
Technique for quantitatively evaluation ofgroundwater chemical processes in groundwatersystem for deducing operative chemical andmicrobial processes from groundwaterchemistry is termed as geochemical modelling.
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Considerations in Geochemical Modeling
Hydrological Considerations
Mineralogical Considerations
Thermodynamic Considerations
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The purpose of geochemical modeling isprimarily to deduce and quantify the
Purpose of geochemical modeling
modify groundwater chemistry in ahydrogeological system.
There are several pre-requisites for inversegeochemical modeling of evolving chemistryin hydrologic systems. The first of them isthe knowledge of flow pattern. When we
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hydrogeological system, the sampling pointsshould be hydraulically connected. Thismeans that flow model has to be solvedbefore geochemical model.
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Geochemical sampling based on flowsystem
Recharge zone
C BA D
River
Lake
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100
200
There are two principle approaches togeochemical modeling:
Approaches for geochemical modeling
chemical equilibrium chemical kinetics
The equilibrium approach is the most common,and assumes very fast (instantaneous)reactions compared to groundwater residencetime.
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The kinetic approach includes time factor, butdata about reaction rates of As-mineralsoxidation are still very limited. There arehowever some exceptions.
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Exceptions are special environmentslike mine tailings with the oxidation ofpyrite.
Approaches for geochemical modeling
In general, we focus mostly onequilibrium models, but applications ofsome kinetic models are also appliedduring the evaluation of contaminatedgroundwater.
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Equillibrium models can be divided into:
Types of Modeling
Forw a rd ( r eact i on pa th m ode ls ) , e.g.,MINTEQA2 (Allison et al., 1993), PHREEQC(Parkhurst, 1995) and PHREEQC-2 (Parkhurstand Appelo, 1999).
I nv erse m ode ls, e.g., NETPATH (Plummer etal., 1994) and inverse modeling module ofPHREE C.
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In fo r w ard m ode l in g , a priori prediction of
Forward and inverse modeling
geochemical processes and thermodynamicconstraints. In other words, these modelsstart with equations and parameters and runforward (in time or space) to producevariables.
In i nve r se m ode l ing , the available
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are used to deduce the operative geochemicalprocesses in hydrologic system. In otherwords, this model starts with the variablesand operate in the inverse direction to derivethe parameters.
A prerequisite of forward modeling is calculation of
Forward modeling
.
Speciation represents calculation of the equilibriumdistribution of mass among complexes and redoxcouples. The speciation module also calculates saturationindices of a water sample with respect to differentminerals.
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Satu r a t i on i ndex (SI) is defined as:
SI = log(IAP/Ksp)
where IAP is i on ac t i v i t y p roduc t and
Ksp is the so lub i l i t y p roduc t for a given temperature.
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This means that when IAP = Ksp
When SI = 0 and water is at thermodynamic
Forward modeling
equ r um w respec o e m nera .
When S I > 0 , water is supersaturated with respect tothe mineral and this mineral should precipitate.
On the other hand, ifS I < 0 , water is undersaturatedwith respect to the mineral and this mineral shoulddissolve.
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However, there is no information about the rate of the
reaction or even if it has proceeded or will proceed. Most reactions never reach equilibrium and reactants aregradually transformed to products (for example, theoxidation of organic matter by dissolved O2).
Speciation calculations are complicated by
Forward modeling
uncertainty about values of thermodynamicconstants for mineral phases because pureminerals are rather the exception than the rule.
Equilibrium constants for solid solutions maydiffer significantly from values in databases ofgeochemical programs.
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of As and other species with organic matter. Onlyfew programs like WHAM (Tipping, 1994)consider the effect of organic complexation.
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Forward (reac t i on pa th ) models are used for prediction of waterchemistry evolution.
Forward modeling
In this case the starting water chemistry is defined and an attemptis made to model water evolution by dissolution and precipitation ofmineral phases and gases etc.
The output of the program has the following form:
I n i t ia l w a ter + React ing phases =
Pred ic ted w a ter + Product ph ases
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During each small step the program transfers a limited amount ofmass from reactant to products. Then the code calculates massdistribution among the products and calculates saturation indices.Pre-determined phases, to which the water sample issupersaturated, are allowed to precipitate.
