H.A. van der Sloot1 and D.S. Kosson2 Hans van der Sloot

Transcription

LEACHING ASSESSMENT METHODOLOGIES
FOR DISPOSAL AND USE OF BAUXITE RESIDUES
H.A. van der Sloot1 and D.S. Kosson2
Hans van der Sloot Consultancy, Langedijk, The Netherlands
Vanderbilt University, Nashville, Tennessee, USA
April 15, 2010
Alumina Technology Roadmap - Bauxite Residue Research
Project 4.3: Evaluation of a risk-based by-product assessment
methodology
Carried out under contract with the International Aluminium Institute
DEDICATION
This report is dedicated to Chanelle Carter in recognition of her research
contributions supporting environmentally protective and sustainable management
of bauxite mining and aluminum production residues. She tragically was killed in
a cycling accident in April 2010. She was a highly regarded colleague and friend
of the authors of this report.
ACKNOWLEDGEMENT AND DISCLAIMER
This development of this report was developed through support from the
International Aluminum Institute (IAI) for the IAI Bauxite & Alumina Committee as
part of a research program to build further knowledge and understanding of the
issue of sustainability within the bauxite-alumina segments of the broader
aluminum industry.
Neither the Hans van der Sloot Consultancy, Vanderbilt University or IAI, or their
employees, nor any of its contractors, subcontractors or their employees, makes
any express or implied: 1. warranty or assumes any legal liability for the
accuracy, completeness, or for the use or results of such use of any information,
product, or process disclosed; or 2. representation that such use or results of
such use would not infringe privately owned rights; or 3. endorsement or
recommendation of any specifically identified commercial product, process, or
service. Any views and opinions of authors expressed in this work do not
necessarily state or reflect those of Vanderbilt University or IAI.
EXECUTIVE SUMMARY
Extraction from bauxite is a primary process for production of aluminum, which results in the
formation of large quantities of waste that are then typically stored in disposal sites near the
production facilities. The international annual bauxite residue production is approximately 120
Mt. The cumulative amount of bauxite residue disposed prior to 2009 is more than 2500 Mt.
Bauxite residue, or red mud, is the largest environmental concern of alumina refineries because
of the production rate of the waste stream and its alkaline nature. Therefore, the aluminum
industry is interested in more fully understanding the environmental considerations and
approaches that would help ensure environmentally protective use and disposal practices. A
primary environmental concern is waterborne release, and subsequent transport and potential
impacts resulting from leaching. The objective of this report was to review the current status,
understanding and approaches to leaching assessment that may facilitate improvements in use
and disposal of red mud, emphasizing emerging assessment techniques in the European Union
and the United States.
The following are conclusions reached based on the assessment contained in this report:

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


Leaching assessment based on combined evaluation of results from pH dependence,
percolation and mass transfer tests provides a robust framework for evaluation of a
wide range of disposal, treatment and use scenarios for red mud.
Use of leaching assessment based on the pH dependence, percolation and mass transfer
tests is in the process of being adopted as part of regulatory frameworks in the
European Union and the United States, and is under consideration in other countries.
This leaching assessment framework has been widely accepted by the scientific
community as evidenced by the peer-reviewed scientific literature. Leaching test
methods are undergoing ruggedness testing in the European Union and inter-laboratory
comparison testing (round-robin testing) in the United States during 2010 and likely
extending into 2011. Use of this approach in the United States will be implemented via
guidance documents that are under development and likely will be recommended for
applications such as beneficial use of secondary materials, hazardous waste delisting,
determination of treatment process effectiveness, where use of TCLP is not required by
statute.
Total elemental content does not correlate with leaching behavior of most elements
and therefore is not recommended as the basis for assessing environmental
compatibility.
Leaching behavior of red mud is consistent within a defined bandwidth for many
elements and samples from several sources. The impacts of neutralization, carbonation
and redox conditions on red mud leaching are evident through the leaching assessment
testing.
Geochemical speciation modeling provides additional insights into the chemistry
controlling observed leaching behavior and facilitates estimating the leaching behavior
of a material under scenarios and time frames not readily tested. Independent
verification testing is needed to confirm geochemical speciation modeling results.
Establishment of a database for leaching characteristics of red mud samples from a
variety of sources will provide a uniform basis for comparison and understanding of
i
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
disposal, treatment and use options. It will also serve as the reference basis for ongoing quality control during red mud production, use and disposal.
Statistical quality control of red mud as a product is possible using reduced testing,
focused on key quality control parameters and at a frequency linked to the probability
of exceeding a specified threshold value.
There is promising evidence that ecotoxicity testing results can be linked to results from
leaching assessment and geochemical speciation, suggesting a pathway to more
integrated testing and evaluation in different contexts.
LeachXS is a software tool set that would efficiently facilitate (i) maintaining a red mud
leaching assessment database, (ii) geochemical speciation modeling of a range of red
mud testing, use, treatment and disposal scenarios, and (iii) statistical quality control of
red mud production. LeachXS and associated databases can be tailored to the aluminum
industry user community.
The following are recommendations for the aluminum industry that follow as a result of this
review:
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Establish a baseline leaching characterization program for red mud produced at
different facilities. This would allow comparisons and understanding of the similarities
and differences among red mud producers and provide a foundation for improving
treatment, use and disposal practices, as well as quality control. Baseline leaching
characterization would include pH dependence, percolation and mass transfer testing.
Additional testing should include physical properties sufficient to understand
geotechnical and hydraulic performance.
Establish a common database of leaching and related properties that can be tied to the
similarities and differences in red mud production processes, sources and management
scenarios. This database should also include field observations of pore water and
leachate from representative red mud management scenarios. A custom LeachXS
database would be suitable for such a database.
If information on lysimeter studies or field data on either landfill or beneficial use are
available those observations should be evaluated in context with the more extended
laboratory test data. If such information is not available or is insufficient, it is advisable
to obtain field test data to verify the basis for estimating long-term performance.
Experience can be obtained from information in other areas (soil, waste, construction)
to facilitate the prediction of long term release from red mud disposal and beneficial
use. This relates to effects of carbonation, oxidation, preferential flow and interaction
between materials in a mixture.
Establish guidance on quality control monitoring for red mud that include simplified
leaching assessment and meets the needs of likely use and disposal scenarios.
Develop and validate to the extent practical a geochemical speciation model for red
mud. This would facilitate simulation-based evaluation of performance under different
use and disposal conditions, including blending of red mud with other materials, prior to
carrying out confirmatory testing, and thereby allow consideration of a wider range of
applications at reduced testing costs.
ii.
Table of Contents
1. Introduction.................................................................................................................1
2. Characteristics of Aluminum Production Residues ...........................................................2
3. Potential uses of wastes from the Aluminum industry .....................................................4
Soil amendment..........................................................................................................4
Construction applications............................................................................................4
Treatment and separation options for beneficiation ....................................................5
Disposal......................................................................................................................5
4. Regulatory situation .....................................................................................................6
European Union (EU) .................................................................................................6
United States ..............................................................................................................7
5. A Unified Approach to Leaching Assessment as Part of Environmental Assessments ..........9
Overview ....................................................................................................................9
Leaching Assessment Fundamentals .......................................................................12
Total composition of the waste material versus leaching .................................12
Basic chemical mechanisms............................................................................12
pH
12
Chemical form of the constituent in the waste material (redox form,
minerals, sorbed phases, etc)............................................................14
Redox
14
Acid-base buffering .........................................................................................15
Organic matter and DOC.................................................................................15
Composition of the water phase and ionic strength .........................................15
Temperature....................................................................................................16
Time
16
Example of geochemical modeling results based on a pH dependence
leach test ...........................................................................................17
Leaching Test Methods ............................................................................................18
Method 1313 (similar to EU TS 14429 and ISO/TS 21268-4): Leaching Test
(Liquid-Solid Partitioning as a Function of Extract pH) for
Constituents in Solid Materials using a Parallel Batch Extraction
Test ...................................................................................................21
Method 1314 (similar to EU TS 14405, ISO/TS 21268-3 and CEN TC
351/TS-3): Leaching Test (Liquid-Solid Partitioning as a Function
of Liquid-Solid Ratio) of Constituents in Solid Materials using an
Up-Flow Percolation Column .............................................................21
iii.
Method 1315 (similar to EU CEN/TS15863 and CEN/ TC351/TS-2): Mass
Transfer Rates of Constituents in Monolithic or Compacted
Granular Materials using a Semi-Dynamic Tank Leaching Test.........26
Fundamental Leaching Characteristics of Red Mud..................................................28
Relationship between results from different leaching tests ..............................28
Characteristic pH dependent leaching behavior of red mud.............................29
Relationship between total content and leachability for red mud......................29
6. Geochemical speciation and reactive transport modeling of Leaching Test Results and
disposal and Use scenarios ..........................................................................................37
Modeling test data pH dependence, percolation, tank leaching ................................38
Modelling of pH dependence test results .........................................................38
Modeling column test results ...........................................................................43
Relationships between Leaching Tests, Geochemical Speciation and Eco-toxicity...43
7. Development of A Databases for Red Mud Characteristics and Leaching .........................47
8. LeachXS™ as a Tool for Evaluating Red Mud Leaching ....................................................48
Description of Conceptual Models ...................................................................49
Inputs Needed to Run LeachXS and the Outputs Generated by LeachXS.......49
Models available within LeachXS-ORCHESTRA .............................................50
Input required and output generated by the LeachXS-ORCHESTRA
models...............................................................................................50
Embedded Databases and Systems including ORCHESTRA..........................53
9. Use of Leaching Testing in Quality Control ....................................................................56
10.Conclusions and recommendations ..............................................................................62
11.
References ........................................................................................................64
iv.
List of Tables
Table 1. Example of physical and chemical characteristics of red mud. ................................3
Table 2. European Union technical committees responsible for leaching assessment in
different fields. .................................................................................................................7
Table 3. A summary of the main physical and chemical factors that influence leaching. .......16
Table 4. General speciation of contaminants in the solid phase and in the leachate of waste
materials. The major phases and species are specified in both the solid phase as well as in the
leachate..........................................................................................................................17
Table 5. Conditions specified as input for geochemical speciation modelling of the pH
dependence test (Chemical Speciation Fingerprint – CSF). ..................................................41
Table 6. Conditions specified as input for geochemical speciation modeling of the pH
dependence test. ............................................................................................................42
Table 7. Types of materials that are included in the LeachXS database of leaching test results.
......................................................................................................................................54
Table 8. Example of k - values and the corresponding risk of non-compliance. ..................58
.
v.
List of Figures
Figure 1. Regulatory framework related to impacts from different sources on soil, surface and
groundwater with different approaches and test requirements. ..................................................6
Figure 2. A common paradigm for environmental assessment. A key issue is the source term
description obtained from leaching assessment...........................................................................9
Figure 3. Different management scenarios can be viewed as a similar problem definition in terms
of source term release (leaching), COPC transport, and impact analysis.....................................10
Figure 4. General leaching behaviour of three groups of constituents as a function of pH. .........13
Figure 5. Leaching behaviour as a function of pH versus total composition. In this figure, the
difference between the total composition in the material is shown versus "potentially leachable"
and "actually leachable" (the red curve) is shown. Note the log scale on the y-axis. ...................14
Figure 6. Absolute levels of leached amounts are different for each material due to influence of
redox, DOC (dissolved organic carbon) and other factors. The leaching patterns of different
groups of elements for all sorts of materials are very systematic, but differ in absolute levels
(leading to a "chemical fingerprint" of a material).....................................................................15
Figure 7. Example of integrated data presentation for pH-static leaching test results and
geochemical speciation modeling. Red data points represent leaching data, black solid line is the
predicted leached concentration. Areas represent the element speciation: white=minerals,
gray=Fe oxide sorption, dark green=complexation to solid organic carbon, light
green=complexation to dissolved organic carbon and light blue=free+inorganically complexed
form (van Zomeren et al., 2006). ...............................................................................................18
Figure 8. Publications in international peer reviewed scientific journals in which pH dependence
test, percolation test and/or tank test is used specifically to characterize the environmental
behavior of construction materials and contaminated materials (n=147)...................................19
Figure 9. Flow diagram modified from the CEN/TC292/WG8 methodology developed for wastes
from the extractive industry. .....................................................................................................20
Figure 10. Example results of a pH dependence test on red mud for vanadium [oxidized (ox) and
reduced (red)]. Relevant pH domains are indicated for assessing different environmental or
exposure scenarios. ...................................................................................................................22
Figure 11. Example percolation test results from oxidized (red lines) and reduced (blue lines) red
mud for a species where leaching is controlled by local solubility (i.e., Al). (a) eluate pH as a
function of LS, (b) eluate cumulative release as a function of pH along with pH dependence test
results, (c) eluate cumulative release (mg/kg) as a function of LS, (d) eluate concentration as a
function of pH along with pH dependence test results, and, (e) eluate concentration (mg/L) as a
function of LS. ...........................................................................................................................23
mud for a highly soluble species where leaching is controlled by availability (i.e., Na). (a) eluate
concentration as a function of pH along with pH dependence test results, (b) eluate
concentration (mg/L) as a function of LS, (c) eluate cumulative release (mg/kg) as a function of
pH along with pH dependence test results, and (d) eluate cumulative release (mg/kg) as a
function of LS. ...........................................................................................................................24
Figuur 13. Example mass transfer rate test results (compacted granular). (left) eluate
concentration in relation to pH dependence test results, (middle) ) eluate cumulative release as a
function of time, and, (right) eluate concentrations as a function of time. .................................27
Figure 14. Relationships between results from the pH dependence test (TS 14429), other single
step extraction tests, sequential chemical extraction (SCE) and total elemental content methods.
vi.
Results are presented on a release basis (mg Ni leached/kg sediment) to facilitate comparison of
leaching test results with total content......................................................................................29
Figuur 15. Characteristic pH dependent leaching behavior of red mud based on comparison of
results of the pH dependence test from available data sets (concentration basis, mg/L). ...........30
Figuur 19. (multiple pages to illustrate selected elements). Characteristic pH dependent leaching
behavior of red mud based on comparison of results of the pH dependence test from available
data sets (concentration basis, mg/L). .......................................................................................34
Figure 20. Comparisons of total elemental content with pH dependence leaching test results for
red mud (release basis, mg/kg). ................................................................................................35
Figuur 21. Comparisons of total elemental content with pH dependence leaching test results for
red mud (release basis, mg/kg). ................................................................................................36
Figuur 22. Geochemical speciation modeling of pH dependence test results for red mud...........40
Figure 23. Comparison of percolation test results with dual porosity geochemical speciation
model based on chemical speciation fingerprint derived from pH dependence test data.
Measured concentrations of Al, Cu, K, Mo and V in column effluent samples (collected eluate
fractions; TS14405) from red mud leaching (red dots) with dual porosity model results (LeachXS)
as continuous (broken line) and simulation results (simulated eluate fractions; blue squares)....45
Cumulative release of Al, K, Ca, Cu, Mo and V in column leaching (TS14405) from red mud (red
dots) with dual porosity model results (broken line) using LeachXS. ...........................................46
Figure 25. Schematic of LeachXS/ORCHESTRA Input and Output Functions and Databases ........49
Figure 26. The relationship between initial characterization testing and quality control testing. 56
Figure 27. The bandwidth of release of vanadium from red mud from worldwide origin with the
average (black dashed line) and 90 % confidence intervals (gray dashed lines). .........................59
Figure 28. The distribution of log - normalized release data for V from red mud.........................60
Figure 29. k-values calculated from the pH dependence test data on red mud (N=26) in relation
to the class limits corresponding with a certain range of risk of non-compliance(RNC) using a 90%
probability of the fraction of samples being less than the threshold)..........................................61
Figure 30. Quality control data for vanadium from red mud in perspective to the summary
statistics for worldwide red mud leaching data as a function of pH with the average (black
dashed line) and 90 % confidence intervals (gray dashed lines) as indicated from Figure 21.......61
vii.
1. INTRODUCTION
A primary process for production of aluminum is extraction from bauxite, which results in the
formation of large quantities of waste that are then typically stored in disposal sites near the
production facilities The international annual bauxite residue production is approximately 120
Mt (Power et al, 2009). The cumulative amount of bauxite residue disposed prior to 2009 is
more than 2500 Mt (Power et al, 2009). Bauxite residue, or red mud, is the largest
environmental concern of alumina refineries because of the production rate of the waste
stream and its alkaline nature.
During the last decade, increased emphasis has been placed on the possible use of Al production
residues in commercial and engineering applications to avoid accumulation in landfills. Use of
aluminum production residues requires demonstration to regulatory satisfaction, through
testing and environmental assessment, that the intended uses will not adversely impact human
health or the environment. Producers and end-users of aluminum production residues should
also require adequate demonstration that the full-life cycle of the intended use (including
material handling, preparation, placement, and use) does not adversely impact human health or
the environment to avoid potential future liability. The alumina industry recognizes that it has a
cradle-to-grave responsibility for the residue and that more work is needed to develop reuse
opportunities and sustainable storage options (Alumina Road Map, 2006).
Detailed literature reviews by Power et al (2009) identified four major themes for improved
management of bauxite residue: improved storage, improved rehabilitation (e.g., treatment to
make the land suitable for use after closure of disposal sites), and value creation in either
mineralogical or metallurgical applications. The research and development program required to
achieve progress on each of these themes was arranged into five priority project areas:
knowledge management, fundamental chemistry and physics, immediate value opportunities,
bioremediation, and industrial synergies.
“The development of a range of large-scale construction and agronomic applications for residue
throughout the world will require a step change improvement in a number of areas of basic
knowledge. In particular, leaching behavior, the concentrations and speciation of trace metals
and naturally occurring radioactive materials, physical and mechanical properties, and
conformance with applicable environmental, safety, engineering and construction standards will
all need to be investigated and systematized” (Power et al, 2009).
Use of aluminum production residues needs to be managed to avoid pathways for potential
adverse impact to human health and the environment, including ingestion, inhalation or
dispersal of fine particulates, erosion, leaching of constituents of potential concern (COPCs),
and/or direct uptake of COPCs by biota. There are well established approaches and
management practices for avoidance of adverse impacts during the handling and preparation of
materials for use, once concerns are recognized. However, evaluation of leaching of COPCs and
direct uptake of COPCs by biota are areas that are currently undergoing substantial evolution in
technical understanding and assessment practices.
The focus of this report is to describe the current state of the science and practice of leaching
assessment, including regulatory context, and the potential application of evolving leaching
assessment methodologies to the evaluation of options for disposal and use of aluminum
production residues.
1.
2. CHARACTERISTICS OF ALUMINUM PRODUCTION RESIDUES
The primary aluminum production residue is commonly called red mud. As produced, red mud
is an alkaline slurry that is first settled in ponds and then the settled material is retrieved and
placed into landfills for further dewatering and storage. At some locations seawater is used to
partially neutralize the material, resulting in lower pH and higher Mg and Ca carbonate content
in the red mud. Atmospheric exposure during handling and storage results in the uptake of
atmospheric carbon dioxide to varying extents, resulting in partial pH neutralization. At one
location, forced carbonation is used prior to disposal to reduce pH and improve the drying
properties of the material. Typical physical and chemical characteristics of red mud are
presented in Table 1. The following constituents present in red mud at significant total
concentrations typically are identified as COPCs requiring further evaluation during
consideration of use and disposal options based on EU landfill criteria (ref): As, Cr, F, V, Mo, Se,
Th, U, Ga, DOC. Reference values for COPCs can be obtained from USEPA (2006, 2009) and
typical marine reference values can be obtained from the State of Louisiana (2009). However,
caution must be exercised when comparing reference values to leaching test results because of
(i) physical and chemical characteristics of the proposed use or disposal scenario usually
requires estimation of release fluxes or concentrations considering field conditions and
appropriate scenario modeling, and (ii) dilution and attenuation that may occur from the point
of release to the point of compliance.
2.
Table 1. Example of physical and chemical characteristics of red mud.
Particle size: typically 90% < 75 μm (some locations have a sand fraction)
Density: 2800 kg/m3 (after drying)
Specific surface area: 10 m2/g
Material
Element
Al
As
B
Ba
Be
Ca
Cd
Cl
Co
Cr
Cu
Fe
Hg
K
Li
Mg
Mn
Mo
Na
Ni
P
Pb
S
Sb
Se
Si
Sn
Sr
Th
Ti
U
V
Zn
# ECN, 2005
Red mud
#
mg/kg
80873
18
11
228
1.00
17716
<0.05
2100
3.4
322
31
194788
Red Mud
##
mg/kg
55943
16
14235
152
22
107748
0.015
Green compost
###
mg/kg
11948
2.0
72.4
373
Soil 1
###
mg/kg
39033
Soil 2
###
mg/kg
46149
1594
69
1433
59
40179
0.6
4670
0.5
4040
0.5
7.0
154
64
16419
12
72
15
14585
11
76
11
23501
5580
17
2290
579
5.2
300
40
1496
31
5450
18
2640
409
3.5
180
23
916
19
0.1
364623
19
33
369298
19
33
205
301
76.0
48
101.0
41
7081
13980
16
13
1428
8376
144
873
12
3
6.7
50057
8226
3077
4.4
98
333
4531
33
20
92
2969
3501
2.7
1.4
21
1.58
0.1
97544
8763
7.6
6.2
132
179
369
184
18815
452
0.92
16
563
329
24
22
12
277
; ## Bloom, 2002 ;### van der Sloot, 2002.
3.
3. POTENTIAL USES OF WASTES FROM THE ALUMINUM INDUSTRY
Most potential uses of red mud will include, to varying extents, pretreatment (mechanical
separation) and/or blending with other materials (e.g., coal fly ash, phosphogypsum waste, local
soils). The following commercial and engineering uses of red mud are under consideration
(Klauber et al, 2009; Power et al, 2009; Garrabrants et al, 2009), along with the intended benefit
from the use:
Soil amendment
Neutralization of acidic or acid producing soils
Improved water and/or phosphorus retention
Construction applications
Structural fill such as for road base and embankments
Raw material for brick, block or paver production
Levee construction, currently being evaluated, in Mississippi and Louisiana (USA)
when blended with phosphogypsum waste
Soil amendment
The alkaline nature of the bauxite residue holds potential for neutralization of acidic and acid
producing soils, like waste rock and sulfide bearing waste from mining activities. In the latter
case, the production of acid rock drainage (ARD) may be prevented. In a recently published
study (Carter et al, 2009) the combination of the pH-dependent leaching test and geochemical
modeling was used to evaluate the partitioning of major, minor and trace elements in relation
to the phosphorus retention capability, the release of non-nutrient constituents, the reduction
of soil acidity and the organic matter retention on Australian soil treated with bauxite residue
derived soil amendments. The approach applied is relevant for any soil improvement or
fertilizer.
Construction applications
Potential uses in construction applications typically are based on substitution of the sand-like
and fine-grained fractions in manufactured products and fill materials (Klauber et al, 2009). The
sand-like fraction of red mud potentially can be used as:
 a partial sand replacement in cement based products,
 for structural fill purposes (road base and embankment),
 as a sand replacement in brick production, or
 as a additive to bituminous products.
The fine grained or mixed sand and fine grained fractions are more limited in the potential uses:
 as additive in Portland cement when color control is not a requirement (Power et al,
2009),
 as a structural fill material without load bearing capacity requirements,
 as a fine grained filler, or
4.

