EPA/600/R-10/129 │September 2010 │www.epa.gov/athens 

State-of-the-Science Report on 

Predictive Models and Modeling 

Approaches for Characterizing and 

Evaluating Exposure to 

Nanomaterials 

Ecosystems Research Division 

National Exposure Research Laboratory 

Office of Research and Development 
EPA/600/R-10/129 

September 2010 

www.epa.gov/athens 

State-of-the-Science Report on 

Predictive Models and Modeling 

Approaches for Characterizing and 

Evaluating Exposure to Nanomaterials 

by 

John M. Johnston 

Ecosystems Research Division 

National Exposure Research Laboratory 

Office of Research and Development 

U.S. Environmental Protection Agency 

960 College Station Rd. 

Athens, GA 30605 

Michael Lowry, Stephen Beaulieu and Evan Bowles 

RTI, International 

3040 Cornwallis Road 

Research Triangle Park, NC 27709 

Contract No. EP-W-09-004 

Work Assignment 1-17 

EMRAD Risk Assessment Support to OSW 

Project Officer: 

Shannon Sturgeon 

Office of Resource Conservation and Recovery 

Washington, DC 20024 

Work Assignment Manager: 

Gerry Laniak 

Office of Research and Development 

Athens, GA 30605 

National Exposure Research Laboratory 

Office of Research and Development 

Washington, DC 20460 
Notice and Disclaimer 

The U.S. Environmental Protection Agency through its Office of Research and 

Development (ORD), funded and collaborated in the research described here under 

Contract No. EP-W-09-004, Work Assignment 1-17 to Research Triangle Institute 

International. It has been subjected to the Agency’s peer and administrative review and 

has been approved for publication as an EPA document. Mention of trade names or 

commercial products does not constitute endorsement or recommendation for use. 

This is a contribution to the EPA Office of Research and Development’s Nanomaterials 

Research Program. 

The appropriate citation for this report is: 

Johnston, J.M., Lowry, M., Beaulieu, S., and Bowles, E. 2010. State-of-the-Science 

Report on Predictive Models and Modeling Approaches for Characterizing and 

Evaluating Exposure to Nanomaterials. U.S. Environmental Protection Agency, Office of 

Research and Development, Athens, GA. EPA/600/R-10/129, September 2010. 
Contents 

Contents 

Acronyms...................................................................................................................................... vii
 

Acknowledgements......................................................................................................................... x
 

Executive Summary........................................................................................................................ 1
 

Chapter 1.0 Introduction ................................................................................................................. 4
 

1.1 Background ...................................................................................................................... 5
 

1.2 Purpose and Scope of this Report .................................................................................... 8
 

1.3 Overview of Review Methodology................................................................................ 10
 

1.3.1 Information Search and Review...............................................................................10
 

1.3.2 Characterization of Models and Approaches...........................................................11
 

1.4 Roadmap to Report......................................................................................................... 12
 

Chapter 2.0 Nanomaterials and their Environmental Transport and Fate .................................... 15
 

2.1 Engineered Nanomaterials and their Classification ....................................................... 15
 

2.1.1 Carbonaceous ENMs ...............................................................................................16
 

2.1.2 Metal ENMs.............................................................................................................16
 

2.1.3 Semiconductor Materials, Including Quantum Dots ...............................................17
 

2.1.4 Nanopolymers/Dendrimers......................................................................................17
 

2.2 Properties of ENMs that Influence Environmental Behavior ........................................ 17
 

2.2.1 Size Characteristics..................................................................................................18
 

2.2.2 Surface Area and Charge Characteristics ................................................................18
 

2.2.3 Chemical Composition and Structure Characteristics .............................................19
 

2.2.4 Reactivity Characteristics ........................................................................................19
 

2.2.5 Partitioning Characteristics......................................................................................19
 

2.3 Key Processes Influencing Environmental Behavior..................................................... 20
 

2.3.1 Aggregation and Deposition ....................................................................................20
 

2.3.2 Disaggregation and Detachment..............................................................................21
 

2.3.3 Settling and Sedimentation ......................................................................................21
 

2.3.4 Filtering and Enhanced Transport in Porous Media ................................................21
 

2.3.5 Particle Diffusion.....................................................................................................22
 

2.3.6 Redox Reactions ......................................................................................................22
 

2.3.7 Biodegradation.........................................................................................................22
 

2.3.8 Hydrolysis................................................................................................................22
 

2.3.9 Photolysis.................................................................................................................23
 

2.3.10 Phase Partitioning ....................................................................................................23
 

2.4 Considerations for the Fate and Transport of ENMs in Environmental Media ............. 24
 

2.4.1 Fate and Transport in Aquatic Systems ...................................................................25
 

 iv 
Contents 

2.4.2 Fate and Transport in Terrestrial Systems ...............................................................27
 

2.4.3 Uptake and Accumulation of Nanomaterials in Biological Systems.......................29
 

2.5 Challenges to Modeling Nanomaterials ......................................................................... 29
 

2.5.1 Complexity of Transport Characteristics and Behaviors and Associated 

Data Gaps ...............................................................................................................................30
 

2.5.2 Variability in Nanomaterial Types and Properties...................................................31
 

2.5.3 Limitations of Traditional Risk Assessment Models...............................................31
 

2.5.4 Need for Near-Term Risk Management Decisions..................................................32
 

Chapter 3.0 Review Methodology for Relevant Models and Methods ........................................ 33
 

3.1 Developing the Search Strategy ..................................................................................... 33
 

3.2 Identifying Key Information Sources............................................................................. 35
 

3.2.1 Journals ....................................................................................................................35
 

3.2.2 Reports .....................................................................................................................38
 

3.2.3 Research Centers......................................................................................................40
 

3.2.4 Informational Web Sites ..........................................................................................41
 

3.3 Summary of Recent Reports and Compendia ................................................................ 44
 

3.3.1 Summary Reports of State of Current Knowledge ..................................................44
 

3.3.2 Governance Frameworks .........................................................................................46
 

3.3.3 Other Relevant Nanomaterial Reports and Compendia...........................................47
 

Chapter 4.0 Model Reviews.......................................................................................................... 49
 

4.1 Model/Method Evaluation Criteria ................................................................................ 49
 

4.2 Review of Environmental Fate and Transport Models .................................................. 50
 

4.2.1 Surface Water Models..............................................................................................51
 

4.2.2 Subsurface Models...................................................................................................55
 

4.2.3 Bioaccumulation Models .........................................................................................58
 

4.2.4 Multimedia Models..................................................................................................61
 

4.3 Alternative Approaches.................................................................................................. 64
 

4.3.1 Adaptive Management and Evaluation Frameworks...............................................66
 

4.3.2 Multi-Criteria Decision Analysis (MCDA) .............................................................68
 

4.3.3 Bayesian Approaches...............................................................................................70
 

Chapter 5.0 Conclusions ............................................................................................................... 74
 

Chapter 6.0 Bibliography.............................................................................................................. 78
 

Appendix A Titles Pertaining to the Use of Exposure Models for Nanomaterials....................... 90
 

Appendix B Exposure Model/Method Summaries for NMs in the Environment ........................ 99
 

v 
Contents 

List of Figures 

Figure 1-1. A predictive modeling strategy to evaluate ENM risks. ...............................................5
 

Figure 1-2. Source-to-outcome framework for ecological exposure research (US EPA, 

2009a). .................................................................................................................................6
 

Figure 1-3. Framework for protecting human health and the environment (US EPA, 2009a)........9
 

Figure 1-4. Model review process for exposure models and alternative approaches. ...................11
 

Figure 1-5. Report roadmap...........................................................................................................12
 

Figure 2-1. Illustration of chemical processes relevant to transport in aquatic (surface 

water) and terrestrial (groundwater, soils, and sediments) environmental systems. .........25
 

Figure 3-1. Summary of review methodology...............................................................................33
 

Figure 3-2. Key search terms organized by theme. .......................................................................34
 

Figure 4-1. Conceptual exposure pathway utilized by the SMARTEN model..............................68
 

Figure 4-2. Conceptual network of ENM flows over value chain and into environmental 

compartments.....................................................................................................................73
 

List of Tables 

Table 2-1. Chemical Properties of Nanomaterials Relevant to Environmental Fate and 

Transport............................................................................................................................17
 

Table 2-2. Comparing the Properties Needed to Model ENMs Versus Other Contaminants .......31
 

Table 3-1. Journals Producing the Highest Number of Relevant Studies......................................35
 

Table 3-2. Major Reports on ENM Research Relevant to Environmental Exposure 

Modeling............................................................................................................................38
 

Table 3-3. Key Research Centers for Nanotechnology Research..................................................40
 

Table 3-4. Publically Available Websites Providing Valuable Information ENM Exposure 

Modeling............................................................................................................................41
 

Table 3-5. Summary Reports on ENM Research Relevant to Environmental Exposure 

Modeling............................................................................................................................45
 

Table 3-6. Governance Frameworks on the Production of Nanomaterials....................................46
 

Table 3-7. Other Relevant Reports and Compendia on the Research of Nanomaterials...............48
 

Table 4-1. Summary Evaluation of Alternative Approaches to ENM Risk Evaluation ................65
 

vi 
 

Acronyms 

Acronyms 

Acronym Definition 

3MRA Multimedia, Multipathway, Multireceptor Risk Assessment 

AAN average agglomeration number 

ADE advection-dispersion equation 

ADME absorption, distribution, metabolism, and elimination 

AGU American Geophysical Union 

AHP Analytical Hierarchy Process 

ALARA as low as reasonably achievable 

ANSI	 American National Standards Institute 

ARAMS Army Risk Assessment Modeling System 

ASTM American Society for Testing and Materials 

BCF bioconcentration factor 

BIOPLUME 2-D transport model of dissolved hydrocarbons 

BSAF biota sediment accumulation factor 

CalTOX California Total Exposure Model for Hazardous Waste Sites 

CBEN Center for Biological and Environmental Nanotechnology 

CEINT Center for Environmental Implications of NanoTechnology 

CFC critical flocculation concentration 

CFT classic filtration theory 

CNT carbon nanotube 

CSC critical salt concentration 

DEFRA Department for Environment, Food and Rural Affairs 

DIAS Dynamic Information Architecture System 

DLVO Derjaguin and Landau, Verwey and Overbeek 

EHP Environmental Health Perspectives 

ENM Engineered nanomaterials 

ENRHES Engineered Nanoparticles: Review of Health and Environmental Safety 

EOR enhanced oil recovery 

EPA U.S. Environmental Protection Agency 

EPI Suite Estimation Programs Interface Suite 

ERED Environmental Residue Effects Database 

ES&T Environmental Science & Technology 

ETH Eidgenössische Technische Hochschule (Swiss Federal Institute of 

Technology) 

FRAMES Framework for Risk Analysis in Multimedia Environmental Systems 

GIS geographic information systems 

HEAST Health Effects Assessment Summary Table 

HSPF Hydrological Simulation Program–FORTRAN 

HWIR Hazardous Waste Identification Rule 

HYDRUS water flow and solute transport model in variably saturated porous media 

ICON International Council on Nanotechnology 

IOM Institute of Occupational Medicine 

IRIS Integrated Risk Information System 

vii 
Acronyms 

ISO International Organization for Standardization 

Kd the equilibrium ratio of the concentration in water to the concentration in the 

solid 

Kh the equilibrium ratio of the concentration in air to the concentration in water 

Koc organic carbon partition coefficient 

MAUT Multi-Attribute Utility Theory 

MAVT Multi-Attribute Value Theory 

MCDA multi-criteria decision analysis 

MEMS MicroElectroMechanical Systems 

MFA material flow analysis 

MIMS	 Multimedia Integrated Modeling System 

MOA modes of action 

MOC Method of Characteristics (2-D transport model for groundwater) 

MODFLOW finite difference flow model 

MRC military relevant compounds 

MWCNT multiwalled carbon nanotube 

NCEA National Center for Environmental Assessment 

NEMS NanoElectroMechanical Systems 

NIOSH National Institute for Occupational Safety and Health 

NM nanomaterial 

NMT Nanotechnology, Molecular Nanotechnology 

NNI National Nanotechnology Initiative 

NNI Nanotechnology Now 

NP nanoparticles 

NPDES National Pollutant Discharge Elimination System 

NRC National Research Council 

NSF National Science Foundation 

PAH polycyclic aromatic hydrocarbon 

PEC predicted environmental concentration 

PM precautionary matrix 

PMFA probabilistic material flow analysis 

PNEC predicted no effect environmental concentration 

PRZM	 Pesticide Root Zone Model 

PZC point of zero charge 

QSAR quantitative structure-activity relationship 

QUAL2K River and Stream Water Quality Model 

RESRAD Residual Radioactivity Models 

SAB Science Advisory Board 

SADA Spatial Analysis Decision Assistance 

SMAA-TRI stochastic multicriteria acceptability analysis 

SMARTEN Strategic Management and Assessment of Risks and Toxicity of Engineered 

Nanomaterials 

STELLA modeling software package 

TMDL total maximum daily load 

TOPSIS Techniques for Order Preference by Similarity to Ideal Solution 

viii 
Acronyms 

TOUGH2 general purpose numerical simulation program for porous and fractured media 

TRACI	 Tools for the Reduction and Assessment of Chemical and Other 

Environmental Impacts 

TRIM Total Risk Integrated Methodology 

UC CEIN University of California Center for Environmental Implications of 

Nanotechnology 

UCLA University of California, Los Angeles 

UK United Kingdom 

UV ultraviolet 

VoI value of information 

WASP Water Quality Analysis Simulation Program 

WRR Water Resources Research 

WWTP wastewater treatment plant 

ix 
Acknowledgements 

The authors would like to thank Michele Conlon (EPA ORD/NERL) for her vision and support 

of this effort. The external peer reviewers for this report—Ms. Christine Hendren, Dr. Igor 

Linkov, and Dr. Sam Luoma—provided a number of comments on the draft that significantly 

improved the quality and transparency of the report. The authors would like to thank Dr. Mark 

Wiesner for reviewing the report and providing helpful guidance, also for sharing information on 

his research at the Center for the Environmental Implications of Nanotechnology at Duke 

University, sponsored by the EPA and the National Science Foundation. Our thanks to Dr. 

MacArthur Long (EPA ORD/NERL) for managing the external peer review. 

x 
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xi 
Executive Summary 

Executive Summary 

This state-of-the-science review was undertaken to identify fate and transport models and 

alternative modeling approaches that could be used to predict exposure to engineered 

nanomaterials (ENMs) released into the environment, specifically, for aquatic systems. The 

development of modeling frameworks that represent the unique complexities of ENM behavior 

in the environment is in its infancy, and a critical mass of researchers actively engaged in model 

development efforts has yet to be achieved. Further, it is widely recognized that there are many 

obstacles to model development and, in general, to conducting environmental risk assessments of 

ENMs that provide meaningful information for risk managers. Nevertheless, the U.S. 

Environmental Protection Agency (EPA) will be required to manage potential risks across the 

life cycle of ENMs, from production through the disposition of wastewaters and residuals 

containing ENMs. Therefore, this state-of-the-science review included traditional modeling 

frameworks as well as approaches that are considered relatively new to environmental modeling 

science and risk management (e.g., adaptive management, multi-criteria decision analysis). In 

essence, this review sought to answer five basic questions: 

1.	 What models and approaches have been used successfully to simulate nanomaterial 

behavior in environmental systems? 

2.	 What models and approaches cannot be used to predict exposures to ENMs in 

ecosystems? 

3.	 What models and approaches can be used in the near term, and what types of predictions 

can be supported by available models? 

4.	 What techniques can be used to address uncertainties and support risk management 

decisions in the near term given obvious gaps in information? 

5.	 What does the state-of-the-science suggest with respect to long-term research goals that 

can be undertaken to improve fate and transport modeling tools for ENMs? 

To describe the state-of-the science landscape of fate, transport, and exposure models, we 

conducted a focused review of the literature (published and grey literature), research centers, 

conference proceedings, and related organizations (e.g., trade associations). We investigated a 

wide range of information sources to identify models and methods that could be used in 

evaluating exposures associated with the environmental release of ENMs. Initially, the 

information search was limited to current models and approaches used to simulate fate and 

transport of engineered ENMs in aquatic systems. However, because the literature on 

environmental exposure modeling of ENMs was extremely limited, the search criteria were 

expanded to include other types of particles (e.g., aerosols, polymers, and colloids) that exhibit 

transport behaviors similar to ENMs. In addition, we expanded the scope of our review to 

include modeling approaches that could be useful to risk managers in the near term (e.g., 

decision analysis methods). Therefore, the literature review identified (1) specific types of 

modeling approaches (e.g., colloid models) considered highly relevant to exposure modeling of 

ENMs, (2) traditional environmental exposure models applied to conventional chemicals, and (3) 

other approaches that could potentially offer modeling solutions for the exposure assessment of 

ENMs. In essence, our expanded search recognized that ENMs behave as both chemicals and 

1 
Executive Summary 

particles and, because few models have been developed to simulate materials that exhibit 

properties of both chemicals and particles, we defined the state-of-the-science for exposure 

modeling in broad terms. 

Our review strategy involved the development of a thematic key word search based, in part, on 

our preliminary review of major reports and literature reviews on the environmental modeling of 

ENMs. We conducted a systematic search of literature databases using different key word 

combinations and tailored those searches to focus on the most prolific researchers and specific 

modeling topics (e.g., excluded health and safety literature). Although we recognized that ENM 

research is a dynamic field, it quickly became apparent that the majority of cutting-edge research 

on environmental exposures and modeling of ENMs is attributable to a relatively small subset of 

scientists and modelers. Specifically, our search strategy included 

 Elsevier’s on-line technical documents service (ScienceDirect) 

 Google Scholar search engine, particularly to identify significant reports 

 ISI’s on-line technical documents in the Web of Science 

 Specific sources on nanotechnology, including nano-specific journals, research centers, 

and nanotechnology trade association web sites 

 Online libraries at the University of North Carolina at Chapel Hill 

(http://www.lib.unc.edu/) and North Carolina State University (http://www.lib.ncsu.edu/) 

 Personal communications with experts in nanotechnology research. 

The results of this review are presented in the bibliography in Section 6, and a subset of these 

results in Appendix A was organized by topical area (e.g., models currently used in fate and 

transport simulations of ENMs) to support at-a-glance usage of the information provided in this 

report. 

The review focused on models and approaches that could be useful in assessing the multimedia, 

multipathway impacts associated with nanomaterial releases into the environment; therefore, the 

evaluation included single media models (e.g., porous media colloid models) that could be 

integrated into a larger modeling framework, as well as multimedia modeling frameworks and 

systems. In characterizing the state-of-the-science, we concentrated on fate and transport models 

for ENMs, i.e., those models designed to predict the migration and transformation of chemicals 

in the environment in support of exposure and risk assessment. Although we recognized that 

bioaccumulation may be an important determinant of exposure for certain types of ENMs (e.g., 

nanoscale metals), the focus of this review was clearly on fate and transport modeling as the 

means to predict exposure. 

The information on fate and transport modeling approaches for ENMs is presented at several 

levels of detail. For example, we developed summaries of models for specific environmental 

media (i.e., surface water, subsurface, and biological media) as well as multimedia models. In 

addition, we present (1) models developed and used specifically to evaluate ENMs, (2) 

established regulatory models used for risk assessment purposes, (3) models that have potential 

applicability to ENMs, and (4) alternative approaches to traditional environmental fate and 

transport models. We identified a short list of applicable models and approaches, and developed 

detailed reviews that could be useful to ENM researchers as a foundation in building a predictive 

2 
Executive Summary 

modeling capacity for estimating environmental exposures to ENMs. The detailed reviews also 

include alternative approaches because risk management decisions are likely to be required 

before the data and modeling science are sufficiently mature to produce reliable quantitative risk 

estimates for exposures to ENMs. 

Lastly, this report suggests several conclusions regarding the state-of-the-science for 

environmental fate and transport models and alternative approaches that could be useful in 

supporting an assessment of the potential environmental exposures to ENMs. These conclusions 

are intended to inform the development of a long-term research strategy and offer insight into 

future directions that may be productive. In summary, the conclusions presented in this report are 

 Research priorities should continue to emphasize the development of empirical studies to 

characterize fate and transport behavior under laboratory and field conditions 

 Field testing of currently available fate and transport models could provide significant 

insight into the limitations of these models when applied to ENMs 

	 Development of new models to replace or modify the partitioning approach used in most 

multimedia fate and transport models should be a priority given the importance of these 

concepts for conventional organics 

	 A parallel research track to adopt alternate approaches describe in this review (e.g., 

decision analysis) should be pursued to meet immediate needs and provide improved 

decision-making support as data and models specific to ENMs continue to evolve 

	 The primary focus of this state-of-the-science review was on ENMs with an organic base 

and, therefore, a similar review specific to metals should be conducted 

	 The development of a standard ENM data model that introduces consistency in 

nomenclature and testing requirements and is driven by fate and transport modeling 

needs would support an integrated approach to data/model development for ENMs. 

3 
 

 

 

1.0 Introduction 

Chapter 1.0 

Introduction
 

Among the many emerging contaminants confronting the U.S. Environmental Protection Agency 

(EPA) are the various nanoscale materials used in manufacturing commercial nanotechnology 

products. Engineered nanomaterials (ENMs)1

 are being incorporated into new commercial 

products at an increasing rate (Wiesner et al., 2006). There are many unknowns regarding the 

ecological or human health risk associated with exposure to nanoscale materials used in the 

manufacturing process or residues released during normal use or after the useful lifetime of the 

product. Environmental processes such as weathering of ENMs may even create a wide spectrum 

of additional, ill-defined transformation products. Perhaps the biggest unknown is whether these 

residues enter (or persist in) the ambient environment in forms or concentrations that pose health 

or environmental concerns. 

To determine the nature and extent of possible exposure to ENMs in the environment, methods 

will be needed to predict their fate and transport in environmental media, understand the 

biologically relevant forms of ENMs that persist in the environment and, ultimately, confirm 

their occurrence in media (e.g., drinking water and foods) to which humans and animals may be 

exposed. Because of the sheer numbers and variety of ENMs that have been and will be created, 

and the profound influence of type, purity, purpose, and characteristics (e.g., coated versus 

uncoated) on the environmental behavior of ENMs, it will be a virtual impossibility to conduct a 

full battery of tests on each nanomaterial that adequately describes interactions among 

environmental compartments and biological systems. At the nano scale, chemicals can exhibit 

behaviors that are unique when compared to behaviors of materials in a larger scale, 

conventional form2

. For example, nano-scale materials may exhibit unique electromagnetic and 

optical properties. In addition, ENMs tend to be more reactive than larger-sized materials due to 

a much higher surface area to mass ratio, potentially resulting in faster kinetics (e.g., oxidationreduction reactions, dissolution) than might otherwise be expected. 

As suggested in Figure 1-1, predictive modeling will be required to represent the relationships 

among: (1) the manner in which ENMs are released into the environment, (2) the behavior, fate, 

and transport of ENMs in various environmental compartments, (3) the exposure of human and 

ecological receptors to ENMs, and (4) the adverse effects to ENMs as exposures occur over time 

and space. However, these models can only be developed if the foundation of basic information 

needs for ENMs is met, including chemical-physical properties, environmental behavior, and 

relevant health and ecological endpoints. Based on the information provided by predictive 

models, decisions can be supported to invest in additional data collection efforts, consider risk 

management options, and so forth. As EPA carries out its mission to safeguard public health and 

1

 It is important to distinguish between (1) intentionally produced, engineered nanomaterials (ENMs), (2) naturally 

occurring nanomaterials (e.g., soil colloids), and (3) incidental nanomaterials produced unintentionally through 

some anthropogenic process (e.g., combustion by-products). Given the potential need for regulations covering their 

production, use, and disposal, the focus of this report is on ENMs, though many of the fate and transport concepts 

and approaches may apply to natural and incidental nanomaterials. 

