The speciation modeling allows to calculate the distribution of aqueous species in a solution. Phreeqc is capable to simulate this speciation calculation and we are going to demonstrate this capability on a study case of aqueous species in seawater. Modeling was done with a Phreeqc executable and results were analysed on a interactive Julia script. Both parts were done on a Jupyter Lab session.

This study case is based on the Exercise 1 of the Phreeqc documentation.

Software links

Install the batch version of Phreeqc from:

https://www.usgs.gov/software/phreeqc-version-3

Install Julia from:

https://julialang.org/downloads/

Install the interactive environment for Julia in Jupyter with this commands on the Julia prompt.

using Pkg

Pkg.add("IJulia")

Install the Dataframe package for julialang

Pkg.add("DelimitedFiles")

Why Julia for geochemical modeling?

Simple answer, why not? Julia is a emerging programing languaje for a series of high-edge calculations. In our perspective, the software has many interesting tools that we want to discover. We made this tutorial in Julia as a way to demostrate that Julia is able to work on hydrogeological / hydrochemical data analysis.

Tutorial

This is a video of the Phreeqc simulation and analysis script with Julia

Coding

The complete Julia script with output data is shown below.

Loading packages and open file

#import require packages
import DelimitedFiles
import Plots

#open the Phreeqc output model as a array
exMat = DelimitedFiles.readdlm("../Model/ex1.out")
exMat[94:100,:]

7×14 Array:
 "Elements"     "Molality"   "Moles"  …  ""  ""  ""  ""  ""  ""  ""  ""  ""
 "Alkalinity"  0.002406     0.002406     ""  ""  ""  ""  ""  ""  ""  ""  ""
 "Ca"          0.01066      0.01066      ""  ""  ""  ""  ""  ""  ""  ""  ""
 "Cl"          0.5657       0.5657       ""  ""  ""  ""  ""  ""  ""  ""  ""
 "Fe"          3.711e-8     3.711e-8     ""  ""  ""  ""  ""  ""  ""  ""  ""
 "K"           0.01058      0.01058   …  ""  ""  ""  ""  ""  ""  ""  ""  ""
 "Mg"          0.05507      0.05507      ""  ""  ""  ""  ""  ""  ""  ""  ""

Analysing the solution composition

#define breakers for the solution part of the output
solutionBeg = findfirst(x->x=="-----------------------------Solution",exMat)[1]+2
solutionEnd = findfirst(x->x=="----------------------------Description",exMat)[1]-1
print("$solutionBeg, $solutionEnd")

95, 108

solMat = exMat[solutionBeg:solutionEnd,1:3]
solMat[1:5,:]

5×3 Array:
 "Alkalinity"  0.002406  0.002406
 "Ca"          0.01066   0.01066 
 "Cl"          0.5657    0.5657  
 "Fe"          3.711e-8  3.711e-8
 "K"           0.01058   0.01058

Plots.plot(solMat[:,1],solMat[:,2],seriestype = [:bar],legend = false)

Solution composition

speciesBeg = findfirst(x->x=="----------------------------Distribution",exMat)[1]+3
speciesEnd = findfirst(x->x=="------------------------------Saturation",exMat)[1]-1
print("$speciesBeg, $speciesEnd")

133, 265

#clipping entire matrix to the species distribution
speMat = exMat[speciesBeg:speciesEnd,1:7]
#replacing specific format of phreeqx
speMat[speMat.=="(0)"].=0.0

speMat[1:22,1:end]