Both MINTEQA2 and PHREEQC used for forward modeling.
Both MINTEQA2 and PHREEQC used for forward modeling,also include surface complexation modeling (SCM) withseveral o tions.
Forward modeling
Unlike the application of adsorption isotherms, which areapplicable only under relatively constant pH and waterchemistry conditions, the approach is far more universal.
Surface charge of adsorbents like hydrous ferric oxide (HFO)changes with increasing pH of water from positive values at
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ow pH towar s negat ve va ues at g pH.
The pH value at which the bulk surface charge changes frompositive to negative is called the zero point of charge (pHZPC).
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The overall adsorption constant is:
Forward modeling
Kads = Kint x exp(-Z. F/R.T)
where Kint is the intrinsic adsorption constant representing achemical component of adsorption
the expression in brackets called the electrostatic (alsocoulombic or Boltzman) term accounts for the electrostaticcom onent of adsor tion
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Z is the change in surface charge due to adsorption, is the surface potential, R is the universal gas constant,
T is temperature.
Forward modeling
intadsorption on HFO by Dzombak and Morel (1990)and are implemented in the d i f f u se d o u b le l a ye r m od el (DDLM), which is a module of theMINTEQA2 and PHREEQC programs.
Other possible approaches are:
cons tan t ca ac i tance m ode l (CCM)
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basic Ster n m ode l (BSM), and
t r ip le laye r m ode l (TLM)
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The DDLM requires as input the followingparameters:
Forward modeling
concentration of adsorbent Mv (g/L),
surface area of adsorbent SA (m2/g), and
number of adsorption sites (mol/mol of HFO).
For surface complexation modeling, the
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information about concentration of an adsorbentsuch as Fe(III) hydroxide can be, for example,obtained by sequential extraction of the solidphase in contact with water.
When the data are not available, the amount of
Forward modeling
precipitated Fe can be estimated from thedecreasing dissolved Fe concentrationdowngradient.
Surface complexation modeling can also include acompetition for adsorption sites with other ions.
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Through inverse modeling, it is possible to determine thepossible reactions between 2 or more sampling points.
-
Inverse modeling
phase composition in the aquifers.
This type of modeling is based on a mass-balanceapproach and does not have thermodynamic constraints.
Inverse modeling in is generally used to verify, if certainreactions for the release of various contaminant speciesof As release and immobilization are possible.
Mass balance for As and As related species such as SO4
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s per orme us ng y rau ca y connecte samp es.
Data on mineralogical composition of the solid phase are
essential because they have to be entered together withwater chemistry.
Inverse or mass balance models are based on massbalance calculations for solid phase and dissolved species(sometimes also isotopes) in a geochemical system.
The input comprises water samples from two or more
Inverse modeling
y rau ca y connecte samp ng po nts an t ecomposition of the solid phase between these points.
The output has the form of
I n i t ia l w a ter + React in g phases
= F ina l w a ter + Product ph ases
However, there are no thermodynamic constraints in the
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program, which can also suggest thermodynamicallyimpossible reactions.
Thus, the code NETPATH based on mass balancecalculations is combined with the WATEQ4F program(Plummer et al., 1976) and with mineralogical analysis todetermine mineral phases, which can dissolve orprecipitate (Glynn and Brown, 1996).
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The mineral database in NETPATH (Plummer etal., 1994) does not include all elements viz. As.
Inverse modeling
However, inverse modeling module ofPHREEQC-2 (Parkhurst and Appelo, 1999)makes it possible to define new elements andmineral phases.
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ur ermore, uncer a n y n ana y ca a a canbe accounted for and a range of concentration
values may be entered instead of fixedconcentration values.
Standard flow/transport packages like Visual MODFLOWwhich includes transport program MT3D, can simulateflow and trans ort in 3-D, but their eochemical
Inverse modeling
capabilities are limited.
They include adsorption isotherms like linear adsorptionisotherm Kd and first order decay.
The Kd value is generally determined from batch orcolumn experiments, when ground water in contact withsolid phase from the investigated aquifer is spiked withthe contaminant species in question viz. As.