2

 The literature often describes materials sized greater than the nanoscale as bulk materials. Consistent with EPA’s 

National Center for Environmental Assessment (NCEA), we have avoided this use of the term in order to prevent 

confusion with large-volume, bulk production of materials, which may include the bulk production of ENMs. 

4 
1.0 Introduction 

the environment from chemical stressors, the information that predictive models provide will be 

an essential component of the decision-making process regarding safe management, use, and 

disposal of ENMs. 

Figure 1-1. A predictive modeling strategy to evaluate ENM risks. 

1.1 Background 

In considering the information presented in this report, it is first necessary to understand the 

context for this effort, namely, EPA’s conceptual framework for exposure science (US EPA, 

2009a). Exposure assessment is the process of measuring and modeling the magnitude, 

frequency, and duration of contact between the potentially harmful agent and a target population, 

including the size and characteristics of that population (Zartarian et al., 2005). As shown in 

Figure 1-2, EPA has adapted a source-to-outcome framework to operationalize this definition 

for the purposes of exposure assessment. Interestingly, this framework includes many of the 

same features as the predictive modeling strategy illustrated in Figure 1-1. For example, EPA’s 

conceptual framework also begins with the release of a stressor (e.g., ENMs) into the 

environment and ends with a dose-response characterization to determine the nature and 

significance of the toxicological endpoints. Following release, ENMs may be transformed and 

move through environmental media; thus, there is an implicit recognition of the importance of 

multimedia (versus single medium) behavior. The intensity of the exposure is defined in terms of 

the concentration in the contact medium, as well as the length of time that a receptor remains in 

contact with the contaminated medium; the exposure becomes a dose only after the stressor has 

crossed the body barrier. However, this figure also describes the interactions of environmental 

factors that contribute to exposure and, importantly, illustrates the types of feedbacks that are 

possible for ecological receptors and the environment. The impacts of exposure to a chemical 

stressor in a defined ecosystem or habitat can include a cascade of effects that represent both 

direct (e.g., significant reduction in a valued species due to direct toxic effects) and indirect (e.g., 

a shift in the vegetative community) effects. Note that this figure was developed, primarily, to 

illustrate the importance of these interactions in spatially defined ecosystems in the sense that 

5 
1.0 Introduction 

ecologists often think about the spatial boundaries of an ecosystem (e.g., an old growth forest). 

The energy and matter fluxes in an ecosystem are tightly coupled to plant and animal 

communities and, as a result, these feedback loops are common. Although similar types of 

feedback loops are possible in human exposure scenarios, human-ecosystem feedback is less 

common for human receptors, because humans tend to be less tightly coupled with their 

ecosystems. 

Figure 1-2. Source-to-outcome framework for ecological exposure research (US EPA, 2009a). 

Based on this figure, it is clear that exposure science—as evidenced by EPA research 

programs—seeks to represent the critical processes and flows of materials within an ecosystem 

as the means of predicting potential effects associated with the introduction of a physical, 

chemical, or biological stressor. This provides a critical context for this state-of-the-science 

report, because it suggests that the focus of this review should, appropriately, be on modeling 

approaches that are capable of representing the complex interactions among ENMs, abiotic, and 

biotic compartments. Of critical importance is the recognition that nanomaterials are particles 

and chemicals. This means that traditional partition coefficients (e.g., solid-water partition 

coefficients) that drive multimedia modeling for most conventional chemicals cannot provide an 

appropriate theoretical basis with which to predict the environmental exposures to ENMs. In 

essence, nanomaterials may exist in aqueous solution both in truly dissolved form and as 

suspended particles; traditional aqueous-solid phase partition coefficients only consider 

dissolved mass versus mass associated with a stationary solid phase. Furthermore, many 

processes are of critical importance to ENMs that may not be relevant to the environmental 

behavior of conventional chemicals (e.g., processes determining the stability of aqueous 

suspensions of nanoparticles). 

Second, it is important to recognize how EPA’s research strategy for nanotechnology (US EPA, 

2009b) is related to the conceptual framework for exposure science. The purpose of EPA’s 

6 
1.0 Introduction 

nanotechnology research program is to conduct focused research to inform risk management 

decisions under various environmental statutes for which EPA is responsible and, more than 

likely, under new environmental statutes that will be developed in the future as the 

nanotechnology industry continues to mature. The EPA research strategy is structured around 

several research themes and associated science questions. The first research theme is “Sources, 

Fate, Transport and Exposure” with the associated science questions: 

Key Science Question 1. What technologies exist, can be modified, or must be developed to 

detect and quantify manufactured nanomaterials in environmental media and biological 

samples? 

Key Science Question 2. What are the major processes and/or properties that govern the 

environmental fate, transport, and transformation of manufactured nanomaterials, and how 

are these related to the physical and chemical properties of those materials? 

Key Science Question 3. What are the exposures that will result from releases of 

manufactured nanomaterials? 

The state-of-the-science report on sampling and analysis (US EPA, 2008) focused on the first 

key science question, providing a comprehensive review of the literature and research on what is 

currently available so that the Agency could identify gaps in the technology and methods to 

detect and quantify ENMs in environmental media. Similarly, this state-of-the-science report 

focuses on the second and third key science questions through a comprehensive review of the 

literature and research on currently available models and modeling approaches, so that the 

Agency can determine what types of research are needed and what the current body of work will 

and will not support with respect to the prediction of ecological (and human health) exposures to 

ENMs released into the environment. 

Lastly, the National Research Council’s (NRC’s) publication Science and Decisions: Advancing 

Risk Assessment (NRC, 2009, commonly referred to as the “silver book”) provides specific 

guidance on transport, fate, and exposure assessment that reiterates major themes in both EPA’s 

conceptual framework for exposure science as well as in the Agency’s research plan for 

nanotechnology. Although the silver book primarily focused on human health risk assessment 

(and, by extension, exposure assessment for human receptors), the insights and recommendations 

expressed by the Committee clearly resonate with ecological exposure science. For example, 

among the recommendations that the NRC Committee provided, the following pertain 

specifically to exposure science: 

 Exposure assessment should characterize sources, routes, pathways and the attendant 

uncertainties linking source to dose 

 Recognition of the multiple possible exposure pathways highlights the importance of a 

multimedia, multipathway exposure framework 

 A critical insight that should be recognized by EPA and other practitioners is that there is 

no ideal transport, fate, or exposure model that can be used under all circumstances 

7 
1.0 Introduction 

	 A lower resolution model (e.g., screening) that produces more timely outputs (at greater 

uncertainty) may be required to support decisions in the near term when available data 

and models are not suitable to support a more refined analysis 

 Guidelines to help the risk analyst or risk manager understand how model uncertainty and 

data limitations affect overall uncertainty in exposure assessment are needed 

 The communication of uncertainty and variability should be part of key computational 

steps of risk assessment—e.g., exposure assessment and dose-response assessment. 

Several themes emerge from the nexus of these three reports that heavily influenced the 

development of this state-of-the science report, from the review strategy through the 

development of criteria with which to evaluate various models and exposure assessment 

approaches. 

	 First, multimedia, multipathway exposure frameworks are preferable because they 

support the development of exposure assessments that reflect the movement of ENMs 

across and between abiotic and biotic compartments. As suggested in Figure 1-1, the 

complexities and feedback loops inherent in a functioning ecosystem should be 

represented to the greatest extent possible. 

	 Second, currently available models are often not ideal (or in some cases, even useful) for 

ENMs; the chemical properties required by traditional fate and transport models to 

simulate major environmental processes (e.g., equilibrium partition coefficients) are not 

likely to be the same properties that drive those processes for ENMs. 

	 Third, the purpose of an exposure assessment for ENMs is, ultimately, to support the 

characterization of potential risks to health and the environment. Consequently, modeling 

approaches should be considered that will support decisions in the immediate future (i.e., 

models that could be used right now) as well as approaches that would require additional 

data and model development. In either case, it will be critical to communicate the 

uncertainty and variability associated with exposure assessments. 

1.2 Purpose and Scope of this Report 

The development of modeling frameworks that represent the unique complexities of 

nanomaterial behavior in the environment is in its infancy, and a critical mass of researchers 

actively engaged in model development efforts has yet to be achieved. Further, it is widely 

recognized that there are many obstacles to model development and, in general, to conducting 

environmental risk assessments of ENMs that provide meaningful information for risk managers 

(e.g., Greiger et al., 2009, 2010; Wiesner et al., 2009). Nevertheless, EPA will be required to 

manage potential risks associated with nanomaterials, from the production stage through ultimate 

discharge and disposal of wastewaters and other residuals containing ENMs. Therefore, this 

state-of-the-science review included traditional modeling frameworks as well as approaches that 

are considered relatively new to environmental modeling science and risk management (e.g., 

adaptive management, multi-criteria decision analysis). Because of the general lack of ENMspecific models, as well as numerous data deficiencies that have been reported in many of the 

references included in Section 6, these alternative approaches may provide a bridge between the 

immediate management needs for ENMs and the longer term exposure research interests and 

8 
1.0 Introduction 

goals of EPA. We recognize that these alternative approaches cut across risk management and 

decision analysis sciences; however, given the significant data deficiencies and lack of 

standardized analytical methods, we believe that it was well within the scope of this report to 

explore near term solutions to characterizing potential exposures to ENMs. Put succinctly, the 

purpose of this report on nanomaterial exposure models was to 

 Provide a targeted review of the literature (published and grey literature), research 

centers, researchers, conference proceedings, and entities (e.g., trade associations) that 

report on current technologies 

 Develop a synthesis of promising models and approaches that could be useful in building 

capabilities in modeling environmental exposures to ENMs, especially for surface water, 

groundwater, soil, and sediments. 

It should be pointed out that this review primarily focused on the fate and transport of ENMs 

with a base substance composed of an organic chemical (e.g., fullerenes, carbon nanotubes). 

Given the breadth of this report, we did not specifically investigate the ENMs with a base 

substance composed of metal (e.g., nano-scale silver). Although many of the concepts and 

models discussed in this report are relevant to all types of ENMs, the fate and transport modeling 

of nanoscale metals deserves separate treatment due to the inherent complexities in the 

environmental behavior of metals (e.g., geochemical speciation, unique sorption-desorption 

dynamics, nonlinear behavior in the subsurface). 

Within the broader context of EPA’s mission to protect human health and the environment, the 

approach taken in this report implies that the domain of models/approaches that were of greatest 

interest were those that fell into the space identified in Figure 1-3 by the dashed box. 

Figure 1-3. Framework for protecting human health and the environment (US EPA, 2009a). 

However, some of the alternative approaches that are described in this report are appropriate for 

the entire framework. Thus, the value of information that could be provided by using, for 

9 
 

1.0 Introduction 

example, a multi-criteria decision analytic (MCDA) framework or a Bayesian network approach, 

extends beyond exposure assessment and crosses over into risk management of outcomes. 

As in the silver book, we recognized that the universe of potential models was so extensive that 

we needed to create a set of science questions to guide this review, and focus the state-of-thescience review on exposure models that can be used or adapted for ENMs. Therefore, we 

developed the following list of questions to guide the review and, ultimately, to organize the 

conclusions of this review. 

1.	 What models and approaches have been used successfully to simulate nanomaterial 

behavior in environmental systems? 

2.	 What models and approaches cannot be used to predict exposures to ENMs in 

ecosystems? 

3.	 What models and approaches can be used in the near term, and what types of 

predictions can be supported by available models? 

4.	 What techniques can be used to address uncertainties and support risk management 

decisions in the near term given obvious gaps in information? 

5.	 What does the state-of-the-science suggest with respect to long-term research goals 

that can be undertaken to improve fate and transport modeling tools for ENMs?3 

This list of questions represents a distillation of the essential goals and purpose of this report and 

provided a compass that was enormously useful in considering which models to include in this 

state-of-the-science review. 

1.3 Overview of Review Methodology 

1.3.1 Information Search and Review 

A wide range of information sources was evaluated to identify models and approaches that could 

be used in evaluating ecosystem exposures associated with the environmental release of 

engineered ENMs. A literature review was performed to identify specific types of modeling 

approaches (e.g., colloid models) considered highly relevant to exposure modeling of ENMs, 

along with other potentially useful modeling frameworks and methods. Sources addressing 

exposure and environmental fate and transport modeling of ENMs were the primary focus of the 

search. The search engines ScienceDirect, the ISI Web of Science, and Google Scholar were 

used extensively to perform the state-of-the-science literature review using different 

combinations of search criteria described in Section 3.1. Titles pertaining to modeling 

environmental transport of ENMs in soils and aquatic systems were identified and further 

evaluated, and key sources of information such as key journals, reports, research centers, and 

informational websites were identified and catalogued. A complete and categorized listing of 

titles relevant to modeling the fate, transport, and exposure to ENMs released into the 

environment is presented in Appendix A. Each of these references was thoroughly reviewed, 

3

 It should be noted that, although this state-of-the-science report is intended to inform the development of long-term 

research goals, the purpose of this report was not to develop long-term research goals. 

10 
1.0 Introduction 

and a subset of these titles (highlighted in blue) was selected for the detailed model evaluations 

provided in Appendix B. 

1.3.2 Characterization of Models and Approaches 

The model evaluation framework implemented for this assessment provides a systematic and 

consistent approach for reviewing and summarizing information about models. The review 

categories were developed to be consistent with the NRC paper, Models in Environmental 

Regulatory Decision Making (NRC, 2007). In this report, the NRC assesses how models support 

the EPA’s environmental regulatory process. The development and application of regulatory 

models is described along with recommended considerations for selecting and using models to 

support EPA programs. The NRC document describes criteria for evaluating whether a model 

and its results provide a sound basis for regulatory decision making. In reviewing the NRC 

document, we compiled a series of key considerations for model evaluation and organized them 

into general categories with specific questions. For example, under the category of Purpose and 

Scope, the types of questions that are relevant include: What is the model purpose?; What 

transport media are considered?; and What spatial and temporal scales does the model consider? 

Because risk management decisions are likely to be required before the data and modeling 

science are sufficiently matured to reliably produce quantitative risk estimates for ecological 

exposures to ENMs, we also examined non-traditional modeling frameworks and methods that 

are more closely related to decision analysis and risk management. These alternative methods do 

not necessarily fit the traditional mold of environmental fate and transport models but may still 

be useful in the risk assessment of nanotechnology in the near term. 

Figure 1-4 presents a flow chart for characterization of the models identified in the search 

strategy. 

Figure 1-4. Model review process for exposure models and alternative approaches. 

11 
1.0 Introduction 

1.4 Roadmap to Report 

Figure 1-5 presents the organization of this state-of-the-science 

report. In Section 2, we begin by providing a brief overview of how 

ENMs are defined and classified, describe properties of ENMs that 

are key determinants of environmental behavior, and identify major 

processes (e.g., aggregation of particles) that strongly influence the 

mobility and fate of ENMs in the environment. The purpose of the 

introductory material in Section 2 is to provide the reader with 

general information on ENMs, a refresher of sorts needed to 

understand the review of exposure models and approaches. Therefore, 

the presentation of this material was intentionally brief and the reader 

is encouraged to review any of a number of references included in the 

bibliography that offer a more thorough treatment of these and other 

issues, notably, EPA’s nanotechnology white paper (US EPA, 2007), 

EPA’s state-of-the-science report on sampling and analysis (US EPA, 

2008), Nanotechnology and the Environment (Wiesner and Bottero, 

2007), Considerations for environmental fate and ecotoxicity testing 

to support environmental risk assessment from engineered 

nanoparticles (Tiede, et al., 2009), and Nanomaterials in the 

environment: behavior, fate, bioavailability, and effects (Klaine et al., 

2008) as excellent sources of information. The remainder of Section 

2 discusses the salient features of exposure modeling for ENMs 

released into the environment and identifies key challenges associated 

with predicting exposures to ENMs (e.g., limitations of current risk 

assessment modeling frameworks). 

Section 3 presents the search strategies and key information sources 

that were included in the search including, for example, major 

reports, journals, and research centers. Section 3 then discusses the 

model/method evaluation criteria that we selected for this state-ofthe-science review. Note that the dynamic nature of ENM-related 

research has resulted in a proliferation of publications over the past several years, with the 

landscape of relevant literature changing on almost a monthly basis. Therefore, we limited our 

search to materials that were either published or extracted from other sources (e.g., personal 

communications) before April 30, 2010. Section 3 concludes with a summary of major 

compendia and reviews of models/methods for ENM risk assessment that have been conducted 

during the past three years. These summary reports were critical in shaping our search strategy 

and, in addition, we relied on the combination of these reports to ensure that we were not 

duplicating previous efforts by other researchers. Rather than attempt to identify every possible 

report, article, or paper related to the fate and transport modeling of ENMs, we developed the 

search/review strategy to describe the state-of-the-science landscape of models and approaches 

that have been, or could be, useful in developing a research strategy for ENMs. Thus, seminal 

journal articles, modeling reviews, and major reports were reviewed in detail to ensure that this 

Figure 1-5. Report 

roadmap. 

12 
1.0 Introduction 

report captured the state-of-the-science with respect to modeling the fate and transport of ENMs 

in soil and aquatic systems, and estimating exposures to ecological receptors. 

Section 4 summarizes the review methodology and presents results of our review of models and 

alternative methods used for, or of potential use in, the exposure modeling of ENMs. The 

descriptions presented in this section distill the information on each model and methodology into 

practical summaries intended to provide the reader with a thumbnail understanding of the 

model/method, and its potential relevance to the exposure assessment of ENMs. From this 

universe of models and methods, we applied the evaluation criteria presented in Section 4.1 to 

select the most promising models and approaches and developed the detailed model/method 

reviews found in Appendix B. 

Section 5 summarizes our conclusions regarding the state-of-the-science review of models and 

methods relevant to the exposure assessment of ENMs released into the environment focusing on 

exposure pathways related to contaminant movement among the soil, subsurface, sediment, 

surface water, and biological compartments (i.e., this review did not evaluate air models). As 

discussed above in Section 1.2, our purpose was not to solve the problem with this review; 

rather, the intent was to provide a comprehensive understanding of the types of modeling 

approaches available and identify those models/methods that could potentially be applied (with 

or without modification) to predict exposures to ENMs in the environment. Therefore, the 

conclusions section offers insight into future directions that may be productive but does not 

provide a definitive research agenda for ecological exposure assessment of ENMs. 

Section 6 presents a full bibliography of relevant reports, publications, web sites, and 

communications. This bibliography represents approximately two-thirds of the larger set of 

references that we identified using our search strategy and initial review. To avoid diluting the 

references with related but non-essential information, we attempted to focus the bibliography 

somewhat narrowly on models and methods relevant to ecological exposure modeling. As 

suggested above in the Section 3 summary, this bibliography is not comprehensive in the sense 

that it includes every journal article with any relationship to fate and transport modeling of 

ENMs. However, this bibliography does represent the state-of-the-science (as published) with 

regard to fate and transport modeling of ENMs and will provide the reader with a thorough 

understanding of the models, approaches, gaps, and current research in this field. 

Appendix A reorganizes and refines the bibliography to focus on key references, in particular, 

references for models/methods that are described in more detail in Appendix B. The references 

are organized around five themes 

 Exposure science and model evaluation 

 Recent reports and compendia 

 Models that simulate particle, aerosol, polymer, and colloid behavior 

 Multimedia models currently used in ENM fate and transport simulations 

 Alternative approaches and models. 

13 
1.0 Introduction 

Lastly, Appendix B presents detailed reviews for exposure models and other relevant alternative 

approaches (e.g., multi-criteria decision analysis) in a standardized format which includes 

 Model Summary 

 Key References 

 Contact/Availability Information 

 Purpose and Scope 

 Evaluation (e.g., mathematical representation; complexity; consideration of uncertainty; 

applicability to ENMs). 

14 
2.0 Nanomaterials and their Environmental Transport and Fate 

Chapter 2.0 

Nanomaterials and their Environmental Transport and Fate 

This section of the state-of-the-science report provides background information on ENMs and 

their behavior once released to environmental systems. This background material is needed to 

understand the review of exposure models and approaches. The presentation of this material is 

intentionally brief, and the reader is encouraged to review any of a number of references 

included in the bibliography that offer a more thorough treatment of these issues (US EPA, 2007; 

Wiesner and Bottero, 2007; Tiede et al., 2009; Klaine et al., 2008). 

Section 2.1 defines nanomaterials and describes several nanomaterial classes. Next, Sections 2.2 

and 2.3 discuss the chemical properties and processes that influence the behavior of ENMs in the 

environment. Section 2.4 provides a summary of key transport behaviors of ENMs in aquatic 

and terrestrial environmental systems. This information provides important context for the 

evaluation of various environmental fate models discussed in Section 4 (e.g., What properties 

and processes need to be considered by models of ENM fate and transport?). Section 2.5 

summarizes several major challenges associated with modeling the environmental behavior of 

ENMs, including transport complexity, variability in types of ENMs, limitations of traditional 

modeling approaches, and the need for near-term decision making. 

2.1 Engineered Nanomaterials and their Classification 

ENMs are generally defined as having at least one dimension less than 100 nm and exhibiting 

properties that are in some way unique relative to the same materials in larger, conventional 

forms. Some researchers (e.g., Auffan et al., 2009) argue that only materials exhibiting novel, 

scaled behaviors (e.g., surface area-normalized toxicity or adsorption) should be considered from 

a regulatory perspective differently from conventionally sized materials. 

Given the emerging and dynamic nature of nanoscience, consistency in the terminology used to 

describe NMs in industry and in the academic literature has not yet been achieved, representing a 

potentially significant source of ambiguity when considering the environmental behavior of 

ENMs. Several national and international organizations (e.g., ANSI, ASTM) are developing 

standards for consistent terminology that will, among other things, provide standard definitions 

of different classes of ENMs. The reader is referred to ASTM Standard E2456-06, Standard 

Terminology Relating to Nanotechnology, for additional information. 

Although there are a number of excellent articles and reports describing types ENMs (e.g., US 

EPA, 2007; Hansen et al., 2007; Ju-Nam and Lead, 2008; Wiesner and Bottero, 2007), we 

adopted the chemical classification scheme following the work of Klaine et al. (2008), which is 

based primarily on the chemical composition of the base substance. Importantly, given the broad 

range of nanomaterial types and properties, this classification system is not internally consistent 

in all cases. For example, many dendrimers are carbonaceous, and carbon nanotubes and metal 

oxides can be semiconductors. In addition, important differences exist between ENMs within a 

given category (e.g., fullerenes versus carbon nanotubes, both of which are carbonaceous 

ENMs). Additional complications arise from modifications often made to ENMs such as surface 

coatings that give them desirable properties, altering their basic chemistry and often significantly 

15 
2.0 Nanomaterials and their Environmental Transport and Fate 

changing their behaviors with respect to environmental fate and toxicity. Despite these 

complexities, the classification scheme described below provides a useful overview of the 

various types of nanomaterials and current applications. Note that the evaluation of fate and 

transport models in this report focuses mainly on the behavior of organic-based, carbonaceous 

nanomaterials. Although many of the concepts are relevant to other classes of nanomaterials, 

important modeling approaches required to evaluate other types of nanomaterials (e.g., 

geochemical speciation models) were not reviewed in this report. 

2.1.1 Carbonaceous ENMs 

This class of ENMs is defined by the presence of carbon atoms in the nanomaterial structure. 

