Prosun DU Short Course Part 3 2011

8/6/2019 Prosun DU Short Course Part 3 2011

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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

7

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.


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


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


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


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


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


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


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


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)


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.


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


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.

Prosun DU Short Course Part 3 2011

Documents

Transcript of Prosun DU Short Course Part 3 2011