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adsorbed concentration S vs. dissolved concentration C isbased on a batch experiment determines the Kd value,which is used to calculate the retardation coefficient R(Fetter, 1999).
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Inverse modeling
d
bwwK
nL
L
v
vR .1
+===
where vw and vc indicate the velocity of ground waterflow and contaminant transport,
Lw and Lc are distances of ground water flow andcontaminant transport,
b is the bulk density of the solid phase, and
cc
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.
Other adsorption isotherms like Freundlich and Langmuirisotherms are also available, and the retardation factor Rbased on these isotherms can be calculated as aderivative of the relation S = f(C) according to C, andsubstitution for Kd into the equation for R (Fetter, 1999).
Geochemical modeling applications of As behavior arepresented here with selected case studies.
Most of the typical applications including forward modeling
Applications of geochemical modeling
w sur ace comp exa on, nverse mo e ng, an ranspormodeling using both the reactive transport code and anapproach based on adsorption isotherms.
Forw ard m odel ing
Prediction of As behavior requires a combination of forwardmodels with speciation and surface complexation
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.some As minerals can precipitate.
However, As minerals like scorodite are not common, anddetermination of the saturation index for As sulphideminerals like orpiment is based on complicated samplingand determination of dissolved reduced S species.
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Another application includes determination ofsaturation indexes for minerals, which could besinks for dissolved Fe.
Applications of geochemical modeling
In Bangladesh and West Bengal, India, thecorrelation between dissolved Fe and As is poor insome cases.
Precipitation of Fe(II) iron minerals like siderite,FeCO3, and vivianite, Fe3(PO4)2.8H2O, wassuggested to explain the lack of correlation by
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race e a . ; c son e a . anBhattacharya et al. (2002b).
Saturation indices for both Fe(II) minerals wereconsistently positive.
Evaporation of recharge water and irrigation watercan be modeled with PHREEQC to determine, if evaporation enrichment can explain high
Applications of geochemical modeling
concen ra ons o s.
During evaporation, constraints can be put onsome minerals and mineral phases like Fe(OH)3can be precipitated.
The same code can be used to calculate theresulting concentration of As after mixing of surface water and ground water at a seepage
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. Mixing ratios can be determined by hydraulic
methods (application of seepage-meters orpiezometers, ground water flow modeling) or byapplication of mixing equation for a conservativetracer (Cl- etc.).
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When the concentration of As decreases, but theAs mass remains the same, then this is the effect
Applications of geochemical modeling
of dilution and no geochemical processes have tobe considered.
When dilution cannot explain attenuation of As,other processes like adsorption on Fe(III) oxideand hydroxides has to be considered.
For example, adsorption of As on Fe(III) oxideand oxyhydroxide was found to be the principal