Although carbonaceous ENMs share this fundamental similarity in their chemical composition, 

there is significant diversity with respect to properties and environmental behavior. For example, 

fullerenes are 60-carbon-atom hollow spheres also known as buckyballs; carbon nanotubes 

(CNTs) include multi and single-walled carbon nanotubes. CNTs exhibit strong thermal and 

electrical conductivity properties. CNTs often have very high aspect ratios, which are similar to 

the high aspect ratios of asbestos, and thus warrant further toxicity evaluation. Carbon ENMs are 

often hydrophobic in aqueous systems. Thus, unmodified carbon ENMs typically aggregate 

together and/or attach to other surfaces in aqueous systems. Significant efforts have been 

undertaken to reduce their hydrophobicity and to increase the stability of aqueous suspensions 

(e.g., through functionalization of CNTs with polyethylene glycol or phospholipids). Common 

applications of carbon ENMs include plastics, catalysts, battery and fuel cell electrodes, supercapacitors, water purification, orthopedic implants, conductive coatings, adhesives and 

composites, sensors, and as components in electronics, aircraft, aerospace and automotive 

industries. 

2.1.2 Metal ENMs 

Metal containing ENMs include metal oxides as well as zero-valent metals. Titanium dioxide has 

been used as a photocatalyst in solar cells, paints, and coatings. Both titanium and zinc oxide 

have been used in sunscreens, cosmetics, and bottle coatings due to their UV blocking properties. 

Cerium dioxide has been applied as a combustion catalyst in diesel fuels, improving emission 

quality. Cerium dioxide has also been used in solar cells, gas sensors, oxygen pumps, and 

glass/ceramics. 

Nanoparticulate, zero-valent iron has been used extensively for remediation of waters, sediments, 

and groundwater through chemical oxidation (e.g., of nitrates, chlorinated solvents) (Kanel et al., 

2008). It is noteworthy that a voluntary moratorium on the use of zero valent iron nanoparticles 

for remediation has been in effect in the UK due to unknown potential environmental transport 

behaviors and impacts. 

The use of nanoparticulate silver for its antimicrobial properties represents by far the most 

prevalent use of ENMs in consumer products (Klaine et al., 2008). Specific applications include 

medical wound dressings, textiles (e.g., undergarments), air filters, toothpaste, baby products, 

clothes washing machines and vacuum cleaners. Colloidal gold has also been used in medicine 

(e.g., tumor therapy) and electronics. 

16 
2.0 Nanomaterials and their Environmental Transport and Fate 

2.1.3 Semiconductor Materials, Including Quantum Dots 

Quantum dots are semiconductor nanocrystals. These ENMs have a reactive semiconductor core 

surrounded by a shell made up of another material (e.g., silica). Quantum dots often include 

surfactant coatings. The uses have primarily been medical (e.g., imaging) as well as in solar cells 

and photovoltaics. 

2.1.4 Nanopolymers/Dendrimers 

This class of ENMs includes polymers with controllable size, topology, and molecular weight. 

They have many applications in biology, material science, and catalysis. Specific applications 

include chemical sensors, electrodes, transfecting agents, prion disease therapy, and drug 

delivery. 

2.2 Properties of ENMs that Influence Environmental Behavior 

This section provides a summary of key properties that can influence the environmental behavior 

of ENMs, particularly in aquatic and terrestrial systems. Given their strong surface reactivity and 

unique behaviors at the nanoscale, different chemical properties can be relevant for ENMs versus 

conventionally sized chemicals. In addition, nanomaterials exhibit properties of chemicals as 

well as particles, thus complicating the understanding of their behavior in the environment and 

requiring a more extensive set of properties when compared to many traditional contaminants. 

For example, nanomaterials exist in aqueous solution both as truly dissolved molecules as well 

as suspended particles, so that many traditional partitioning relationships (e.g., Kd [the 

equilibrium ratio of the concentration in water to the concentration in the solid] relating solidaqueous phase concentrations) are insufficient to characterize nanomaterial behavior. 

Table 2-1 provides a summary listing of the properties discussed in this section and groups the 

properties into several categories of relevance to ENMs. A primary references for much of the 

discussion of ENM properties in this section is Chappell (2008). 

Table 2-1. Chemical Properties of Nanomaterials Relevant to Environmental Fate and Transport 

Size 

Characteristics 

Surface Area and 

Charge 

Characteristics 

Chemical 

Composition and 

Structure 

Reactivity 

Characteristics 

Partitioning 

Characteristics 

Characteristics 

Average particle size 

Particle size range 

Effective particle size 

Aggregate size 

Average 

agglomeration 

number 

Surface area 

Specific surface area 

Surface charge 

Hydrophobicity 

Point of zero charge 

Zeta potential 

Elemental 

composition 

Crystalinity 

Surface coatings 

Surface 

functionalization 

Aspect ratio 

Degradability 

Hydrolysis rate 

Biodegradation rate, 

Photolysis rate 

Redox reaction rates 

Solubility 

Effective solubility 

Volatility 

Partition 

coefficients 

Bulk density 

Speciation 

Mean speciation 

17 
2.0 Nanomaterials and their Environmental Transport and Fate 

2.2.1 Size Characteristics 

A sample of ENMs generally contains a distribution of variably sized nanoparticles. Thus, 

parameters characterizing the size distribution of nanoparticles can be critical. For example, the 

average particle size and particle size range are descriptors often provided by manufacturers. 

Nanoparticles in aqueous suspensions may be surrounded by more or less permanent layers of 

molecules due to inter-particle attractive forces. This behavior may result in a far larger effective 

particle size relative to the size of the original nanoparticle. Particle aggregates may also be 

characterized by the aggregate size and average agglomeration number (AAN). The AAN is the 

average number of primary particles contained in an aggregate of particles. The AAN is typically 

strongly dependent on environmental conditions (e.g., pH, ionic strength) as well as nanoparticle 

surface coatings. Note that some research communities differentiate between aggregates and 

agglomerates based on the degree of reversibility of the bonds holding particles together, 

whereby aggregates are strongly bonded particles and agglomerates are more loosely bound 

(British Standards Institute, 2007; Luoma, 2008). 

2.2.2 Surface Area and Charge Characteristics 

The surface area describes the total exposed surface for a particle. The strong surface activity of 

ENMs accounts for much of the unique behavior associated with particles in the nano versus 

larger size range. As the particle size decreases approximately below 20 nm, a very large fraction 

of the total number of atoms exists at the particle surface, which can give rise to quantum 

mechanical effects and associated unique properties and behaviors. The related specific surface 

area is the ratio of the surface area to the mass for a particle. Some researchers suggest that, due 

to their surface activity, surface area may be a better measure of potential health effects than 

concentration, which is mass based (Bell, 2007). For example, some effects may be correlated 

more closely with the surface area than the concentration (Oberdörster, 1996; Oberdörster et al., 

2007; Stoeger et al., 2006, 2007). 

The surface charge is a measure of the density of charged entities on a particle surface and may 

affect its propensity to interact with other surfaces and ions. The surface charge is typically pH 

dependent (e.g., with oxide minerals). The surface charge is related to the hydrophobicity and 

solubility, often determining the stability of nanoparticle suspensions, interactions with other 

materials (e.g., attachment to natural colloids or immobile soil solids). Hydrophobicity describes 

a particles interaction with water. More hydrophobic materials interact to a lesser degree with 

water molecules, resulting in a reduced affinity for aqueous solutions and greater difficulty in 

creating stable nanoparticle suspensions. Nanoparticles are often treated in order to decrease 

their hydrophobicity (e.g., functional groups added to CNTs). The point of zero charge (PZC) is 

the pH at which the number of positively charged sites on a surface that interact with protons is 

equal to the negatively charged sites. This parameter is critical for determining the stability of 

nanoparticle suspensions and thus their aqueous mobility. Below the PZC, water donates more 

protons than hydroxide groups, and so the adsorbent surface is positively charged (and attracts 

anions). Conversely, the surface is negatively charged above PZC (attracting cations/repelling 

anions). The PZC determines how readily particles will adsorb to surfaces. At the PZC, a 

colloidal system exhibits zeta potential of zero (i.e., the particles remain stationary when an 

electric field is applied). The zeta potential refers to the electrical potential at a short distance 

18 
2.0 Nanomaterials and their Environmental Transport and Fate 

from a particle surface. This potential arises from the surface charge and is often used to 

approximate the surface potential of the particle. 

2.2.3 Chemical Composition and Structure Characteristics 

Elemental composition describes what elements make up ENMs. Importantly, the composition 

of ENMs is often modified with surface coatings or other treatments. A related issue is that the 

purity of manufactured ENMs varies widely, so that other chemicals are often present within a 

given batch of ENMs. The crystalinity refers to the stable three-dimensional arrangement of 

atoms. The crystalinity can determine or strongly influence other parameters such as surface 

area, charge, and aspect ratios. In order to maintain stable aqueous suspensions of nanoparticles 

(without particle aggregation and settling), many ENMs are modified with surface coatings such 

as polymers, polyelectrolytes, and surfactants. In addition to the desired changes in the resulting 

ENM surface chemistry, such modifications may also alter a chemical’s environmental transport 

behavior and toxicity. When surface modifications involve changes in surface functional groups 

it is referred to as surface functionalization. The aspect ratio describes the ratio between the 

longest and shortest lengths of a particle. This parameter can be important for mobility and 

uptake in organisms. 

Speciation refers to chemical form. Different chemical species of the same element often have 

very different properties influencing environmental fate and toxicity (e.g., solubility, volatility). 

For some ENMs (e.g., CNTs), surface functionalization can lead to different speciation 

properties for different parts of the surface, which may be represented by a “mean speciation” of 

the material’s surface (Chappell, 2008). Speciation can strongly influence other properties such 

as particle size, solubility, and particle charge and, as with all chemicals, speciation can 

significantly affect the mobility and toxicity of the nanomaterial. 

2.2.4 Reactivity Characteristics 

The degradability of ENMs refers to their persistence under various environmental conditions. 

Generally, only organic ENMs biodegrade, and the rates of biodegradation vary widely. Mineral 

ENMs typically do not biodegrade; however, they may be differentially susceptible to other 

degradation/transformation processes such as oxidation. 

2.2.5 Partitioning Characteristics 

The solubility refers to whether the material dissolves in water or other substances (e.g., acids, 

bases, solvents, biological media). In many studies, the dissolved fraction is defined 

operationally using filters (e.g., dissolved materials are those that pass through a 200 or 450 nm 

filter). However, the behavior of ENMs below this size fraction can be very different (e.g., 

aggregation, attachment) than truly dissolved molecules. Therefore, more complete descriptions 

of nanoparticle suspensions (e.g., particle size distribution, surface activity) and a better 

understanding of solubility are necessary to fully describe aqueous nanomaterial systems. The 

effective solubility reflects the presence of suspended ENM aggregates in addition to truly 

dissolved molecules. Thus, effective solubilities can vary significantly with environmental 

19 
 

2.0 Nanomaterials and their Environmental Transport and Fate 

conditions (e.g., pH, ionic strength) and can greatly exceed the solubility of conventionally sized 

materials (Fortner et al., 2005). 

2.3 Key Processes Influencing Environmental Behavior 

This section describes key environmental processes that influence the mobility and fate of ENMs 

in aqueous and terrestrial environments. In considering whether a model may be useful for 

predicting exposures to ENMs, it is critical to understand what processes should be represented 

within the modeling construct. 

2.3.1 Aggregation and Deposition 

Both aggregation and deposition of 

nanoparticles can be considered as a twostep process by which particles are first 

transported to the proximity of a surface 

(including for example another particle) 

followed by an attachment step. In 

aggregation, particles interact with other 

moving surfaces while in deposition 

particles interact with stationary surfaces 

(including previously deposited particles). 

As particles are transported and collide in 

water or air, they may attach to each other 

through forces of attraction. Particle–particle 

interactions that control attachment may 

result from relatively weak van der Waals 

forces, stronger polar and electrostatic 

interactions, or covalent bonding (Brar, 

2009). The aggregation/disaggregation and 

attachment/detachment behavior of 

particulates in aqueous systems may be 

predicted mechanistically using classic 

DLVO theory and its extensions (see Box 2-

1). The term agglomeration is sometimes 

used interchangeably with aggregation; 

however, some sources use agglomeration to 

describe particle groupings that are held 

together by weaker forces than aggregation. 

For example, irreversible groupings of 

primary particles may be referred to as 

“hard” aggregates in contrast to “soft” 

(reversible) agglomerates (Brar, 2009). 

Box 2-1. DLVO Theory and Its Extensions 

Classic DLVO (Derjaguin and Landau, Verwey and 

Overbeek) theory describes the attractive and repulsive 

forces between charged surfaces in a liquid medium. 

The sum of these forces determines whether attraction 

or repulsion forces will control particle aggregation and 

attachment behavior. Fundamental DLVO theory 

accounts for van der Waals attractive forces and 

electrostatic repulsion (Grasso et al., 2002). Classic 

DLVO theory has been found inadequate to fully 

describe particulate behavior in some situations. 

Additional forces that may be important in environmental 

systems but are not considered by DLVO theory include 

hydrogen bonding and the hydrophobic effect, hydration 

pressure, non-charge transfer Lewis acid base 

interactions, and steric interactions (Grasso et al., 

2002). An additional force active at the nanoscale with 

potential importance for nanomaterials is Born repulsion 

(Brant et al., 2007). Other explanations for non DLVO 

behavior include heterogeneous surface charge 

conditions (Bradford and Toride, 2007). 

Although models of particulate behavior in aqueous 

systems based on DLVO theory and its extensions may 

be useful, Brant et al. (2007) emphasize several 

important issues to consider for potential application of 

the theory for ENMs. Nanoparticles at the lower size 

range (approximately smaller than 20 nm) can 

increasingly resemble molecular solutes, and 

intermolecular forces can become more important. 

Particle diffusion (Section 2.3.5) can occur at very fast 

rates for nanoparticles, thereby increasing their potential 

contact time with other particles and possible 

attachment surfaces. This contact efficiency may 

increase the rate of attachment (and decrease mobility) 

beyond DLVO predictions. Development of extensions 

to DLVO theory to account for ENM behaviors is an 

active area of research (e.g., Loux and Savage, 2008). 

Because aggregates tend to settle out of solution more readily than primary particles, the 

aggregation process can have a fundamental influence on the mobility of ENMs. For example, 

20 
2.0 Nanomaterials and their Environmental Transport and Fate 

aggregates that settle out will tend to preferentially reside in sediments at the bottom of surface 

water bodies rather than being entrained within flowing water (if their settling rate is large 

compared with the hydraulic residence time of the system). In addition, aggregates in porous 

media may become trapped (filtered) and thus immobile. Aggregates also have a lower surface 

area to mass ratio than primary particles, so they may be relatively less reactive (Johnson et al., 

1996). 

The degree of aggregation and deposition is dependent on particle characteristics as well as 

properties of the environmental system. Important particle characteristics include type, size, and 

surface properties. Important environmental characteristics include pH, ionic strength, dissolved 

carbon content, and the presence of dissolved organic matter and multivalent cations. In general, 

greater aggregation (and settling) occurs under higher ionic strength and pH conditions. A study 

by Fortner et al. (2005) demonstrated a dependence of C60 suspensions aggregate size 

distribution on mixing rate, pH, and ionic strength, whereby lower pH and ionic strength led to 

smaller particles (less aggregation). In many cases, the ionic strength of natural waters is 

sufficiently large for particles to aggregate and settle to the bottom sediments. 

2.3.2 Disaggregation and Detachment 

Disaggregation and detachment are essentially the inverse processes relative to aggregation and 

deposition. Specifically, disaggregation occurs when an aggregate suspended in solution 

separates in to its component particles. Detachment occurs when a particle detaches from a 

stationary surface it had previously attached to. In general, processes that favor particle stability 

(low attachment probabilities) also tend to favor disaggregation and detachment. 

2.3.3 Settling and Sedimentation 

Settling is the process whereby particulates in aqueous solution sink due to gravity. Settling may 

lead to sedimentation, the deposition of settled particulates onto sediments present at the bottom 

of a water body. In very simple systems, the process of settling may be simulated using the 

Stokes equation, which shows that the settlement velocity is exponentially dependent on the 

particle diameter. Accordingly, larger particles are much more likely to settle out of suspension. 

Particles with a settling velocity greater than a critical velocity equal to the mean depth divided 

by the hydraulic residency time will preferentially be removed from suspension. Particles with a 

lower settling velocity than the critical velocity will be removed proportionally to the ratio of the 

settling velocity and the critical settling velocity (Boxall, 2007a). However, in some cases, 

Stokes law may underpredict the settling velocity of an aggregate by an order of magnitude or 

more (Wiesner, 1999). 

2.3.4 Filtering and Enhanced Transport in Porous Media 

Filtering refers to the process whereby aqueous phase particulates are deposited in porous media. 

Mechanisms for particle filtering include attachment to the porous medium as well as straining, 

which occurs when particulates are too large to pass through pore spaces. The formation of 

particle aggregates can increase the likelihood of straining and the associated particulate filtering 

in porous media. 

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2.0 Nanomaterials and their Environmental Transport and Fate 

An alternative potential mechanism in porous media is due to enhanced particle transport from 

size exclusion. Because of their size and charge characteristics, particulates may be excluded 

from some regions of the pore space, flowing primarily within the larger pore spaces. When this 

occurs, the average velocity of particulates can exceed the average water velocity, thus allowing 

particulates to travel faster than inert solutes (van de Weerd et al., 1998). 

2.3.5 Particle Diffusion 

Diffusion refers to the process whereby particles spread from areas of higher concentration to 

areas of lower concentration. Particle diffusion constants are predicted to be inversely 

proportional to the particle size, so that the rate of diffusion increases as particle size decreases 

towards the nanoscale (Brant et al., 2007). 

2.3.6 Redox Reactions 

Some ENMs are specifically designed to stimulate redox reactions. The redox chemistry often 

provides the desirable mechanisms responsible for useful applications of ENMs. For example, 

zero-valent iron has been used extensively in the remediation of waters contaminated with 

chemicals such as chlorinated solvents. In its nanoscale form, zero-valent iron can undergo the 

chemical reduction reactions that degrade contaminants faster than larger sized zero-valent iron. 

Oxidation-reduction reactions can be critical for many ENMs. 

2.3.7 Biodegradation 

Biodegradation refers to the biologically mediated transformation of chemicals (parent 

compounds) into other forms (daughter products). Ultimately, the products of biodegradation 

may be carbon dioxide and water; however, other chemicals may also be formed (in some cases 

more toxic than the parent compounds). Many ENMs are composed of inherently nonbiodegradable inorganic chemicals (e.g., metals) and not expected to biodegrade. However, some 

carbon ENMs have been shown to be metabolized biologically (Filley et al., 2005). In addition, 

some polymer based ENMs (and surface coatings) are known to be biodegradable. For some 

ENMs (e.g., polymers evaluated for use in drug transport), biodegradability is integral to the 

materials design and function (US EPA, 2007). 

2.3.8 Hydrolysis 

Hydrolysis describes the reaction of a chemical with water, whereby a chemical bond is broken 

between a carbon atom and some functional group and a new carbon-oxygen bond is formed 

with oxygen derived from the water molecule. Hydrolysis generally breaks chemicals down into 

simpler molecules, which are usually (but not always) less toxic. Given their much greater 

surface area to mass ratios, ENMs are generally anticipated to undergo transformations such as 

hydrolysis more readily and at a faster rate than larger-sized materials. However, few ENMs 

have been thoroughly characterized with respect to hydrolysis, particularly under variable and 

complex environmental conditions. 

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2.0 Nanomaterials and their Environmental Transport and Fate 

2.3.9 Photolysis 

Photolysis refers to a reaction whereby a chemical compound is broken down by photons. 

Photolysis generally breaks chemicals down into simpler molecules, which may be (but are not 

always) less toxic. Some nanomaterials, such as TiO2 and C60 are photocatalytic in the sense 

that when in water and exposed to UV light, they convert energy in photons to chemical 

compounds that may directly or indirectly react with other compounds in water. 

2.3.10 Phase Partitioning 

Phase partitioning refers the transfer of mass between aqueous, solid, and air phases, including 

the processes of dissolution, volatilization, and solid/aqueous partitioning (sorption). 

Traditionally, equilibrium mass partitioning between solid and aqueous media has been 

described using partition coefficients (Kd, or the equilibrium ratio of the concentration in water to 

the concentration in the solid). Equilibrium mass partitioning between water and air has 

traditionally been characterized using Henry’s constants (Kh, or the equilibrium ratio of the 

concentration in air to the concentration in water). Other partitioning coefficients may 

characterize the mass uptake into other phases (e.g., plants, fatty tissue). Solubility describes the 

equilibrium partitioning between a pure solid or liquid phase and water. For non-equilibrium 

partitioning, mass transfer may be described kinetically using parameters that characterize the 

rate of mass transfer from one phase to another (e.g., first-order mass transfer rate coefficients). 

The field of environmental science has studied the partitioning of various chemicals under a wide 

range of conditions, yielding extensive summaries of partitioning data used to predict where 

chemicals will tend to accumulate in the environment (e.g., air, water, soil, fatty tissues). In 

addition, various approaches for estimating parameters for chemicals based on their chemical 

structure (e.g., quantitative structure-activity relationships or QSARs) have been developed. This 

concept of predicting chemical fate based on partitioning behavior is central to most 

fate/transport and exposure models, and the theoretical underpinnings of phase partitioning (for 

conventionally sized chemicals) have been widely accepted in risk assessment modeling. 

However, the utility of existing partition coefficient values and estimation approaches for ENMs 

is limited given the unique properties and behaviors of chemicals at the nano-scale as discussed 

below. 

ENMs will dissolve to differing degrees into aqueous solution, yielding truly dissolved 

molecules in addition to nanoscale particulates and larger-sized aggregates suspended in 

solution. Therefore, nanomaterials in solution exhibit properties and behaviors of chemicals as 

well as particles. Understanding the dissolution of ENMs is significantly complicated by 

commonly used operational definitions of solubility (e.g., materials passing through 200 nm 

filters being considered dissolved; see Section 2.2.5). The presence of nanoparticles and 

associated stable aggregates can greatly increase total concentrations in solution relative to the 

molecular solubility of a compound. For example, Fortner et al. (2005) show that C60 fullerenes 

may form negatively charged, water-stable colloidal aggregates, increasing the effective aqueous 

concentration by approximately 11 orders of magnitude greater than the estimated molecular 

solubility of C60. Given the potentially strong influence of suspended nanoparticulates on the 

effective aqueous concentrations of ENMs, traditional solubility and partitioning relationships 

23 
2.0 Nanomaterials and their Environmental Transport and Fate 

will have limited applicability for predicting the dissolution and the relative mass of ENMs in 

solid and aqueous phases. 

As with aqueous/solid phase mass transfer, the process of chemical volatilization (i.e. 

aqueous/air phase mass transfer) has traditionally been described using partition coefficients, 

specifically Henry’s constant, which is the ratio of the vapor concentration of a chemical to the 

aqueous concentration of that chemical. The volatilization of ENMs is also complicated by the 

potentially significant enhanced solubility effects of nanoparticle suspensions as described 

above. Therefore, the utility of traditional Henry’s constant values may be limited for predicting 

the partitioning of ENMs to air. 

Given their greater surface area per mass ratio and reactivity, it would stand to reason that ENMs 

could partition at higher rates and thus reach equilibrium more quickly than conventional 

materials. This behavior may be particularly relevant for mass transfer processes that are 

kinetically limited (that reach equilibrium relatively slowly). However, this difference in ENM 

versus conventional chemical behavior has not been extensively evaluated, particularly under 

variable and complex environmental conditions. 

Phase partitioning is also related to biological media and the bioaccumulation of chemicals. 

