Chemical speciation modeling tries to predict the compositition of a system using thermodynamic data, reaction kinetics and transport phenomena.
Background:Chemical speciation describes the distribution of an element amongst chemical species in a system. Such information is cruciual for understanding environmental fate and transport, bioavailabiliy and toxicity. Speciation analysis is meant to provide such information. However, because trace element concentrations are sometimes very low, speciation and sample matrix are complex, analytical technques are unfortunately not always effective for determining overall speciation. In these cases, it may be useful to identify the various classes of species of an element in order to determine their summed concentration in each class. Such fractionation can be based on different class properties, such as their solubility, affinity, size or molecular mass, to name only few. In certain situations, the fractions can be analyzed further for individual species based on subsequent analyses and calculations.
In cases, where analytical techniques cannot obtain the full species distribution, the determination of the chemical speciation relies on the use of analytical methods in conjunction with chemical speciation models.
Chemical Equilibrium Modeling
Equilibrium models can be used when the reactions considered are fast or the system has sufficient time to reach equilibrium. Under such conditions the system can be described by using thermodynamic equilibrium modeling. The components of the model are selected such that all species can be formed by the components, and no component can be formed by a combination of other components.
Essential components of creating the model are the formulation of
objectives, the documentation of the scientific basis for the analysis
and the experts judgements and assujmptions made. The following steps
are necessary for creating a valuable model:
Choice of scenario
Based on the current level of understanding of the system, a sequence of future events is postulated.
Formulation of conceptual models
The
processes envisaged in the scenario descriptions are described by
appropriate scientific theories, models and data by making also some
physical and chemical approximations.
Construction of the mathematical model
The
conceptual models are translated into a suitable numerical form for
calculating the relevant system properties under the given assumptions
and postulated events.
Calculations
Effective calculation
requires well tested computer code that provides numerical stability
also for coupled processes such as reactions and mass-transport.
Uncertainty of the modelng
Unfortunately speciation modeling involves significant uncertainty. In general the uncertainty can be differentiated into four distinct types:
decision rule uncertainty, model uncertainty, parameter uncertainty, and parameter variability.
Models are by choice and necessity incomplete. Therefore, modeling may be performed at different levels of sophistication and necessary elements are approximations, estimates and expert judgement, based on the perception of the problem at hand. The challenge is to find a model that represents the physico-chemical phenomena sufficiently well, to determine the numerical data required and to estimate the consequences of the inherent uncertainties in the models.
Thermodynamic data
A crucial part of any equilibrium modeling calculation is the selection of equilibrium constants that quantify the strength of interactions between metals and ligands. Precise thermodynamic data are necessary because they provide the scientific understanding of the processes taking place. Uncertainty studies have reviealed that uncertainty for thermodynamic values is much greater around the very dilute range (trace element speciation) and the more concentrated range, where data for the thermodynamic constants are comparatively sparse. Minimal uncertainty is predicted in conditions where one species is dominant over all others, but when many species are close in stability, uncertainty increases substantially. However, the uncertainty in the global models often by far outweighs the uncertainty in the thermodynamic input data. Anyhow, as far as possible, uncertainty analysis should be included in moddeling evaluation especially when complex species distribution is important.
Several databases exist that report experimental results for speciation constants. Most aqueous speciation programs come supplied with a standard
thermodynamic database. Unfortunately, many of these databases have been
shown to contain significant errors.
EPA - Data Systems and Software at U.S. Environmental Protection Agency
Chemistry WebBook - NIST Standard Reference Database Number 69, last update 2018
(thermochemical, thermophysical, and ion energetics data compiled by
NIST under the Standard Reference Data Program)
JESS Database is the world's largest single source of thermodynamic information
relating to electrolytes, reactions in aqueous media, and hydrocarbon phase equilibria.
The full contents of certain databases are available to inspect online
Thermochimie - a thermodynamic database initially created and developed by Andra
(French National Radioactive Waste Management Agency), for more than
twenty years (1995).
In October 2014, Radioactive Waste Management Limited (NDA, UK)
joined the project and the "ThermoChimie consortium" was formed.
In March 2018, Ondraf/Niras (National Agency for Radioactive Waste
Management, Belgium) also joined the "ThermoChimie consortium".
ThermoML - an XML-based IUPAC standard for storage and exchange of experimental thermophysical and thermochemical property data
THEREDA - the THErmodynamic REference DAtabase project from a German consortium
A recent review compared the quality and consistency of data provided by these databases:
Chemical Speciation Modeling Software
Multicomponent thermodynamic equilibrium speciation modeling has
been incorporated into publicly available and commercial modelling
software.
Programs based on the Law of mass Action
EQ3/6 -
licencing
procedure from LLNL - US$ 500 for non-US academic institutions.