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s n or s n ac m ne ra nage mpac e s e oNevada (Webster et al., 1994).
The process can be modeled using surfacecomplexation approach.
Applications of geochemical modelingfor investigations on arsenic
Example Approach Solid phase As data Program Objectives of
modeling
References
1 x
adsorption and potential As
sources/sinks
,
2 Forward with SCM XRF, oxalate extraction, batchexperiments
MINTEQA2,ECOSAT
Determine factors controllingAs distribution
Lumsdon et al., 2001
3 Forward with SCM Electron microprobe analysis
(EMPA)
MINTEQA2 Determine factors controlling
As distribution
Davis et al. 1994
4 Forward with SCM Batch experiments Models based on
CCM, BSM andTLM
Determine the impact of
competitive adsorption
Gao and Mucci, 2001
5 Forward with 1-D
transport and SCM
Sequential extraction PHREEQC Release of As along flow path Parkhurst, 1995
6 Inverse Transmission electron
microscopy (TEM), X-rayabsorption spectroscopy
XAS
NETPATH Mass balance for potential As
source
Schreiber et al., 2000
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7 Inverse and forwardwith SCM
Scanning electron microscopy(SEM)/energy dispersivespectroscopy (EDS), partial
sequential extraction
NETPATH andMINTEQA2
Identification of geochemicalprocesses
Carillo-Chavez et al.,2000
8 Inverse Partial sequential extraction PHREEQC Water chemistry evolution Armienta et al., 2001
9 Isotherms based onSCM (Freundlich,
Langmuir) andtransport
Sequential extraction(oxalate step)
ECOSAT,MODFLOW/MT
3D
Mobility of As hot spots DPHE/BGS, 2000
1SCM S urface Complexation Modeling
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1 du004 Puntia, Rajshahi 25.5m du004 c5
916. 1033. 9901 .0000 .90 00 .0000 .0000 .000 0 .0000 .0000
TEMP = 24.700000
PH = 6.870000EH(0) = -.153000
DOC = .900000
DOX = .000000
CORALK = 0
FLG = MG/L
DENS = 1.000000
PRNT = 0
Geochemical modeling using PHREEQC: Input data
PUNCH = 1
EHOPT(1) = 0 Use measured Eh to calculate Fe species distribution
EHOPT(2) = 0 Use measured Eh to calculate Mn species other than +2
EHOPT(3) = 0 Use measured Eh to calculate Cu +1 species
EHOPT(4) = 0 Use measured Eh to calculate As species distribution
EHOPT(5) = 0 Use measured Eh to calculate Se species distribution
EHOPT(6) = 0 Use measured Eh to calculate Ion Activity Products
EHOPT(7) = 0 Use measured Eh to calculate atmospheric pO2
EHOPT(8) = 0 Use measured Eh to calculate H2S from SO4
EHOPT(9) = 0 Use measured Eh to calculate U species distribution
EMPOX = 0
ITDS = 1033.000000
COND = 916.000000
SIGMDO = .000000
SIGMEH = .000000
SIGMPH = .000000
Species Index No Input Concentration
--------------------------------------------
Ca : 0 : 188.00000000
Mg : 1 : 44.00000000
Na : 2 : 28.40000000
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K : 3 : 1.93000000
Cl : 4 : 255.00000000
SO4 : 5 : 36.97000000
HCO3 : 6 : 473.10000000
Fe total : 16 : 5.22000000
H2S aq : 13 : .00000000
CO3 : 17 : .00000000
SiO2 tot : 34 : 30.60000000
NH4 : 38 : .00000000
B tot : 86 : .00000000
PO4 : 44 : .64400000
Al : 50 : .07300000
F : 61 : .00000000
NO3 : 84 : .00000000
Mn : 109 : .70500000
As total : 249 : .03750000
As3 tot : 261 : .01698000
As5 tot : 262 : .02050000
1 du004 Puntia, Rajshahi 25.5m du004 c5 Date = 10/13/99 13:05
916. 1033. 9901 .0000 .9000 .0000 .0000 .0000 .0000 .0000
DOX = .0000 DOC = .9 INPUT TDS = 1033.0
Anal Cond = 916.0 Calc Cond = 1482.2 Activity H2S calc from SO4 and pe = 7.84E-12