Traditional environmental fate evaluations have relied on bioconcentration factors (BCF), which 

is generally defined as the equilibrium ratio of chemical concentrations in an organism relative to 

the chemical concentrations in the environmental medium of interest. BCFs characterize the 

uptake of chemicals to organisms in aqueous environments as well as the uptake of chemicals by 

plants from soil. Due to the potentially strong influence of ENMs on effective solubility 

described above, the utility of existing BCFs for the evaluation of the potential to accumulate 

ENMs in organisms is limited. Furthermore, many researchers have proposed mechanisms of 

biological uptake that may be possible only at the nanoscale (e.g., direct penetration of 

nanoparticles into organisms). 

In summary, because that ENMs exhibit properties of chemicals and particles, partitioning data 

heavily utilized in traditional environmental fate modeling (e.g., values measured or estimated 

for Kd and Kh) will not be valid for ENMs. Nevertheless, the ability to predict in which medium 

substances prefer to exist will still be a critical concept for environmental modeling of ENMs. 

However, methods of measuring and predicting partitioning for ENMs will need to be modified 

relative to approaches currently used for chemicals in conventional forms. Specifically, there will 

need to be (1) new model constructs that accurately capture the complexities of ENM 

partitioning, and (2) new experimental data will be required to parameterize these models 

accurately under different environmental conditions. 

2.4 Considerations for the Fate and Transport of ENMs in Environmental Media 

This section summarizes properties and processes that need to be considered by models of ENM 

fate and transport. This information provides important context for the evaluation of various 

environmental fate models discussed in Section 4. The discussion first considers behavior in 

aquatic systems (Section 2.4.1) and subsequently considers behavior in terrestrial systems 

(Section 2.4.2). For the purposes of this discussion, aquatic systems are surface waters, whereas 

24 
2.0 Nanomaterials and their Environmental Transport and Fate 

terrestrial systems include soils, groundwater, and sediments. Figure 2-1 provides a summary of 

fate and transport processes and their relevance to behaviors of ENMs in aqueous versus 

terrestrial systems. This figure ties together the discussion of processes in Section 2.3 with the 

consideration of ENM behaviors in environmental systems, focusing on processes specific to 

particles like many ENMs versus traditional processes that are often considered in models 

currently applied for environmental risk assessment. 

Figure 2-1. Illustration of chemical processes relevant to transport in aquatic (surface water) and 

terrestrial (groundwater, soils, and sediments) environmental systems. 

2.4.1 Fate and Transport in Aquatic Systems 

Traditional evaluation of chemical fate in aquatic environmental systems has focused on the 

following processes and parameters (US EPA, 1996): 

 Dissolution characterized by the solubility 

 Volatilization characterized by the Henry’s constant 

 Adsorption to sediments and/or suspended particulates (i.e., solid-aqueous phase 

partitioning) 

 Biological uptake characterized by bioconcentration/bioaccumulation factors 

 Photolysis characterized by photolysis rates 

 Hydrolysis characterized by hydrolysis rates 

 Biodegradation characterized by biodegradation rates. 

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2.0 Nanomaterials and their Environmental Transport and Fate 

Many traditional environmental fate assessments consider some or all of the processes listed 

above. Associated evaluation techniques (e.g., decision frameworks and models) have been 

developed to predict potential environmental concentrations and to support regulations for a 

range of environmental contaminants such as pesticides, industrial solvents, and agricultural 

chemicals. Somewhat less common are environmental fate models that consider processes of 

aggregation, attachment, settling, and sedimentation. These processes, which involve the 

behavior of aqueous-phase particulates, are critical in understanding and predicting the 

environmental fate of ENMs in aqueous systems. 

A key consideration influencing the mobility of ENMs in surface waters is whether a distribution 

of suspended nanoparticles (a nanoparticle suspension) will be stable. In other words, will the 

nanoparticles tend to aggregate, forming larger assemblies that may settle out, will the 

nanoparticles tend to hetero-aggregate with other solids in aqueous solution (e.g., soil particles or 

naturally occurring colloids), or will the nanoparticles remain in a stable suspension? The 

aggregation/disaggregation process is generally dependent on the solution pH and ionic strength, 

the presence of other solutes, the properties of naturally occurring particles, and on the properties 

of the specific ENM (e.g., surface charge, size distribution). Typically, higher pH and ionicstrength solutions give rise to larger particles and subsequent settling. The ionic strength of 

natural waters (particularly seawater) is often sufficient for particles to aggregate and settle to 

bottom sediments. 

Researchers into ENM behavior in aqueous systems emphasize that surface water chemistry can 

have a profound influence on the extent of particle aggregation, thus controlling whether 

nanoparticle suspensions are stable and flow with surface water or are unstable, tending to 

aggregate and settle (Brar, 2009; Boxall, 2007a). The more prevalent compounds in aquatic 

ecosystems—proteins, humic acids, organic matter, and natural colloids—may have a profound 

and complex influence on nanoparticle suspensions and behavior in surface water. Additional 

research is required to characterize behaviors of ENMs under highly variable natural conditions. 

In addition to nanoparticle interactions such as aggregation, ENMs have the capacity to sorb to 

sediments, other suspended particulates, and/or other solid surfaces (Oberdörster, 2005a). ENMs 

sorbed to other particulates that are stably suspended in solution (e.g., colloids) would have 

enhanced mobility relative to nanoparticles that aggregate and settle out of solution. 

An additional complexity associated with ENMs in the environment is the impact of 

transformation processes on ENM properties and behavior. So-called weathering processes 

occurring in natural systems may include aggregation, hydrolysis, loss or acquisition of surface 

coatings, photolysis, etc. Each of these processes may have a marked influence on the ENM 

surface chemistry, reactivity, bioavailability, toxicity, etc. under natural conditions (Brar, 2009). 

Given their unique properties and behaviors at the nano scale, ENM may be expected to exhibit 

different transformation behaviors (e.g., rates, dependencies) than conventionally sized materials. 

The study of ENM transformations is an active field of research (e.g., Hotze et al., 2010). 

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2.0 Nanomaterials and their Environmental Transport and Fate 

2.4.2 Fate and Transport in Terrestrial Systems 

Traditional evaluation of chemical fate in terrestrial (soil and groundwater) systems has focused 

on the following processes and parameters (US EPA, 1996) 

 Dissolution characterized by the solubility 

 Volatilization characterized by the Henry’s constant 

 Adsorption to organic matter (e.g., organic carbon partition coefficients, Koc) 

 Adsorption to inorganic matter 

 Biological uptake by plants characterized by BCFs 

 Photolysis (at the soil surface) characterized by photolysis rate 

 Redox reactions and associated rates 

 Hydrolysis characterized by hydrolysis rate 

 Biodegradation characterized by biodegradation rates. 

Many traditional environmental fate assessments consider some or all of the processes listed 

above. Associated evaluation techniques have been developed to predict potential environmental 

concentrations and to support regulations for a range of environmental contaminants. Somewhat 

less common are environmental fate models that consider processes of aggregation, attachment, 

straining, and enhanced porous media transport. These processes, which involve the behavior of 

particulates, are critical in understanding and predicting the environmental fate of ENMs in 

terrestrial systems. 

Until the last two decades, subsurface transport was thought to be mediated only by mobile 

liquid and gaseous phases. However, we now recognize that a separate, solid phase consisting of 

particles may be present and mobile under some conditions and may facilitate or retard 

contaminant transport in porous media (Sen and Khilar, 2006). In some cases, contaminants have 

migrated much farther than would be predicted based on their solubility and sorption 

characteristics, a behavior that can be explained by colloid-facilitated transport. Such facilitated 

transport may occur when colloids (and nanoparticles) are excluded from some regions of the 

pore space due to their size and charge characteristics (e.g., occlusion from zones with small 

pores and electrostatic repulsion from solid surfaces). Under these conditions, the average 

velocity of the particulates may exceed the average water velocity (i.e., colloids can travel faster 

than inert solutes) (van de Weerd et al., 1998). In contrast to the enhanced mobility associated 

with colloid facilitated transport, colloid transport has also been associated under some 

conditions with reduced contaminant transport through porous media (Sen, Mahajan, et al., 2002; 

Sen, Nalwaya, et al., 2002; Bekhit and Hassan, 2005). Retardation of transport may occur when 

colloidal particles are filtered (trapped) within a porous medium due to their size and/or due to 

attachment to immobile solid surfaces. Filtering of particulates will be enhanced under 

conditions favoring aggregation and attachment. A related transport retardation mechanism may 

occur when particulates plug the pore space and thereby reduce the hydraulic conductivity and 

groundwater flow velocities. 

Colloidal particles may be contaminants (e.g., radioactive metal particulates, nanoparticles with 

potentially toxic properties) or the particles may mediate the transport of other contaminants in 

27 
 

2.0 Nanomaterials and their Environmental Transport and Fate 

the subsurface (e.g., chemicals or nanoparticles sorbed to and thus potentially migrating with the 

colloidal particles). With their high surface area to volume ratios, the surface activity of colloids 

may be very high, potentially leading to faster kinetics and irreversible association (e.g., fast, 

irreversible sorption). 

Colloids generally may undergo the following relevant behaviors in the subsurface: 

 Attachment (also called colloid deposition) generally refers to the binding of colloidal 

particles onto the stationary soil solid surface 

 Detachment (also called colloid release) generally refers to the release of colloids from 

the soil surface into the flowing liquid phase 

 Aggregation refers to the process whereby colloids join together forming larger sized 

aggregates; in many cases these particles may be too large to fit through aquifer pore 

spaces, and are filtered by the porous medium and no longer migrate with the flowing 

groundwater (they may also clog a porous medium, reducing its hydraulic conductivity) 

 Disaggregation refers to the breaking apart of colloid aggregates (the opposite of 

aggregation). 

There is typically an abundance of natural colloidal particles attached to soil grain surfaces in the 

environment (e.g., clay colloids, humic and fulvic acids). Under certain conditions, these colloids 

can be released from the soil matrix and transported with the mobile liquid phase. Mechanisms 

leading to colloid mobility could include an increase in the groundwater flow rate (e.g., around a 

pumping well), which may result in sufficient advective shear forces for colloid detachment. An 

additional important detachment mechanism is tied to the aqueous geochemistry. For example, a 

decrease in the salt concentration below the critical salt concentration (CSC) may release 

colloids from the solid by decreasing the attractive forces and increasing the repulsive forces 

between the colloids and the soil (Blume et al., 2005). Note that this behavior is analogous to the 

critical flocculation concentration (CFC) in surface waters, which refers to the concentration 

above which flocculation is favored (e.g., flocculation in wastewater treatment settlement 

reactors). These properties are also related to the zeta potential and the point of zero charge as 

described in Section 2.2.2. Groundwater generally has higher ionic strength than rainfall but 

lower ionic strength than marine and many freshwater systems. Typically, higher ionic strengths 

increase the likelihood of particle aggregation and attachment due to the reduced inter-particle 

electrostatic repulsion. 

The primary relevant processes determining colloid transport behavior include advection, 

dispersion, particle-particle physico-chemical interactions, and particle-soil physico-chemical 

interactions. Other potentially relevant phenomena include acid-base relationships, steric 

repulsions (e.g., with long-chain polymers), and magnetic interactions (e.g., with iron) (Tosco 

and Sethi, 2009). 

As described in Section 2.3, nanoparticle surface modifications and coatings can greatly 

influence their transport behavior. One application of such surface coatings is for zero-valent 

iron nanoparticles utilized in the remediation of groundwater contamination (e.g., chlorinated 

solvents). If the iron nanoparticles are made more hydrophilic through surface modifications, 

nanoparticle aggregation is greatly reduced along with potential sorption and filtration by the 

28 
2.0 Nanomaterials and their Environmental Transport and Fate 

aquifer material (US EPA, 2008). This modification to iron nanoparticles can greatly increase the 

mobility of the nanoparticles in groundwater, thus increasing the effectiveness of iron 

nanoparticles for remediation. Of course, similar enhanced transport would not be desirable for 

nanoparticles that may have environmental risk implications. 

Surfactants are commonly utilized in the engineering of nanoparticles to help stabilize 

nanoparticle suspensions. These surfactants can have a clear impact on the partitioning between 

aqueous, solid, and particulate phases and thus on the fate of nanoparticles in subsurface 

systems. In addition to engineered surfactants, many systems have natural surfactants (e.g., 

natural organic carbon), and some researchers have found evidence that such natural surfactants 

can help stabilize aqueous nanomaterial suspensions, thus enhancing subsurface transport. 

Several studies have evaluated the mobility of nanosized materials in a porous medium under 

different conditions (Zhang, 2003; Lecoanet and Wiesner, 2004; Lecoanet et al., 2004). 

2.4.3 Uptake and Accumulation of Nanomaterials in Biological Systems 

Published, quantitative research on the bioavailability, uptake, and bioaccumulation of ENMs in 

plants and animals is scarce (Klaine et al., 2008). However, the body of research on effects of 

ENMs is far more developed and provides some clues as to the potential for bioaccumulation of 

ENMs released into the environment. In fact, the vast majority of research involving ENMs and 

biological systems is focused on interactions (e.g., positive effects on plant growth) and potential 

toxicity, especially on standard animal models such as Daphnia magna as well as on test 

organisms that will likely be exposed to ENMs through close contact (e.g., soil bacteria). Based 

on first principles, it is evident that organisms exposed to environmentally relevant 

concentrations of ENMs will be capable of accumulating these materials. Nano-sized particles— 

due to their size—can diffuse through cell membranes, can by engulfed by cells, or can adhere to 

cells. Because some ENMs are designed specifically for drug delivery purposes, it is reasonable 

to assume that they will interact with proteins and other cellular components and, especially, be 

taken up by the gut (Klaine et al., 2008). Despite the fact that little published research exists to 

determine how efficiently different types of organisms may accumulate and, possibly, translocate 

ENMs within the body, it is clear that potential interactions at the cellular level may allow for the 

relatively efficient accumulation of ENMs and, possibly, some level of magnification in the food 

chain. 

2.5 Challenges to Modeling Nanomaterials 

Predicting the behavior of ENMs in the environment requires an understanding of: (1) the 

potential sources of ENMs; (2) the distribution of ENMs once released into the environment; and 

(3) the transformations and persistence of ENMs in the environment (Lowry and Casman, 2009). 

Significant modeling challenges make current estimations of ENM fate highly uncertain. This 

section of the state-of-the-science report discusses the challenges listed below: 

 Complexity of ENM transport characteristics and associated data gaps 

 Variability in nanomaterial types and properties 

 Limitations of current modeling approaches 

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2.0 Nanomaterials and their Environmental Transport and Fate 

 Need for near term risk management decisions. 

2.5.1 Complexity of Transport Characteristics and Behaviors and Associated Data Gaps 

The locations, concentrations, and properties of ENMs released to the environment will affect 

their distribution, concentration, and ultimately their effects on the health of receptors, including 

humans and ecosystems (Lowry and Casman, 2009). Two aspects of this complexity bear noting. 

First, as described in detail above, properties and processes that define nanomaterial transport 

(e.g., stability of dispersions; simultaneous chemical and particle characteristics) can be quite 

different from properties and processes considered in most traditional risk assessment (e.g., 

Henry’s constants, Kd for aqueous-solid phase partitioning). Second, at each stage of modeling 

ENM transport, there are large uncertainties that cannot be quantified given currently available 

data and models. These uncertainties do not purely surround the values of traditional parameters, 

but they also include potentially unique causal mechanisms of transport that, as of this report, 

have not been fully addressed (SCENIHR, 2007). Thus, identifying the chemical and physical 

properties required to predict the transport of ENMs in a natural system is crucial to developing 

predictive exposure models. ENMs do not conform to the behavior of conventional chemicals 

(Lowry and Casman, 2009) and, therefore, studies must be conducted to determine the driving 

properties for specific ENMs (e.g., key chemical properties) in specific environmental settings 

(e.g., key water quality characteristics). Before adequate models for ENMs can be developed, 

the critical properties and mechanisms must be understood and characterized. 

It should be noted, however, that there is more than just a lack of knowledge surrounding 

properties specific to ENM transport. Grieger et al. (2009) address the issue of uncertainty in 

ENMs by evaluating 31 reports and papers pertaining to ENMs and environmental health and 

safety. The authors present a table describing each uncertainty identified. Each uncertainty is 

categorized into locations and sub-locations that define the lack of knowledge area within the 

different environmental, human health and safety aspects of ENM exposure. A few categories of 

uncertainties that are of importance to environmental fate and transport taken from this chart 

include: 

 Initial concentration levels 

 Release points during the ENM lifecycle (production, use, and waste) 

 The form of the released ENM (agglomerates, composites, mixtures) 

 Environmental processes relevant to ENMs 

 Degradation 

 Bioaccumulation 

 Cellular uptake. 

Thus, to predict or assess the risks that ENMs may pose in the environment, we must understand 

the sources, characteristics, transformations, and the effect on the surface properties of those 

materials. 

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2.0 Nanomaterials and their Environmental Transport and Fate 

2.5.2 Variability in Nanomaterial Types and Properties 

Nanomaterials exist as a widely divergent array of chemicals with very different behaviors and 

properties. In fact, ENMs should not be considered as a single class of substances due to their 

extreme diversity (e.g., Hansen, 2009; Wiesner and Bottero, 2007). Due to the wide variety in 

ENM properties, size, morphology, chemical composition, and the even greater variability in the 

types of surface coatings, an understanding of ENM processes from first principles is needed 

before we can generalize these findings across classes or types of ENMs (Lowry and Casman, 

2009). The sheer range of types and properties poses a particular challenge in terms of 

developing and understanding chemical behaviors of ENMs relevant to environmental transport. 

2.5.3 Limitations of Traditional Risk Assessment Models 

In general, conventional models used for chemical environmental fate and exposure assessment 

are not directly applicable in their current form for manufactured ENMs (US EPA, 2007). For 

example, the Estimation Programs Interface Suite (EPI Suite) has little applicability to ENMs, 

because it is based on equilibrium partition coefficients and does not consider the behavior of 

particulates (US EPA, 2009b). Even though some models (e.g., EPA’s MINTEQA2) and 

approaches (e.g., DLVO theory) may be useful in modeling ENMs, these models/methods will 

need to be modified and validated to ensure that they adequately represent the chemical 

properties and/or transformation processes relevant to ENMs (US EPA, 2009b). Therefore, while 

most established models may still provide insight, their potential application is relatively narrow 

for ENM risk assessment purposes (Wiesner et al. 2009). 

The chemical properties required by traditional risk assessment models are unlikely to be the 

same properties required to model the environmental transport of ENMs. Chemical and physical 

properties of ENMs (Section 2.2) are strongly related to the processes that control movement 

(Section 2.3). Therefore, typical chemical properties for predicting chemical fate and transport 

such as water solubility, octanol-water partition coefficient, and vapor pressure are not as 

important for ENMs as particle size, surface charge and surface potential (US EPA, 2009b). 

Metcalfe et al. (2009) produced Table 2-2 comparing some the characteristics needed for 

environmental fate and transport modeling of ENMs versus conventional compounds. This 

evaluation further supports the consensus that established risk assessment models (and, indeed, 

current thinking) must be adapted before they can be applied reliably to predict the 

environmental behavior of ENMs. 

Table 2-2. Comparing the Properties Needed to Model ENMs Versus Other Contaminants 

Characteristic Nanoparticles Other contaminants 

Distribution in water Dispersivity Solubility 

Distribution in porous media Filtration Adsorption/desorption 

Biologically availability Sorption? Lipophilicity 

Cellular uptake Vesicular transport? Passive or facilitated diffusion 

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2.0 Nanomaterials and their Environmental Transport and Fate 

Table 2-2. Continued 

Characteristic Nanoparticles Other contaminants 

Toxic mechanisms Steric hindrance, photo-chemical 

effects, oxidative damage, 

inflammation 

Interactions with cellular 

macromolecules and receptors, 

narcosis 

Target trophic systems Bottom of the food chain? Top of the food chain 

2.5.4 Need for Near-Term Risk Management Decisions 

A critical issue with the development of environmental fate and transport models for ENMs and 

their subsequent validation is the relatively long time required to gain knowledge upon which to 

make decisions versus the very rapid pace of nanotechnology development (Owen et al., 2009). 

Therefore, the development of regulations based on quantitative risk assessments (using tools 

including fate and transport models) will be an inherently slow governance process. Given that 

reliable, mechanistic ENM risk assessment may not be available for years or even decades 

(Grieger et al. 2009), risk managers are in need of tools over the short term to aid in the decision 

making process. Thus, in the absence of reliable quantitative and qualitative data or evaluation 

methods, novel approaches must be attempted to gain insight for near term decision making. 

Given this critical issue and need, Section 4 discusses several alternative approaches that derive 

more from risk management and decision analysis sciences than from traditional fate and 

transport analysis. 

32 
 

3.0 Review Methodology for Relevant Models and Methods 

Chapter 3.0 

Review Methodology for Relevant Models and Methods 

This section describes the methodology used to search for relevant information on the fate and 

transport modeling of ENMs released in the environment. Figure 3-1 depicts the review 

methodology used to identify information sources that describe the state-of-the-science for 

exposure modeling of ENMs. Section 3.1 presents the search strategy developed to gather 

information on the type of fate and transport models needed to estimate exposure concentrations 

for ENMs in aquatic systems. This section includes the criteria, tools, and methodology used to 

frame the retrieval of appropriate references and other information. Section 3.2 describes the 

sources of information that we identified as particularly useful in describing the state-of-thescience for ENM exposure modeling, including key journals, reports, research centers, and 

informational websites. Section 3.3 summarizes the recent reports and compendia that are 

relevant to the area of nanotechnology research but only peripherally related to this report. 

Figure 3-1. Summary of review methodology. 

3.1 Developing the Search Strategy 

Identifying references, published studies, and research describing the state-of-the-science in 

modeling the environmental fate and transport of ENMs was the primary objective of the search. 

We investigated a wide range of information sources, and developed an iterative search strategy 

to identify specific types of modeling approaches (e.g., colloid models) considered highly 

relevant to exposure modeling of ENMs, along with other potentially useful modeling 

frameworks and methods. 

The search engines ScienceDirect and Google Scholar were used extensively to perform a 

comprehensive literature review using different combinations of search criteria as described 

below. Based on peer review comments, we also performed a search of the literature using the 

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3.0 Review Methodology for Relevant Models and Methods 

ISI Web of Knowledge database (http://apps.isiknowledge.com/), which includes the Web of 

Science. The purpose of this additional search was to ensure that the use of ScienceDirect, 

Google Scholar, and local university searches provided adequate coverage in describing research 

related to the state-of-the-science. 

Information pertaining to modeling ecological exposures to ENMs, particularly in aquatic 

systems, was further evaluated. The websites of National Science Foundation (NSF)–funded 

research centers were explored, as were publicly available conference proceedings and 

nanotechnology-related informational websites. Initially, the information search was limited to 

current models and approaches used in simulating fate and transport of engineered ENMs in 

aquatic systems. However, because technical reports that describe exposure modeling of ENMs 

were extremely limited, the search criteria were expanded to include other ultrafine particle types 

such as aerosols, polymers, and colloids. In addition, the search included information that could 

be relevant to fate and transport modeling (e.g., physical and chemical properties). 

Elsevier’s on-line technical documents service (ScienceDirect) was used as the primary source 

for our literature searches. ScienceDirect is one of the largest online collections of published 

scientific research available, containing over 9.6 million articles from over 2500 journals, and 

over 6,000 e-books, reference works, book series and handbooks. Searches were also conducted 

using the Google Scholar search engine, as well as the online libraries of North Carolina State 

University (http://www.lib.ncsu.edu/) and the University of North Carolina at Chapel Hill 

(http://www.lib.unc.edu/). As suggested above, the ISI Web of Science was used to verify that 

we had characterized the landscape of published literature relevant to the fate and transport, and 

exposure modeling of ENMs. This resource includes over 12,000 journals and more than 46 

million records. The ISI Web of 

Science is particular helpful in 

identifying articles with the 

greatest impact to the field; for 

instance, the influence of a paper 

on the field can be determined 

based on the number of papers that 

have cited it. 