22×7 Array:
 "OH-"           2.705e-6   1.647e-6    -5.568   -5.783  -0.215   -2.63
 "H+"            7.984e-9   6.026e-9    -8.098   -8.22   -0.122    0.0 
 "H2O"          55.51       0.9806       1.744   -0.009   0.0     18.07
 "C(4)"          0.002182    ""           ""       ""      ""       "" 
 "HCO3-"         0.001485   0.001003    -2.828   -2.999  -0.171   26.98
 "MgHCO3+"       0.000256   0.000161    -3.592   -3.793  -0.201    5.82
 "NaHCO3"        0.0001658  0.0001936   -3.781   -3.713   0.067    1.8 
 "MgCO3"         8.747e-5   0.0001022   -4.058   -3.991   0.067  -17.09
 "NaCO3-"        6.682e-5   4.99e-5     -4.175   -4.302  -0.127    2.88
 "CaHCO3+"       4.453e-5   3.081e-5    -4.351   -4.511  -0.16     9.96
 "CO3-2"         3.752e-5   7.803e-6    -4.426   -5.108  -0.682   -1.97
 "CaCO3"         2.703e-5   3.158e-5    -4.568   -4.501   0.067  -14.6 
 "CO2"           1.186e-5   1.385e-5    -4.926   -4.858   0.067   34.43
 "UO2(CO3)3-4"   1.252e-8   1.173e-10   -7.902   -9.931  -2.028    0.0 
 "UO2(CO3)2-2"   1.837e-9   5.716e-10   -8.736   -9.243  -0.507    0.0 
 "MnCO3"         2.55e-10   2.979e-10   -9.593   -9.526   0.067    0.0 
 "MnHCO3+"       6.475e-11  4.294e-11  -10.189  -10.367  -0.178    0.0 
 "UO2CO3"        7.662e-12  8.95e-12   -11.116  -11.048   0.067    0.0 
 "(CO2)2"        3.015e-12  3.522e-12  -11.521  -11.453   0.067   68.87
 "FeCO3"         1.796e-20  2.098e-20  -19.746  -19.678   0.067    0.0 
 "FeHCO3+"       1.505e-20  1.124e-20  -19.823  -19.949  -0.127    0.0 
 "Ca"            0.01066     ""           ""       ""      ""       ""

Plots.plot(speMat[4:15,1],speMat[4:15,2],
    ylabel="Species", xlabel="Molality",
    seriestype = [:bar],legend = false,orientation = :h, fmt = :png)

Analysis of most abundant species

#filter to eliminate rows with total molalities
speFil = speMat[speMat[:,3].!="",:]
speFil[1:10,:]

10×7 Array:
 "OH-"       2.705e-6   1.647e-6   -5.568  -5.783  -0.215   -2.63
 "H+"        7.984e-9   6.026e-9   -8.098  -8.22   -0.122    0.0 
 "H2O"      55.51       0.9806      1.744  -0.009   0.0     18.07
 "HCO3-"     0.001485   0.001003   -2.828  -2.999  -0.171   26.98
 "MgHCO3+"   0.000256   0.000161   -3.592  -3.793  -0.201    5.82
 "NaHCO3"    0.0001658  0.0001936  -3.781  -3.713   0.067    1.8 
 "MgCO3"     8.747e-5   0.0001022  -4.058  -3.991   0.067  -17.09
 "NaCO3-"    6.682e-5   4.99e-5    -4.175  -4.302  -0.127    2.88
 "CaHCO3+"   4.453e-5   3.081e-5   -4.351  -4.511  -0.16     9.96
 "CO3-2"     3.752e-5   7.803e-6   -4.426  -5.108  -0.682   -1.97

#creating a mutable struct object in Julia
mutable struct Specie
    Name::String
    Molality::Float64
    Activity::Float64
end

#empty list to store the species structs
speciesList=Vector(undef, 0)

#iteration to store the solution species on the list
for row in range(1,length(speFil[3:end,1]),step=1)
    push!(speciesList, Specie(speFil[row,1],speFil[row,2],speFil[row,3]))
end

speciesList[1:5]

5-element Array:
 Specie("OH-", 2.705e-6, 1.647e-6)    
 Specie("H+", 7.984e-9, 6.026e-9)     
 Specie("H2O", 55.51, 0.9806)         
 Specie("HCO3-", 0.001485, 0.001003)  
 Specie("MgHCO3+", 0.000256, 0.000161)

#sort values by the Molality
sort!(speciesList, by=x->x.Molality, rev=true)
#get the first 10 species with higher molality
speciesList[1:10]