Information is available about some useful
tools for performing pH-, Eh-, or concentration scans with EQ3/6
and download them. There is also a collection of
tips and remarks available
PHREEQC home page at U.S.G.S. informs about code
development, GUIs, and couplings to other software.
The most recent version is 3.0 and includes an interfcae for MS Windows.
There is a form-based user-interface,
WEB-PHREEQ for remote calculations
(Department of Geosciences, North Dakota State University).
There is also a separate graphical interface to PHREEQC,
PHREEQCI from U.S.G.S.
MINTEQA2 from the U.S. EPA, current version is 4.03 of May 2006.
New:
Visual Minteq from KTH Stockholm, current version is 3.1 of December 2013.
and
MINTEQA2 for Windows from Allison Geoscience Consultants, Inc.
CHEPROO - CHEmical PRocesses Object-Oriented is a tool to be coupled with transport codes,
it is maintained by UPC in Barcelona
CHEAQS Next by Wilko Verweij (NL) is a computer program for calculating
CHemical
Equilibria in
AQuatic
Systems., which is a follow-up of CHEAQS Pro
CHESS allows to simulate the
equilibrium state of complex solutions including minerals, colloids,
organics and gases.
It comes with Java-based GUI (JCHESS) and graphing tool (JPLOT).
Maintained in the past by Jan van der Lee, at present not accessible.
Cost: 2900 Euro (Console Mode Version of CHESS is available for free for
Linux users).
Geochemist's Workbench® from the University of Illinois
at Urbana-Champaign. Cost: 700 USD for the essential package and 1300 USD per year for the standard package (including reaction paths).
HSC Chemistry® from Outotec Research Oy / Finland. basic licence starting at 1500 Euro.
JESS by Peter M May (Murdoch University, Australia) and
Kevin Murray (Insight Modelling Services,
Garsfontein East, South Africa)
MINEQL+ Version 5.0 32-bit Win 7,8,10 -GUI interface to MINEQL-successor from W.Schecher
(ERS - Environmental Research Software, Hallowell, ME). Cost: 635 USD.
WATEQ4F provided free of charge by U.S.G.S.
Programs based on the Minimization of Gibbs Free Energy
CHEMSAGE© from GTT Aachen / Germany. Cost: 2495 USD.
FACT Facility for the Analysis of Chemical Thermodynamics,
by the Centre for Research in Computational Thermochemistry (CRCT)
at the University Montréal,
closely cooperating with GTT
GEMS-PSI by D.Kulik at PSI / Switzerland (latest version is 3.5 from May 2019)
Related reviews
Darren Rowland, Peter M. May, Progress in Aqueous Solution Modelling: Better Data and Better Interfaces, J. Solution Chem., 48 (2019) 1066–1078. DOI: 10.1007/s10953-019-00871-5
Tamas Kiss, Eva A. Enyedy, Tamas Jakusch, Orsolya Domotor, Speciation of Metal Complexes of Medicinal Interest: Relationship between Solution Equilibria and Pharmaceutical Properties, Curr. Med. Chem., 26/4 (2019) 580-606. DOI: 10.2174/0929867325666180307113435
Peter M.May, Darren Rowland, JESS, a Joint Expert Speciation System – VI: thermodynamically-consistent standard Gibbs energies of reaction for aqueous solutions, New J. Chem., 42/10 (2018) 7617-7629. DOI: 10.1039/C7NJ03597G
Tamas Kiss, Eva A. Enyedy, Tamas Jakusch, Development of the application of speciation in chemistry, Coord. Chem. Rev., 352 (2017) 401-423. DOI: 10.1016/j.ccr.2016.12.016
Agnieszka Jeske, Use of innovative and advanced computer simulation of chemical speciation of heavy metals in soils amd other environmental samples. Soil Sci. Annu., 65/2 (2014) 65-71. DOI: 10.2478/ssa-2014-0010
Jeane M. Van Briesen, Mitchell Small, Chris Weber, Jessica Wilson, Modelling Chemical Speciation: Thermodynamics, Kinetics and Uncertainty, in : Grady Hanrahan Ed.), Modelling of Pollutants in Complex Environmental Systems, ILM Publications (2010) 133-149.
Peter M.May, Darren Rowland, Thermodynamic Modeling of Aqueous Electrolyte Systems: Current Status, J.Chem. Eng. Data, 62 (2017) 2481−2495. DOI:10.1021/acs.jced.6b01055
D.G. Lumsdon, L.J. Evans,
Predicting chemical speciation and computer simulation, in: A.M. Ure, C.M. Davidson (eds.), Chemical speciation in the environment, Blackwell Science, London, 2002, 89-130.
DOI: 10.1002/9780470988312.ch5
last time modified: January 14, 2024