We adopted a broad search 

strategy by incorporating 

combinations of key words, and 

this set of search criteria was used 

to identify models and approaches 

that could potentially be used to 

simulate the fate and transport of 

ENMs. Figure 3-2 outlines the 

most productive search terms—by 

technical theme—that we used in 

various combinations to identify 

additional references. 

Figure 3-2. Key search terms organized by theme. 

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The largest number of documents retrieved from the expanded searches pertained primarily to 

exposure modeling of ENMs, ENM toxicity, or safety assessments for workers involved in the 

manufacturing of nanoparticles (NPs). In general, we found relatively few references (compared 

with the results of our search) that could be considered specific to modeling the fate and 

transport of, or characterizing the exposure to, ENMs released into the environment. 

Relevant information sources were identified from several areas of study (e.g., mining, metals 

transport, sedimentation processes) that had potential applicability. Understanding the key 

processes and research challenges specific to ENMs was critical in the assessment of models 

developed for other purposes. For instance, many researchers have pointed out that the 

environmental behavior of some ENMs is similar to colloids; thus, part of our revised search 

strategy focused on identifying colloidal fate and transport models for aqueous systems. In 

addition, research on ENMs in other fields was considered (e.g., use of iron oxide ENMs in 

groundwater remediation). For completeness, modeling tools were also searched across EPA 

research offices/programs, other federal agencies, states and EPA regions, and international trade 

and research organizations. 

A complete listing of titles relevant to modeling the fate, transport, and exposure to ENMs 

released into the environment is presented in Appendix A. Each of these references was 

thoroughly reviewed, and a subset of these titles (highlighted in blue) was selected for the 

detailed model evaluations provided in Appendix B. 

3.2 Identifying Key Information Sources 

The search strategy produced a tremendous number of journal articles, major reports, research 

centers, and informational websites; however, few useful conference proceedings were identified 

that provided materials relevant to fate and transport modeling of ENMs. The following 

subsections highlight the most important information sources that we identified. 

3.2.1 Journals 

Table 3-1 lists the journals that produced the majority of relevant research articles that we 

identified. Although a much wider variety of journals was included in the search, this list 

presents those journals that published the highest numbers of relevant articles on modeling the 

fate and transport of engineered ENMs (and similarly sized particles) in aquatic systems and 

soils. These journals include well-established titles dealing with hydrology and environmental 

modeling, as well as more recently published titles focusing specifically on nanotechnology. 

Table 3-1. Journals Producing the Highest Number of Relevant Studies 

Journal title First published 

Water Resources Research 1965 

Environmental Science and Technology 1967 

Environmental Pollution 1970 

Environmental Health Perspectives 1972 

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Table 3-1. Continued 

Journal title First published 

Journal of Environmental Quality 1972 

Advances in Water Resources 1977 

Journal of Contaminant Hydrology 1986 

Waste Management 1989 

Ecotoxicology and Environmental Safety 1995 

Environmental Toxicology and Chemistry 1996 

Journal of Nanoparticle Research 1999 

Nanomedicine: Nanotechnology, Biology, and Medicine 2005 

Nanotoxicology 2007 

Brief descriptions of these journals are provided below, adapted from each journal’s website. 

	 Water Resources Research (WRR), 1965: a peer-reviewed scientific journal published 

by the American Geophysical Union (AGU). AGU states that WRR is an 

“interdisciplinary journal integrating research in the social and natural sciences of water.” 

	 Environmental Science & Technology (ES&T), 1967: Published by the American 

Chemical Society, the journal combines magazine and research sections. The news and 

features section of ES&T presents objective reports and analyses of the major advances, 

trends, and challenges in environmental science, technology, and policy for a diverse 

professional audience. The research section seeks to publish papers that are particularly 

significant and original. The types of papers published in the research section of ES&T 

are research article, policy analysis, critical review, correspondence (comment/rebuttal), 

and correction/addition (errata). 

	 Environmental Pollution, 1970: an international journal that focuses on papers that 

report results from original research on the distribution and ecological effects of 

pollutants in air, water and soil environments and new techniques for their study and 

measurement. Findings from re-examination and interpretation of existing data are also 

included. The editors are focusing on papers that provide new insights into environmental 

processes and or the effects of pollutants. 

	 Environmental Health Perspectives (EHP), 1972: a monthly journal of peer-reviewed 

research and news published by the U.S. National Institute of Environmental Health 

Sciences, National Institutes of Health, Department of Health and Human Services. 

EHP's mission is to serve as a forum for the discussion of the interrelationships between 

the environment and human health by publishing in a balanced and objective manner the 

best peer-reviewed research and most current and credible news of the field. 

	 Journal of Environmental Quality, 1972: covers various aspects of anthropogenic 

impacts on the environment, including terrestrial, atmospheric, and aquatic systems. 

Emphasis is given to the understanding of underlying processes rather than to monitoring. 

Contributions reporting original research or brief reviews and analyses dealing with some 

aspect of environmental quality in natural and agricultural ecosystems are accepted from 

all disciplines for consideration by the editorial board. 

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	 Advances in Water Resources, 1977: provides a forum for the presentation of 

fundamental scientific advances in the understanding of water resources systems. The 

scope of Advances in Water Resources includes any combination of theoretical, 

computational, or experimental approaches used to advance fundamental understanding 

of surface or subsurface water resources systems or the interaction between these 

systems. 

	 Journal of Contaminant Hydrology, 1986: an international journal publishing scientific 

articles pertaining to the contamination of groundwater. Emphasis is placed on 

investigations of the physical, chemical, and biological processes influencing the 

behavior of organic and inorganic contaminants in both the unsaturated and saturated 

zones. Articles on contamination of surface water are not included unless they 

specifically deal with the link between surface water and groundwater. 

	 Waste Management, 1989: an international journal devoted to the presentation and 

discussion of information on the generation, prevention, characterization, monitoring, 

treatment, handling, reuse and ultimate residual disposition of solid wastes, both in 

industrialized and in economically developing countries. 

	 Ecotoxicology and Environmental Safety, 1995: focuses on integrated mechanistic 

research related to short- and long-term pathways and interactions of substances and 

chemical mixtures in environmental systems and subsystems on their bioavailability, 

circulation, and assimilation in target organisms, as well as biological responses of these 

organisms, and damage mechanisms (endocrine disruption, genotoxicity); and on their 

subsequent fate in the food chain, including humans. 

	 Environmental Toxicology and Chemistry, 1996: seeks to publish papers describing 

original experimental or theoretical work that significantly advances understanding in the 

area of environmental toxicology, environmental chemistry and hazard/risk assessment. 

Emphasis is given to papers that enhance capabilities for the prediction, measurement, 

and assessment of the fate and effects of chemicals in the environment, rather than simply 

providing additional data. 

	 Journal of Nanoparticle Research, 1999: is an academic journal published by Springer. 

It focuses mainly on physical, chemical and biological phenomena and processes in 

structures of sizes comparable to a few nanometers. It covers the synthesis, assembly, 

transport, reactivity, and stability of nanostructures and devices obtained via precursor 

NPs, in various fields such as physics, chemistry, biology and health care. 

	 Nanomedicine: Nanotechnology, Biology, and Medicine, 2005: presents theoretical 

and experimental research results related to nanoscience and nanotechnology in life 

sciences, including Basic, Translational, and Clinical research, and commercialization of 

results. Article formats include Communications, Original Articles, Reviews, 

Perspectives, Technical and Commercialization Notes, and Letters to the Editor. In 

addition, regular features on our website will address commercialization, funding 

opportunities, and societal, Public Health, and ethical issues of nanomedicine. 

	 Nanotoxicology, 2007: is the first peer-reviewed academic journal devoted entirely to the 

publication of research that addresses the potentially toxicological interactions between 

nano-structured materials and living matter. The journal publishes the results of studies 

that enhance safety during the production, use and disposal of ENMs. 

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3.2.2 Reports 

The reports shown in Table 3-2 were produced by governmental agencies and significant 

research institutions. 

Table 3-2. Major Reports on ENM Research Relevant to Environmental Exposure Modeling 

Report title Date Institute Author 

Engineered Nanoparticles: Review of Health 2009 

and Environmental Safety (ENRHES) 

EMERGNANO: A review of completed and 2009 

near completed environment, health and safety 

research on ENMs and nanotechnology 

Nanomaterial Research Strategy 2009 

Approaches to Safe Nanotechnology: 2009 

Managing the Health and Safety Concerns 

Associated with Engineered ENMs 

Sampling and Analysis of ENMs in the 2008 

Environment: A State-of-the-Science Review 

Environmental Fate and Ecotoxicity of 2007 

Engineered Nanoparticles 

Nanotechnology White Paper	 2007 

Nanotechnology: A Research Strategy for 2006 

Addressing Risk 

Edinburgh Napier University, 

EU Joint Research Center, 

Institute for Occupational 

Medicine, Technical University 

of Denmark 

UK Department for 

Environment, Food and Rural 

Affairs (DEFRA) 

US EPA 

National Institute for 

Occupational Safety and 

Health (NIOSH) 

US EPA 

Norwegian Pollution Control 

Authority 

US EPA 

Woodrow Wilson International 

Center for Scholars 

Stone, V. (project 

coordinator) of 

Edinburgh Napier 

University 

Institute for 

Occupational 

Medicine 

Morris, J., Wentsel, R. 

(US EPA) 

NIOSH 

Varner, K. 

Bioforsk 

Nanotechnology Work 

Group (US EPA) 

Maynard, A. 

Brief descriptions of the reports are provided below, adapted from the executive summary. 

	 Engineered Nanoparticles: Review of Health and Environmental Safety (ENRHES), 

2009: presents a comprehensive and critical scientific review of the health and 

environmental safety of four classes of ENMs: fullerenes, CNTs, metals and metal 

oxides. The review considers sources, pathways of exposure to the health and 

environmental outcomes of concern, followed by a risk assessment based upon this 

information. The report includes an illustration of the state-of-the-art as well as on-going 

work, while identifying knowledge gaps in the field. Prioritized recommendations have 

been developed and set in the context of informing policy makers in the development of 

methods to address exposure as it relates to the potential hazards posed by engineered 

NPs, and in the development of appropriate regulation. 

	 IOM’s EMERGNANO project for UK Department for Environment, Food and 

Rural Affairs (DEFRA), 2009: this project involved a detailed review and analysis of 

research carried out worldwide on Environment, Health, and Safety aspects of engineered 

NPs, including issues relating to hazard, exposure and risk assessment and regulation, 

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and made an assessment of how far 18 of 19 Research Objectives from this report’s 

predecessor have been met and which gaps still remain to be filled. 

	 US EPA’s Nanotechnology Research Strategy, 2009: The purpose of the Nanomaterial 

Research Strategy is to guide the EPA’s Office of Research and Development’s program 

in conducting focused research to inform ENM safety decisions that may be made under 

the various environmental statutes for which EPA is responsible. This report focuses on 

four areas that take advantage of EPA’s scientific expertise as well as fill gaps not 

addressed by other organizations. The four research themes are: (1) Identifying sources, 

fate, transport, and exposure, (2) Understanding human health and ecological effects to 

inform risk assessments and test methods, (3) Developing risk assessment approaches, 

and (4) Preventing and mitigating risks. 

	 NIOSH, Approaches to Safe Nanotechnology: Managing the Health and Safety 

Concerns Associated with Engineered ENMs, 2009, this report aims to provide an 

overview of what is known about the potential health hazards of engineered NPs and 

measures that can be taken to minimize workplace exposures. It provides a detailed list of 

potential health concerns, reviews numerous related studies, and offers a number of 

recommendations to improve safety regulations and monitoring. 

	 US EPA’s State-of-the-Science Review, 2008: this state-of-the-science review was 

undertaken to identify and assess currently available sampling and analysis methods to 

identify and quantify the occurrence of ENMs in the environment. The environmental 

and human health risks associated with ENMs are largely unknown, and methods needed 

to monitor the environmental occurrence of ENMs are very limited or nonexistent. 

Because this research is current and ongoing, much of the applicable information is found 

in gray literature (e.g., conference proceedings, communications with research scientists 

and other experts). 

	 Norwegian Pollution Control Authority, Environmental Fate and Ecotoxicity of 

Engineered Nanoparticles, 2008: This report is an overview of the scientific knowledge 

on potential negative effects that engineered NPs may have on the environment. So far, 

scientific evidence show that some NPs have toxic effects under laboratory conditions, 

but practically nothing is known about their mobility and uptake in organisms under 

environmental conditions. This report was written by researchers at Bioforsk Soil and 

Environment. 

	 US EPA’s Nanotechnology White Paper, 2007: the purpose of this paper is to inform 

EPA management of the science needs associated with nanotechnology, to support 

related EPA program office needs, and to communicate these nanotechnology science 

issues to stakeholders and the public. The Nanotechnology Research Framework outlines 

how EPA will strategically focus its own research program to provide key information on 

potential environmental impacts from human or ecological exposure to ENMs in a 

manner that complements other federal, academic, and private-sector research activities. 

	 Woodrow Wilson International Center for Scholars, Nanotechnology: A Research 

Strategy for Addressing Risk, 2006: this report addresses the current state of 

nanotechnology risk research and what needs to be done to help ensure the technology’s 

safe development and commercialization. A strategic research framework is developed 

that identifies and prioritizes what the author believes are the critical short-term issues. 

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Recommendations are made on how a viable strategic research plan might be 

implemented. 

3.2.3 Research Centers 

We identified several leading U.S. research centers that are at the forefront of studies on the 

behavior of ENMs in the environment. Each of these research centers are the recipients of grants 

from the National Science Foundation and/or the EPA, and are performing cutting edge research 

on a variety of technical issues central to understanding the fate and transport characteristics of 

ENMs in the environment. We recognize that there are also other research centers in the U.S. as 

well as international research centers (especially in the EU) that are involved in ENM research; 

however, our search strategy did not identify other centers (domestic or international) engaged in 

significant research that was specific to environmental fate and transport modeling of ENMs in 

aquatic systems. In addition, the intent was to identify centers that focused on the environmental 

behavior of ENMs rather than on characterizing ENM properties or developing engineering 

applications for nanomaterials. Table 3-3 lists the university and the name of the research center, 

and provides a link to the center’s website. 

Table 3-3. Key Research Centers for Nanotechnology Research 

Academic Institution Research Center	 Web Address 

Duke University Center for Environmental Implications of 

NanoTechnology (CEINT) 

http://www.ceint.duke.edu/ 

Rice University Center for Biological and Environmental 

Nanotechnology (CBEN) 

http://cben.rice.edu/ 

University of California, Los 

Angeles 

University of California Center for 

Environmental Implications of 

Nanotechnology (UC CEIN) 

http://cein.cnsi.ucla.edu/pages/ 

Brief descriptions of each research center are provided below, adapted from the center’s website. 

	 Duke University’s CEINT was created in 2008 with funding from the National Science 

Foundation and the US EPA. As described on its website, CEINT is elucidating the 

relationship between a vast array of ENMs and their potential environmental exposure, 

biological effects, and ecological consequences. Headquartered at Duke University, 

CEINT is a collaboration between Duke and a number of leading universities and 

researchers (comprehensive list found here: http://www.ceint.duke.edu/participatinginstitutions ). CEINT performs fundamental research on the behavior of nano-scale 

materials in laboratory and complex ecosystems. Research includes all aspects of ENM 

transport, fate and exposure, as well as ecotoxicological and ecosystem impacts. CEINT 

is developing risk assessment models to provide guidance in assessing existing and future 

concerns surrounding the environmental implications of ENMs. This address provides a 

detailed list of CEINT’s partners and participating institutions: 

	 CBEN’s mission is to discover and develop ENMs that enable new medical and 

environmental technologies. The mission is accomplished by the following: 

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o	 Fundamental examination of the wet/dry interface between ENMs, complex aqueous 

systems, and ultimately our environment (Theme 1). 

o	 Engineering research that focuses on multifunctional NPs that solve problems in 

environmental and biological engineering (Themes 2, 3). 

o	 Educational programs that develop teachers, students, and citizens who are well 

informed and enthusiastic about nanotechnology. 

o	 Innovative knowledge transfer that recognize the importance of communicating 

nanotechnology research to the media, policymakers, and the general public. 

The Center’s research focuses on investigating and developing nanoscience at the wet/dry 

interface. Water, the most abundant solvent present on Earth, is of unique importance as 

the medium of life. The Center’s research activities explore this interface between ENMs 

and aqueous systems at multiple length scales, including interactions with solvents, 

biomolecules, cells, whole-organisms, and the environment. These explorations form the 

basis for understanding the natural interactions that ENMs will experience outside the 

laboratory, and also serves as foundational knowledge for designing biomolecular/ENM 

interactions, solving bioengineering problems with nanoscale materials, and constructing 

nanoscale materials useful in solving environmental engineering problems. 

	 UC CEIN is the sister center to Duke’s CEINT. Headquartered at the University of 

California, Los Angeles (UCLA), it includes the University of California at Santa 

Barbara and other UC partners. According to UC CEIN’s website, UC CEIN will explore 

the impact of libraries of engineered ENMs on a range of cellular life forms, organisms 

and plants in terrestrial, fresh water and sea water environments. By being able to predict 

which ENM physicochemical properties are potentially hazardous, the UC CEIN will be 

able to provide advice on the safe design of engineered ENMs from an environmental 

perspective. While UCLA serves as the lead campus for the UC CEIN, researchers from a 

range of other institutions and organizations are involved in the UCLA-based UC CEIN 

research, and a comprehensive list of domestic and international partners may be found 

here: http://cein.cnsi.ucla.edu/pages/institutions. 

3.2.4 Informational Web Sites 

The publicly-available websites shown in Table 3-4 were identified because they provided 

valuable information related to fate and transport of engineered ENMs in aquatic ecosystems. 

The table provides a brief summary of their mission along with the current web address. 

Table 3-4. Publically Available Websites Providing Valuable Information ENM Exposure Modeling 

Website Mission Web Address 

National Nanotechnology coordinate Federal nanotechnology research http://www.nano.gov/ 

Initiative (NNI) and development 

International Council on improve communication between http://icon.rice.edu/ 

Nanotechnology (ICON) stakeholders involved with risk assessment 

and research 

NanoRISK newsletter addressing nanotechnology risk http://www.nanorisk.org/ 

assessment 

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Table 3-4. Continued 

Website Mission Web Address 

NanoScienceWorks not-for-profit portal for the nanoscience 

research community 

http://www.nanoscienceworks.org/ 

Nanotechnology Now 

(NN) 

created to serve the information needs of 

business, government, academic, and public 

communities 

http://www.nanotech-now.com/ 

Nanowerk serve as portal for nanotechnology and 

nanosciences; provide links and editorial 

content 

http://www.nanowerk.com/ 

Project on Emerging 

Nanotechnologies 

ensure that risks are minimized, consumer 

engagement remains strong, potential 

benefits realized 

http://www.nanotechproject.org/ 

Brief descriptions of these websites are provided below, adapted from each website. 

 http://www.nanoscienceworks.org/ 

NanoScienceWorks.org is a not-for-profit community portal for the nanoscience research 

community. The website provides a comprehensive variety of nanotechnology and 

nanoscience-related information, in addition to an encyclopedia and a free monthly 

newsletter. 

“NanoScienceWorks.org serves the nano community as a gateway to the news, journals, 

books, and articles that support and drive nano research and development. We invite you to 

explore these resources, view our slidecasts, and join our networking database of nanoinvolved people and institutions from around the world.” 

 http://icon.rice.edu/ 

The creation of a sustainable nanotechnology industry requires meaningful and organized 

relationships among diverse stakeholders. The International Council on Nanotechnology 

(ICON) aims at providing such interactions for a broad set of members. Managed by Rice 

University’s Center for Biological and Environmental Nanotechnology, 

ICON activities promote effective nanotechnology stewardship through risk assessment, 

research and communication. By pooling the resources of the nanotechnology industry, 

government and non-government organizations and academia, ICON can cost-effectively 

provide a wide range of synergistic projects that serve the interests of all stakeholders. CBEN 

provides a financial and administrative structure for ICON, such that ICON members are also 

members of CBEN. 

 http://www.nano.gov/ 

The National Nanotechnology Initiative (NNI) is the program established in fiscal year 2001 

to coordinate Federal nanotechnology research and development. The NNI provides a vision 

of the long-term opportunities and benefits of nanotechnology. By serving as a central locus 

for communication, cooperation, and collaboration for all Federal agencies that wish to 

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participate, the NNI brings together the expertise needed to guide and support the 

advancement of this broad and complex field. 

 http://www.nanowerk.com/ 

Nanowerk is committed to educate, inform and inspire about nanosciences and 

nanotechnologies. As the leading nanotechnology and nanosciences portal, nanowerk.com 

delivers useful, entertaining and cutting-edge information from all things nano. Nanowerk 

has become the premier nanotechnology portal due to the depth, rich scope and relevance of 

our unique editorial content and the comprehensive resources that we put at users' fingertips. 

Editorial content Scientists appreciate the publicity they receive through our articles and in 

turn help spread the word about Nanowerk among their colleagues and scientific 

communities. On average Nanowerk run between 70-100 news articles every week. T news 

section is separated into Business News and Research & General News. Nanowork also 

produces a newsletter deals explicitly with the risks involved in nanotechnology. “Much of 

nanotechnology today is about producing nanoscale particles that, due to their size, have 

significantly more catalytic active surfaces. [This newsletter tries] to support a debate on the 

very real issues that we are facing today: the fact that engineered ENMs such as carbon 

nanotubes or titanium dioxide particles are finding their way from scientists’ laboratories into 

commercial products and we don’t understand the risks they pose to health and 

environment.” Nanorisk is a bi-monthly newsletter published by Nanowerk LLC. 

 http://www.nanotechproject.org/ 

The Project on Emerging Nanotechnologies was established in April 2005 as a partnership 

between the Woodrow Wilson International Center for Scholars and the Pew Charitable 

Trusts. The Project is dedicated to helping ensure that as nanotechnologies advance, possible 

risks are minimized, public and consumer engagement remains strong, and the potential 

benefits of these new technologies are realized. The Project on Emerging Nanotechnologies 

collaborates with researchers, government, industry, NGOs, policymakers, and others to look 

long term, to identify gaps in knowledge and regulatory processes, and to develop strategies 

for closing them. 

 http://www.nanotech-now.com/ 

Nanotechnology Now (NN) covers future sciences such as Nanotechnology, Molecular 

Nanotechnology (MNT), MicroElectroMechanical Systems (MEMS), 

NanoElectroMechanical Systems (NEMS), Nanomedicine, Nanobiotechnology, 

Nanoelectronics, Nanofabrication, Computational Nanotechnology, Quantum Computers, 

and Artificial Intelligence. NN was created to serve the information needs of business, 

government, academic, and public communities. And with the intention of becoming the 

most informative and current free collection of nano reference material. We will cover: 

related future sciences, issues, news, events, and general information, and make this a place 

to come for information, stimulating debate, and research info. 

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	 http://www.nanorisk.org/ 

This newsletter deals explicitly with the risks involved in nanotechnology. “Much of 

nanotechnology today is about producing nanoscale particles that, due to their size, have 

significantly more catalytic active surfaces. [This newsletter tries] to support a debate on the 

very real issues that we are facing today: the fact that engineered ENMs such as carbon 

nanotubes or titanium dioxide particles are finding their way from scientists’ laboratories into 

commercial products and we don’t understand the risks they pose to health and 

environment.” Nanorisk is a bi-monthly newsletter published by Nanowerk, LLC. 