in combination with other binders for structural fill and barrier functions (e.g., levee
construction as currently being considered in Mississippi and Louisiana, USA).
Treatment and separation options for beneficiation
Separation of size fractions, such as the separation of a sand like fraction and fines, can be
beneficial for specific applications. This type of mechanical separation can be carried out at large
scale, but the relevance of such separation depends on the local source material.
There are potential options for blending red mud fractions with other materials so the resultant
mixture properties are an improvement over the individual components constituting the mix.
Geochemical speciation modeling provides a mechanism to explore the behavior of possible
synergistic mixtures of materials prior to initiating testing. Possible other secondary materials
that may be potentially blended with red mud to improve the overall characteristics of both
materials include coal fly ash, phosphogypsum, and acid producing mining wastes. Whenever
materials are blended, both the mechanical and leaching characteristics of the resulting blended
material should be evaluated. Usually, screening mixture mechanical properties prior to
leaching evaluation is more cost effective. In addition, the emerging development of
geochemical speciation models for a range of secondary materials provides the opportunity for
simulation-based screening of mixtures for leaching properties prior to laboratory testing.
Disposal
Although disposal is the least preferred option in any waste management scheme, avoiding
disposal in the near future is unlikely, which implies that improvement of disposal scenarios is
important. Several disposal options are practiced (Power et al, 2009), including seawater
discharge, accumulation in lagoon, dry stacking and dry disposal. Two factors during disposal
that can have significant impact on leaching are neutralization through uptake of atmospheric
carbon dioxide, and control of infiltration by reduction in permeability and drainage control.
5.
4. REGULATORY SITUATION
European Union (EU)
Figure 1 illustrates the EU regulatory system dealing with environmental and health impacts on
soil, surface and groundwater. It is rapidly obvious that different policies and regulatory
approaches and test methods have traditionally evolved for different types of materials and
different types of environmental concerns. This situation is not unusual in other jurisdictions as
well. In some cases, contaminant total content is the primary decision basis (fertilizer use),
while in other fields single step extraction tests are usually applied. When materials from the
same source are used in different applications, a variety of different tests may be required to
assess the impact from the resulting products. A more uniform approach (often referred to as a
harmonized approach) to testing and evaluation would be helpful to provide a consistent basis
for evaluation of potential environmental impacts from a range of materials in a variety of
management scenarios, and thus allowing a useful comparative analysis of options. EU-wide
efforts towards regulatory harmonization are underway and are being accomplished through
multiple Technical Committees (TCs, Table 2). The approach towards harmonization of leaching
assessment can be deduced from Table 2, as many fields are covered by the proposed
characterization methods.
POLICY
POLICY
85/337/EEC (impact assessment)
POLICY
79/409/EEC (birds)
92/43/EEC (habitat)
PROTECTED
AREAS
CEN
CEN TC 230
DRINKING
WATER
URBAN
SECTOR
AGRICULTURE
CEN
96/82/EC (seveso)
96/61/EC (IPPC)
INDUSTRY
CEN TC 308
& 345
CEN
CEN TC 292
POLICY
98/83/EC (DWD)
RESEARCH
RESEARCH
POLICY
RESEARCH
POLICY
76/160/EEC
(BWD)
POLICY
86/278/EEC (sludge)
99/31/EC (landfill)
91/271/EEC (UWW)
89/106/EEC (CPD)
CEN
CEN TC 351
POLICY
80/68/EEC
(groundwater)
POLICY
91/414/EEC (pesticides)
91/276/EEC (nitrates)
POLICY
98/8/EC (biocides)
2000/60/EC (Water Framework Directive)
From a presentation by Philippe Quevauviller at a CEN Workshop on 25-3-2006, Coimbra - Portugal
From a presentation by Philippe Quevauviller at a CEN Workshop on 25-3-2006, Coimbra - Portugal
Figure 1. Regulatory framework related to impacts from different sources on soil, surface and
groundwater with different approaches and test requirements.
6.
Table 2. European Union technical committees responsible for leaching assessment in
different fields.
Committee
Topic
Test number
Description
CEN/ TC 292
Waste
TS14405
Percolation test
TS14429
pH dependence test
TS14997
automated pH static
TS15863
monolith leach test
PrEN15875
Static ARD test
ISO/TC21268-3
Percolation
ISO/TC21268-4
pH dependence
ISO/CD 12784 1-5
Sorption parameters
CEN TC/292 WG8
Mining waste
CEN/TC345 and ISO/TC
190
Soil
#
(Fe and Al oxide, DOC
and POM )
CEN/TC351
#
Construction
products
TS-3
Percolation
TS-2
Monolith leach test
Waste from the extractive industry better describes the scope of the TC.
United States
In the United States, disposal is governed by the Resource Conservation and Recovery Act
(RCRA), which is federal regulation that may be delegated to states for implementation.
Individual states have the option of having more stringent requirements than the federal
requirements. Under RCRA, wastes may be classified as hazardous based on either origin or
specific characteristics, including toxicity characteristic based on testing using the toxicity
characteristic leaching procedure (TCLP). However, certain mining wastes, including red mud,
are specifically excluded from consideration as a hazardous waste under RCRA sub-title C for
disposal, and disposal operations are therefore regulated under different jurisdictions. There
are no national regulations regarding “beneficial use” of secondary materials and therefore
individual states develop their own guidelines or regulations. In practice, state guidelines or
regulations rely heavily on either guidance from USEPA and/or state regulatory associations.
As discussed later in this document, USEPA has been supporting the development of alternative
leaching procedures in conjunction with Vanderbilt University for applications where use of
TCLP is not required based on the RCRA statute (see www.Vanderbilt.edu/leaching). These
methods include procedures for pH dependence testing, percolation (column) testing of
7.
granular materials and mass transfer rate (e.g., diffusion) testing of monolithic materials. Interlaboratory comparison (round-robin) testing is being initiated during 2010 and is likely to
continue into 2011 as one of the final steps towards adoption as part of SW-846 Standard
Methods. Associated guidance documents are also under development that are likely to
recommend these testing procedures as part of evaluation of secondary materials for beneficial
use. These efforts are being led by the Waste Characterization Branch of the Office of Solid
Waste and Emergency Response (Washington, DC) in coordination with the Office of Research
and Development (Research Triangle Park, NC).1
1
Points of contact are Mr. Greg Helms, USEPA, Waste Characterization Branch, Office of Solid Waste and
Emergency Response, phone: +1-703-308-8845, [email protected] and Ms. Susan Thorneloe, Office of
Research and Development, National Risk Management Research Laboratory, Phone: +1-919-541-2709,
[email protected]
8.
5. A UNIFIED APPROACH TO LEACHING ASSESSMENT AS PART OF ENVIRONMENTAL
ASSESSMENTS
Overview
When most solid materials, either products or wastes, are placed where contact with soils or
water is likely, understanding leaching often becomes the first step in the pathway to evaluating
impacts to water resources and soils. The overall assessment process (Figure 2) can be
described as the steps of assessing (i) the source term, (ii) COPC transport from the source to
the point of compliance or receptor, and (iii) impact analysis (e.g., comparison with compliance
standards or evaluation of ecological or human health risks). Leaching assessment for COPCs is
intended to provide the source term in the overall assessment. Waterborne contaminant
transport from the source term to the point of compliance is estimated with groundwater or
surface water transport models with a range of complexity, depending on the degree of
refinement of the information needed. Often, the outcome of the contaminant transport model
can be summarized as a dilution and attenuation factor (DAFtransport) which is specific to the
particular COPC and location, or estimated as a regional range of values. Ecological and human
health effects are then evaluated based on either accepted thresholds or toxicity information.
Through this series of steps, leaching assessment results can be used to judge the acceptability
of a proposed management scenario. It is often practical to back calculate from point of
compliance or risk thresholds, the threshold for COPC release from the source term that is
needed to maintain adequate environmental and human health protection. This allows
decoupling of the transport and impact assessment steps from the source term evaluation.
Also, using this approach, a range of potential management scenarios for a specific material can
be evaluated using a similar problem definition paradigm (Figure 3).
Roadbase
[conc]
SOURCE TERM
L/S
TRANSPORT
IMPACT
Point of compliance
Different for each scenario material, changes over time
(carbonation, redox), etc.
Transport in unsaturated
zone and saturated zone to
point of compliance Similar for each scenario
In first instance a generic sensitive soil
system is assumed, which can later be
adapted to the actual situation
Figure 2. A common paradigm for environmental assessment. A key issue is the source term
description obtained from leaching assessment.
.
9.
Landfil
l
Drinking
water well
Contaminated
soil
Drinking water
pipes
Mining
Road base
Coastal protection
Plant
Industrially
contaminated
soil
Roof
runoff
Construction
sewer
Agriculture
Figure 3. Different management scenarios can be viewed as a similar problem definition in terms
of source term release (leaching), COPC transport, and impact analysis.
There have been two, fundamentally different approaches to leaching assessment applied for
environmental assessments and regulatory decision making:
1. Develop individual laboratory leaching tests to simulate or serve as analogues to
anticipated field conditions. In this case, results of laboratory extracts are considered
to be the environmental assessment source term. Back calculated thresholds from
transport and impact assessment steps are then used to assign thresholds for direct
comparison with chemical analyses from laboratory extracts. This approach resulted in
the use of a wide range of leaching tests, with each leaching test developed to evaluate
a specific material type (e.g., soils, waste type, building materials) and to reflect a
different set of anticipated field conditions (e.g., monofill disposal, co-disposal, road
base use, construction applications). Furthermore, the results of testing have limited
comparability and multiple testing regimes are needed to evaluate alternative
management scenarios, often with an inconsistent set of underlying assumptions. In
the simplest application of this approach, the results from a single extract test that is
assumed to be conservative is compared with the applicable thresholds for each COPC.
For example, the EP Toxicity test developed in the United States, assumed a worst case
mismanagement scenario of co-disposal with municipal solid waste in a landfill.
However, the resulting test (now modified and called TCLP, but retaining the original codisposal premise) has been used to evaluate a range of disposal and use scenarios that
have no relationship to the underlying test method assumptions.
2. Develop a set of laboratory leaching tests that measure intrinsic properties of the
material being evaluated, and then use a common set of laboratory test results to
parameterize mass transfer models that provide COPC release estimates for individual
scenarios (Kosson, et al, 2002; van der Sloot and Dijkstra, 2004). The intrinsic
properties that are measured using this approach are (i) constituent aqueous-solid
equilibrium as a function of pH (implemented through a pH-dependent parallel batch
10.
test), (ii) constituent aqueous-solid partitioning as a function of liquid-to-solid ratio (LS;
implemented either through a percolation column test or a variable LS parallel batch
test), and (iii) mass transfer rate (diffusion and dissolution; implemented as a monolith
or compacted granular sequential extraction rate test). The underlying assumptions to
this approach are (i) multi-component leaching can be described in terms of
thermodynamic equilibrium and mass transfer rate principles (Bird, Stewart and
Lightfoot, 2007); (ii) pH and LS are the primary variables that control release, (iii)
knowledge of the underlying chemical speciation of the primary constituents and COPCs
in the solid phase can be inferred from analysis of the variable pH and LS test results,
and (iv) knowledge of the underlying chemical speciation allows estimation of a system
performance under a wider range of physical-chemical conditions (e.g., redox
conditions, water contact rates, etc.).
Advantages of this approach are (i) that a single set of testing data can be used to
provide comparison and assessments for a range of disposal and use options, and (ii)
meaningful comparisons of leaching behavior of different material types can be made.
A disadvantage of this approach is that initial material characterization requires more
extensive testing, however, this disadvantage has been addressed in several ways. First,
establishment of a common database for leaching assessment (LeachXS™, see section 8)
allows initial assessment of general material types with prior information before testing
specific material samples. Second, the database allows for simplified testing by posing
the question “Is this material essentially the same as the relevant materials in the
database with more complete testing?” If this is the case, prior information can be used
for leaching assessment purposes and quality control during on-going production.
Third, a hierarchical approach to testing has been proposed that facilitates reduced
testing for screening, compliance and quality control purposes (Kosson et al, 2002).
Finally, detailed characterization testing costs have been estimated to be approximately
US$15,000, which is small in contrast to overall management costs for large waste
streams.
The second approach described above serves as the foundation for evolving standardized
testing methods and assessment methodologies in both the EU and United States, although
each jurisdiction seeks to apply this approach within different regulatory frameworks.
For all leaching assessments, understanding water contact and flow is essential; maximum
leaching concentrations are observed at low flow conditions where local equilibrium in the pore
solution is obtained, while maximum fluxes are obtained at high rates of percolation through a
material where dissolution and diffusion processes control leaching rates. Thus, estimates or
measurements of hydrologic characteristics of materials and system (e.g., infiltration rates,
material permeability and porosity, groundwater velocity) should be included during
development of detailed leaching assessments.
11.
Leaching Assessment Fundamentals
In this section, an overview of chemical processes controlling the release of contaminants from
waste materials is provided (van der Sloot and Dijkstra, 2004). This “horizontal” approach of
testing and data interpretation forms the basis for integrated leaching assessment.
Total composition of the waste material versus leaching
Somewhat contra-intuitive, the total composition (in the sense of mg of an element / kg of
material) has only a limited influence on the maximum leaching of most elements. Exceptions
are non-reactive soluble salts, of which the maximum leached amount over time is often similar
to the total amount present in the material. The release of other elements is primarily caused by
geochemical mechanisms and physical factors, and leached amounts therefore seldomly
correlate with its total content.
Basic chemical mechanisms
Three different chemical mechanisms control the release of contaminants; by the dissolution of
a mineral (solubility control), by adsorption processes (sorption control) or by its availability (or
total content) in the waste material. Some contaminants show affinity for adsorption to
reactive surfaces. Positively charged heavy metal cations (e.g. Cu+2) that are not controlled by
the dissolution of a mineral, are often controlled by adsorption to (negatively charged) surfaces
present in the material such as organic material or oxide surfaces (sorption control).
A number of inorganic constituents are not very reactive and show neither solubility control nor
sorption control. Examples are the very soluble salts such as NaCl. Upon contact with water they
will dissolve instantaneously and quantitatively. Those elements are availability controlled, as
the total available concentration can be released from the material.
pH
The pH of the material and the pH of its environment are crucial in determining the release of
many constituents. This is valid for all sorts of materials (monolith, granular, cements, soil,
waste, sediment etc.). The pH value of the surrounding fluid determines the maximum water
phase concentration at that pH value, and each material has its own pH-dependent release
curve (see Figure 4). Release curves are similar and systematic for different groups of elements,
only the absolute level may differ between different materials. This implies that the solubility
controlling phases are the same; only the relative importance of the influencing factors may
differ from one material to another (Fe oxides, Mn oxides, Al oxides, clay, organic matter).
The strong influence of pH on release is because the dissolution of most minerals, as well as
sorption processes, is pH dependent. That means that the release of virtually all contaminants
that are solubility controlled or sorption controlled show pH dependent release. The general
shape of the release curves is shown in Figure 4.
12.
100
10
1
salts (Na, K, Cl,
Br,..)
0.1
0.01
1000
Cations (Ni,
Cu, Zn, Cd,
Pb, Al, Fe, )
100
10
1
0.1
4
6
8
pH
10
12
14
100
10
Anions (Mo,
Cr(VI), As,
Se, Sb, SO4)
1
0.1
0.01
2
Leached (mg/kg)
1000
Leached (mg/kg)
Leached (mg/kg)
1000
0.01
2
4
6
8
pH
10
12
14
2
4
6
8
pH
10
Figure 4. General leaching behaviour of three groups of constituents as a function of pH.
Cations, anions and soluble salts have a distinct leach pattern, caused by their chemical
speciation, and vary orders of magnitude as a function of pH. The pH values of materials vary
greatly. Cement-based materials superimpose a pH of around 12 (or higher) on its environment,
whereas predominantly inorganic waste has a pH of around neutral (pH 6-8). The actual pH at
which leaching takes place depends on the pH of the material itself, the pH of the surrounding
environment and the buffering capacity of the material. This is illustrated in Figure 5. The total
amount of a contaminant does not change as a function of the pH. It is clear that the potentially
leachable amount is also significantly lower than the total amount. The potentially leachable
amount is used as an input parameter in geochemical model calculations to predict the leaching
behaviour of waste materials. The red line shows the actual leaching behaviour of metals (in this
example Cd) as a function of pH. In a landfill, the pH of the leachate is around 6-8, indicated by
the pink dashed box. The actual leaching behaviour of a metal can change when the
composition of the waste changes (i.e. more iron oxides for sorption processes, more DOC
causes increased leaching at neutral to high pH, reducing environment generally causes a lower
metal solubility). More information on specific chemical properties are given in the paragraphs
below.
When the chemical processes that lead to release of contaminants are understood, a basis is
formed for long-term prediction of the emissions from waste materials. The basic
characterisation and geochemical modelling approach is therefore an important part of the total
environmental risk assessment of landfills. This approach was also followed in the first Dutch
sustainable landfill project (Mathlener et al., 2006; van Zomeren et al., 2006; van Zomeren and
van der Sloot, 2006a; van Zomeren and van der Sloot, 2006b).
13.
12
14
Figure 5. Leaching behaviour as a function of pH versus total composition. In this figure, the
difference between the total composition in the material is shown versus "potentially
leachable" and "actually leachable" (the red curve) is shown. Note the log scale on the
y-axis.
Chemical form of the constituent in the waste material (redox form, minerals, sorbed
phases, etc)
Aside from these basic chemical mechanisms, the chemical form of a contaminant determines
its characteristic leaching behaviour (e.g. the pH dependence shown in the above figures).
Contaminants may be in the oxidised or reduced form (e.g. Chromium may be present as CrO4-2
or Cr+3) which is important for their leaching behaviour.
Heavy metals tend to complex strongly with natural humic substances present in natural waters,
soils and waste materials. Complexed forms of heavy metals are generally highly soluble and
therefore are released more rapidly than uncomplexed forms of heavy metals (see also 'organic
matter').
Redox
Oxidation /reduction state of the material or its environment ("redox") influences the chemical
form of a contaminant. For heavy metals, the oxidation of an initially reduced material usually