10-element Array:
 Specie("H2O", 55.51, 0.9806)        
 Specie("Cl-", 0.5657, 0.3568)       
 Specie("Na+", 0.4785, 0.3434)       
 Specie("Mg+2", 0.04754, 0.01372)    
 Specie("SO4-2", 0.01432, 0.002604)  
 Specie("K+", 0.0104, 0.006483)      
 Specie("Ca+2", 0.009634, 0.002409)  
 Specie("MgSO4", 0.00717, 0.008375)  
 Specie("MgSO4", 0.00717, 0.008375)  
 Specie("NaSO4-", 0.006637, 0.004482)

names=String[]
molalities=Array(undef, 0)
activities=Array(undef, 0)
for row in speciesList[1:10]
    push!(names, row.Name)
    push!(molalities, row.Molality)
    push!(activities, row.Activity)
end

Plots.plot(names,molalities,seriestype = [:bar],legend=false, yaxis=:log, fmt = :png)

Plots.plot(names,activities,seriestype = [:bar],legend=false,yaxis=:log, fmt = :png)

Working with saturation indices

satBeg = findfirst(x->x=="------------------------------Saturation",exMat)[1]+2
satEnd = findfirst(x->x=="**For",exMat)[1]-1
print("$satBeg, $satEnd")

268, 298

#clipping entire matrix to the saturation distribution
satMat = exMat[satBeg:satEnd,1:7]

satMat[1:5,:]

5×7 Array:
 "Anhydrite"   -0.93  -5.2   -4.28  "CaSO4"          ""  ""
 "Aragonite"    0.61  -7.73  -8.34  "CaCO3"          ""  ""
 "Calcite"      0.75  -7.73  -8.48  "CaCO3"          ""  ""
 "Chalcedony"  -0.52  -4.07  -3.55  "SiO2"           ""  ""
 "Chrysotile"   3.36  35.56  32.2   "Mg3Si2O5(OH)4"  ""  ""

#creating a mutable struct object in Julia
mutable struct Saturation
    Name::String
    SI::Float64
    logIAP::Float64
end

#empty list to store saturation values
satList=Vector(undef, 0)

#loop to store the saturation values on the list
for row in 1:length(satMat[1:end,1])
    push!(satList, Saturation(satMat[row,1],satMat[row,2],satMat[row,3]))
end

satList[1:5]

5-element Array:
 Saturation("Anhydrite", -0.93, -5.2)  
 Saturation("Aragonite", 0.61, -7.73)  
 Saturation("Calcite", 0.75, -7.73)    
 Saturation("Chalcedony", -0.52, -4.07)
 Saturation("Chrysotile", 3.36, 35.56)

#sort values by the Saturation
sort!(satList, by=x->x.SI, rev=true)
#get the first 10 species with higher saturation index (SI)
satList[1:10]

10-element Array:
 Saturation("Hematite", 14.17, 10.17)  
 Saturation("Pyrolusite", 6.97, 48.35) 
 Saturation("Goethite", 6.08, 5.08)    
 Saturation("Talc", 6.03, 27.43)       
 Saturation("Chrysotile", 3.36, 35.56) 
 Saturation("Dolomite", 2.39, -14.7)   
 Saturation("Manganite", 2.39, 27.73)  
 Saturation("Hausmannite", 1.55, 62.58)
 Saturation("Sepiolite", 1.15, 16.91)  
 Saturation("Calcite", 0.75, -7.73)

names=String[]
saturations=Array(undef, 0)
for row in satList
    push!(names, row.Name)
    push!(saturations, row.SI)
end

Plots.plot(names[1:10],saturations[1:10],orientation = :h, seriestype = [:bar], legend=false, fmt = :png)

Plots.plot(names[end-10:end],saturations[end-10:end],orientation = :h, seriestype = [:bar], legend=false,fmt = :png)

Input data

You can download the input data for the tutorial here.

Blog

Geochemical Speciation Modeling and Analysis with Phreeqc and Julia - Tutorial

Software links

Why Julia for geochemical modeling?

Tutorial

Coding

Loading packages and open file

Analysing the solution composition

Solution composition

Analysis of most abundant species

Working with saturation indices

Input data

Hatari Labs

Our courses:

Blog

Software links

Why Julia for geochemical modeling?

Tutorial

Coding

Loading packages and open file

Analysing the solution composition

Solution composition

Analysis of most abundant species

Working with saturation indices

Input data

Hatari Labs

Our courses:

Follow Us

Join us on social media for the latest webinars, tutorials and courses.