3.3 Summary of Recent Reports and Compendia 

As compared to chemicals in their conventional form, the unique properties of ENMs have led to 

concerns about the potential health and ecological risks that might be associated with exposure to 

ENMs following environmental release. Given the rapid growth in the manufacture and use of 

ENMs, there have been a number of recent research initiatives and published reports that focus 

on developing basic information about ENMs. In general, these reports fall into one of the 

following four categories: 

1.	 Reports that characterize the basic physical and chemical properties of ENMs 

2.	 Reports that address aspects of modeling (e.g., particle transport) relevant to the 

predictive risk assessment of environmental releases of ENMs 

3.	 Reports that summarize the state of current knowledge on the health and 

environmental risk assessment of ENMs 

4.	 Reports that propose governance frameworks for the handling and safe management 

of ENMs. 

The first category covers a wide range of research, much of which is geared towards the 

manufacturing aspects of ENMs. Although this information on ENMs is critical to fate and 

transport modeling, it was not the focus of this effort and, to some degree, was included in EPA’s 

state-of-the-science review on sampling and analysis of ENMs in the environment (US EPA, 

2008). As discussed in Section 1, the second category was the primary focus of this report, and 

the results of the model reviews are presented in Sections 4.2 and 4.3. Sections 3.3.1 and 3.3.2 

summarize recent reports and compendia that fall into the last two categories. Section 3.3.3 

addresses recent reports and compendia that pertain to the risk assessment of ENMs but were 

beyond the scope of this report. 

3.3.1 Summary Reports on the Current State of Knowledge 

This section describes several recent articles on the health and environmental risk assessment of 

ENMs that provide significant insight into the current state of risk assessment science as it 

pertains to ENMs. Table 3-5 lists selected articles that we believed provide the most salient 

discussion of issues, uncertainties, and available data for the risk assessment of ENMs. It should 

be emphasized, again, that ENMs cover a wide range of compounds and that our use of this term 

should not be interpreted as a science-based simplification of nanoscale materials. Rather, it is a 

convention adopted for the purposes and readability of this report. 

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Table 3-5. Summary Reports on ENM Research Relevant to Environmental Exposure Modeling 

Report title Date Author 

The known unknowns of nanomaterials: Describing and characterizing 2009 Grieger et al. uncertainty within environmental, health, and safety risks 

Redefining risk research priorities for nanomaterials 2010 Grieger et al. 

Nanomaterial risk assessment and risk management: Review of regulatory 2009 Linkov, I., Satterstrom, F.K. frameworks 

Decreasing Uncertainties in Assessing Environmental Exposure, Risk, and 2009 Wiesner et al. Ecological Implications of Nanomaterials 

	 Grieger et al. (2009) address the shortfalls surrounding the modeling of ENMs by 

identifying the uncertainties associated with these models. By discovering these known 

unknowns, the paper intends to generate a pathway to shrink the knowledge gaps that 

currently persist in the modeling of ENMs. The article identifies 31 reports and articles 

published by leading scientists and authorities for ENMs, classifying the uncertainties 

addressed in each report and presenting them in a table that categorizes each uncertainty 

with respect to environmental health and safety. From the assessment, the article 

concludes the current level of knowledge to be in an early state in need of additional 

studies to decrease knowledge gaps. The article thus estimates that current quantitative 

risk assessments may produce premature results. Lastly, the article recommends that the 

focus of the research be given to the assessment and development of test procedures and 

equipment to fully characterize ENMs so that uncertainties will be most effectively 

reduced in the near term. 

	 Grieger et al. (2010) discuss the recent advancement in the risk assessment approach for 

ENMs. The authors argue that due to the timeframe for the current approach to 

responsible development of ENMs, alternative approaches must be explored. The article 

evaluates possible alternative approaches and adaptive evaluation frameworks 

(precautionary matrices, multi-criteria decision analysis, etc.) and governance 

frameworks (International Risk Governance Council, Environmental Defense and 

Dupont, etc.) currently in place. Intended for decision makers, this report suggests the 

need for surveillance of ENMs in light of the high levels of uncertainty with ENMs. 

	 Linkov and Satterstrom (2009) review the current ENM risk management frameworks 

using a regulatory pyramid. Thirteen frameworks and related documents were reviewed 

in all, including the USEPA white paper on nanotechnology (US EPA, 2007), DEFRA 

(2005), and SCENIHR (2005). The authors reviewed the frameworks by the following 

categories: (1) science and research aspects; (2) legal and regulatory aspects; (3) social 

engagement and partnerships; and (4) leadership and governance. The regulatory pyramid 

consists of 4 levels relating to the time frames of immediate, short term, medium term, 

and long term ordered from bottom to top respectively. The authors find that appropriate 

tools in the bottom (immediate) are largely lacking and recommend an adaptive 

framework be utilized to manage nanotechnology risk. 

	 Wiesner et al. (2009) address the uncertainties surrounding environmental exposure, risk 

and ecological implications of ENMs. The authors characterize these uncertainties by 

asking 4 questions concerning: (1) ENM properties and environmental conditions that 

45 
3.0 Review Methodology for Relevant Models and Methods 

control the spatial and temporal distribution of ENMs in the environment; (2) 

fundamental differences between natural, incidental and manufactured ENMs; (3) nano 

effects on bioavailability, toxicity, and other environmental end points; and (4) effects 

ENMs may have on ecosystems; The authors then use the findings from these 4 questions 

to suggest methods for conducting risk assessments of emerging ENMs. 

3.3.2 Governance Frameworks 

This section summarizes recent proposals to govern the production and risk management of 

ENMs in the face of significant uncertainty due to a lack of research data on the environmental 

behavior and effects of ENMs. Renn (2008) define risk governance as a process that includes the 

totality of actors, rules, conventions, processes and mechanisms concerned with how relevant 

risk information is collected, analyzed and communicated, and how management decisions are 

taken. Table 3-6 lists the articles and reports that represent the most recent governance 

proposals. 

Table 3-6. Governance Frameworks on the Production of Nanomaterials 

Report title Date Author 

Essential features for proactive risk management 2009 Murashov and Howard 

Strategic approaches for the management of environmental ris

uncertainties posed by nanomaterials 

k 2009 Owen et al. 

Moving toward exposure and risk evaluation of nanomaterials:

and future directions 

 challenges 2009 Thomas et al. 

The Framing Nano Governance Platform: A New Integrated Approach to the 2009 Widmer et al. 

Responsible Development of Nanotechnologies 

	 Murashov and Howard (2009) propose a proactive approach to the management of 

occupational health risks based on six guidelines. The authors suggest first to utilize 

qualitative risk assessments based on expert judgments and extrapolations from existing 

data for similar materials. Secondly, develop strategies that quickly adapt to 

accumulating risk assessment information and to refine risk management requirements. 

The authors promote the ISO (International Organization for Standardization) Concept 

Database, which aims to update risk databases in real time to aid in decision making. The 

third guideline is to embody an appropriate level of precaution due to the lack of 

qualitative risk assessment because of the amount of uncertainty present. The fourth 

guideline is to generate global governance so that risk management is equivalent across 

the spectrum of emerging technology firms. The fifth guideline suggests the ability to 

elicit strong voluntary cooperation among firms. Lastly, the authors suggest that there be 

a high level of stakeholder involvement based on the belief that involving all stakeholders 

will broaden the knowledge of potential benefits and risks as they pertain to ENMs and 

their production. 

	 Owen et al. (2009) identifies one area of uncertainty surrounding the modeling of 

ENM—the complexity of their behavior in natural systems. The article then outlines two 

methods to address this issue: (1) a hazard-driven approach; and (2) an exposure-driven 

approach. The hazard-driven approach suggests a large data gathering task utilizing 

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3.0 Review Methodology for Relevant Models and Methods 

extensive toxicity assessments over many types of organisms using endpoints that would 

cover all potential exposure routes. The exposure-driven approach would focus on the 

development and subsequent validation of a conceptual model of exposure for the ENM 

of concern by utilizing a life cycle assessment that considers sources and pathways of 

exposure during production, use and end-of-life. The model would need to incorporate 

properties specific to the transport of ENMs rather than properties used for traditional 

chemical assessments. The article concedes that both approaches would depend on a 

considerable time lag between data gathering and resultant decision making based on the 

information obtained. Therefore, the article suggests that environmental surveillance 

approaches could be used in the near term to act as a safety net, but also advocates that it 

is not yet clear how fit for purpose this monitoring may be. 

	 Thomas et al. (2009) implicate the need for exposure assessment due to the explosive 

growth of nanotechnology. This call for action stresses this need as a critical factor in the 

risk assessment of ENMs, which is important in fostering their sustainable development. 

The article argues that an assessment that identifies and characterizes the contact and 

uptake of compounds into organisms which may result in health effects is essential in 

eliminating harmful chemical exposures of humans and the environment. The authors 

explore the avenues of exposure from a life cycle perspective, exposure metrics, and 

exposure assessment activities relevant to ENMs to explain the shortcomings of current 

efforts, as well as justify the need for more action. 

	 Widmer et al. (2009) describe the FramingNano governance platform, which was 

undertaken to create proposals for a workable governance platform bases on: (a) the 

analysis of regulatory processes of nanotechnology; (b) consultation with stakeholders to 

define key issues; and (c) dissemination of information on governance of nanotechnology 

to allow input to its development. The overall objective of the FramingNano Governance 

Platform is to promote responsible development of nanotechnology without hindering 

innovation and commercial growth. As a result, the platform proposes guidance on 4 

different levels on nanotechnology development: (1) Technical and organizational – 

prioritizing research needs; (2) Communication and dialogue – effective information 

passing to aid in policy implementation; (3) Institutional – management of policy for 

responsible development of nanotechnologies; and (4) International harmonization – to 

aid in global governance. The research conducted by the FramingNano project concluded 

that governance and regulation of nanotechnologies is a dynamic process and must be 

continuously adapted as emerging scientifically relevant information becomes available. 

3.3.3 Other Relevant Nanomaterial Reports and Compendia 

This section (Table 3-7) presents other articles that may be of importance to the progression of 

nanotechnology risk assessment but fell outside the scope of this review (i.e., toxicology). We 

did not report the summaries of these reports and compendia, but recommend these articles for 

readers that wish to gain insight on related aspects of risk assessment. 

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3.0 Review Methodology for Relevant Models and Methods 

Table 3-7. Other Relevant Reports and Compendia on the Research of Nanomaterials. 

Report title Date Author 

Factors Influencing the Partitioning and Toxicity of Nanotubes in the Aquatic 2008 Kennedy et al. 

Environment 

Ecotoxicity of engineered nanoparticles to aquatic invertebrates: a brief 2008 Baun et al. 

review and recommendations for future toxicity testing 

Nanoparticles: Their potential toxicity, waste and environmental 2009 Bystrzejewska-Piotrowska 

management (literature review) et al. 

Safety Assessment for Nanotechnology and Nanomedicine: Concepts of 2010 Oberdörster et al. 

Nanotoxicology 

48 
4.0 Model Reviews 

Chapter 4.0 

Model Reviews 

In this section of the state-of-the-science report we evaluate several modeling approaches and 

specific models according to their relevance and applicability for predicting ENM transport. The 

focus of the review is on aquatic and terrestrial systems, including transport in surface water, 

sediments, groundwater, and soils. The evaluation also considers biological uptake and 

multimedia models (air models are not considered). In addition, the focus of this review is on 

organic-based nanomaterials. Many of the approaches reviewed are relevant to other 

nanomaterials (notably metals); however, evaluation of these materials can require additional 

modeling tools (such as geochemical speciation modeling) that were outside the scope of this 

review. 

Section 4.1 presents the model evaluation framework, which provides a systematic approach for 

reviewing information about models and is based on National Research Council guidelines 

(NRC, 2007). The remainder of Section 4 presents results of the model reviews. Summary 

descriptions of specific models and approaches are provided in this section. Appendix B 

provides more detailed reviews for the models and approaches that show more promise for ENM 

transport modeling. The model reviews are divided into two main categories: fate and transport 

models (Section 4.2) and alternative approaches (Section 4.3). 

4.1 Model/Method Evaluation Criteria 

The model evaluation framework implemented for this assessment provides a systematic and 

consistent approach for reviewing and summarizing information about models. The review 

categories were developed to be consistent with the National Research Council paper, Models in 

Environmental Regulatory Decision Making (NRC, 2007). In this report, the NRC assesses how 

models support the EPA’s environmental regulatory process. The development and application 

of regulatory models is described along with recommended considerations for selecting and 

using models to support EPA programs. The NRC document describes criteria for evaluating 

whether a model and its results provide a sound basis for regulatory decision making. Using the 

NRC document as a guide, we compiled a series of key considerations for model evaluation and 

organized them into the general categories used in the model reviews. Appendix B provides 

model reviews that are structured according to these categories. We have provided reviews for 

models that have been developed for and/or applied to ENMs as well as other models that have 

potential applicability (in their present form) for the evaluation of ENM transport in the 

environment. 

Purpose and Scope. What is the model purpose? What transport media are considered (i.e., 

groundwater, surface water, multimedia)? What processes are simulated? What is the conceptual 

basis and what are the primary assumptions? What spatial and temporal scales does the model 

consider? What kind of results does the model produce (e.g., media concentrations, risk, 

probability)? 

Background and History. How extensively has the model been used and applied (e.g., a single 

academic study versus an established model used for extensive regulatory decision making)? 

49 
 

 

4.0 Model Reviews 

What kind of peer review has the model been subjected to? Has the model been verified through 

comparison with other models or laboratory experimental results? Has the model been validated 

through comparisons with measured environmental data? 

Complexity. What physical and chemical processes are considered? What is the mathematical 

representation (e.g., analytical or numerical solution)? How extensive are the input data 

requirements? 

Consideration of Uncertainty. Does the model account for uncertainty and, if so, how? Is the 

model process based or statistical? Is the model deterministic or probabilistic? Does the model 

implementation include tools for sensitivity and/or uncertainty analysis? 

Availability and Usability. Is a user-friendly interface available? Does the model rely on 

proprietary algorithms and/or user interfaces? Is the model documentation complete and 

transparent? Is the source code available for potential enhancements/modifications? 

Applicability to ENM Behavior. Does the model consider key processes and chemical 

properties relevant for the specific environmental medium or media considered in determining 

ENM behavior (e.g., aggregation/disaggregation, attachment/detachment in aqueous systems)? 

How does the model account for the gaps in the data that are necessary for traditional models? 

How well does this model respond to updated scientifically relevant information? What kind of 

information can be gathered from these types of models? What interpretations can be made from 

the findings of these types of models? 

4.2 Review of Environmental Fate and Transport Models 

In this section we describe several environmental fate and transport models and their potential 

applicability for evaluating ENMs. The model categories considered are based primarily on 

NRC’s review of regulatory modeling practices at EPA (NRC, 2007). There is overlap in model 

constructs between some of these categories. For example porous media transport mechanisms 

may be important both for sediments in a surface water model as well as a groundwater transport 

model. Nevertheless, the model categorization used in this document is consistent with the 

typical structure and scope of many existing environmental fate models. 

Models within the following general categories are considered: 

 Surface water models. Most surface water quality models account for interactions 

between surface water and underlying sediments (e.g., sedimentation and resuspension). 

Thus, this category may include models that evaluate fate and transport within the water 

column as well as within underlying sediments. 

 Subsurface models. This category includes models that simulate environmental fate 

within soils, the unsaturated zone (below the soil zone and above the groundwater table), 

and saturated groundwater. 

 Biological uptake models. This category focuses on models predicting the uptake of 

chemicals into biological organisms and associated potential bioaccumulation. 

50 
4.0 Model Reviews 

	 Multimedia models. These models account for processes and mass transfer across 

multiple environmental media. There is redundancy between multimedia models and the 

other model categories. In fact, some multimedia modeling systems explicitly include 

media-specific submodels (e.g., the EXAMS surface water model within the Multimedia, 

Multipathway, Multireceptor Exposure and Risk Assessment [3MRA] modeling system). 

Nevertheless, multimedia models deserve focused consideration given their extensive use 

in many traditional environmental risk assessments. 

Within each of the model categories listed above, we have evaluated models of three types: 

	 Models specific to nanomaterials. These models were developed for and/or used 

specifically to evaluate ENM transport. The models considered were identified during the 

literature review documented in Section 3. 

	 Established regulatory models. Given the focus of this assessment on supporting EPA’s 

evaluation of models for regulatory support, we have provided a discussion of several 

existing, established models currently used by EPA to evaluate chemical fate in the 

environment. In most cases, these models are not appropriate for modeling ENMs in their 

present form, because they do not consider critical properties and processes for ENMs. 

The discussion will highlight limitations of such models and modeling approaches. The 

regulatory models evaluated correspond to those listed in NRC’s review of EPA 

regulatory modeling practice (NRC, 2007). Although these models represent only a 

subset of the available models used by EPA, they are among the most widely applied, and 

they are representative of typical risk assessment modeling practice by the agency. 

	 Other models. In some cases, existing models developed for other contaminants may 

provide potentially useful approaches for simulating ENM environmental transport. For 

example, given their particulate nature, modeling approaches for colloid transport in 

porous media may be relevant to nanoparticles. 

4.2.1 Surface Water Models 

As discussed in Section 2.4.1, many traditional environmental fate models of chemicals in 

aquatic systems consider some or all of the following important processes: dissolution, 

volatilization, adsorption, biological uptake, photolysis, hydrolysis, and biodegradation (see 

Section 2.4.1). Less common are environmental fate models that consider processes of 

aggregation, attachment, and sedimentation, all processes critical to understanding and predicting 

the environmental fate of ENMs (Sections 2.3 and 2.4). Models predicting ENM behavior in 

aquatic environments should account for these critical processes related to particulates in natural 

systems. The evaluations of surface water fate and transport models in this section primarily 

consider whether models account for these key processes. 

4.2.1.1 Surface Water Models of Nanomaterials 

This section describes surface water models identified in the literature that have been developed 

and/or applied specifically for the evaluation of ENM fate in the environment. Appendix B 

provides more detailed reviews of each of these models. 

51 
4.0 Model Reviews 

Mackay et al. (2006) developed a stochastic probability model predicting the environmental 

stability of nanoparticle suspensions in aqueous solutions and the associated uncertainty. The 

model simulates settlement utilizing critical buoyancy properties and the Boltzmann equation. 

Rates of aggregation are estimated based on molecular collision and adhesion coefficients. 

Appendix B provides a more detailed review of the Mackay et al. (2006) model. 

Boncagni et al. (2009) implemented an experimental study of the exchange of titanium dioxide 

nanoparticles between streams and streambed sediments. They evaluated the degree of 

aggregation and sedimentation under a range of conditions (pH and water flow velocity). They 

utilized the process based model of colloids in surface water systems developed by Packman et 

al. (2000) to interpret the results. The model was formulated based on advective pumping theory, 

colloid filtration, and settling. The Packman model is discussed further in Section 4.2.1.3 and is 

reviewed in Appendix B. 

Koelmans et al. (2009) performed a compartmental modeling analysis of mass transfer between 

surface water and sediments, considering particulate transport processes. The model estimated 

steady state concentrations of carbon-based nanoparticles by accounting for processes of 

sedimentation, aggregation, degradation, and burial in deeper sediment layers. The model 

assumes: (1) a distinct, mixed biologically active layer; (2) transport of manufactured carbon 

nanoparticles to sediment is through sedimentation; and (3) the removal of manufactured carbon 

nanoparticles can be modeled as a first order decay process. Their analysis suggested that 

concentrations of manufactured carbon-based nanoparticles in aquatic sediments will likely be 

negligible relative to levels of black carbon nanoparticles (incidental ENMs generated as 

combustion byproducts). Appendix B provides a more detailed review of the Koelmans et al. 

(2009) model. 

4.2.1.2 Regulatory Surface Water Models 

This section discusses several established surface water models that have been used in risk 

assessments to support EPA regulatory programs. The models evaluated correspond to those 

listed in NRC’s review of EPA regulatory modeling practice (NRC, 2007). Although these 

models represent only a subset of the available surface water models used by EPA, they are 

among the most widely applied, and they are representative of typical risk assessment modeling 

practice by the agency. 

The three reviewed models are moderate complexity, conceptual models (see Box 4-1). The 

models do consider some of the key processes characterizing particulate transport in aqueous 

systems, including sedimentation, resuspension, and particulate advection. However, their 

current applicability for predicting ENM behavior is significantly limited by lack of knowledge 

and lack of available, empirical data characterizing ENMs. Furthermore, it is as yet unknown 

whether the associated lumped-parameter formulations will be adequate for simulating the 

environment behavior of ENMs or whether alternative modeling constructs will be required. For 

example, if sufficient empirical knowledge becomes available to support model 

parameterization, can mass transfer between the water column and underlying sediments be 

adequately modeled using a lumped mass-transfer-rate formulation? Given the limited 

applicability of regulatory surface water models for evaluating ENMs at this time, we provide 

52 
 

 

4.0 Model Reviews 

only a brief review of each model 

and have not included more detailed 

reviews of these models in 

Appendix B. 

HSPF (The Hydrological 

Simulation Program–FORTRAN) 

is a modeling package for 

simulating watershed hydrology and 

water quality. HSPF adopts a basinscale approach, incorporating 

pollutant source models and fate 

and transport in one dimensional 

stream channels. The model 

accounts for watershed hydrology, 

including sediment runoff processes 

along with in-stream hydraulic and 

sediment-chemical interactions. 

Simulation results include time 

series of the runoff flow rate, 

sediment load, and contaminant 

concentrations, as well as water 

quantity and quality. HSPF 

considers up to three sediment types 

(sand, silt, and clay) in addition to a 

single dissolved organic chemical 

and transformation products of that 

chemical. The model considers the 

following reaction processes: 

hydrolysis, oxidation, photolysis, 

biodegradation, volatilization, and 

sorption. The model accounts for 

particulate settling and potential 

resuspension. Resuspension is 

modeled based on the shear stress at 

the sediment water interface and the 

capacity to transport particulates at 

a particular flow. Mass transfer with 

sediments is modeled as 

sorption/desorption and 

deposition/scour processes. Fate and 

transport mechanisms within the 

sediments (e.g., pore water flow, 

bioturbation) are not modeled. 

HSPF has been used in hydrologic 

Box 4-1. Complexity and Empiricism in Environmental Risk 

Assessment Modeling 

Fate and transport models used in risk assessment often do not 

include extremely detailed, mechanistic formulations of fate 

processes. Rather, these models often encapsulate detailed 

processes using simpler formulations. One example would be a 

surface water model that describes the mass transfer between a 

water column and underlying sediments using a single mass 

transfer rate even though the underlying processes may be quite 

varied and complex (sedimentation, scouring, adsorption, 

bioturbation, diffusion, pumping exchange). In this case, the mass 

transfer rate could be considered a lumped parameter designed to 

capture the cumulative effect of a range of relevant processes. 

Models that adopt such an approach have been referred to as 

conceptual models in that the model provides a conceptual 

framework for the underlying processes (Wainright and Mulligan, 

2004). 

One can consider a continuum of model types from empirical 

models that rely only on data with no underlying conceptualization 

of the system (e.g., a fitted regression equation) to physical models 

that are based on a detailed, mechanistic and spatially explicit 

understanding of the underlying processes. In general, physical 

models are fully distributed spatially; empirical models are fully 

lumped with no explicit spatial representation. Conceptual models 

(i.e., most risk assessment fate and transport models) are typically 

semi-distributed, falling in the middle of the continuum between 

physical and empirical models. 

Conceptual models may appear to compromise scientific rigor by 

ignoring known complexities. However, such an approach is often 

necessary in order to predict environmental behaviors given the 

typically extreme variability, complexity, and uncertainty associated 

with natural systems. In other words, additional scientific detail and 

complexity do not necessarily increase model reliability, particularly 

when dealing with highly variable and uncertain systems. However, 

it is generally not possible to make predictions using conceptual 

models a priori (i.e., based on theoretical considerations alone). 