enhances leached amounts while reduction will have the opposite effect. This relates to the
chemical form of the elements of interest. An example of the effect of the redox state of
materials is given in Figure 5 and 6.
14.
Acid-base buffering
The acid- base buffering capacity of a material determines how the pH develops over time under
influence of external factors. Examples are the neutralization of cementitious products due to
the uptake of atmospheric carbon dioxide. In such cases, the alkaline buffering capacity of the
alkaline material determines the time needed until the pH drops from strongly alkaline (pH > 12)
towards a neutral pH value (pH ~ 8).
Organic matter and DOC
Solid and dissolved organic matter or humic substances (often expressed as "DOC", dissolved
organic carbon) consists of complex molecules that have a high affinity to bind heavy metals.
The presence of DOC can enhance leaching by several orders of magnitude. As a result, a new
partitioning between DOC-bound metal and free metal will be established. Organic matter is
usually present in relatively large amounts in organic environments (soils, sediments, sludges).
An example of the effect of organic matter and DOC in waste materials is given in Error!
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Composition of the water phase and ionic strength
The ionic strength influences the solubility of other components (generally, a higher salt
strength increases the leaching of contaminants). Other components present in the solution
may cause enhanced leaching due to complexation, such as metal complexes with chloride or
carbonates.
Leached (mg/kg)
1000
redox
changes
100
DOC
change
10
1
0.1
0.01
2
4
6
8
10
12
14
pH
Figure 6. Absolute levels of leached amounts are different for each material due to influence of
redox, DOC (dissolved organic carbon) and other factors. The leaching patterns of
15.
different groups of elements for all sorts of materials are very systematic, but differ in
absolute levels (leading to a "chemical fingerprint" of a material).
Temperature
Temperature increase generally leads to a higher solubility of contaminants. In addition, an
increase in temperature increases chemical reaction rates, and thus also increases transport by
diffusion.
Time
Time is an important factor for the amount released when
a) In general, the time scale applies to the disposal scenario of the landfill;
b) The rate at which processes proceed, which may be limiting for the release in case of slow
reaction kinetics (slow dissolution of minerals) or diffusion. It may not be feasible to allow
such reactions to run to completion, as the time to reach that stage may be far too long. In
that case, one has to estimate the possible consequences of such slow processes on the
overall release.
c) The change of material properties or environmental conditions over time. Examples are the
degradation of organic matter, changes in permeability of the landfill, carbonation of alkaline
waste materials (altering its release properties) or the increased surface area of a monolithic
waste material due to erosion.
Test methods that include several steps provide insight into the short and long term effects of
leaching. Such tests may give information for interpolation or extrapolation towards shorter or
longer leaching periods. A summary of factors influencing release is given in Table 3 (obviously,
not all factors are equally relevant and depend on the scenario taken into account).
Table 3. A summary of the main physical and chemical factors that influence leaching.
Chemical processes
- Dissolution
- pH
- Chemical form
-Total composition/ availability
- Redox
- Acid-base buffering
- DOC
- Composition water
phase/ionic strength
- Temperature
- Time
Physical factors
- Percolation
- Diffusion
- Surface wash off
- Granular/monolithic
- Size (particles or monoliths)
- Porosity
- Permeability
- Tortuosity
- Erosion
External factors
- Amount of water
- Contact time
- pH of environment
- Temperature
- Redox of environment
- DOC / Adsorption
16.
Example of geochemical modeling results based on a pH dependence leach test
The results of the pH dependence test and the results of organic matter fractionation and
reactive Fe/Al-oxide extractions are used for geochemical modeling. With geochemical
modeling, the underlying chemical processes leading to release of contaminants can be
estimated. A general overview of chemical processes that can be identified in both the solid and
the liquid phase is given in Table 4.
An example of the measured and predicted leaching behavior is given in Figure 7. The leaching
data from a laboratory pH-static leaching test is represented as a function of pH by the red data
points. The black solid line represents the predicted total concentration of the considered
element in solution, which should ideally meet the data points for good understanding of the
chemical processes that determine the leaching behavior. Moreover, Figure 7 shows the
calculated chemical speciation of the element in both the solid matrix and the sample solution.
The predicted leaching behavior is therefore the intersection between the calculated speciation
in the solid matrix (minerals, sorption to Fe-Oxides and binding to solid organic matter) and in
the solution (free+inorganic and complexed by dissolved organic carbon). This type of data
presentation integrates the predicted total leached concentration as well as the different
species that determine the leached concentrations.
The upper line in Figure 7 gives the total available concentration (input into model). The white
area shows the amount of the element bound as minerals in the solid phase. Sorption to FeOxides is represented by the gray area while complexation to solid organic matter is dark green.
These areas represent the total amount in the solid matrix as a function of pH. In the leachate
solution, the light blue area is the total amount of the free ion and the inorganically complexed
form. The light green area represents the amount of the element that is organically complexed.
Table 4. General speciation of contaminants in the solid phase and in the leachate of waste
materials. The major phases and species are specified in both the solid phase as well
as in the leachate.
Solid phase
Dissolved (leachate)
Mineral phases (e.g. CuO, Pb(OH)2)
Free ion (e.g. Cu2+, Pb2+)
Bound to solid organic matter
Inorganic complexes (e.g. [Pb(OH)4]2-)
Adsorbed to Fe/Al-(hydr-)oxides
Complexed to DOC
Adsorbed to clay
17.
1.E+00
1.E+00
Total available
concentration
Ca (mol/l)
1.E-01
1.E-01
Mineral phases
(solid)
Intersection
between solid
and dissolved
1.E-02
1.E-02
1.E-03
1.E-03
Data
POM-bound
(solid)
Free + inorganic
complexes
(dissolved)
FeOxide
(solid)
DOC-bound
(dissolved)
1.E-04
1.E-04
11
22
33
44
55
66
7
8
9
10
11
12
13
14
pH
Figure 7. Example of integrated data presentation for pH-static leaching test results and
geochemical speciation modeling. Red data points represent leaching data, black solid
line is the predicted leached concentration. Areas represent the element speciation:
white=minerals, gray=Fe oxide sorption, dark green=complexation to solid organic
carbon, light green=complexation to dissolved organic carbon and light
blue=free+inorganically complexed form (van Zomeren et al., 2006).
Leaching Test Methods
The following sections provide summaries of leaching test methods for measuring (i) solidaqueous partitioning as a function of pH, (ii) solid-aqueous partitioning as a function of LS, and
(iii) mass transfer rates for monolithic or compacted granular materials. These tests, mostly
applied in combination, provide the level of understanding needed to assess environmental
impact as needed for environmental assessments and to derive regulatory criteria. These tests
provide the basic characterization against which results from simpler tests (separate or part of
the characterization test) used in quality control can be judged. These test methods have
evolved collaboratively and in parallel in both the EU and the US. The specific details of the EU
and US methods differ somewhat, in response to different jurisdictional requirements and
choices, however, the essential aspects of the tests are the same and results of the tests provide
similar and comparable information. The method summaries provided are intended as
examples, with full descriptions provided at www.Vanderbilt.edu/leaching; EU draft standards
are available from the European standardization organization CEN and from ISO.
Both EU and US sets of methods are currently undergoing standardization, with each jurisdiction
having somewhat different standardization requirements. The performance characteristics in
terms of repeatability and reproducibility are lacking. Inter-laboratory comparisons for the US
18.
EPA methods is to begin during Spring 2010. The standards prepared by the different CEN and
ISO bodies are available as Technical Specifications, with ruggedness2 validation and interlaboratory comparisons needed (see CEN Guide on validation tasks in the process of
standardisation of environmental test methods, April 2008, ENV TC 215rev, supported by SABE
Resolution 06/2008 - Validation policy). Since the methods have been in use for some time,
extensive experience is available, and only limited additional ruggedness work will be needed.
However, since the methods have been developed by different groups in different fields,
differences in individual test specifications need to be evaluated and reconciled to insure
uniformity of the standards for a wide spectrum of materials and products, as well as a broad
range of substances (inorganic, organic substances and radionuclides). Appendix A provides a
comparison of test method specifications.
A full comparison of test methods and the
background information on selection of test conditions is included in two documents currently
under development for issuing during Spring 2010 (Ruggedness report, in preparation 2010;
SW846 background document on leaching, in preparation 2010). The aim of the ruggedness and
inter-laboratory comparison work is harmonized standards with a world-wide coverage,
recognizing that some test requirements may vary in different jurisdictions (e.g. quality control
requirements). Figure 8 provides the number of publications each year in the peer-reviewed
literature that indicate use of these approaches and the increasing acceptance of the proposed
approach by the technical community.
25
Publications pH-dependence
test, percolation test, tank
test
journal publications
20
15
10
5
0
90
19
92
19
94
19
96
19
98
19
00
20
02
20
04
20
06
20
08
20
year
Figure 8. Publications in international peer reviewed scientific journals in which pH dependence
test, percolation test and/or tank test is used specifically to characterize the
environmental behavior of construction materials and contaminated materials (n=147).
2
“Ruggedness” refers to comparison of proposed test conditions and either analysis of prior information or laboratory
testing to select final test specifications.
19.
Evaluation of
ARD potential
Acid/base accounting
tests
Sulphidic waste
Evaluation of
other substances
No further ARD testing
No
yes
No
Evaluation
** Dissolution of salt waste can be captured by the own pH condition of the pH dependence leaching test (single step extraction)
Percolation test or
other to provide time
dependent release
Supply and/ or verify
consistency with previous
characterisation
Non-sulphidic waste
yes
Waste management options specific for
the considered waste
pH dependence
leaching test **
Chemical/mineralogical
analysis
No or unsure
Sufficient
Information (methods, waste)
available?
Geochemical
characterisation
yes
* Suf f icient inf ormation means
- data about the chemical and physical properties of the material exist
- potential environmental risks are already known and considered in the technical design of the f acility
Evaluation
Kinetic testing
Yes or
unsure
Assessment of
physical stability and
associated risks
Physical/ geotechnical
characterisation
Sample collection and
preparation
No or unsure
Sufficient
Information (site, waste) available? *
Site characterisation
WASTE FROM THE EXTRACTIVE INDUSTRY
Recent efforts in CEN/TC 292 (Characterization of waste), focusing on wastes from the extractive
industry (Working group 8), provides an example methodology for deciding which test to use
under specific circumstances (Figure 9). In this scheme, red mud would be considered a nonsulphidic waste. However, we recommend inclusion of the mass transfer rate test as part of
waste characterization to allow consideration of scenarios where slow percolation or local
equilibrium at the external waste interface do not control release. A similar activity has been
initiated in CEN/TC 351 for construction products.
Figure 9. Flow diagram modified from the CEN/TC292/WG8 methodology developed for wastes
from the extractive industry.
20.
Method 1313 (similar to EU TS 14429 and ISO/TS 21268-4): Leaching Test (Liquid-Solid
Partitioning as a Function of Extract pH) for Constituents in Solid Materials using a
Parallel Batch Extraction Test
EPA Draft Method 1313 (USEPA 2009a) is designed to provide aqueous extracts representing the
liquid-solid partitioning (LSP) curve of constituents as a function of eluate pH. The protocol
consists of nine parallel extractions of a granular or particle-size reduced solid material in dilute
acid or base. Particle-size reduction facilitates the approach to solid-liquid equilibrium during
the test duration. A mass of solid material equivalent to a specified dry mass (value depends on
sample heterogeneity and particle size) is added to nine extraction bottles. Deionized water is
added to supplement the calculated acid or base addition such that the final liquid-solid (LS)
ratio is 10 mL/g-dry. Addition of acid or base is based on a pre-test titration procedure to
determine the required equivalents/gram yielding a series of eluates in the pH range between 2
and 13. The extraction vessels are sealed and tumbled in an end-over-end fashion for a
specified contact time that depends on the particle size of the sample. Liquid and solid phases
are roughly separated via settling or centrifugation and an aliquot is removed for measurement
of eluate pH and conductivity. The remainder of the eluate is filtered (0.45 µm filter) by
pressure or vacuum filtration and saved for chemical analysis. The eluate concentrations of
constituents of interest are reported and plotted as a function of eluate pH. “Own pH” is defined
as the endpoint pH after extraction with deionized water only at LS=10 mL/g. These
concentrations may be compared to quality control and assessment limits for interpretation of
method results. Figure 10 provides an example of results from a pH dependence test and
indications of the relevant pH domains for different environmental and human health exposure
scenarios.
Method 1314 (similar to EU TS 14405, ISO/TS 21268-3 and CEN TC 351/TS-3): Leaching
Test (Liquid-Solid Partitioning as a Function of Liquid-Solid Ratio) of Constituents in
Solid Materials using an Up-Flow Percolation Column
Draft Method 1314 (USEPA 2009b) is designed to provide the LSP of constituents in a granular
solid material as a function of LS ratio under percolation conditions. A 5-cm diameter x 30 cm
column is moderately packed with solid material. Eluant is introduced to the column in up-flow
pumping mode to minimize air entrainment and flow channeling. For most materials, the
default eluant is deionized water; however, a solution of 1.0 mM calcium chloride in deionized
water is used when testing materials with either high clay content (i.e. to prevent deflocculation
of clay layers) or high organic matter (i.e. to minimize mobilization of dissolved organic carbon).
The eluant flow rate is to be maintained between 0.5-1.0 LS/day to increase the likelihood of
local equilibrium within the column. Liquid fractions are collected as a function of the
cumulative LS ratio and saved for chemical analysis. The cumulative mass release is plotted as a
function of cumulative LS ratio. Example results are provided in Figure 11 and Figure 12.
21.
pH dependent Concentration of V
100
INGESTION
INHALATION
Concentration (mg/l)
10
CEMENT
STABILIZATION
ACIDIC
ENVIRONMENT
1
SOIL
LIMING
NATURAL
SOIL
0,1
0,01
Red Mud TS14429 ox
Red Mud TS14429 red
0,001
2
4
6
8
10
12
14
pH
Figure 10. Example results of a pH dependence test on red mud for vanadium [oxidized (ox) and
reduced (red)]. Relevant pH domains are indicated for assessing different
environmental or exposure scenarios.
Results can be used in combination from the above leaching tests to provide additional insights
into mechanisms controlling constituent release. Figures 11 and 12 present results from the pH
dependence test in conjunction with the results from the percolation column test. When
results of the percolation column test are plotted in concentration units (mg/L) along with the
pH dependence test results as a function of pH, solubility controlled release (i.e., a saturated
eluate solution with respect to the constituent) during the column test is indicated when the
column test results coincide with the pH dependent test results. Washout of a highly soluble
constituent is indicated by a nearly vertical set of percolation data points in contrast to the pH
dependence test results. In addition, when plotted on a release basis (mg/kg), the pH
dependence test result at “own pH” coincides with the cumulative release from the column test
at LS=10 mL/g.
22.
pH development as function of L/S
13,5
Red mud 1a red2
13
100000
(a)
12,5
10000
12
1000
100
Red10 mud 1a ox
Red mud 1a ox
pH
Emission (mg/kg)
Red mud 1a
11
1
1
3
5
7
9
11
11,5
13
10,5
pH
10
Red mud 1a red
9,5
0,1
1
10
L/S (L/kg)
pH dependent Emission of Al
Cumulative release of Al
1,0E+05
Cumulative release (mg/kg)
100000
Emission (mg/kg)
10000
1000
100
(b)
10
1
(c)
1,0E+04
1,0E+03
1,0E+02
1,0E+01
1,0E+00
1
3
5
7
9
11
13
0,1
1
pH
pH dependent Concentration of Al
Al concentration as function of L/S
100000
100000
10000
Concentration (mg/L)
10
L/S (L/kg)
1000
100
10
1
(d)
0,1
1
3
10000
1000
100
(e)
10
5
7
9
pH
11
13
0,1
1
10
L/S (L/kg)
mud for a species where leaching is controlled by local solubility (i.e., Al). (a) eluate pH
as a function of LS, (b) eluate cumulative release as a function of pH along with pH
dependence test results, (c) eluate cumulative release (mg/kg) as a function of LS, (d)
eluate concentration as a function of pH along with pH dependence test results, and,
(e) eluate concentration (mg/L) as a function of LS.
23.
pH dependent Concentration of Na
Na concentration as function of L/S
100000
100000
10000
1000
100
10000
1000
100
1
3
5
7
9
11
13
0,1
pH
10
Cumulative release of Na
pH dependent Emission of Na
1,0E+06
Cumulative release (mg/kg)
1000000
Emission (mg/kg)
1
L/S (L/kg)
100000
10000
1000
1,0E+05
1,0E+04
1,0E+03
1
3
5
7
9
pH
11
13
0,1
1
10
L/S (L/kg)
mud for a highly soluble species where leaching is controlled by availability (i.e., Na).
(a) eluate concentration as a function of pH along with pH dependence test results,
(b) eluate concentration (mg/L) as a function of LS, (c) eluate cumulative release
(mg/kg) as a function of pH along with pH dependence test results, and (d) eluate
cumulative release (mg/kg) as a function of LS.
24.
The combined interpretation of results from pH dependence and column leaching tests provides
insights into the different processes controlling release of individual constituents. Figure 11 and
12 illustrate the relationships between pH dependence test results and percolation test results
for red mud for several elements. Consideration must be given to the change in eluate pH and
ionic strength as the column test eluates progress as a function of LS. The following is a
summary of the insights that can be gained:
1. Highly soluble constituents (e.g., Na, Cl, B) will have greater initial concentrations (mg/L)
in the first few fractions of the column test than observed during the pH dependence
test and the majority of the overall leached fraction will elute by ca. LS=2. The fraction
(mg/kg) of the highly soluble constituents that elutes during the pH dependence test will
be approximately equal to the cumulative fraction (mg/kg) that elutes at LS=10.
2. For constituents where leaching is controlled by pore water dissolution-precipitation of
solid phases (i.e., minerals), the concentrations observed in eluates from the column
test will be approximately equal to the concentrations observed from the pH
dependence test when compared at the same eluate pH. Thus, column test results will
will be consistent with pH dependence test results when plotted as a function of eluate
pH.
3. Constituents where leaching is controlled by ion exchange or adsorption-desorption
phenomena will elute during a column test consistent with an equilibrium liquid-solid
partitioning isotherm (either linear or non-linear depending on concentration domain
and amount of adsorptive surfaces). The relationships between pH dependence and
column test results will be inconsistent on a concentration basis, but the cumulative
release from the column test will be be approximately equal to the cumulative fraction
(mg/kg) that elutes at LS=10.
4. When significant amounts of dissolved organic carbon are present (DOC; e.g., humic
substances or analogues thereof), results of pH dependence and column tests will be
more strongly dependent of the concentration of DOC in the eluate than pH.
5. Concentrations observed in eluates from the column test at low LS (e.g., LS≤0.5) are
indicative of pore water concentrations and initial leachate concentrations that can be
expected in the field under percolation conditions where significant flow channeling
does not occur.
The following additional observations are made that are specific to red mud with respect to
results of pH dependence and column tests:

Al release is not affected by the oxidized or reduced state of the red mud, as the pH
dependence test data overlap.

Comparison of pH dependence test results from red mud samples that are
uncarbonated and with imposed carbonation indicates that Al leaching is not affected
by the difference in using HNO3 or CO2 for neutralization.

Eluate concentrations from column tests on oxidized and also carbonated red mud are
consistent with the pH dependence test curve. The oxidation process simultaneously
resulted in carbonation. In addition, the cumulative release curve for Al follow a slope 1
curve (dotted line) in the cumulative release time plot, which is indicative of solubility
control. In the concentration – L/S plot the concentration is constant over the entire L/S
range, which also points at solubility control.
25.

Eluate concentrations for Al measured from the column test on reduced red mud are
greater than indicated by the pH dependence test curve. In addition, the cumulative
release curve for Al reaches a plateau in the cumulative release vs. LS plot, which is
indicative of wash out and/or change in exposure conditions. In the concentration – L/S
plot the concentration is initially constant and then drops off steadily, which points at
wash-out at low L/S followed by solubility control as the pH decreases. In the pH curve
showing release (mg/kg), the data from the column test on reduced red mud fall on the
pH dependence test curve, as does the cumulative release at L/S=10 for the column test
on oxidized red mud.

Eluate pH as a function of L/S from the column test on reduced red mud is indicative of
changes in release behavior.