Anal EPMCAT = 14.5219 Anal EPMAN = 15.7531 Percent difference in input cation/anion balance = -8.1335
Calc EPMCAT = 13.8902 Calc EPMAN = 15.1177 Percent difference in calc cation/anion balance = -8.4632
Total Ionic Strength (T.I.S.) from input data = .02216
Effective Ionic Strength (E.I.S.) from speciation = .02090
Geochemical modeling using PHREEQC: Recalculated data
Sato
Input Sigma Fe3/Fe2 Sigma NO3/NO2 Sigma NO3/NH4 Sigma SO4/S= Sigma S/S= Sigma H2O2/O2 Sigma H2O/O2
Sigma
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Eh - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-.153 .000 -.153 .000 9.900 .000 9.900 .000 9.900 .000 9.900 .000 9.900 .000 9.900
.000
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - pe - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-2.589 .000 -2.589 .000 100.000 .000 100.000 .000 100.000 .000 100.000 .000 100.000 .000 100.000
.000
As5/As3 Sigma As3/As Sigma Se6/Se4 Sigma Se4/Se Sigma Se/Se= Sigma U6/U4 Sigma Sigma
Sigma
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Eh - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
.003 .000 -.297 .000 9.900 .000 9.900 .000 9.900 .000 9.900
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- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - pe - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
.049 .000 -5.028 .000 100.000 .000 100.000 .000 100.000 .000 100.000
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Geochemical modeling using PHREEQC: Saturation Index
Phase Log IAP/KT Log IAP Sigma(A) Log KT Sigma(T)
39 Adularia .673 -19.923 -20.596
40 Albite -.502 -18.523 -18.021
140 Al(OH)3 (a) -.676 10.144 10.820
50 Alunite -.803 -2.166 -1.363
42 Analcime -2.518 -15.233 -12.714
17 Anhydrite -2.044 -6.403 -4.360
21 Aragonite .087 -8.247 -8.334 .020
497 Arsenolite -23.764 -26.576 -2.812
472 Basaluminite .326 23.026 22.700
19 Brucite -6.123 10.737 16.860
12 Calcite .231 -8.247 -8.478 .020
97 Chalcedony .264 -3.291 -3.555
20 Chrysotile -6.606 25.631 32.237
99 Cristobalite .300 -3.291 -3.591
154 Diaspore 3.247 10.144 6.897
11 Dolomite (d) -.371 -16.902 -16.532
401 Dolomite (c) .181 -16.902 -17.083
340 Epsomite -4.670 -6.812 -2.142
112 Ferrihydrite -4.339 .552 4.891
51 Gibbsite (c) 2.017 10.144 8.127 .200
110 Goethite 1.553 .553 -1.000 .800
18 Gypsum -1.823 -6.403 -4.581
64 Halite -6.757 -5.175 1.581
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47 Halloysite 1.179 13.706 12.527
205 Jarosite K -21.753 -30.939 -9.187 1.100
46 Kaolinite 6.245 13.706 7.461
128 Laumontite 3.868 -27.121 -30.989
10 Magnesite -.631 -8.655 -8.024
189 Manganite -12.633 12.707 25.340
141 Prehnite -.981 -12.684 -11.703
183 Pyrolusite -24.440 16.988 41.428
101 Quartz .694 -3.291 -3.985
190 Rhodochrs(d) -.577 -10.967 -10.390
492 Scorodite -11.523 -31.772 -20.249
153 Sepiolite(d) -7.057 11.603 18.660
36 Sepiolite(c) -4.165 11.603 15.768
9 Siderite (d) .356 -10.094 -10.450
395 SiO2 (a) -.576 -3.291 -2.714
146 Strengite -5.141 -31.540 -26.399
106 Vivianite -.290 -36.290 -36.000
1 du001 Mokamtala, Bogra 24m du001 a5
Effective
T pH TDS ppm Ionic Str pO2 Atm ppm O2 Atm pCO2 Atm ppm CO2 Atm log pCO2 CO2 Tot Ncrb Alk
aH2O
30.20 6.530 617.2 .00998 6.48E-65 2.07E-60 8.63E-02 3.80E+03 -1.064 7.31E-03 1.03E-05
.9997
I Species Anal ppm Calc ppm Anal Molal Calc Molal % of Total Activity Act Coeff -Log Act
l 1 4 4 4 1 1 4 1 4 4 1
Geochemical modeling using PHREEQC: Aqueous Species
. . . - . - . . - . .