Rather, conceptual models must be grounded in empirical evidence 

in order to produce realistic predictions of environmental behavior. 

For example, model input parameters such as mass transfer rates 

and reaction terms may be adjusted until model predictions are 

reasonably close to measured data (i.e., model calibration). In 

addition, conceptual models may parameterized using regressions 

previously developed from empirical data (e.g., correlations relating 

surface water volatilization rates to wind speed). Large-scale (e.g., 

national) risk assessments may not have sufficient available data to 

support model calibration; nevertheless, the model input 

distributions are typically based on empirical knowledge gained 

through laboratory experimentation and field data collection. 

In summary, models often used in regulatory risk assessment are 

moderately complex, conceptual models. Parameterization of such 

models must be based on empirical evidence in order to make 

reliable predictions. Very limited knowledge (experimental and field 

data) of ENM transport is available to support the use of such 

modeling frameworks at this time. 

53 
4.0 Model Reviews 

and water quality evaluations, including analysis of pesticide runoff and agricultural best 

management practices. 

WASP provides a dynamic compartment-modeling approach for aquatic systems, including both 

the water column as well as the underlying sediments. The model can evaluate 1, 2, and 3 

dimensional systems and a variety of pollutant types, including particulates. The model considers 

processes of advection, dispersion, point and diffuse mass loadings and boundary mass transfers. 

Sediment transport processes include advection, dispersion, settling (and sedimentation), as well 

as erosion to the water column from the sediment layer. Example uses of the WASP model 

include evaluations of eutrophication, phosphorus loading, bacterial contamination, as well as 

PCB, VOC, and heavy metal pollution. 

QUAL2K provides a relatively simple model for simulating flow and water quality in rivers and 

streams. The model has been used to evaluate the environmental impact of pollution discharges 

along rivers from point and non-point sources. The model has been used extensively to support 

National Pollutant Discharge Elimination System (NPDES) wastewater discharge permit 

applications, total maximum daily load (TMDL) studies, and environmental impact statements 

for proposed development. A wide range of chemical and biological pollutants within a river can 

be modeled, including carbonaceous biochemical oxygen demand, nitrogen and phosphorus, 

suspended solids, algae, pathogens, phytoplankton and detritus. Physical-chemical processes 

simulated by the model include water quality kinetics, chemical equilibrium, advection, 

dispersion, settling, and interactions with the atmosphere (re-aeration) and riverbed (sediment 

oxygen demand). Water quality parameters predicted throughout the modeled river domain 

include dissolved oxygen concentration, pH, salinity and temperature, in addition to the various 

pollutant quantities. 

4.2.1.3 Other Surface Water Models 

This section describes surface water models that appear to have potential utility for modeling 

ENMs but that were not developed specifically for that purpose. 

Packman et al. (2000) developed a process-based model to simulate the transport of colloids in 

surface water systems, including the mass exchange between the water column and underlying 

sediments. The model accounts for particle settling and sedimentation processes as well as 

pumping exchange of particulates due to water flow through sediment bedforms induced by 

stream flow. Their formulation also considered particulate filtration in the porous bed sediments. 

Using their model, solute and colloid exchanges may be predicted without fitting coefficients and 

only requiring measurable hydraulic and particle parameters as inputs. One limitation of the 

model is that it does not account for changes in the particulate suspension (e.g., due to time 

varying pH or ionic strength). The Packman et al. (2000) model was used by Boncagni et al. 

(2009) to simulate the behavior of titanium dioxide nanoparticles in surface water systems (see 

Section 4.2.1.1). Appendix B provides a more detailed review of the model. 

54 
4.2.2 Subsurface Models 

As discussed in Section 2.4.2, many traditional 

environmental fate models of chemicals in 

terrestrial systems consider some or all of the 

following important processes: dissolution, 

volatilization, adsorption to organic and inorganic 

matter, biological uptake, photolysis, hydrolysis, 

and biodegradation. Less common are 

environmental fate models that consider 

processes of aggregation, attachment, and 

sedimentation, all processes critical to 

understanding and predicting the environmental 

fate of ENMs. Models predicting ENM behavior 

in the terrestrial environments should account for 

these critical processes related to particulates in 

natural systems. The evaluations of subsurface 

fate and transport models in this section primarily 

consider whether models account for these key 

processes. Some established modeling 

approaches show promise for modeling 

subsurface ENMs, particularly colloid transport 

models (US EPA, 2007). Box 4-2 provides some 

additional background information about 

subsurface colloid transport modeling. 

4.0 Model Reviews 

Box 4-2. Subsurface Colloid Transport 

Modeling 

Colloid transport is often modeled using an 

advection-dispersion equation modified to account 

for colloid attachment and detachment. The most 

common approach relies on CFT (also called 

clean bed filtration theory). This formulation 

accounts for first-order kinetic attachment and 

assumes that detachment is negligible (Tosco et 

al., 2009). However, experiments have shown that 

CFT theory is not always valid. In addition to 

detachment, CFT does not account for so-called 

blocking effects (also known as ripening), whereby 

a maximum, threshold concentration of colloids is 

able to attach to the solid. Such blocking behavior 

may be described using a Langmuirian isotherm 

approach. Other mechanisms not considered in 

CFT include straining and enhanced transport (see 

Section 2.3.4). In addition, CFT approaches do 

not typically consider the potentially strong effects 

of solution chemistry on colloid 

attachment/detachment behavior (as described by 

DLVO theory and its extensions). Some models 

have extended CFT in order to account for some 

of these additional processes. 

4.2.2.1 Subsurface Models of Nanomaterials 

This section describes subsurface models identified in the literature that have been developed 

and/or applied specifically for the evaluation of ENM fate in the environment. Appendix B 

provides more detailed reviews of each of these models. 

Tosco and Sethi (2009) developed a one-dimensional model called MNM1D (micro and 

nanoparticle transport model in porous media in 1D geometry). The model considers constant or 

transient hydrochemical parameters (ionic strength) and describes attachment and detachment 

phenomena. The model accounts for multiple attachment sites, one based on linear and another 

based on Langmuirian isotherms (thus accounting for blocking effects as described in Box 4-2). 

The governing partial differential equations were solved using a finite difference solution, and 

the model was validated through comparison with other models (HYDRUS 1D and Stanmod). 

The model was developed in Matlab and may be downloaded from the website 

www.polito.it/groundwater/software. Appendix B provides a more detailed review of the Tosco 

and Sethi (2009) model. 

Ju and Fan (2009) developed a nanoparticle transport model for use in enhanced oil recovery 

(EOR) applications. The proposed EOR approach involves the injection of polysilicon 

nanoparticles to change the solid matrix from an oil wet to a water wet system. The model 

55 
4.0 Model Reviews 

includes both oil and water phases and accounts for blocking as well as permeability reduction. 

Thus, the model accounts for complex processes (e.g., multiphase flow, permeability reduction 

from particle straining). However, the model is one dimensional. Given that the purpose of this 

model is to support enhanced oil recovery involving multiphase flow processes, we have not 

performed a more detailed review of this model. 

Li et al. (2008) developed a model to evaluate the transport of fullerene (C60) nanoparticles. 

This model accounts for nonequilibrium attachment kinetics and maximum retention capacity 

(site blocking). The authors developed a correlation for the maximum retention capacity 

allowing prediction based on flow velocity, nanoparticle size, and mean grain size of the porous 

medium. The authors determined that patch-wise surface charge heterogeneity on the sand grains 

is probably the reason that observations deviated from classical DLVO theory. They concluded 

that modifications to clean-bed filtration theory and accounting for surface heterogeneity are 

necessary to predict nC60 transport behavior in saturated porous media. Appendix B provides a 

more detailed review of the Li et al. (2008) model. 

Liu et al. (2009) modeled experimental transport results for engineered multiwalled carbon 

nanotubes (MWCNTs) using a one-dimensional model. This model includes a new theoretical 

collector efficiency relationship to describe colloid attachment behavior. The model is based on 

traditional colloid filtration theory (CFT) modified with a site-blocking term. The model 

provided good agreement with experimental results. Appendix B provides a more detailed 

review of the Liu et al. (2009) model. 

Cullen et al. (2010) simulated the transport of nano-fullerenes (C60) and MWCNTs using a twodimensional finite element model. The model considered heterogeneity in permeability. The 

model is based on classical CFT modified with a maximum retention capacity term. Their results 

indicated that carbon nanotubes are more mobile than C60. This study utilized the commercially 

available model, COMSOL Multiphysics version 3.4a. Their results show that nanoparticle 

transport and maximum concentrations are very sensitive to collision efficiency factors and 

blocking factors (parameters controlling colloidal attachment). As these authors emphasized, 

accurate methods to predict these parameters from soil and nanoparticle characteristics have not 

been developed, especially for natural environmental conditions. Appendix B provides a more 

detailed review of the Cullen et al. (2010) model. 

4.2.2.2 Regulatory Subsurface Models 

This section discusses several established subsurface models that have been used in risk 

assessments to support EPA regulatory programs. The models evaluated correspond to those 

listed in NRC’s review of EPA regulatory modeling practice (NRC, 2007). Although these 

models represent only a subset of the available subsurface models used by EPA, they are among 

the most widely applied, and they are representative of typical risk assessment modeling practice 

by the agency. 

None of the three evaluated regulatory models accounts for key processes of ENM subsurface 

transport, including aggregation, attachment, and porous media filtering. The models therefore 

56 
4.0 Model Reviews 

are not suitable in their present form for evaluating ENM transport. We have thus provided only 

brief reviews of these models below. 

It is worth mentioning that some researchers have simulated colloid facilitated transport (see 

Section 2.3.4) using conventional porous media transport models (such as the three regulatory 

models reviewed in this section). For example, Contardi et al. (2001) and Vilks et al. (1998) 

utilized a transport model accounting only for advection, dispersion, sorption, and decay. They 

recognized that the model did not account for colloid behavior explicitly; however they utilized 

an approximate approach to decrease the degree of sorption in order to account for colloid 

facilitated transport. This modeling approach essentially utilizes a lumped-parameter approach to 

simulate complex processes (see Box 4-1). If such an approximate approach is effective for 

simulating ENMs, the regulatory models described in this section may have potential use. 

However, given the unique and complex behaviors exhibited by ENMs in porous media, it seems 

unlikely that this approach would provide reliable predictions for a broad range of conditions. 

The PRZM modeling package couples a model of pesticide and nitrogen fate in the crop root 

zone with a variably saturated flow and transport model of the deeper unsaturated zone. The onedimensional root zone model is solved using a finite difference approach (formulated from 

multiple homogeneous compartments in series). The deeper unsaturated zone model is based on 

a finite-element solution of Richard’s equation for flow and an advection-dispersion equation for 

transport. PRZM accounts for processes of advection, dispersion, sorption, biodegradation 

(including up to two degradation products). The model also simulates surface runoff and 

sediment erosion, including the transport of contaminants sorbed to sediments. The model 

includes a Monte Carlo pre-and post-processor that supports probabilistic simulations. PRZM 

has been used extensively to evaluate the fate of pesticides in agricultural settings. Other than 

surface runoff the model does not include processes specific to particulate transport such as 

aggregation, attachment, and porous media filtering. Therefore, PRZM is not suitable in its 

current form to simulate the behavior of ENMs. 

MODFLOW is a modular three-dimensional finite-difference groundwater flow model 

developed by the U.S. Geological Survey and first published in 1984. MODFLOW is one of the 

most widely used groundwater flow and transport models. Although the original version of the 

model only considered groundwater flow, MODFLOW’s modular structure has allowed 

integration of many additional capabilities. The MODFLOW modeling system now includes 

capabilities to simulate coupled groundwater/surface-water systems, solute transport, variabledensity and unsaturated-zone flow, aquifer-system compaction and land subsidence, parameter 

estimation, and groundwater management. The model is based on a finite difference numerical 

solution to the groundwater flow and transport equations. The model does not include processes 

specific to particulate transport such as aggregation, attachment, and porous media filtering. 

Therefore, MODFLOW is not suitable in its current form to simulate the behavior of ENMs in 

the environment. 

BIOPLUME is a two-dimensional finite difference model utilized to simulate processes of 

natural attenuation of organic contaminants in ground water. Attenuation processes considered 

include advection, dispersion, sorption, and biodegradation. BIOPLUME was developed from 

the U.S. Geological Survey solute transport model MOC. The model considers the fate and 

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transport of the contaminant as well as several aerobic and anaerobic electron acceptors, 

including oxygen, nitrate, sulfate, iron (III), and carbon dioxide. Three kinetic formulations are 

available to simulate biodegradation reactions, including first-order decay, instantaneous 

reaction, and Monod kinetics. BIOPLUME does not include processes specific to particulate 

transport such as aggregation, attachment, and porous media filtering. Therefore, BIOPLUME is 

not suitable in its current form to simulate the behavior of ENMs in the environment. 

4.2.2.3 Other Subsurface Models 

This section describes subsurface models that appear to have potential use for modeling ENMs 

but that were not developed specifically for that purpose. Several researchers have developed 

modeling approaches for simulating colloid transport in porous media. We have provided a brief 

summary of several of these studies below. More detailed reviews are provided in Appendix B 

for the established models TOUGH2 and HYDRUS. 

Corapcioglu and Choi (1996) developed a one-dimensional model describing colloid transport in 

unsaturated porous media with four phases (aqueous, air, solid matrix, and colloid). They 

concluded that the air–water interface could strongly limit colloid transport due to colloid 

attachment to the air–water interface. Johnson et al. (2007) incorporated geochemical 

heterogeneity and random sequential deposition dynamics. Sun et al. (2001) developed a twodimensional colloid transport model for heterogeneous porous media. Ryan et al. (1999) 

considered the importance of the geochemical environment on colloid attachment/detachment 

behavior by developing a two-dimensional model accounting for physical and geochemical 

heterogeneity. Bradford and Toride (2007) attempted to account for non-CFT behavior using a 

conventional advection-dispersion equation (ADE) model with first order kinetic deposition and 

release; they allowed some parameters to vary stochastically in successfully simulating 

experimental results. Bekhit and Hassan (2005) developed a 2D colloid transport model 

accounting for potentially facilitated and retarded colloid transport. 

Moridis et al. (2003) utilized the TOUGH2 (Pruess, 1991) model to develop three-dimensional 

simulations of a proposed nuclear waste disposal facility and associated colloid transport. This 

effort accounted for colloid transport using the EOS9nT module (Moridis et al., 1999). 

Appendix B provides a more detailed review of the EOS9nT module of TOUGH2. 

HYDRUS is a software package for simulating water, heat, and solute movement in two- and 

three-dimensional variably saturated media. The model includes several optional mechanisms of 

colloid transport as documented in Šimůnek et al. (2006). Appendix B provides a more detailed 

review of the HYDRUS model. 

4.2.3 Bioaccumulation Models 

Although the focus of this state-of-the-science review was on fate and transport models, we 

recognize that the bioaccumulation of ENMs may, for some materials, represent a significant 

exposure pathway. Therefore, we include this short summary of bioaccumulation models for 

completeness, focusing primarily on organic chemicals. It should be noted that there is a 

considerable body of research and attendant models available to estimate tissue concentrations of 

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organic chemicals, and the past few years have seen considerable advances in our ability to 

predict uptake and accumulation of metals. 

There are several types of mathematical modeling approaches that have been developed and used 

in predicting exposure concentrations in biota, especially in aquatic systems. These approaches 

can be classified as (1) quantitative structure-activity models, (2) mass balance models, and (3) 

food web bioaccumulation models. The distinction among these models are certainly blurred 

because QSAR elements are found—either explicit or imbedded—in all bioaccumulation models 

and, similarly, kinetics (i.e., absorption, distribution, metabolism, and elimination [ADME]) tend 

to be represented in most models, often using the log of the octanol-water partition coefficient as 

a surrogate; nevertheless, it is useful to organize the types of predictive bioaccumulation models 

into these categories. 

Based on this review, no models or modeling approaches were identified that (1) have been 

applied to ENMs or, (2) because of their theoretical underpinnings, could readily be used to 

predict the bioaccumulation of ENMs in aquatic biota. Therefore, this section summarizes each 

approach and provides a brief discussion of the potential relevance and applicability to ENM 

bioaccumulation. Given the plethora of published research on the development and validation of 

models to predict the uptake and accumulation of conventional chemicals, we identified a 

handful of articles and reports that provide an excellent overview of methods as well as the 

uncertainties associated with predictive bioaccumulation models. The primary sources of 

information identified for this review were 

 Bioaccumulation Assessment Using Predictive Approaches (Nichols et al., 2009) 

 Uncertainties in ecological, chemical, and physiological parameters of a bioaccumulation 

model: Implications for internal concentrations and tissue-based risk quotients 

(DeLaender et al., 2010) 

 Evaluation of Chemical Bioaccumulation Models for Aquatic Ecosystems – Final Report 

(Aqua Terra, 2004). 

QSAR Models. The earliest approaches to predict chemical concentrations in aquatic organism 

is based on the relationship between an organism’s BCF4

 and the log of the n-octanol-water 

partition coefficient (log Kow). As pointed out by Nichols et al. (2009), a curvilinear relationship 

is obtained when plotting the BCF versus the log Kow up to a log Kow value of roughly 6. The use 

of log Kow as a predictor of bioconcentration potential is based on the behavior of hydrophobic 

organic chemicals, namely, the partitioning of hydrophobic organics into the lipid tissue of 

animals. Because n-octanol tends to be a useful surrogate for lipid, this approach has proven to 

be very useful within the range of chemicals for which uptake across the gills (versus uptake 

through the food web) tends to be the driving exposure route. More recent development of 

QSAR algorithms adjusts the baseline (i.e., the BCF based strictly on the log Kow) by chemicalspecific attributes such as ionizability. This same type of approach has proven to be extremely 

useful in predicting toxicity for chemical classes with similar modes of action (MOA) such as 

organic chemicals that cause adverse effects via narcosis (see, for example, Netzeva et al., 2007). 

4

 For the purposes of this discussion, the BCF is defined simply as the ratio of the chemical concentration in fish per 

unit mass over the chemical concentration in water per unit volume. 

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However, because these QSAR methods are often derived from empirical studies, and because 

they rely heavily on the assumption that n-octanol is an appropriate surrogate for lipid (implying 

that the chemical partitions to lipid preferentially), these methods are unlikely to support 

predictions for ENMs without considerable research demonstrating how ENMs partition from 

the gut into other tissues following exposure. It is reasonable to assume that the mechanism for 

partition for many ENMs (certainly quantum dots) is very different than the mechanism for 

conventional organic chemicals and, therefore, QSAR approaches based on the log KOW are 

unlikely to produce reliable predictions without extensive study into the actual mechanisms that 

drive partition of ENMs. 

Mass Balance Models. These types of models predict bioaccumulation in various body parts, in 

essence, by representing processes associated with chemical uptake (e.g., the amount of water 

that passes across the gill) and elimination (e.g., the dilution of chemical mass associated with 

growth of the animal). The model conceptualizes the animal as one or more compartments (or 

boxes) and the concentration of chemical in each box is a function of the processes that affect the 

throughput of the chemical mass (Aqua Terra, 2004). In this engineering type approach, some 

mass of chemical enters the box (e.g., parent compound that is not biotransformed), some mass 

of chemical remains in the box (i.e., accumulation), and some mass of chemical leaves the box 

(i.e., elimination). Most of these approaches are developed to solve the equation for steady-state 

conditions and, therefore, supporting studies must demonstrate that steady state has been 

achieved to provide reliable data for model validation. Naturally, the development of suitable 

study data must address ADME and, as Nichols et al. (2009) point out, there is already a need to 

improve the representation of ADME processes for conventional chemicals. The authors point 

out that metabolism has long been a significant source of uncertainty for hydrophobic chemicals 

(consider the importance of metabolism in predicting the tissue concentrations of polycyclic 

aromatic hydrocarbons [PAHs]). Given the hydrophobicity of certain classes of ENMs (e.g., low 

solubility of fullerenes), significant research may be required before a mass balance approach 

may be applied reliably to ENMs. However, mass balance approaches may be developed that 

simplify the ADME paradigm by eliminating processes that are not relevant to ENMs (e.g., 

certain elimination mechanisms may not be relevant for ENMs that bind strongly to cellular 

proteins). 

Food Web Bioaccumulation Models. Whereas mass balance models are designed to predict 

uptake and accumulation from water only exposures, food web models account for the exposure 

to chemicals via water passing across the gills as well as through the diet (i.e., consuming prey 

species that have accumulated some level of the chemical). These types of models can be limited 

to aquatic food webs or they can be extended to terrestrial organisms that consume aquatic 

animals. The primary difference between the mass balance class of models and food web models 

is that food web models consider (and solve for, mathematically) multiple trophic levels 

simultaneously. For chemicals that are efficiently metabolized by lower trophic level organisms 

the predicted tissue concentrations may approximate the predictions generated using a mass 

balance approach because water is the dominant exposure pathway (i.e., the chemical may not 

biomagnify up the food chain). In contrast, if dietary exposure to a chemical is dominant, food 

web models will produce a much higher estimate of tissue concentrations than a mass balance 

model that only considers water only exposures. As suggested above, the food web class of 

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models is also based on kinetics principles (i.e., ADME), and require suitable studies to measure 

parameters related to the chemistry (e.g., log Kow), ecology (e.g., nonlipid organic carbon in 

detritus), and physiology (e.g., diffusion resistance for uptake) to produce useful predictions (De 

Laender et al., 2010). This strongly suggests that, for food web models to be considered for 

ENMs, substantial research would be needed across all trophic levels. 

It should be noted that, although these classes of bioaccumulation models may not be 

immediately useful in predicting tissue concentrations in aquatic ecosystems for the purposes of 

exposure assessment, the theoretical bases of these approaches have been developed and 

validated over many years and, in general, model performance has been considered appropriate 

to support risk management decisions. A variety of models have been created and used for both 

organic chemicals and metals (e.g., EPA’s Bioaccumulation and Aquatic System Simulator, or 

BASS, Barber, 2008) and, therefore, there is significant potential for further development of 

these concepts to represent processes that are specific to ENM behavior in aquatic ecosystems. 

4.2.4 Multimedia Models 

Multimedia models treat various environmental media (e.g., surface water, groundwater and 

atmosphere) as an integrated system, synthesizing information about chemical partitioning, 

reaction, and intermedia transport. Multimedia models have been used to estimate regional and 

global contaminant migration based on mass balance relationships (Fenner et al., 2005). 

Multimedia models have also been used to assess transport at more local scales, including risk 

assessments of point contamination sources (e.g., industrial sources of hazardous waste). This 

section reviews several multimedia models that have been developed specifically to evaluate 

ENMs (Section 4.2.4.1). In addition, several multimedia modeling frameworks established 

within the risk assessment community are discussed (Section 4.2.4.2). 

Many multimedia models are compartmental models based on a mass balance formulation. Such 

models estimate the transport of material through (often homogeneous) compartments during the 

life cycle of a chemical and may include the following steps: (1) characterize the source and 

production volumes of material (compounds or chemicals); (2) estimate the emissions of material 

to environmental compartments (air, sediment, soil, surface water, etc.); (3) specify the fate in 

the environment; and (4) derive distributions of predicted environmental concentrations (PECs) 

and predicted no effect environmental concentrations (PNECs) for the studied material. 

One approach to calculate PECs and PNECs is material flow analysis (MFA) (also known 

substance flow analysis, SFA), which is a method of analyzing the flows of material or substance 

in a well-defined system. Generally, the goal of a MFA is to obtain an understanding of the 

material flows, calculate indicators, and develop strategies and measures for improving the 

material flow system. MFA can be used to determine flows to and amounts of materials within 

the studied environmental compartments. It is also possible to extend MFA into a probabilistic 

material flow analysis (PMFA), in which the goal is to derive probability distributions of PECs. 