The majority of highly soluble species like Na are leached completely before before L/S =
2.
Method 1315 (similar to EU CEN/TS15863 and CEN/ TC351/TS-2): Mass Transfer Rates
of Constituents in Monolithic or Compacted Granular Materials using a Semi-Dynamic
Tank Leaching Test
Draft Method 1315 (USEPA 2009c) provides mass transfer rates (release rates) of constituents
contained in low permeability material under diffusion-controlled release conditions. The
procedure consists of continuously leaching water-saturated monolithic or compacted granular
material in an eluant-filled tank with periodic renewal of the leaching solution. The vessel and
sample dimensions are chosen such that the sample is fully immersed in the leaching solution at
a liquid-surface area ratio of 9 mL/cm2. Monolithic samples may be cylinders or parallelepipeds
while granular materials are compacted into cylindrical molds at optimum moisture content
using modified Proctor compaction methods. At nine pre-determined intervals, the leaching
solution is exchanged with fresh reagent water and the previous leachate is collected. For each
eluate, the pH and conductivity are measured and analytical samples are saved for chemical
analysis. Eluate concentrations are plotted as a function of time, as a mean interval flux and as
cumulative release as a function of time. Observed diffusivity and tortuosity may be estimated
through analysis of the resulting leaching test data.
Figure 13 presents results from the pH dependence test in conjunction with the results from the
compacted granular mass transfer test. Diffusion based mass transfer tests are designed to
maintain the constituents of interest as a dilute solution with respect to equilibrium
concentrations (i.e., less than the concentration observed at liquid-solid equilibrium) to
maximize the release flux. In this way, dissolution and diffusion in the pores of the solid phase
controls the release rate. When eluate concentrations from the mass transfer test are equal or
close to the concentrations observed in the pH dependence test at the same pH, the mass
transfer test eluate is approaching equilibrium and therefore the assumption of “dilute”
conditions during leaching in the mass transfer test is no longer valid (as shown in Figure 13). In
addition, initially lower pH in the mass transfer test eluate than observed for the “own pH” in
the pH dependence test may indicate carbonation of the external layer of the test sample used
during mass transfer rate testing.
26.
Concentration of Al as function of pH
Cumulative release of Al
10
1
0,1
10
100
0,01
100
10
1
1
3
5
7
9
11
13
0,01
MXA1 - Monolith
MXA2 - Monolith
MXA1 - pH dependence
MXA1 - Monolith
Concentration of Mo as function of pH
1
10
100
0,01
MXA2 - Monolith
slope=0.5
0,001
9
11
10
1
13
0,01
MXA2 - Monolith
0,1
0,01
1
10
100
0,01
20,01
40,01
time (days)
MXA1 - Monolith
Concentration of Cd as function of pH
MXA2 - Monolith
60,01
80,01
time (days)
slope=0.5
MXA1 - Monolith
Cumulative release of Cd
100
MXA2 - Monolith
0,001
0,1
pH
MXA1 - Monolith
100
Concentration of Mo as function of time
0,1
7
10
1
Cum. release (mg/m²)
0,01
1
MXA1 - Monolith
Cumulative release of Mo
0,1
5
0,1
time (days)
100
3
0,1
time (days)
1
1
1
0,01
0,1
pH
Concentration of Al as function of time
1000
1000
MXA2 - Monolith
Concentration of Cd as function of time
10
0,1
1
0,1
0,01
10
1
0,01
0,001
0,001
0,0001
0,1
1
3
5
7
9
11
13
0,01
0,0001
0,1
pH
MXA1 - Monolith
MXA2 - Monolith
1
10
100
0,01
0,1
time (days)
MXA1 - Monolith
MXA2 - Monolith
1
time (days)
slope=0.5
MXA1 - Monolith
MXA2 - Monolith
Figuur 13. Example mass transfer rate test results (compacted granular). (left) eluate concentration in relation to pH dependence test
results, (middle) ) eluate cumulative release as a function of time, and, (right) eluate27.
concentrations as a function of time.
10
100
Fundamental Leaching Characteristics of Red Mud
Relationship between results from different leaching tests
Figure 14 illustrates the relationships amongst several single step leaching tests for the release
of nickel from a contaminated river sediment as a function of pH, as well as in comparison with
different total content methods (true total, partial total) in both linear and log scale. The
relevant pH domain for the sediment under field conditions is depicted by the box using dashed
lines (vertical lines represent the pH domain, the lower horizontal line represents the analytical
detection limit, and the upper horizontal line represents a regulatory limit). The variation
between the total content methods, although significant, is irrelevant because the amount
leached is only a small fraction and independent of the total content, and strongly a function of
endpoint pH in the leaching test. Total elemental content is used as a determining characteristic
in many regulations, particularly for the evaluation of sludge, compost, soil and sediments.
Generally, for many elements in a variety of materials, the total content is not correlated with
leaching and often the readily leachable amount is only a small fraction of the total content (for
example, see Kosson et al, 2009). Thus, decisions based on leaching, rather than total content,
more realistically consider likely constituent release and thereby potential impacts to human
health and the environment. It can be argued that decisions based on total content are
“conservative” (by over-estimating constituent release), however, when total content based
release estimates are several orders of magnitude greater than actual release, then resulting
decisions can be inconsistent with accepted performance of non-waste derived materials and
natural background levels in the environment. When evaluating potential impact to soil and
groundwater or uptake by organisms and plants, the chemical form and the actual
concentration in the pore solution are important, which cannot be obtained from total
composition analysis. Approaches using correlations between leachability and total content are
by definition poor, as changes in pH, organic matter or redox state are not captured in such
correlations.
In Figure 14, the consistency of the pH dependence test (TS 14429) is also illustrated. Results of
the single step extraction tests generally match well when plotted as a function of end point pH,
but also clearly illustrate the limitations of using a single point extraction test to understand the
leaching behavior of a material over the range of pH conditions that can be encountered under
field conditions. The sequential chemical extraction (SCE) test results are in good agreement
with the pH dependence test results provided the SCE results are expressed as the cumulative
amount leached in subsequent steps and plotted at the end point pH of the individual extraction
step. In summary, Figure 14 illustrates three main points in relation to the pH dependence test:
(i) the test forms a better basis for assessing leaching rather than total content; (ii) the test
provides a reference base for many single step test procedures; and (iii) the test provides insight
into important changes in release from extractions as a function of end-point pH.
28.
80
100
"Total"
70
Le ache d (mg/k g)
10
Le ache d (mg/k g)
Total (HF, HClO4)
1
0.1
60
Total (A qua Regia)
50
Total (HNO3,tef lonbomb)
40
Total (HNO3, HClO4)
Ni
30
PrEn14429
20
EN12457-2
PrEN 14429
CaCl2
10
Ni
SCE
0
0.01
1
3
5
7
9
11
13
1
3
pH
5
7
9
11
13
pH
Figure 14. Relationships between results from the pH dependence test (TS 14429), other single
step extraction tests, sequential chemical extraction (SCE) and total elemental content
methods. Results are presented on a release basis (mg Ni leached/kg sediment) to
facilitate comparison of leaching test results with total content.
Characteristic pH dependent leaching behavior of red mud
The range or band-with characteristic leaching behavior of red mud can be evaluated by
comparison of the pH dependent leaching results from the available data sets. Figure 15 to 19
presents this information for a range of constituents. In case concentrations are forming a solid
line at a fixed concentration, that is an indication of detection limit issues. Some of the data are
older and thus did not have the more sensitive analytical data. In particular the data for Hg are
biased. The actual concentration of Hg from neutral to high pH is quite low.
Relationship between total content and leachability for red mud
Figure 20 and 21 provide comparisons of total elemental content with pH dependence leaching
for a several constituents in red mud. The maximum amount of an element leached over the
domain of the pH dependent test (usually the maximum release occurs either at pH < 4 or
pH>12) is considered a good estimate of the maximum leachable under typical field conditions,
which often is referred to as the “available fraction” or “availability”. For many constituents in
red mud, the leachable fraction is only a small fraction of the total content (e.g., Cr, Th, V).
However, for some elements, the leachable fraction is close to the total content (e.g., Ca, Mo,
U). The difference between the available fraction and the total content of an element is a
consequence of the chemical speciation in the solid phase. For example, lead and chromium
incorporated into alumino-silicate phases typically will not be leachable under typical field
conditions.
29.
pH dependent Concentration of As
pH dependent Concentration of Al
10
1000
10000
100
10
1
0.1
1
0.1
0.01
0.001
0.0001
0.01
1
3
5
7
9
11
1
13
3
5
7
9
11
13
11
13
pH
pH
pH dependent Concentration of Ba
pH dependent Concentration of B
100
10
10
1
0.1
0.01
1
0.1
0.01
0.001
0.0001
0.00001
0.001
1
3
5
7
9
11
1
13
3
5
7
pH dependent Concentration of Ca
pH dependent Concentration of Be
10000
10
1
9
pH
pH
0.1
0.01
0.001
0.0001
0.00001
1000
100
10
1
0.1
0.000001
1
3
5
7
9
11
1
13
3
5
7
9
11
13
11
13
pH
pH
pH dependent Concentration of Cl
pH dependent Concentration of Cd
10000
100
10
1
0.1
0.01
0.001
1000
100
10
0.0001
1
0.00001
1
3
5
7
9
pH
11
13
1
3
5
7
9
pH
results of the pH dependence test from available data sets (concentration basis, mg/L).
>
30.
pH dependent Concentration of Cu
pH dependent Concentration of Cr
100
1
10
0.1
0.01
0.001
0.0001
10
1
0.1
0.01
0.001
0.0001
1
3
5
7
9
11
13
1
3
5
7
pH
13
11
13
11
13
11
13
100
10
1
0.1
10
1
0.1
0.01
0.001
0.0001
0.01
1
3
5
7
9
11
1
13
3
5
7
9
pH
pH
pH dependent Concentration of Ga
pH dependent Concentration of Hg
10
0.1
1
0.1
0.01
0.001
0.0001
0.01
0.001
0.0001
0.00001
0.000001
0.0000001
1
3
5
7
9
11
13
1
3
5
7
pH
9
pH
pH dependent Concentration of La
pH dependent Concentration of K
100
1000
10
100
11
pH dependent Concentration of Fe
pH dependent Concentration of F
100
9
pH
10
1
0.1
1
0.1
0.01
0.001
0.0001
0.00001
0.01
1
3
5
7
9
pH
11
13
1
3
5
7
9
pH
31.
pH dependent Concentration of Mg
pH dependent Concentration of Li
10000
1
1000
0.1
0.01
0.001
0.0001
100
10
1
0.1
0.01
0.001
0.0001
0.00001
0.00001
1
3
5
7
9
11
1
13
3
5
7
9
11
13
11
13
11
13
11
13
pH
pH
pH dependent Concentration of Mn
pH dependent Concentration of Mo
1000
10
100
10
1
0.1
0.01
0.001
1
0.1
0.01
0.001
0.0001
0.0001
0.00001
1
3
5
7
9
11
1
13
3
5
7
9
pH
pH
pH dependent Concentration of Na
pH dependent Concentration of Ni
100000
100
10
10000
1000
100
1
0.1
0.01
0.001
0.0001
10
0.00001
1
3
5
7
9
11
13
1
3
5
7
pH
9
pH
pH dependent Concentration of Pb
pH dependent Concentration of P
100
100
10
10
1
0.1
1
0.1
0.01
0.001
0.0001
0.00001
0.01
1
3
5
7
9
pH
11
13
1
3
5
7
9
pH
>
32.
pH dependent Concentration of Sb
pH dependent Concentration of S
0.1
1000
100
10
0.01
0.001
0.0001
0.00001
1
1
3
5
7
9
11
1
13
3
5
7
10000
10
1000
100
1
0.1
0.01
0.001
0.0001
13
100
10
1
0.1
0.01
0.00001
1
3
5
7
9
11
1
13
3
5
7
9
11
13
pH
pH
pH dependent Concentration of Sn
pH dependent Concentration of Th
10
10
1
1
11
pH dependent Concentration of Si
pH dependent Concentration of Se
0.1
0.01
0.001
0.0001
0.00001
0.1
0.01
0.001
0.0001
0.00001
1
3
5
7
9
11
13
1
3
5
7
pH
9
11
13
11
13
pH
pH dependent Concentration of U
pH dependent Concentration of Tl
10
1
9
pH
pH
0.1
0.01
0.001
1
0.1
0.01
0.001
0.0001
0.00001
0.0001
1
3
5
7
9
pH
11
13
1
3
5
7
9
pH
>
33.
pH dependent Concentration of V
pH dependent Concentration of Zn
100
10
10
100
1
0.1
0.01
1
0.1
0.01
0.001
0.0001
0.001
0.00001
1
3
5
7
9
11
13
pH
1
3
5
7
9
11
13
pH
pH dependent Concentration of DOC
1000
100
10
1
1
3
5
7
9
11
13
pH
Figuur 19. (multiple pages to illustrate selected elements). Characteristic pH dependent leaching
behavior of red mud based on comparison of results of the pH dependence test from
available data sets (concentration basis, mg/L).
34.
pH dependent Emission of Al
pH dependent Emission of As
1000000
1000
100000
100
)
g
k
/
g
m
(
n
o
i
s
s
i
m
E
)
g 10000
k
/
g
1000
m
(
n
o
100
i
s
s
i
m
10
E
10
1
0,1
0,01
1
0,001
0,1
1
3
5
7
9
11
1
13
3
5
7
pH dependent Emission of Ca
11
13
11
13
11
13
pH dependent Emission of Cr
100000
10000
1000
10000
)
g
k
/
g 1000
m
(
n
o
i
100
s
is
m
E
)
g
k
/
g
m
(
n
o
i
s
is
m
E
10
100
10
1
0,1
0,01
1
0,001
1
3
5
7
9
11
13
1
3
5
7
pH
9
pH
pH dependent Emission of Ga
pH dependent Emission of Cu
1000
1000
100
)
g
k
/
g
(m
n
io
s
s
i
m
E
9
pH
pH
100
)
g
k
/
g
(m
n
io
s
is
m
E
10
1
0,1
0,01
10
1
0,1
0,01
0,001
0,001
1
3
5
7
9
pH
11
13
1
3
5
7
9
pH
Figure 20. Comparisons of total elemental content with pH dependence leaching test results for
red mud (release basis, mg/kg).
35.
pH dependent Emission of Na
pH dependent Emission of Pb
1,0E+06
1000
100
)
g
k 1,0E+05
/
g
m
(
n
o
i
s
s
i 1,0E+04
m
E
)
g
k
/
g
m
(
n
o
i
s
is
m
E
10
1
0,1
0,01
0,001
1,0E+03
0,0001
1
3
5
7
9
11
13
1
3
5
7
pH
pH dependent Emission of Se
13
11
13
11
13
11
13
1000
100
100
)
g
k
/
g
(m
n
io
s
is
m
E
10
1
0,1
0,01
10
1
0,1
0,01
0,001
0,001
0,0001
1
3
5
7
9
11
13
1
3
5
7
pH
9
pH
pH dependent Emission of V
pH dependent Emission of U
10000
100
10
)
g
k
/
g
m
(
n
o
i
s
s
i
m
E
11
pH dependent Emission of Th
1000
)
g
k
/
g
m
(
n
io
s
is
m
E
9
pH
1000
)
g
k 100
/
g
(m
10
n
io
s
s
i
1
m
E
1
0,1
0,01
0,001
0,1
0,0001
0,01
1
3
5
7
9
11
1
13
3
5
7
9
pH
pH
pH dependent Emission of Hg
pH dependent Emission of Mo
100
1,0E+00
1,0E-01
)
g
k
/
g
m
(
n
o
i
s
is
m
E
)
g
k 1,0E-02
/
g
(m 1,0E-03
n
io
s
s
i 1,0E-04
m
E
1,0E-05
10
1
0,1
0,01
0,001
1,0E-06
1
3
5
7
9
pH
11
13
1
3
5
7
9
pH
Figuur 21. Comparisons of total elemental content with pH dependence leaching test results for
red mud (release basis, mg/kg).
36.
6.
GEOCHEMICAL SPECIATION AND REACTIVE TRANSPORT MODELING OF LEACHING
TEST RESULTS AND DISPOSAL AND USE SCENARIOS
Geochemical speciation modeling simulates the aqueous-solid-gas partitioning of the system
constituents based on (i) the solid phase minerals, solid solutions, adsorptive surfaces and
organic matter, (ii) complexation and chelation in the aqueous phase, (iii) thermodynamic
relationships for precipitation/dissolution and phase equilibrium, (iv) aqueous-gas equilibrium
(especially important for carbon dioxide and oxygen), (v) kinetic information (when available),
and (vi) charge and mass balances. In practice, local equilibrium and homogeneity is assumed at
the representative volume element level (e.g., at the pore-scale) to facilitate calculations. Mass
transfer models (e.g., for diffusion or percolation) are then coupled with local equilibrium
geochemical speciation models to simulate overall constituent behavior as a function of the
desired scenario.
Two approaches are taken for determining the initial set of solid phases and solution conditions
used to represent a material leaching system for environmental purposes and are often used in
combination: (i) use direct observation of solid phases through analytical chemistry techniques
such as x-ray diffraction and electron microscopy, and (ii) using the liquid-solid partitioning
behavior of a material under a range of solution conditions to back infer the controlling solid
phase speciation. Direct observation of solid phases is most useful for identifying the major
phases present in a material and typically is limited to species that are present at solid phase
concentrations greater than several thousand parts per million to a few tenths of a percent.
Most often, analytical techniques are not sensitive enough to determine the phases present for
many trace constituents that are of environmental concern, and observation of a particular
phase does not mean that it is necessarily the phase controlling solid-liquid partitioning
behavior.
Using solid-liquid partitioning data to back infer the solid phases present is useful for developing
a virtual model of the solid phases that appear to be controlling the liquid-solid partitioning.
However, this approach alone often will not result in a unique solution and has a very large
number of degrees of freedom in the model. To constrain such a model, as many elements as
possible are modeled simultaneously, as well as using expert judgment. The resulting “chemical
speciation fingerprint” then can be tested for robustness by comparing the suitability of
simulations based on experimental data for conditions other than used for model
parameterization. Major species can also be confirmed using analytical techniques on the same
sample or as provided in literature reports. This is the approach generally taken by the authors
of this report, whereby the initial chemical speciation fingerprint is inferred from pH
dependence leaching test data and the resulting geochemical speciation model is then tested by
comparison with percolation and diffusion test results.
In the sections that follow, geochemical speciation using the chemical speciation fingerprint
approach and using LeachXSTM (which embeds ORCHESTRA for geochemical speciation) as the
simulation tool is described. Other geochemical speciation tools such as Geochemist’s
Workbench, PhreeqC or Minteq may be used with a similar approach, but the LeachXS tools are
specifically designed for compatibility with the leaching test methods described earlier and
leaching assessment scenarios. LeachXS is described in more detail in Section 8 of this report.
37.
Modeling test data pH dependence, percolation, tank leaching
Solid and liquid phase chemical speciation was calculated for the pH dependence test conditions
using LeachXSTM-ORCHESTRA (www.leachxs.net; van der Sloot et al, ??; van der Sloot et al, ??;
Meeussen, 2003). Aqueous speciation reactions and selected mineral precipitates were taken
from the MINTEQA2 database. Ion adsorption onto organic matter was calculated with the
NICA-Donnan model (Kinniburgh et al., 1999), with the generic adsorption reactions as
published by Milne et al. (Milne et al., 2001; Milne et al., 2003). Adsorption of ions onto iron and
aluminium oxides was modelled according to the generalized two layer model of Dzombak and
Morel (Dzombak and Morel, 1990).
The input to the model consists of fixed element availabilities estimated based on the maximum
release from the pH dependence test, selected possible solubility controlling minerals, active Feand Al-oxide sites (Fe- and Al-oxides were summed and used as input for HFO as described in
(Meima and Comans, 1998), particulate organic matter and a description of the DOC