52 Al(OH)2 1 .045 7.448E-07 14.99 6.713E-07 .9013 6.173
181 Al(OH)3 0 .038 4.837E-07 9.73 4.848E-07 1.0023 6.314
53 Al(OH)4 -1 .353 3.715E-06 74.76 3.348E-06 .9013 5.475
261 As3 tot 0 .017 2.324E-07
250 H3AsO3aq 0 .029 2.318E-07 67.52 2.323E-07 1.0023 6.634
262 As5 tot 0 .008300 1.109E-07
257 HAsO4 -2 .007012 5.015E-08 14.61 3.309E-08 .6598 7.480
256 H2AsO4 -1 .008547 6.070E-08 17.68 5.470E-08 .9013 7.262
0 Ca 2 47.400 44.846 1.183E-03 1.120E-03 94.63 7.489E-04 .6687 3.126
29 CaHCO3 1 4.730 4.682E-05 3.96 4.220E-05 .9013 4.375
31 CaSO4 aq 0 1.833 1.347E-05 1.14 1.350E-05 1.0023 4.870
4 Cl -1 86.161 86.119 2.432E-03 2.431E-03 99.97 2.187E-03 .8997 2.660
6 HCO3 -1 289.500 277.405 4.747E-03 4.550E-03 62.29 4.115E-03 .9045 2.386
85 H2CO3 aq 0 159.537 2.574E-03 35.24 2.580E-03 1.0024 2.588
16 Fe total 2 22.700 4.067E-04
7 Fe 2 16.911 3.030E-04 74.51 2.000E-04 .6598 3.699
309 FeHCO3 1 10.661 9.130E-05 22.45 8.229E-05 .9013 4.085
63 H 1 .000326 3.233E-07 .00 2.951E-07 .9128 6.530
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3 K 1 22.400 22.381 5.732E-04 5.728E-04 99.93 5.154E-04 .8997 3.288
1 Mg 2 18.800 17.807 7.738E-04 7.330E-04 94.73 4.934E-04 .6731 3.307
21 MgHCO3 1 2.306 2.705E-05 3.50 2.438E-05 .9013 4.613
22 MgSO4 aq 0 1.364 1.134E-05 1.47 1.137E-05 1.0023 4.944
109 Mn 2 .824 .622 1.501E-05 1.134E-05 75.53 7.479E-06 .6598 5.126
311 MnCO3 aq 0 .049 4.276E-07 2.85 4.285E-07 1.0023 6.368
119 MnHCO3 1 .353 3.044E-06 20.28 2.743E-06 .9013 5.562
2 Na 1 69.000 68.811 3.003E-03 2.996E-03 99.75 2.703E-03 .9023 2.568
26 OH -1 .000938 5.522E-08 .00 4.976E-08 .9013 7.303
44 PO4 -3 3.414000 .000002 3.597E-05 1.720E-11 .00 6.750E-12 .3924 11.171
46 HPO4 -2 .580 6.047E-06 16.81 3.990E-06 .6598 5.399
47 H2PO4 -1 1.982 2.045E-05 56.85 1.843E-05 .9013 4.734
34 SiO2 tot 0 41.500 6.911E-04
23 H4SiO4aq 0 66.330 6.907E-04 99.93 6.922E-04 1.0023 3.160
5 SO4 -2 15.310 12.426 1.595E-04 1.295E-04 81.18 8.617E-05 .6656 4.065
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Geochemical modeling using PHREEQC: Aqueous Species1 du001 Mokamtala, Bogra 24m du001 a5
Effective
T pH TDS ppm Ionic Str pO2 Atm ppm O2 Atm pCO2 Atm ppm CO2 Atm log pCO2 CO2 Tot Ncrb Alk
aH2O
30.20 6.530 617.2 .00998 6.48E-65 2.07E-60 8.63E-02 3.80E+03 -1.064 7.31E-03 1.03E-05
.9997
I Species Anal ppm Calc ppm Anal Molal Calc Molal % of Total Activity Act Coeff -Log Act
50 Al 3 .134000 .000024 4.969E-06 8.816E-10 .02 3.459E-10 .3924 9.461
52 Al(OH)2 1 .045 7.448E-07 14.99 6.713E-07 .9013 6.173
181 Al(OH)3 0 .038 4.837E-07 9.73 4.848E-07 1.0023 6.314
53 Al(OH)4 -1 .353 3.715E-06 74.76 3.348E-06 .9013 5.475
261 As3 tot 0 .017 2.324E-07
250 H3AsO3aq 0 .029 2.318E-07 67.52 2.323E-07 1.0023 6.634
262 As5 tot 0 .008300 1.109E-07
257 HAsO4 -2 .007012 5.015E-08 14.61 3.309E-08 .6598 7.480
256 H2AsO4 -1 .008547 6.070E-08 17.68 5.470E-08 .9013 7.262
0 Ca 2 47.400 44.846 1.183E-03 1.120E-03 94.63 7.489E-04 .6687 3.126