concentration as a function of pH (polynomial curve fitting procedure). The DOC analysis of the
extracts does not distinguish the reactive part of the dissolved organic matter. Based on
experience with soil and other residue samples (van Zomeren and Comans, 200x), where the
hydrophilic, fulvic and humic acid fractions in DOC was quantified, reactive fractions of DOC are
defined as a function of pH (the lowest proportion of reactive forms is present at neutral pH and
increases at both low and high pH). A polynomial fit is created through the 8 data points to
interpolate quantification of the reactive DOC at intermediate pH values in modelling. The
speciation of all elements then is calculated in one problem definition of the model with the
same parameter settings, and sensitivity analysis is used to refine the model. This limits the
degrees of freedom in selecting parameter settings, as improvement of the model description
for one element may deteriorate the outcome for other elements. It was found that total
(leachable) carbonate concentration plays an important role in the model calculations. This
parameter is estimated based on the total inorganic carbon content of the solid phase. The
concentration is adjusted until the Ca as calcite shows a reasonably good match with the
observed leaching data. There is a clear need for more data on total (available) carbonate
concentrations in various materials to enhance model predictions.
The mineral phases that were allowed to precipitate were selected after calculation of their
respective saturation indices (SI) in the original pH dependence leaching test eluates. Saturation
indices were calculated for all > 650 minerals in the thermodynamic database and a selection of
the most likely and relevant phases was made based on the degree of fit over a wider pH range
and the closeness of the SI value to 0 (ideal fit) and expert judgment on suitability of possible
minerals for the waste mixture (e.g. exclusion of high temperature minerals). Generally,
minerals were selected if the SI was in the range of -0.2 to 0.2 for more than two pH data points.
Modelling of pH dependence test results
Geochemical speciation modelling of pH dependence test results for red mud is illustrated in
Figure 22 for selected constituents. The percolation test data are included in Figure 22 for
comparison. The conditions specified as input for the modeling are provided in Table 5 and 6.
The agreement between measurement at LS=10 and the model description (minerals, iron
interaction, clay interaction and organic matter interaction) is quite good considering the
complex nature of the waste. The prediction of the eluate concentrations at LS=0.2 reasonably
agrees with the percolation test data, which is an indication of a practical model description for
38.
leaching behaviour. This combination of composition data, solid phases and sorption
parameters settings can be used as a basis for estimating release and system responses from
39.
9
10 11 12 13 14
pH
0
1
3
5
ANC
7
9
11
13
pH
[SeO4-2] as function
of pH
1,0E-02
1,0E-06
1,0E-03
1
2
3
4
5
6
7
8
9
10 11 12 13 14
Concentration (mol/l)
[Al+3] as function of pH
1,0E+00
1,0E-05
1,0E-01
2
3
4
5
6
7
8
9
10 11 12 13 14
1,0E+00
1,0E-06
1,0E-01
1,0E-07
1,0E-02
1,0E-08
1,0E-03
1,0E-04
1,0E-09
1
2
2
3
3
4
4
5
5
6
6
7
7
8
pH
8 9
9
9
10 11 12 13 14
7
9
11
1,0E-05
1,0E-06
1,0E-06
1,0E-07
1
2
3
3
4
4
5
5
6
6
7
7
8
8
pH
9
9 10 11 12 13 14
10 11 12 13 14
DOC-bound
POM-bound
FeOxide
Clay
pH
Clay
Partitioning liquid-solid, [Ca+2]
0,1
1,0E-07
0,01
1,0E-08
0,001
0,0001
1,0E-09
12
23
34
45
56 67 78 8 9 910 1011 1112 1213 1314 14
Free
DOC-bound
POM-bound
Free
DOC-bound POM-bound FeOxide
Clay
Ni[OH]2[s]
FeOxide
Clay
AA_Calcite
AA_Portlandite
BaCaSO4[50%Ba]
beta-TCP
Partitioning
liquid-solid, [VO2+]
Fluorite
Laumontite
Uranophane
1,0E-04
1,0E-02
[H4SiO4] as function of pH
2
pHpH
pH
1,0E+00
1,0E-05
1,0E-05
Partitioning liquid-solid, [H4SiO4]
1,0E-01
1,0E-06
1,0E-01
1,0E-06
1,0E-02
1,0E-07
1,0E-02
1,0E-07
1,0E-03
1,0E-08
1,0E-04
1
1,0E-03
1,0E-08
2
3
4
5
6
7
8
9
10 11 12 13 14
pH
1,0E-05
2
3
4
5
6
7
8
pH
9
10 11 12 13 14
1
1,0E-04
2
3
4
5
6
7
8
9
10 11 12 13 14
pH
1,0E-05
Free
1,0E-06
2
1
DOC-bound
POM-bound
2 3 4 5 6 7
Free
FeOxide
Laumontite
ZnSiO3
DOC-bound
Clay
Montmorillonite
pH
Free
13
14
pH
FeOxide
Clay
Pb2V2O7
8 9 10 11 12 13 14
POM-bound
Albite[low]
Uranophane
pH
1
2
DOC-bound
Model L/S=0.2
Al+3 fractionation in solution
100%
90%
80%
70%
60%
50%
40%
30%
100%
20%
1
2
3
4
5
6
7
8
9
10
11
12
13
14
pH
1
2
3
4
5Free6
7
8
9 10
DOC-bound
11
12
13
14
pH
Ni+2 fractionation in solution
Free
DOC-bound
100%
90% Ca+2 fractionation in solution
80%
100%
70%
90%
60%
80%
70%
50%
60%
40%
50%
30%
40%
20%
30%
10%
20%
0%
10%
1
2
3
4
5
6
7
8
9 10 11
0%
1
2
3
4
5
6
pH
8
9
7
10
11
12
12
13
13
14
14
pH
Free
DOC-bound
VO2+ fractionation
in solution
Free
DOC-bound
100%
90%
80%
70%
60%
100%
50%
40%
30%
20%
10%
0%
H4SiO4 fractionation in solution
1
2
3
4
5
6
7
8
9
10
11
12
13
pH
Free
100%
1
2
3
4
5
DOC-bound
6
7
8
9
pH
Free
DOC-bound
Figuur 22. Geochemical speciation modeling of pH dependence test results for red mud.
40.
10
11
12
4
8
9 10 11 12 13 14
12 34 56 78 91011121314
pH
FeOxide
pH
AA_OH-hydrotalcite[cr]
SeO4-2 fractionation in the solid phase
100%
10%
0%
3
1,0E+00
1,0E-01
1,0E-02
1,0E-03
1,0E-04
1,0E-05
1,0E-06
5
6
7
POM-bound
Clay
Montmorillonite
SeO4-2 fractionation in solution
1,0E-04
1
10 11 12 13 14
1,0E-03
1
13
1,0E-02
1,0E-05
1,0E-03
10 11 12 13 14
[VO2+] as function of pH
8
AA_Al[OH]3[am]
Partitioning liquid-solid,
[Ni+2]
Albite[low]
Laumontite
Montmorillonite
1,0E-06
[Ca+2] as function of pH
1,0E-05
1,0E+01
1
7
DOC-bound
pH
FeOxide
Free
Free
DOC-bound
POM-bound
pH
1,0E-04
6
Partitioning liquid-solid, [Al+3]
1
[Ni+2] as function of pH
1,0E-04
1
5
1,0E-01
pH
1,0E-07
1,0E-05
4
1,0E-04
1,0E-04
1,0E+01
1
3
pH
Partitioning liquid-solid,
[SeO4-2]
1,0E-03
1,0E-06
2
Free
POM-bound
-5
Clay
1 Montmorillonite
3
5
-3
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
8
Fraction of total concentration (%)
7
1,0E-02
1,0E-03
1,0E-04
1,0E-05
1,0E-06
3Red
4 mud
5
6Percolation
7
8
9 10 11 12
12 34 56 78 91011121314
13
14
14
100%
90%
Al+3 fractionation in the solid phase
80%
100%
70%
60%
80%
50%
40%
60%
30%
40%
20%
10%
20%
0%
0%
1
2
3
4
5
6
7
8
9 10 11 12 13 14
1
2
3
4
5
6
7
8
9 10 11 12 13 14
6
Model 1,0E+00
L/S=10
1,0E-01
5
pH
pH
FeOxideClay Clay
POM-bound POM-bound
AA_Al[OH]3[am]
Albite[low]
Ni+2 fractionation in theLaumontite
solid phase
100%Montmorillonite
Ca+2 fractionation in the solid phase
90%
80%
100%
70%
90%
60%
80%
50%
70%
60%
40%
50%
30%
40%
20%
30%
10%
20%0%
10%
1
2
3
4
5
6
7
8
9 10 11 12
0%
1
2
3
4
5
POM-bound
POM-bound
6
7
pH
8
9
FeOxide
Clay
FeOxide
10
11
12
13
13
14
14
pH
Ni[OH]2[s]
Clay
VO2+
fractionation
in the solid phase
AA_Calcite
AA_Portlandite
beta-TCP
Laumontite
Uranophane
100% Fluorite
90%
80%
70%
H4SiO4 fractionation in the solid phase
60%
100%
50%
90%
40%
80%
30%
70%
20%
60%
10%
50%0%
40%
1
2
3
4
5
6
7
8
9 10 11 12 13
30%
pH
20%
10%
POM-bound
FeOxide
Clay
Pb2V2O7
0%
4
3
1,0E-10
1
1,0E-12
-1
1
Mg+2 fractionation in the solid phase
Red mud pH dependence
3
5
1,0E-08
2
7
1,0E-06
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
1
9
1,0E-04
EC
10
1,0E-04
1,0E-07
1,0E-05
Mg+2 fractionation in solution
Partitioning liquid-solid, [Mg+2]
1,0E-02
ANC/BNC
Concentration
(mol/kg) (mol/l)
Conductivity
Concentration
(mS/cm)
(mol/l)
[Mg+2] as function of pH
1,0E+00
80
1,0E-01
701,0E-02
1,0E-03
601,0E-04
1,0E-05
50
1,0E-06
401,0E-07
1,0E-08
301,0E-09
1,0E-10
20
1
2
3
4
5
6
7
8
9
10
11
12
13
POM-bound
FeOxide
Clay
Albite[low]
Laumontite
Montmorillonite
Uranophane
ZnSiO3
14
pH
14
Table 5. Conditions specified as input for geochemical speciation modelling of the pH dependence test (Chemical Speciation Fingerprint – CSF).
Prediction case
A-Red mud + column AA2
Speciation session
Red mud + column AA
Material
Red mud AU (P,2,2)
Solved fraction DOC
Sum of pH and pe
L/S
0,2
13,00
10,0000 l/kg
Clay
1,000E-02 kg/kg#
HFO
SHA
1,500E-03 kg/kg
5,000E-03 kg/kg#
Percolation material
Avg L/S first perc. fractions
Red mud AU (C,1,1)
0,1617 l/kg
Polynomial coeficients
C0 -3,853E+00
DOC/DHA data
pH
[DOC] (kg/l)
DHA fraction
[DHA] (kg/l)
1,00
3,606E-04
0,20
7,211E-05
3,48
4,02
2,720E-04
1,470E-04
0,10
0,08
2,720E-05
1,176E-05
5,77
8,440E-05
0,06
5,064E-06
6,55
7,44
1,160E-04
1,520E-04
0,05
0,06
5,800E-06
9,120E-06
8,62
1,810E-04
0,07
1,267E-05
9,58
10,64
2,430E-04
3,010E-04
0,08
0,09
1,944E-05
2,709E-05
11,69
3,190E-04
0,11
3,509E-05
12,54
14,00
3,390E-04
3,690E-04
0,13
0,20
4,407E-05
7,380E-05
C2 -3,546E-02
C4 -3,404E-04
C1 -2,253E-01
C3 8,325E-03
C5 0,000E+00
# The clay and HFO content were adjusted from measured values, because the specific activity of these phases in red mud was not directly
measured.
41.
Table 6. Conditions specified as input for geochemical speciation modeling of the pH dependence test.
Reactant concentrations
Reactant
mg/kg
Reactant
mg/kg
Reactant
mg/kg
Reactant
mg/kg
Ag+
Al+3
not measured
2,568E+04
CrO4-2
Cu+2
2,169E+00
9,118E-01
Mg+2
Mn+2
7,265E+02
2,181E+00
SO4-2
Sb[OH]6-
1,945E+03
2,390E-02
H3AsO4
1,059E+01
F-
2,756E+02
MoO4-2
1,492E+01
SeO4-2
9,324E+00
H3BO3
Ba+2
4,757E+00
3,860E+00
Fe+3
H2CO3
3,036E+02
2,200E+04
Na+
NH4+
5,084E+04
not measured
H4SiO4
Sr+2
1,483E+04
1,166E+02
Br-
not measured
Hg+2
not measured
Ni+2
4,426E-01
Th+4
5,336E+00
Ca+2
Cd+2
1,803E+04
1,713E-02
IK+
not measured
6,636E+01
NO3PO4-3
not measured
1,795E+02
UO2+
VO2+
5,803E-01
2,456E+01
Cl-
2,042E+03
Li+
9,812E-02
Pb+2
3,486E+02
Zn+2
5,170E+00
Selected Minerals
AA_Al[OH]3[am] *
AA_Calcite *
B_UO2[OH]2
Ca2V2O7
Ba[SCr]O4[96%SO4]
*
Ca3[VO4]2
Chloroapatite *
Magnesite
Pb[OH]2[C]*
Strontianite
Cr[OH]3[C]
Manganite
Pb2V2O7 *
Th[SO4]2[s] *
AA_CO3-hydrotalcite
BaCaSO4[50%Ba] *
Ca4Pb[PO4]3OH * Cu[OH]2[s]
Pb3[PO4]2 *
ThF4:2.5H2O
AA_Gypsum
AA_OH-hydrotalcite[cr] *
BaSrSO4[50%Ba]
beta-TCP *
Ca4Zn[PO4]3OH
Calcite
Fe2[SeO3]3:2H2O Montmorillonite *
Fe3[OH]8
Ni[OH]2[s]
MnHPO4[C] *
Pb3[VO4]2
PbMoO4[c] *
ThF4[s]
Tyuyamunite
AA_Portlandite *
Bixbyite *
Carnotite
Ferrihydrite *
NiCO3[s]
Rhodochrosite Uranophane *
Al[OH]3[a]
Albite[low] *
Brucite
Bunsenite
CaZincate
Cd[OH]2[C]
Fluorite *
Laumontite *
Nsutite
Otavite
Schoepite
Strengite *
ZnSiO3 *
Willemite
Zincite
* The minerals marked with * are those that appear to be present as relevant phases. All others are too low to be of significance in this particular sample.
The AA just marks the thermodynamic data for these samples being from the Lothenbach & Winnefeld paper (200X). Future simulations may include
AA_3CaO_Al2O3_6H2O as a relevant mineral. Albite and potentially laumontite are surrogates for desilication product, a sodium alumino silicate mineral
formed in the process (similar to sodalite), which is not in the database (but albite, analbite and other similar minerals can model it reasonably well).
42.
other samples of red mud and the performance of red mud under different chemical (e.g.,
reducing, carbonated) and management scenarios.
When similar information from a variety of other materials (in the form of Chemical Speciation
Fingerprints -CSF’s) is available in the LeachXS database, this provides options to evaluate
beneficial combinations of bauxite residues with other materials.
Modeling column test results
Geochemical speciation modeling of percolation test results is illustrated in Figure 23 and 24 for
selected elements. Modeling can be based on either (i) a single porosity approach that assumes
uniform flow through the column, (ii) a dual porosity model (2 domain model) that assumes a
linear mass transfer coefficient between uniform flow in the “mobile” domain and the
“immobile” domain, or (iii) a two domain model that assumes uniform flow in one domain
(“mobile”) and diffusion processes through the second domain to or from the interface between
the domains. The dispersion coefficient, derived from a highly soluble species (e.g., sodium), is
used to represent the hydraulic characteristics in the single porosity model. The elution
behavior during the percolation test of sodium or other highly soluble species can also be used
to derive the hydraulic characteristics for the dual porosity model (e.g., fraction of the immobile
domain and mass transfer coefficient). Application of the two domain percolation-diffusion
model requires mass transfer rate test results (i.e., from the compacted granular test) as well as
results from the percolation test to parameterize the model. For all cases, results from the pH
dependence test are used to derive the necessary chemical speciation fingerprint.
Relationships between Leaching Tests, Geochemical Speciation and Eco-toxicity
Recent leach testing, biotic eco-toxicity testing and geochemical speciation modeling with
contaminated soil, municipal solid waste incinerator bottom ash and Cu treated wood has
suggested that the eco-toxicity of a material is related to the free dissolved concentration of the
constituent(s) of concern (Postma et al, 2009; van der Sloot et al, 2009). Examples have been
developed for copper and PAH toxicity. For both cases, the toxicity has been shown to correlate
with the concentration of the free dissolved species as estimated using geochemical speciation
modeling to distinguish dissolved organic carbon-bound amount from the freely dissolved
amounts in solution. In these cases, the total dissolved concentration would be equal to the
freely dissolved concentration plus the DOC-bound concentration. This approach appears
promising and warrants further examination as a methodology for understanding and predicting
eco-toxicity.
Relationships between lab and field data and the relevance for environmental impact
assessment
In assessing impact from red mud disposal or beneficial uses of red mud, laboratory studies
alone are generally not sufficient, as an understanding of processes occurring at field scale is
needed to be able to provide suitable long-term release predictions. Such processes include
43.
carbonation, oxidation, preferential flow, variations in wet dry cycles, temperature differences
(seasonal or climatic differences), and covered or atmosphere-exposed conditions. The
combination of laboratory test data and field experiments and measurements can provide the
input for such predictive modeling. Recent modeling for the Dutch Soil Quality Decree (2007)
has made use of a mechanistic description of the fate and transport in the unsaturated and
saturated zones. For this type of impact modeling for regulatory purposes, a mechanistic source
term has not been applied although technically this is already feasible. The most critical element
in such modeling is the effect of significant pH changes with time due to carbonation from the
surface and the neutralization of alkaline leachate in the underlying soil. The understanding of
the key processes governing release of potentially critical substances will allow better
management choices than will be possible based on total content or too simple leaching test
data currently practiced.
44.
14
1,0E-02
13,5
pH
13
12,5
12
11,5
0,01
0,1
1
10
1,0E-05
1,0E-06
Red mud column TS14405
Model (Avg/fraction)
0,01
100
1
Model
0,1
1
10
100
1,0E-05
Al
K
1,0E-04
1,0E-07
11
0,1
0,01
0,001
Cu
1,0E-06
1,0E-07
1,0E-08
1,0E-09
0,01
0,1
1
10
100
0,01
0,1
1
10
100
1,0E-02
1,0E-03
1,0E-03
Mo
1,0E-04
1,0E-05
1,0E-06
1,0E-07
V
1,0E-03
1,0E-04
1,0E-05
1,0E-06
1,0E-08
0,01
0,1
1
L/S (L/kg)
10
100
0,01
0,1
1
10
100
L/S (L/kg)
Measured concentrations of Al, Cu, K, Mo and V in column effluent samples (collected
eluate fractions; TS14405) from red mud leaching (red dots) with dual porosity model
results (LeachXS) as continuous (broken line) and simulation results (simulated eluate
fractions; blue squares).
45.
Cumulative release of Al+3
Cumulative release of K+
100
Cum. release (mg/kg)
100000
10000
1000
100
10
1
0,01
0,1
1
10
100
0,01
10
100
100
1
Cumulative release of VO2+
Cumulative release of MoO4-2
100
10
1
10
1
0,1
0,01
0,1
1
10
0,01
100
1
10
100
1
100
10
1
0,1
0,1
0,01
0,001
0,01
0,01
0,1
Cumulative release of Cu+2
Cumulative release of Ca+2
0,1
0,1
1
L/S
10
100
0,01
0,1
1
10
100
L/S
Cumulative release of Al, K, Ca, Cu, Mo and V in column leaching (TS14405) from red
mud (red dots) with dual porosity model results (broken line) using LeachXS.
46.
7. DEVELOPMENT OF A DATABASES FOR RED MUD CHARACTERISTICS AND LEACHING
It has been recognized that relevant information on bauxite residues is too widely scattered.
Power et al (2009) describe a database on bauxite residue information: the Bauxite Residue and
Disposal Database (BRaDD). The absence of a means of collecting and managing information on
bauxite residue was identified as a key knowledge gap, and prompted the creation of this
database to contain details on refinery practices on bauxite residue storage, disposal, and
technologies gathered from a wide range of sources. A variety of reasons are identified why the
restriction to existing publicly available data resources places a severe limitation on the ability to
collect, systematize and interrogate information:
-
Each source of information provides only a limited amount of relevant data, and the
data type is not consistent across publications
-
Relevant information is published in a variety of languages
-
Relevant information is scattered over many public domains
-
Nature and scope of the information is owner and/ or refinery specific and not consistent
in form and content
The LeachXS database embedded in the LeachXS™ expert system as described below would
form a useful extension of the capabilities sought, as it focuses on release behavior from bauxite
residues under a variety of exposure conditions (Carter et al, 2008; Van der Sloot et al, ?? ) as
well as possible beneficial uses (Carter et al, 2009).
47.
8. LeachXS™ AS A TOOL FOR EVALUATING RED MUD LEACHING
LeachXS is a database/expert decision support system for characterization and environmental
impact assessment based on estimated contaminant release as derived from leaching tests
developed jointly by The Energy Research Centre of The Netherlands (ECN; Petten, The
Netherlands), Vanderbilt University (Nashville, Tennessee, USA), and DHI (Hölshorn, Denmark)3.
LeachXS contains an extensive materials database and has predefined models using Orchestra
and extended graphical and reporting capabilities for a range of possible questions. Model input
formulations are available for the pH dependent leaching test, the percolation test, lysimeter
and field leachate data (van der Sloot et al., 2001; van der Sloot et al., 2003; van der Sloot et al.,
2007a; van der Sloot et al., 2007c). The coupled LeachXS - ORCHESTRA combination allows for
very quick data retrieval, automatic input generation for modelling, processing of calculated
results and graphical and tabular data presentation.
Databases used by LeachXS include leaching for 600+ materials, scenarios, and regulations to
allow comparisons of test data versus specific utilization or disposal conditions. The system
assists in evaluation and laboratory guidance, data management and evaluation, source term
description, impact evaluation, and decision analysis. Applicable materials for assessment using
LeachXS include:







Treated Wastes
Stabilized Waste
Construction Materials
Cement Mortars and Concrete
Soils and Contaminated Soil
Sludge
Coal Combustion Residues







Compost
Sediments
Municipal Wastes
Industrial and Hazardous Wastes
Mining Wastes
Preserved Wood
Blast furnace and other slags
Geochemical speciation and chemical reaction/transport modeling capabilities are integrated
into the LeachXS system using the ORCHESTRA (Objects Representing CHEmical Speciation and
TRAnsport) modeling environment as illustrated in Figure 25Error! Reference source not
found.Error! Reference source not found..
ORCHESTRA (Meeussen 2003) is a modeling framework for defining geochemical equilibrium
models and to combine these models with mass transport (including diffusion, convection, etc.)
for user-defined processes. The ORCHESTRA chemical module can calculate chemical
thermodynamic equilibrium in a similar way to other speciation codes, but is internally
organized differently. Instead of defining all model equations within the source code, equations
are defined in a separate user accessible text file in ORCHESTRA. ORCHESTRA has been used in
practice for a wide range of applications that include aqueous speciation, precipitation, different
3
LeachXS has been developed as an integrated leaching database, geochemical speciation and assessment tool over a
period of more than 10 years by the Developers. Hans van der Sloot, David Kosson, and Ole Hjelmar are the technical
team leaders in development of LeachXS and LeachXS Lite for ECN, Vanderbilt and DHI, respectively.
.
48.
forms of surface complexation, ion exchange, diffusion (including radial and electroneutral),
convection, solid solutions, colloid adsorption, and biota uptake.
Materials
( Leaching data,
Composition, Physical
characteristics)
Materials
Leaching
Database
Scenarios
(e.g., fill
characteristics,
geometry, infiltration,
hydrology)
Scenario
Database
Excel
Spreadsheets
(Data, Figures)
LeachXS
(Materials and
Scenarios
Evaluation)
Reports
(Figures, Tables,
Scenario and Material
Descriptions)
Regulatory
(Regulatory
thresholds and
criteria from different
jurisdictions)
Regulatory
Database
Thermodynamic
Databases
Orchestra
(Geochemical
Speciation and
Reactive Transport
Simulator)
Other Models
(Source Term and
Parameters for
Fate, Transport,
and Risk Models)
Figure 25. Schematic of LeachXS/ORCHESTRA Input and Output Functions and Databases
Description of Conceptual Models
The LeachXS system is based on evaluating the leaching behavior of materials using equilibriumbased information obtained from pH dependence leaching tests (e.g., EPA Method 1313, CEN/TS
14405, CEN/TS 14497, or ISO/TS21268-4) and dynamic release information from a percolation
type test (e.g., EPA Method 1314 or CEN TS 14405) and monolith leach testing (e.g., EPA Method
1315 or CEN TS 15683). The latter tests have a time dependent element in them, which is closely
linked to the actual release under field conditions except field conditions have the added
complexity of different hydrological conditions and long term external influences on the
material. The combination of these methods allows an evaluation of the long term release (see
Kosson et al. (2002)). A key aspect in the basic approach in LeachXS is the ability to display
information from a variety of different tests over different scales in the same graphical
representation, which allows conclusions to be drawn on long term behavior.
Inputs Needed to Run LeachXS and the Outputs Generated by LeachXS
The inputs required to run LeachXS depend on the type or types of leaching results (i.e., single
material with pH dependent leaching test, single material with percolation or lysimeter test, or
mixtures of materials with pH dependent leaching tests) that are available for the material.
49.
Models available within LeachXS-ORCHESTRA
LeachXS-ORCHESTRA can be used to simulate different contaminant release situations. These
include a number of commonly used experimental test procedures that can be used to evaluate
model performance under well-defined conditions. The same chemical and physical model
components can be used as part of larger scale release scenario’s in support of environmental
impact assessment.
The predefined LeachXS-ORCHESTRA release models are:
1. A model that calculates pH dependent element solubility in a batch system (pH –
dependence tests).
2. A model that calculates leaching of elements from a solid matrix by reaction-diffusion
(monolith or compacted granular leach tests)
3. A model that calculates leaching of elements from a solid matrix by reaction-diffusionconvection (column test)
4. A number of predefined release scenario models that make use of the same process
sub-models (reaction, (gas) diffusion, convection, etc.) as the models above, but with
different systems configurations and boundary conditions.
LeachXS-ORCHESTRA also contains a unique model setup that assists users in the generation of a
chemical model description for a given material. Using this option, pH dependent solubility data
is used to calculate mineral saturation indices for a large set of minerals. These saturation
indices can subsequently be used to select a set of mineral precipitation reactions that describes
the observed leaching behavior best. The input for this model is pH dependent total element
solubilities, dissolved organic carbon, and adsorbing surfaces, while the output is a chemical
model including a set of precipitation reactions (minerals). This chemical model, or chemical
speciation fingerprint, can subsequently be used in any of the models mentioned above. Within
LeachXS a number of generic models for different materials have been derived and are
available.
Materials, for which a chemical speciation fingerprint has been assessed, can be modeled as a
mixture assuming proportionality with the ratio of the constituting parts for composition and
reactive surfaces. Any mixing ratio can be modeled, but experimental verification of adequate
prediction is needed at a few selected conditions to evaluate the model performance.
Input required and output generated by the LeachXS-ORCHESTRA models
Input for the chemical models
All models listed above contain one (or more) chemical equilibrium modules that describe the
chemical reactions and distribution of elements over different physical-chemical forms in a
material. These chemical models require as input:

A set of chemical equilibrium reactions representative for the material under
consideration. LeachXS contains predefined sets for several typical materials (different
50.
soil types, cementitious materials) in the database, but alternative sets may be provided
by users.

A set of total (or available) element concentrations, which can be estimated from total
composition measurements, or using a pH dependence leaching test (maximum leached
concentrations).

Estimated amount of adsorbing surfaces (organic matter, hydrous ferric oxide,
aluminum hydroxide, clay), which can be estimated by determining oxalate extractable
Fe and Al.

Liquid to solid ratio.
Output of the chemical models
The main result of a chemical equilibrium calculation is the distribution of elements over the
different possible physical and chemical forms (different dissolved, adsorbed, precipitated,
gaseous species, etc.) according to the set of thermodynamic equilibrium reactions given as
input. Other generated outputs are:




pH,
redox potential,
ionic strength, and
electrical conductivity.
The ORCHESTRA model generates the basic numerical output of these calculations, while
LeachXS processes this output and presents data in graphical form.
For a reactant of interest, the following general model data can be displayed graphically:





pe as function of pH,
Dissolved Humic Acid (HA) as function of pH,
Conductivity as function of pH,
Acid/Base neutralization capacity as function of pH, and
REDOX capacity as function of pH.
The geochemical speciation results from ORCHESTRA can be viewed and exported. These results
include:

Solubility prediction results, including a comparison of measured test results and
modeled solution composition (free + DOC associated);

Solid and liquid phase partitioning, including visualization of partitioning of the
constituent between free, DOC associated, clay-bound, Fe-oxide bound, solid organic
matter (POM) bound, mineral precipitate or incorporated in a solid solution; and,

A combination of solubility prediction and partitioning for a specific reactant in the
selected reactant series. Additional graphs including solid phase partitioning and liquid
phase partitioning can be displayed.
Input for the physical transport models
The transport modules calculate mass transport based upon the liquid-solid equilibrium
information provided by the chemical modules along with the rate of flow or diffusion for each
51.
element. These transport modules keep track of how much of each element is present as a
function of time and space. Transport modules are initialized with the initial chemical conditions
of the system at the start of the simulation using the CSF previously generated. Apart from the
chemical information, the transport model also needs a set of physical input parameters
including:








physical dimensions of the system,
porosity of the material(s),
density of the solid material(s),
effective tortuosity of the material(s),
flow rates (for column or percolation simulations),
refresh rates (for monolith leaching tests or diffusion simulations),
water saturation or gas volume (for unsaturated conditions), and,
chemical composition of each solid material and each solution that is used in the
simulation, including amounts of adsorbing surfaces.
Output of the transport models
The output of the reactive transport models is the total content and chemical speciation of each
element as a function of time and space:

chemical composition of the system as function of time and space (concentration
profiles),

distributions of elements over mineral, aqueous, and gaseous phases, pH, redox, and
conductivity, and,

total fluxes of elements over chosen system internal or external system boundaries.
For a reactant of interest, the following general model data can be displayed graphically (ECN
2007):






pH as function of LS,
pe as function of LS,
dissolved humic acid (HA) as function of LS,
conductivity as function of LS,
acid/base neutralization capacity as function of LS, and,
REDOX capacity as function of LS.
For the percolation or lysimeter test results, the geochemical speciation results from
ORCHESTRA can be viewed and exported including:

Leachate concentrations. The measured percolation or lysimeter test concentrations
and the predicted concentrations are graphed as functions of LS as well as the
concentrations predicted for the collected fractions.

Cumulative release. The cumulative leached amount (as derived from the percolation or
lysimeter test data) is compared with the predicted cumulative release as a function of
LS.

Concentration profiles at a specified time. The partitioning between dissolved and solid
phases is represented as a function of the depth in the column at the specified time.
52.

Concentration profiles at a specified depth. The partitioning between dissolved and solid
phases is represented at a specified depth in the column as a function of time.

Animated time profiles for a single reactant. An animation can be generated of the
partitioning of a constituent between dissolved and solid phases as a function of depth
in the column over the time span of the percolation test.
Embedded Databases and Systems including ORCHESTRA
LeachXS relies on various embedded databases along with ORCHESTRA to estimate contaminant
release from the results of leaching tests, which are summarized as follows.
Materials Leaching Database
The materials leaching database contains results of laboratory leaching tests, lysimeter test
results, and field data from more than 600 materials and wastes (Table 7)4. In some cases,
interrelated laboratory, lysimeter, and field scale data are available. The total number of
individual inorganic and organic substances and radionuclide analyses in the database is more
than 600,000 entries. The level of detail for individual samples vary in terms of constituents
analyzed and the tests performed on the same sample.
A set of tools is also available for importing additional materials into the LeachXS materials
database. Leaching tests are interpreted using an expert system to provide estimates of the
short and long term release of constituents of interest. Custom databases focused on the needs
for individual users or materials can be developed. For example, a custom database on coal
combustion residues has been developed for USEPA. An analogous database could be
developed focusing on red mud.
Regulatory Database
A regulatory database allows comparison of test results against criteria for specific utilization or
disposal conditions. Constituents included are inorganic constituents as well as a selection of
organic species. The system is also suitable for radionuclides. Pre-defined sets of regulatory
criteria for different jurisdictions can be added. End users also can enter and save reference
criteria specific to their own applications.
ORCHESTRA and the Thermodynamic Database
The geochemical modeling includes mineral solubility (extended MINTEQ database), sorption on
Fe-oxide, Al-oxide, dissolved organic carbon, and particulate organic carbon interaction. The
object-oriented structure of the chemical model definitions makes it possible to implement a
new object-oriented framework for implementing chemical models. The framework consists of
three basic object types (i.e., entities, reactions, and phases) that form the building blocks from
which chemical models are comprised. The hierarchical approach also ensures consistent and
compact model definitions including, for example, aqueous complexation, activity correction,
precipitation, surface complexation ion exchange, electrostatic interactions, NICA (ref), and
CDMUSIC (ref).
4
Some of the materials and test information contained in the main LeachXS database is proprietary and therefore not
distributed with the software licenses.
53.
Table 7. Types of materials that are included in the LeachXS database of leaching test results.
Table 3. Samples listed in the LeachXS database
Sample Name
Activated carbon NL
Aggregates
Al-production ash
Amphibolite
APC MSWI residues
Arc oven slag, bulk
Armour stone
Artificial Aggregate
Asfalt concrete
Asphalt
Asphalt Granulate
Atmospheric_dust
Beech_wood
Biomass ashes
Bitumen
Blast furnace bulk slag
Blasting waste
Bleach soil
Brown coal ash
C&D waste
Car shredder waste
Cement mortar
Sample Name
Cement stab. MSWI Fly ash
Cement stabilised jarosite slag
Cement_stabilised_soil
Ceramic Brick
Ceramic tiles
Ceramic Paving_Brick
Clay
Coal combustion bottom ash
Coal fly ash
Compost
Concrete products
Contaminated soil
Copper slag
Dolomite
Dredge spoil
Drilling mud
Drinking water pipes
Drinking water purification sludge
Electro oven slag, steelworks
Expanded clay pellets
Fe-Cr catalyst residue
Filtercake waste water treatment
Sample Name
Flotation sludge
Fluorescence sludge
Fly ash rockwool production
Form sand
Foundary sand
Galvanic waste
Glass oven rubble
Glaze/enamel sludge
Gneiss
Granite coarse
Granite fine
Granodiorite
Gravel (fresh water environment)
Harbour sediment
Heavy sewage sludge amended soil
Hydraulic mixed aggregate
Incinerated sewage sludge
Industrial Fly ash
Industrial water treatment sludge
Jarosite slag Electric arc
Lava stone
Lavaliet
Table 3. Samples listed in the LeachXS database (continued)
Sample Name
LD slag, steelworks
Leather sludge
Light weight concrete
Lime silicate brick
Limestone
Masonry aggregate
Mine stone
Mining residues TiO2 mining
Mixed Landfill waste
Moraines
MSWI Bottom ash
Nickel sludge
Paint residue
Paper
papersludge
Pb metal sheet Roofing
Pb slag
PbO glass
Phosphor_gypsum
Phosphorus slags
Pigment sludge
Plastic waste material
Sample Name
Porphyr Älvdal
Porphyr Småland
Preserved wood CCA
PVC_pipe
Quartsite
Rail support beam
Railroad_aggregate_untreated
Recycled concrete aggregate
Recycling sifter sand
Red mud
River Sediment harbour
Roman Aquaduct core
Roofing felt
Roofing material
Sand (fresh water environment)
Screen glass, sandblasted
Sewage sludge
Slate
Sludge form sewer water purification
Soil cleaning residue
Spent catalyst
Stabilised waste
Sample Name
Steel slag
Straw
Sulphidic mining waste
Tannery sludge
Tapistry and mats
Thermally treated Sand
TV screen cone glass, sand blasted
Various glass types
Vegetable, fruit and garden waste
Vitrified MSWI Fly ash
WAELZ slag
Wastewater treatment sludge
White glass, mixed 9 sources
Willemite
Wollastonite
Wood_bark
Zinc_Soil
54.
LeachXS Lite
The Energy Research Centre of The Netherlands (“ECN”, in Petten, The Netherlands), Vanderbilt
University (“Vanderbilt”, in Nashville, Tennessee, USA) and DHI (in Hørsholm, Denmark)
(collectively, “Developers”) have committed to collaborate with the United States
Environmental Protection Agency (“EPA”) in the development of a derivative software
application, LeachXS Lite™. LeachXS Lite will be a leaching assessment tool for the display and
analysis of leaching and related characterization data that has been developed by EPA
supported research on coal combustion residues (CCR). LeachXS Lite is a limited capability
version that derived from the commercial product, LeachXS™, which is owned by and was
developed jointly by the Developers.
LeachXS Lite will be distributed free of charge by EPA and the Developers. LeachXS Lite will
provide a way to manage the data generated as part of leaching methods under development
for inclusion in EPA SW-846, Test Methods for Evaluating Solid Waste, Physical/Chemical
Methods, and serve as a limited decision support tool for evaluating constituent leaching from a
range of solid materials, including wastes, byproducts and recycled materials under disposal and
use conditions. LeachXS Lite will allow end-users to (i) input data, (ii) display data in comparison
to a limited number of other data sets, (iii) augment the supplied databases with userdeveloped data, and (iv) download data and figures to Excel spreadsheets. A database
containing the leaching test results and total elemental content data from evaluation of
approximately 75 coal combustion residues will be included as part of the initial release of
LeachXS Lite as a beta version. The initial beta version is anticipated to be widely available
during first half of 2010.
The main functionality of LeachXS Lite will include:

Leaching database functions, including data importing, data display, units conversion,
and exporting of leaching data and other characteristic data for solid materials, eluates
and field leachates (e.g., physical properties, material identification, waste codes,
locations, sampling dates, etc.). Data importing and exporting utilizes Microsoft Excel
spreadsheets (pre-defined templates); users are required to separately acquire a license
for use of Microsoft Excel;

CCR data generated by or on behalf of EPA incorporated into a LeachXS Lite database;
and

Data analysis functions, including comparison of leaching results from multiple leaching
test types and field data, comparison with pre-defined or user-defined thresholds and
limits, and comparison with statistically pre-defined “families” of leaching data based on
commonality of leaching behavior amongst similar materials.
55.
9. USE OF LEACHING TESTING IN QUALITY CONTROL
A tiered approach to leaching testing allows the frequency and the focus of the testing to be
adapted based on the intended use of the test results. The most detailed understanding is
needed for initial characterization of a material to understand its potential behavior under
different disposal and use scenarios. For initial characterization, pH dependence, percolation
and mass transfer testing is recommended, as well as physical and hydraulic testing.
Accumulation of test results in a common database allows for comparison of similar red mud
materials from different sources and referencing behavior of red mud to the leaching behavior
of other materials (e.g., soils, sediments, other secondary materials or wastes).
Once the leaching characteristics of a product type or class are established, much simpler
conformity testing will suffice for potentially critical parameters and at a frequency consistent
with the risk of approaching/ exceeding set limit values by notified regulations (Figure 26). If
sufficient information is available, statistical tools can be used to assist with the determination
of the testing needed for specific substances and the frequency of testing needed for quality
control purposes. Initial characterization testing provides the frame of reference for evaluation
of the quality control testing results.
Initial type testing/
Frequency
of testing
“characterization”
testing
Characterisation
Factory production
“compliance”
testing
control
Level of
detail
Figure 26. The relationship between initial characterization testing and quality control testing.
A statistical approach for quality control testing can be based on an upper tolerance limit, often
referred to as a “k-value” approach (van der Sloot et al, 2008). An explanation and example of
this approach follows.
In the framework of the assessment of environmental properties of construction products for CE
marking (European conformity with specifications) under the Construction products Directive
(1988), products need to be evaluated on their potential impact on environment and health
(Essential requirement 3). Since several products can be considered to have no impact, a system
has been set up to classify materials as without testing (WT), without further testing (WFT) and
further testing (FT). This basis for the classification is still in debate. Although in some cases it
56.
may be possible to decide on WT for a product or product type, generally, the approach shall be
based on individual constituents within the material. So a product or material can be WT for a
number of constituents, WFT for another set of constituents and FT for a limited number of
substances. In this way, the number of constituents requiring on-going testing is reduced. To
be able to come to a conclusion about risk of non-compliance, a statistical approach is needed.
A sufficient number of observations on release of all relevant substances is needed to be able to
draw a conclusion on the risk of exceeding a class limit or regulatory criterion. Furthermore, the
unit of material (i.e., batch size) that serves as the basis of compliance must be defined (e.g.,
1000 tons of material). This approach to decide on the relevant constituents for compliance or
quality control testing and to determine the required frequency of testing is equally relevant for
wastes from the extractive industry, such as red mud, as it is for construction products.
The following approach provides information to judge for a given waste type which are the
potential constituents of concern and with what frequency testing will be required. Initially full
characterization will be needed, which can later be reduced to a single measurement, provided
such single step test data are placed in perspective with prior characterization information.
Leaching data are log-transformed (i.e., log(x)) to obtain a normal distribution, because leaching
data typically are log-normally distributed and the range often spans several orders of
magnitude, which otherwise would bias the average towards the higher concentrations.
-
Testing the waste by characterization test methods to assess the bandwidth of release
for the waste type to be judged. The number of repetitions depends on the variability in
the production and in the source materials. The determination of the frequency of
subsequent tests depends on the standard deviation of the measurements obtained
from prior testing. Thus, improved estimates of the standard deviation, which are
obtained through increased testing, can reduce future testing. As a practical matter,
prior information of a similar material from other sources (e.g., a red mud leaching
database) and four sets of pH dependence test results from four different batches of the
material provides a useful starting point. In addition, this initial data set can be used to
identify the interval within the pH domain of expected field conditions where the
maximum eluate concentration or release for each constituent of potential concern will
occur. Subsequent testing then can be focused on the pH interval within the domain of
expected field conditions which will cover the release of all of the constituents of
potential concern.
-
Evaluation of the test data against a class limit or regulatory criterion (e.g. EU Landfill
Directive - Inert) using a statistical approach, where the risk (probability) of noncompliance is based on a upper tolerance interval5 derived from the number of
observations, the standard deviation of this number of observations and the difference
between the limit value and the average value of the release data obtained by a
standardized test protocol.
-
The acceptable risk of some fraction of the batches failing the threshold criteria is
negotiated with the regulator. For example, a 90% probability that 90% of the batches
have each of the constituents of concern less than the threshold value. The k-value for
a specified acceptable risk can then be used to identify for which constituents the risk of
5
See Engineering Statistics Handbook, National Institute of Standards and Technology (NIST),
http://www.itl.nist.gov/div898/handbook/prc/section2/prc263.htm accessed on Feb. 20, 2010. See also
http://statpages.org/tolintvl.html accessed on Feb. 20, 2010
57.
non-compliance is negligible, medium or high to exceed the limit. This then determines
the frequency of testing (Table 8).
k-values are calculated as follows:
In general, the upper tolerance interval is calculated as
ത − ݇ଵ‫ݏ‬
ܻ௎ = ܻ
ത = mean value of the sample values; ݇ଵ = k-value; and
Where ܻ௎ = upper tolerance limit; ܻ
‫ = ݏ‬standard deviation of the sample values. For the application here, at any given pH value for
ത = log-mean of the sample values; and
a selected constituent, ܻ௎ = log (regulatory threshold); ܻ
‫ = ݏ‬standard deviation of the log-transformed sample values. Thus, rearranged,
݇ଵ =
ത
ܻ௎ − ܻ
‫ݏ‬
݇ଵ can be further related to the number of samples ܰ, and the confidence ߛ (probability or risk)
of the specified percent of the population ‫ ݌‬that will not exceed the threshold value by:
ܽ= 1−
మ
௓భష
ം
ଶ(ே ିଵ)
݇ଵ =
ଶ
and ܾ = ܼଵି௣
−
ଶ
ܼଵି௣ + ට ܼଵି௣
− ܾܽ
మ
௓భష
ം
ே
ܽ
where ܼଵି௣ and ܼଵିఊ are the critical values from the normal distribution (for example,
ܼ଴.଴ହ = −1.645 and ܼ଴.ଵ଴ = −1.282).
Table 8. Example of k - values and the corresponding risk of non-compliance.
K-value
N=5
> 6.12
4.67 -6.12
2.74 -4.67
1.46 -2.74
0.69 -1.46
< 0.69
K-value
N=10
> 4.63
3.53 -4.63
2.07 -3.53
1.07 -2.07
0.44 -1.07
< 0.44
Chance of
compliance
> 99.9 %
99% -99.9%
90% -99%
70% -90%
50% -70%
< 50%
Risk of noncompliance
< 0.1%
0.1% -1%
1% -10%
10% -30%
30% -50%
> 50%
Testing
frequency
Every year
Every 10 batches
Every 4 batches
Every other batch
Every batch
Parameter
relevance
WFT
FT
FT
FT
FT
FT
In Figures 27 through 30, an example is given for the release of V from red mud. Figure 27 gives
the range of characterization test data from the pH dependence leaching test (TS14429, 2005),
which have been compared with the Dutch Soil Quality Decree (2007) for isolated applications
(selected as the limit values for vanadium provided). The square box in the figure provides the
expected relevant pH domain for the material (here pH 7 – 13), the limit of detection for
substance (lower horizontal line) and the limit value (upper horizontal line). A total of 26 data
sets have been collected from worldwide sources. All data have been transformed to lognormalized values. In Figure 28 the distribution of vanadium release after log-normal
transformation are shown indicating that this is indeed justified.
58.
The k-value to fall in a certain range for the risk of non-compliance depends on the number of
observations in the calculation of the average release. Table 8 gives a set of typical values. The
table also indicates tentatively the testing frequency and the classification type WFT or FT.
100
V Concentration (mg/L)
10
1
0,1
0,01
V
0,001
1
3
5
7
9
11
13
pH
Figure 27. The bandwidth of release of vanadium from red mud from worldwide origin with the
average (black dashed line) and 90 % confidence intervals (gray dashed lines).
59.
12
120
10
100
8
80
6
60
4
40
2
20
0
Cumulative %
Frequency
Distribution pH=6.62 V Concentration (mg/L)
0
-2,6 -2,4 -2,2
-2
-1,8 -1,6 -1,4 -1,2
-1
-0,8 -0,6 -0,4
Bins (log scale)
Figure 28. The distribution of log - normalized release data for V from red mud.
Figure 29 presents the k-values calculated for the release of vanadium from the pH dependence
test on red mud from worldwide sources. The horizontal lines indicate different fractions of noncompliance (RNC) at a 90% confidence level (ߛ=0.9), which are indicated by the 100 ‫ ݌‬values
respectively 0.1, 1, 3, 5 and 10 % chance of exceeding the limit value at a give pH (note that
greater k-values indicate reduced risk in exceeding the limit value). These results indicate that
for vanadium, neutralization will reduce the risk of exceeding the limit and comply with the
regulatory criterion. Since the k-value is not greater than 4.63 for this case, testing at some
limited frequency will be required. Once a reference base for a material like red mud is
available at the international level or local level, the testing can be reduced to one or a few
selected key conditions, which can then be sufficient to demonstrate compliance, provided the
information is placed in context with the prior characterization data (Figure 30). This then
triggers further testing only in cases where test results fall well outside the range of the
available data set. This would imply carrying out a more extended test (concise test, 1997) or a
full characterization to identify the reason for the deviation.
The approach described here is equally applicable to both use and disposal applications,
although constituents of concern, limit values and acceptable risk tolerances may vary
depending on the use or disposal scenario, as well as regulatory jurisdiction.
60.
pH dependent K-value for V
4,5
Low pH limit
RNC ( p=0.1)
4
High pH limit
3,5
RNC ( p=1)
K-value
3
RNC ( p=3)
2,5
RNC ( p=5)
2
RNC ( p=10)
1,5
1
0,5
0
0
2
4
6
8
10
12
14
pH
Figure 29. k-values calculated from the pH dependence test data on red mud (N=26) in relation
to the class limits corresponding with a certain range of risk of non-compliance(RNC)
using a 90% probability of the fraction of samples being less than the threshold).
pH dependent release of V
1000
Release (mg/kg)
100
10
1
0,1
0,01
1
3
5
7
9
11
13
pH
Figure 30. Quality control data for vanadium from red mud in perspective to the summary
statistics for worldwide red mud leaching data as a function of pH with the average
(black dashed line) and 90 % confidence intervals (gray dashed lines) as indicated from
Figure 21.
61.
10. CONCLUSIONS AND RECOMMENDATIONS
The following are conclusions reached based on the assessment contained in this report:









Leaching assessment based on combined evaluation of results from pH dependence,
percolation and mass transfer tests provides a robust framework for evaluation of a
wide range of disposal, treatment and use scenarios for red mud.
Use of leaching assessment based on the pH dependence, percolation and mass transfer
tests is in the process of being adopted as part of regulatory frameworks in the
European Union and the United States, and under consideration in other countries. This
leaching assessment framework has been widely accepted by the scientific community
as evidenced by the peer-reviewed scientific literature. Leaching test methods are
undergoing ruggedness testing in the European Union and inter-laboratory comparison
testing (round-robin testing) in the United States during 2010 and will likely be
extending into 2011. Use of this approach in the United States will be implemented via
guidance documents that are under development and likely will be recommended for
applications such as beneficial use of secondary materials, hazardous waste delisting,
determination of treatment process effectiveness, where use of TCLP is not required by
statute.
Total elemental content does not correlate with leaching behavior of most elements
and therefore is not recommended as the basis for assessing environmental
compatibility.
Leaching behavior of red mud is consistent within a defined bandwidth for many
elements and samples from several sources. The impacts of neutralization, carbonation
and redox conditions on red mud leaching are evident through the leaching assessment
testing.
Geochemical speciation modeling provides additional insights into the chemistry
controlling observed leaching behavior and facilitates estimating the leaching behavior
of a material under scenarios and time frames not readily tested. Independent
verification testing is needed to confirm geochemical speciation modeling results.
Establishment of a database for leaching characteristics of red mud samples from a
variety of sources will provide a uniform basis for comparison and understanding of
disposal, treatment and use options. It will also serve as the reference basis for ongoing quality control during red mud production, use and disposal.
Statistical quality control of red mud as a product is possible using reduced testing,
focused on key quality control parameters and at a frequency linked to the probability
of exceeding a specified threshold value.
There is promising evidence that ecotoxicity testing results can be linked to results from
leaching assessment and geochemical speciation, suggesting a pathway to more
integrated testing and evaluation in different contexts.
LeachXS is a software tool set that would efficiently facilitate (i) maintaining a red mud
leaching assessment database, (ii) geochemical speciation modeling of a range of red
mud testing, use, treatment and disposal scenarios, and (iii) statistical quality control of
red mud production. LeachXS and associated databases can be tailored to aluminum
industry user community.
62.
The following are recommendations for the red mud industry that follow as a result of this
review:






Establish a baseline leaching characterization program for red mud produced at
different facilities. This would allow comparisons and understanding of the similarities
and differences among red mud producers and provide a foundation for improving
treatment, use and disposal practices, as well as quality control. Baseline leaching
characterization would include pH dependence, percolation and mass transfer testing.
Additional testing should include physical properties sufficient to understand
geotechnical and hydraulic performance.
Establish a common database of leaching and related properties that can be tied to the
similarities and differences in red mud production processes, sources and management
scenarios. This database should also include field observations of pore water and
leachate from representative red mud management scenarios. A custom LeachXS
database would be suitable for such a database.
If information on lysimeter studies or field data on either landfill or beneficial use are
available those observations should be evaluated in context with the more extended
laboratory test data. If such information is not available or is insufficient, it is advisable
to obtain field test data to verify the basis for estimating long-term performance.
Experience can be obtained from information in other areas (soil, waste, construction)
to facilitate the prediction of long term release from red mud disposal and beneficial
use. This relates to effects of carbonation, oxidation, preferential flow and interaction
between materials in a mixture.
Establish guidance on quality control monitoring for red mud that include simplified
leaching assessment and meets the needs of likely use and disposal scenarios.
Develop and validate to the extent practical a geochemical speciation model for red
mud. This would facilitate simulation-based evaluation of performance under different
use and disposal conditions, including blending of red mud with other materials, prior to
carrying out confirmatory testing, and thereby allow consideration of a wider range of
applications at reduced testing costs.
63.
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67.
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68.

H.A. van der Sloot1 and D.S. Kosson2 Hans van der Sloot

Transcription

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