29 CaHCO3 1 4.730 4.682E-05 3.96 4.220E-05 .9013 4.375
31 CaSO4 aq 0 1.833 1.347E-05 1.14 1.350E-05 1.0023 4.870
4 Cl -1 86.161 86.119 2.432E-03 2.431E-03 99.97 2.187E-03 .8997 2.660
6 HCO3 -1 289.500 277.405 4.747E-03 4.550E-03 62.29 4.115E-03 .9045 2.386
85 H2CO3 aq 0 159.537 2.574E-03 35.24 2.580E-03 1.0024 2.588
16 Fe total 2 22.700 4.067E-04
7 Fe 2 16.911 3.030E-04 74.51 2.000E-04 .6598 3.699
309 FeHCO3 1 10.661 9.130E-05 22.45 8.229E-05 .9013 4.085
63 H 1 .000326 3.233E-07 .00 2.951E-07 .9128 6.530
3 K 1 22.400 22.381 5.732E-04 5.728E-04 99.93 5.154E-04 .8997 3.288
1 Mg 2 18.800 17.807 7.738E-04 7.330E-04 94.73 4.934E-04 .6731 3.307
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21 MgHCO3 1 2.306 2.705E-05 3.50 2.438E-05 .9013 4.613
22 MgSO4 aq 0 1.364 1.134E-05 1.47 1.137E-05 1.0023 4.944
109 Mn 2 .824 .622 1.501E-05 1.134E-05 75.53 7.479E-06 .6598 5.126
311 MnCO3 aq 0 .049 4.276E-07 2.85 4.285E-07 1.0023 6.368
119 MnHCO3 1 .353 3.044E-06 20.28 2.743E-06 .9013 5.562
2 Na 1 69.000 68.811 3.003E-03 2.996E-03 99.75 2.703E-03 .9023 2.568
26 OH -1 .000938 5.522E-08 .00 4.976E-08 .9013 7.30344 PO4 -3 3.414000 .000002 3.597E-05 1.720E-11 .00 6.750E-12 .3924 11.171
46 HPO4 -2 .580 6.047E-06 16.81 3.990E-06 .6598 5.399
47 H2PO4 -1 1.982 2.045E-05 56.85 1.843E-05 .9013 4.734
34 SiO2 tot 0 41.500 6.911E-04
23 H4SiO4aq 0 66.330 6.907E-04 99.93 6.922E-04 1.0023 3.160
5 SO4 -2 15.310 12.426 1.595E-04 1.295E-04 81.18 8.617E-05 .6656 4.065
1 du001 Mokamtala, Bogra 24m du001 a5
Effective
T pH TDS ppm Ionic Str pO2 Atm ppm O2 Atm pCO2 Atm ppm CO2 Atm log pCO2 CO2 Tot Ncrb Alk
aH2O
30.20 6.530 617.2 .00998 6.48E-65 2.07E-60 8.63E-02 3.80E+03 -1.064 7.31E-03 1.03E-05
.9997
I Species Anal ppm Calc ppm Anal Molal Calc Molal % of Total Activity Act Coeff -Log Act
Geochemical modeling using PHREEQC: Aqueous Species
Al .1 . . E- . 1 E-1 . . E-1 . . 1
52 Al(OH)2 1 .045 7.448E-07 14.99 6.713E-07 .9013 6.173
181 Al(OH)3 0 .038 4.837E-07 9.73 4.848E-07 1.0023 6.314
53 Al(OH)4 -1 .353 3.715E-06 74.76 3.348E-06 .9013 5.475
261 As3 tot 0 .017 2.324E-07
250 H3AsO3aq 0 .029 2.318E-07 67.52 2.323E-07 1.0023 6.634
262 As5 tot 0 .008300 1.109E-07
257 HAsO4 -2 .007012 5.015E-08 14.61 3.309E-08 .6598 7.480
256 H2AsO4 -1 .008547 6.070E-08 17.68 5.470E-08 .9013 7.262
0 Ca 2 47.400 44.846 1.183E-03 1.120E-03 94.63 7.489E-04 .6687 3.126
29 CaHCO3 1 4.730 4.682E-05 3.96 4.220E-05 .9013 4.375
31 CaSO4 aq 0 1.833 1.347E-05 1.14 1.350E-05 1.0023 4.870
4 Cl -1 86.161 86.119 2.432E-03 2.431E-03 99.97 2.187E-03 .8997 2.660
6 HCO3 -1 289.500 277.405 4.747E-03 4.550E-03 62.29 4.115E-03 .9045 2.386
85 H2CO3 aq 0 159.537 2.574E-03 35.24 2.580E-03 1.0024 2.588
16 Fe total 2 22.700 4.067E-04
7 Fe 2 16.911 3.030E-04 74.51 2.000E-04 .6598 3.699
309 FeHCO3 1 10.661 9.130E-05 22.45 8.229E-05 .9013 4.085
63 H 1 .000326 3.233E-07 .00 2.951E-07 .9128 6.530
3 K 1 22.400 22.381 5.732E-04 5.728E-04 99.93 5.154E-04 .8997 3.288
36
. . . . . . . .
1 Mg 2 18.800 17.807 7.738E-04 7.330E-04 94.73 4.934E-04 .6731 3.30721 MgHCO3 1 2.306 2.705E-05 3.50 2.438E-05 .9013 4.613
22 MgSO4 aq 0 1.364 1.134E-05 1.47 1.137E-05 1.0023 4.944
109 Mn 2 .824 .622 1.501E-05 1.134E-05 75.53 7.479E-06 .6598 5.126
311 MnCO3 aq 0 .049 4.276E-07 2.85 4.285E-07 1.0023 6.368
119 MnHCO3 1 .353 3.044E-06 20.28 2.743E-06 .9013 5.562
2 Na 1 69.000 68.811 3.003E-03 2.996E-03 99.75 2.703E-03 .9023 2.568
26 OH -1 .000938 5.522E-08 .00 4.976E-08 .9013 7.303
44 PO4 -3 3.414000 .000002 3.597E-05 1.720E-11 .00 6.750E-12 .3924 11.171
46 HPO4 -2 .580 6.047E-06 16.81 3.990E-06 .6598 5.399
47 H2PO4 -1 1.982 2.045E-05 56.85 1.843E-05 .9013 4.734
34 SiO2 tot 0 41.500 6.911E-04
23 H4SiO4aq 0 66.330 6.907E-04 99.93 6.922E-04 1.0023 3.160
5 SO4 -2 15.310 12.426 1.595E-04 1.295E-04 81.18 8.617E-05 .6656 4.065
8/6/2019 Prosun DU Short Course Part 3 2011
19/20
Geochemical modelling using Visual MINTEQ(current ver 3.0)
http://www2.lwr.kth.se/English/OurSoftware/vminteq/
37
Geochemical modelling using VisualMINTEQ (current ver 3.0)
http://www2.lwr.kth.se/English/OurSoftware/vminteq/
Visual MINTEQ is a Windows version of MINTEQA2 ver 4.0,re ease y e n .
MINTEQA2 is a chemical equilibrium model for the calculationof metal speciation, solubility equilibria etc. for natural waters.
Development of the Windows version of MINTEQA2 is beingsupported by the two Swedish research councils VR andMISTRA and the program is distributed via Internet from free
38
.
Visual MINTEQ has been developed to make the powerfulfeatures of MINTEQA2 more easily accessible for graduate andpost-graduate students in soil and water chemistry. VisualMINTEQ has also been modernised to include new options foradsorption modelling.
8/6/2019 Prosun DU Short Course Part 3 2011
20/20
Visual MINTEQ (current ver 3.0): Salient uses
Ion speciation using equilibrium constants from the MINTEQA2database, which has been updated using the most recent NIST
http://www2.lwr.kth.se/English/OurSoftware/vminteq/
ata to conta n > 3000 aqueous spec es an > 600 so sSolubility calculations involving solid phases .
Adsorption calculations with adsorption isotherms, five surfacecomplexation models (Diffuse Layer, Constant Capacitance,Triple Layer, Basic Stern and Three Plane), with the 1-pK or 2-pK formalisms, and with the CD-MUSIC concept.
- -
39
- -be simulated using the Gaussian DOM, the Stockholm HumicModel, or the NICA-Donnan model.
Calculations with redox couples and gases (e.g. CO2)Presentation of results from Visual MINTEQ runs on separateoutput tables; export of results to Excel.
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