The PMFA is designed to calculate concentrations of possible contaminants in environmental 

compartments and life stages associated with these contaminants. 

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The goal of many multimedia modeling analyses is to estimate PECs of potential hazards as well 

as PNECs such that the risk quotient (PECs/PNECs) can be calculated. From this risk quotient, 

risk managers can determine which chemicals are at greater risk (typically a risk quotient >1). 

Sensitivity and uncertainty analysis (when available) can also be beneficial in the development 

of intervention strategies for the chemicals associated with higher risk. 

4.2.4.1 Multimedia Models of Nanomaterials 

This section describes multimedia models identified in the literature that have been developed 

and/or applied specifically for the evaluation of ENM fate in the environment. Each of the 

models will be further reviewed in Appendix B. 

Boxall et al. (2007a) developed a deterministic model by deriving dilution equations predicting 

the environmental concentrations of ENMs in surface water, sludge, and soil. This model 

determines the PECs of specific ENMs after a life cycle that includes production, use, emission, 

and disposal. The model uses estimations and data values for parameters such as concentration of 

the ENM within the product, daily usage of the product, fraction of the ENM removed during 

sewage treatment, and sludge application rates. Uncertainty is introduced into the model when 

data is not available for some of the necessary parameters. Uncertainty is also evaluated by 

allowing certain parameters (e.g. concentration of ENM within the product) to vary to calculate a 

range of PECs. 

Blaser et al. (2008) modeled the emissions of silver (Ag) from biocidal products that held nanosilver. The model was designed to estimate the emissions of silver and analyze the mass flow as 

a result of emission, assess the fate and estimate the PECs of silver in a river system, estimate the 

PNECs through critical evaluation of available toxicity data for environmentally relevant forms 

of silver, and characterize the risk. Many simplifying assumptions such as neglecting emissions 

from production or solid waste, as well as the removal of marine environments from the system 

provide a simple model that may not encapsulate the characteristics of the broader life cycle of 

ENMs. 

Mueller and Nowack (2008) present a model intended to address the quantities of engineered 

ENMs released into the environment from a life-cycle perspective. Using material flow analysis, 

three types of nanoparticles were studied: nano-silver, nano-titanium oxide, and carbon 

nanotubes. The model incorporated estimated worldwide production, particle release from 

products, and flow coefficients within the compartments selected for the model. The different 

life cycles of the three products generated varied results for the PECs. These generated PECs 

were then compared to the PNECs specific to each material in order to estimate potential risk. 

Gottschalk et al. (2010a) developed a probabilistic material flow analysis (PMFA) to calculate 

distributions PECs and PNECs in a system comprised of 11 compartments. The paper used 

PMFA specifically to address the lack of data concerning environmental fate, exposure, 

emission, and transmission characteristics of ENMs. This stochastic approach allows the model 

to represent uncertainties based on estimated input parameters. The authors propose that the use 

of Monte Carlo simulations and Markov Chain Monte Carlo modeling is appropriate to estimate 

PECs when faced with limited of data. 

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4.2.4.2 Regulatory Multimedia Models 

The six multimedia risk assessment models described in this section represent current, accepted 

approaches for multimedia modeling within the regulatory community. None of these models 

provides a comprehensive solution for estimating the fate of ENMs in the environment. Many of 

the associated sub-models do not account for key processes of particulate transport in the 

environment such as aggregation, attachment, settling, and porous media filtering. In addition, 

these traditional multimedia models are strongly reliant on chemical property estimation tools 

(e.g., QSARs) that were developed for chemicals other than ENMs (see Section 2.3.10). Some of 

the multimedia modeling frameworks are highly abstracted and only describe mass transfer 

between environmental compartments using simple mass transfer functions rather than 

mechanistic formulations that account explicitly for underlying processes. Such highly abstracted 

multimedia models may be useful for screening type evaluations of ENM transport; indeed, 

multimedia models specific to ENMs discussed in the previous section fall within this category. 

However, parameterization of such models generally will require knowledge (e.g., empirical 

data) that is currently unavailable for ENMs. Given these significant limitations, we have only 

provided brief summary descriptions of the traditional multimedia models described below. 

FRAMES (Framework for Risk Analysis in Multimedia Environmental Systems and subsequent 

versions) and MIMS (the Multimedia Integrated Modeling System) are two multi-agency 

software frameworks regarded as the best available in United States for multimedia risk 

assessment. These two frameworks borrow concepts and codes from other frameworks, such as 

STELLA and DIAS. The most notable use of FRAMES is the integration of 17 scientific models 

in the 3MRA model to accomplish multimedia, multi-pathway, and multi-receptor risk 

assessment. MIMS is an object-oriented framework, especially suitable for models with different 

spatial and temporal scales. Its conceptual design supports interchanging models and data sets 

(and modeling of physical, chemical, biological, and human systems), cross-platform portability 

to support off-the-shelf models and distributed computing. MIMS differs from FRAMES in that 

it provides mechanisms to allow feedbacks between models (i.e., dynamically coupled system). 

The 3MRA Modeling System is a suite of 17 environmental risk assessment modules originally 

designed to support the Hazardous Waste Identification Rule (HWIR). It uses FRAMES to allow 

integration of these varied modules and data. This model has been peer-reviewed by EPA’s 

Science Advisory Board (SAB) and is currently supported by ongoing activities at ORD to 

develop automated systems to populate the extensive databases required to run simulations. 

TRIM (Total Risk Integrated Methodology) is an elaborate collection of multiple models (e.g., 

fugacity-based models, simple air quality models, human exposure models) developed to 

perform deterministic multimedia health and ecological risk assessments for hazardous air 

pollutants. It uses the MIMS framework for integration of the risk assessment process. TRIM 

provides risk metrics tables that can be used to further analyze, interpret, and visualize the 

results. (http://www.epa.gov/ttn/fera/trim_gen.html) 

The RESRAD (Residual Radioactivity Models) family of codes is a comprehensive set of 

components that allow probabilistic multimedia risk assessment that are fully interoperable. The 

codes support multimedia modeling and provide capabilities for sensitivity and uncertainty 

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analysis. RESRAD uses an OpenLink software framework for integration of environmental risk 

analysis and management. In addition, the technical support for RESRAD is extremely good. 

RESRAD uses as low as reasonably achievable (ALARA) analysis or a cost-benefit analysis that 

can help in the cleanup decision-making process. The code is supported by Argonne National 

Labs and frequently updated to enhance functionality; for example, recent updates include nonradioactive chemicals. (http://web.ead.anl.gov/resrad/home2/) 

CalTOX (California Total Exposure Model for Hazardous Waste Sites) is an Excel-based 

fugacity model for multimedia risk assessment. Due to the simplicity of the model, its results 

have been incorporated into life-cycle assessment models (e.g., TRACI). The CalTOX model has 

been used primarily for the assessment of contaminated soils as the primary source of 

contamination; however, it has been adapted for multiple purposes, including the support of risk 

ranking schemes and life cycle assessments. The California Exposure Modeling Research Center 

at Berkeley has an active program that involves the continuing development of this model, in 

part, to run nested spatial scale calculations. (http://eetd.lbl.gov/ie/ERA/) 

ARAMS (Army Risk Assessment Modeling System) is a multimedia risk assessment tool that 

specially addresses human health and the ecological risks associated with military relevant 

compounds (MRCs); however, it is applicable to any setting with contaminated sources or 

media. ARAMS uses FRAMES to integrate environmental models and databases. ARAMS 

considers temporal and spatial distribution of contaminants and lends itself to sensitivity and 

uncertainty analyses. ARAMS has functional links to multiple existing databases, such as the 

Integrated Risk Information System (IRIS), Health Effects Assessment Summary Table 

(HEAST), Environmental Residue Effects Database (ERED), and BSAF 

(http://el.erdc.usace.army.mil/arams/). 

SADA (Spatial Analysis Decision Assistance) is unique among all the other risk assessment 

models presented because it is a decision analysis and support tool for risk assessment. SADA 

combines risk assessment with geographic information systems (GIS) and statistical analysis 

methods and sampling design to determine remedial design and cost-benefit analysis. Use of GIS 

provides the capability to explore data that is spatially distributed 

(http://www.tiem.utk.edu/~sada/index.shtml). 

4.3 Alternative Approaches 

It is apparent that risk assessment of ENMs will depend critically and sensitively on the issues 

and uncertainties surrounding their fate and transport in the environment (Wiesner et al., 2009). 

Because of this, we must address the question: How can environmental behavior and risk be 

characterized for an emerging technology? The logical first step might be to modify a 

conventional risk assessment to incorporate the environmental interactions and properties 

relevant to ENMs. However, traditional risk assessment modeling will introduce substantial and 

unquantifiable uncertainties due to paucity of data surrounding the persistence of 

environmentally relevant forms of ENMs. Thus, while these models may provide some insight 

into the complex systems surrounding ENM transport in the environment, they offer limited 

guidance as to the actual potential for adverse health and environmental effects. This creates a 

need to develop models and approaches that can explicitly address the uncertainties surrounding 

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these complexities and behaviors of ENMs in the environment, and provide meaningful 

information to risk managers. Therefore, we must rethink the existing assessment paradigms with 

respect to the nature of ENMs and their transformations, biological interactions, and 

environmental transport so that effective risk management can be developed for ENMs. 

Other authors have supported this position (Linkov et al., 2009a). For example, Grieger et al. 

(2009) argue that although conventional risk assessments are needed for responsible 

development, the process may take decades, leaving decision makers with little support in the 

near term. Given the immediate demands placed on decision makers, we must design more 

adaptive risk governance frameworks and alternative methods to support the characterization of 

potential risks associated with ENMs released into the environment (Hansen, 2009). Lowry and 

Casman (2009) also stress the need for developing new frameworks to describe the potential 

risks of ENMs in the environment. They suggest integration of laboratory results into risk 

analytic frameworks such that preliminary risk analyses can prioritize and identify the most 

relevant data gaps needed to aid in traditional risk assessment. Thus, with the current limitations 

of traditional risk assessment and the future impact on every aspect of our lives and society that 

nanotechnology is expected to have, this state-of-the-science review has incorporated alternate 

approaches to more traditional fate and transport models including5

: 

 Adaptive management and evaluation frameworks 

 MCDA 

 Bayesian approaches. 

The above approaches can be used for relatively near-term decision making for exposure to 

ENMs and will be discussed in the rest of this section. Appendix B provides more detailed 

reviews of each of these approaches. Table 4-1 summarizes advantages and limitations of each 

of these alternative approaches. 

Table 4-1. Summary Evaluation of Alternative Approaches to ENM Risk Evaluation 

Alternative Advantages Limitations Approach 

Adaptive  flexible and adaptable because of the  limited quantitative data 

Management and acceptance of available data  lacks thorough testing and validation Evaluation  explores atypical pathways of exposure Frameworks 

5

 Two additional alternative approaches were identified in the search process for this report: Precautionary matrices 

(PM) and Value of Information (VoI). PM is a simple scoring tool designed for use in early assessments of the 

potential exposure risk of a substance to human health and the environment (Höck et al., 2008). The associated 

safety matrix incorporates information about potential harmful effects, product life cycle, chemical properties, and 

potential exposure routes in order to gain a general understanding of the risks that may arise from these substances. 

The designers stress that while this tool cannot replace traditional risk assessments, these matrices can be used to 

prioritize research needs for emerging technologies. Given the simplicity of the approach and the relatively narrow 

applicability of the technique, we have not provided additional review information for PM. VoI is intended to 

quantify the improvement in expected value from obtaining new information before making a decision and can 

reveal methods to reduce risk or increase potential value. While VoI may be a valuable tool for risk management, its 

potential application for evaluating ENM risk has yet to be documented. Therefore, the approach has not been 

reviewed in this report. 

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Multicriteria 

Decision Analysis 

Bayesian 

Networks 

	 balances societal benefits against 

unintended side effects and risks 

	 combines multiple lines of evidence to 

estimate the toxicity, risk, or exposure to 

ENMs given limited information on physical 

and chemical properties 

	 scientifically sound decision analytical 

framework 

	 ranks or groups all the alternatives through 

a structured process rather than suggesting 

a single replacement 

	 generally robust to imperfect knowledge 

	 easily updated/modified as new scientifically 

relevant information becomes available 

	 provides optimal decisions based on the 

parameters assigned to the model 

	 does not predict environmental fate 

 interpretation of MCDA is subject to parameter 

definitions defined by the user 

	 outcomes may depend on the decision maker 

which can be influenced by personal goals 

and preferences 

 some networks can be too large and complex 

for current Bayesian algorithms 

4.3.1 Adaptive Management and Evaluation Frameworks 

An adaptive evaluation framework, which is a form of adaptive management, is an alternative 

method that can be used to resolve challenges in modeling ENMs. Adaptive management is an 

atypical environmental management method in which the process involves: (1) Setting goals and 

management objectives; (2) development of a model of the system being managed; 

(3) development of a range of management choices; (4) monitoring and evaluating outcomes of 

management decisions; and (5) development of a mechanism in which new information can be 

incorporated into the system for future decisions (learning attribute). Along with this process, 

adaptive management allows for, and encourages, revisiting and revising goals and objectives of 

the project as well as a collaborative structure for stakeholder participation and learning (Linkov 

et. al, 2006). Adaptive management can be divided into two approaches: passive and active. Both 

follow the first three steps of the adaptive management process however passive management 

studies only one alternative experiment at a time, while active management implements multiple 

alternative strategies and examines the outcomes. 

Following the above structure, adaptive evaluation frameworks can be used to circumvent the 

lack of data needed for the traditional risk assessment of ENMs by identifying many exposure 

potentials based on criteria typically not included in the risk assessment paradigm. Possible 

added avenues of evaluation include the location of the ENM within a product (Hansen et al., 

2007), the product life cycle and potential release points, and anticipated volumes of production 

(Metcalfe et al., 2009). Adaptive evaluation frameworks typically unite these parameters with 

more common evaluation parameters such as basic physical and chemical information available 

for the ENM. 

It is important to note that because each model approach relies on differing parameters, there are 

multiple techniques in which these types of specific frameworks can be developed. However, 

most frameworks use a conceptual guideline in the early development of the framework to map 

potential pathways of exposure. These pathways can be as simple or complex as the developer 

chooses. For instance, the categorization framework presented by Hansen et al. (2008) considers 

only consumer exposure to products containing ENMs, thus neglecting possible exposure from 

many environmental factors. However, a more extensive example of a conceptual framework for 

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potential exposure can be shown in Figure 4-1, which is the exposure pathway conceptualized 

by the SMARTEN (Metcalfe et al., 2009) technique for adaptive frameworks. This framework 

utilizes information from the ENM manufacture as well as generalized environmental processes 

and exposure pathways to make predictions about the environmental fate and effects of ENMs. 

Once pathways are identified, adaptive management requires alternative solutions be explored in 

order to produce lower risk exposure potentials. The decision maker takes a decision which is 

then interpreted as a hypothesis that needs to be tested and validated. Validation could involve 

monitoring exposure levels such as environmental surveillance (Metcalfe et al., 2009) or 

evaluating potential exposures due to the location and concentration levels of ENMs within the 

product (Hansen et al., 2008). The findings are evaluated to determine if the hypothesis is to be 

confirmed or rejected. If rejected, a new hypothesis is generated and the process starts again. 

Therefore, adaptive evaluation frameworks view the management of a risk as a process 

consisting of many small decisions rather than a single decision (Hansen, 2009). 

Because technologies are evolving that constantly generate new safety and health information, 

adaptive evaluation frameworks must be able to accommodate new data so that the most accurate 

risk assessment can be performed on emerging ENMs. This methodology allows risk assessors to 

be more proactive in evaluating all aspects of the life cycle of an ENM, thus aiding in decisions 

to produce lower risk ENM products. 

Some of the emerging adaptive evaluation frameworks are: (a) Categorization frameworks 

(Hansen et al., 2007); and (b) SMARTEN (Metcalfe et al., 2009), which reports a governance 

framework for adaptive evaluation frameworks. Hansen et al. (2007) will be evaluated in detail 

in Appendix B using the guidelines from Section 4.1. 

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Figure 4-1. Conceptual exposure pathway utilized by the SMARTEN model. 

4.3.2 Multi-Criteria Decision Analysis (MCDA) 

This section presents a basic overview of MCDA techniques, including characteristics shared by 

different approaches. Within MCDA, almost all methodologies share similar steps of 

organization and decision matrix construction, but each methodology synthesizes information 

differently (Yoe, 2002; Figueira et al., 2005; Belton and Stewart, 2002). Different methods 

require diverse types of value information and follow various optimization algorithms. Some 

techniques rank options, some identify a single optimal alternative, some provide an incomplete 

ranking, and others differentiate between acceptable and unacceptable alternatives. 

The MCDA methods are classified into two general categories of elementary methods and multiobjective methods that are considered more sophisticated and briefly discussed in the following. 

4.3.2.1 Elementary Methods 

Elementary MCDA methods (e.g., maximum method, conjunctive method, lexicographic 

method, and TOPSIS method) can be used to reduce complex problems to a singular basis for 

selection of a preferred alternative. However, these methods do not necessarily weight the 

relative importance of criteria and combine the criteria to produce an aggregate score for each 

alternative. While elementary approaches are simple and can, in most cases, be executed without 

the help of computer software, these methods are best suited for single-decision maker problems 

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with few alternatives and criteria, a condition that is rarely characteristic of environmental 

projects. 

In the maximum method, each alternative is scored based on the performance of its weakest 

attribute. The analogous maximax method scores each alternative based on the performance of 

its strongest attribute. Comparison of the alternatives requires that all attributes be scored on 

comparable scales. 

The conjunctive method is designed to screen alternatives based on whether they exceed 

minimum performance thresholds for all criteria. One useful application of the conjunctive 

approach is to decrease a large number of alternatives to allow more detailed evaluation of a 

subset. The conjunctive method does not require attributes to be scored on a common scale, 

thereby limiting the effort needed for the analysis. In the analogous disjunctive method, 

alternatives pass the screening test if they exceed the minimum performance threshold for at least 

one attribute (as opposed to all attributes in the conjunctive method). 

In the lexicographic method, the criteria are ordered in terms of importance. The alternative with 

the best performance is the alternative with the strongest performance for the most important 

criterion. If multiple alternatives are tied with respect to the most important criterion, these 

alternatives are compared for the next criterion, and so on, until the highest performing 

alternative is selected. 

In the TOPSIS method (technique for order preference by similarity to ideal solution), the 

selected alternative should be as close to the ideal as possible and as far from the negative ideal 

as possible. The ideal is defined as a hypothetical alternative with the highest individual criteria 

scores. The negative ideal is the combination of minimum scores. 

4.3.2.2 Multi-objective Methods 

Some of the main multi-objective decision analysis methods include Multi-Attribute Utility 

Theory (MAUT), Multi-Attribute Value Theory (MAVT), Analytical Hierarchy Process (AHP), 

and outranking. Table 4-1 summarizes important elements, strengths and weaknesses of these 

methods (Linkov et al., 2007). The first three methods are more complex methods that use 

optimization algorithms, whereas outranking uses a dominance approach. The optimization 

approaches employ numerical scores to communicate the merit of each option on a single scale. 

Scores are developed from the performance of alternatives with respect to individual criteria and 

then aggregated into an overall score. Individual scores may be simply summed or averaged, or a 

weighting mechanism can be used to favor some criteria more heavily than others. The goal of 

MAUT is to find a simple expression for the net benefits of a decision. Through the use of utility 

or value functions, MAUT transforms diverse criteria into one common scale of utility or value. 

MAUT relies on the assumptions that the decision maker is rational (preferring more utility to 

less utility, for example), that the decision maker has perfect knowledge, and that the decision 

maker is consistent in his judgments. The goal of decision makers in this process is to maximize 

utility or value. Because poor scores on criteria can be compensated for by high scores on other 

criteria, MAUT is part of a group of MCDA techniques known as compensatory methods. 

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MAVT refers to decision analysis without formal uncertainty analysis, while MAUT refers to 

methodologies that formally account for uncertainty. In the literature, MAVT is typically treated 

as a subset of MAUT, and the more general term (MAUT) is more commonly used. 

Similar to MAUT, AHP (Saaty, 1994) aggregates various facets of the decision problem using a 

single optimization function known as the objective function. The goal of AHP is to select the 

alternative that results in the greatest value of the objective function. Like MAUT, AHP is a 

compensatory optimization approach. However, AHP uses a quantitative comparison method 

that is based on pair-wise comparisons of decision criteria rather than utility and weighting 

functions. All individual criteria must be paired against all others and the results compiled in 

matrix form. For example, in examining the choices in the selection of an ENM, AHP would 

require the decision maker to answer questions such as, ‘‘With respect to the selection of an 

ENM, which is more important, its economic impacts or its environmental impacts?’’ The user 

uses a numerical scale to compare the choices and AHP moves systematically through all pairwise comparisons of criteria and alternatives. AHP thus relies on the supposition that humans are 

more capable of making relative judgments than absolute judgments. Consequently, the 

rationality assumption in AHP is more relaxed than in MAUT. 

Unlike MAUT and AHP, outranking is based on the principle that one alternative may have a 

degree of dominance over another (ODPM, 2004). Dominance occurs when one option performs 

better than another on at least one criterion and no worse than the other on all criteria (ODPM, 

2004). However, outranking techniques do not presuppose that a single best alternative can be 

identified. Outranking models compare the performance of two (or more) alternatives at a time, 

initially in terms of each criterion, to identify the extent to which a preference for one over the 

other can be asserted. Outranking techniques then aggregate the preference information across all 

relevant criteria and seek to establish the strength of evidence favoring selection of one 

alternative over another. For example, an outranking technique may entail favoring the 

alternative that performs the best on the greatest number of criteria. Thus, outranking techniques 

allow inferior performance on some criteria to be compensated for by superior performance on 

others. They do not necessarily, however, take into account the magnitude of relative 

underperformance in a criterion versus the magnitude of over-performance in another criterion. 

Therefore, outranking models are known as partially compensatory. Outranking techniques are 

most appropriate when criteria metrics are not easily aggregated, measurement scales vary over 

wide ranges, and units are incommensurate or incomparable. 

4.3.2.3 Recent Reports and Models 

There are many different forms of MCDA available for use, such as the stochastic multicriteria 

acceptability analysis (SMAA-TRI), AHP, and MAUT. Linkov et al. (2007) and Linkov et al. 

(2009b) explore some of these techniques and how they can be utilized in ENM decision making. 

These reports will be evaluated in detail in Appendix B using the guidelines from Section 4.1. 

4.3.3 Bayesian Approaches 

Bayesian Networks, or BayesNets, provide a framework for adaptable risk assessment that can 

account for various types of uncertainty and may be easily updated/modified as new 

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4.0 Model Reviews 

scientifically relevant information becomes available. Much of this discussion is based upon 

work at CEINT directed by Dr. Mark Wiesner. Bayesian approaches are a major focus of their 

efforts to develop approaches for evaluating ENM behavior in the environment. 

BayesNets are probabilistic networks. A network, or graph, provides a mathematical structure 

composed of nodes (vertices) and edges. Edges join pairs of vertices and represent a pairwise 

relationship between two nodes. Two nodes are said to be connected if a path of edges exists that 

can be followed from one vertex to the other. Graphs can be undirected or directed. In a directed 

graph, relationships move in one direction, whereby one vertex influences another but not viceversa. In an undirected graph, influence can occur in either direction. A network is probabilistic 

if probabilities (also known as weights) are assigned to the edges. The weights represent the 

likelihood of a relationship occurring between nodes. 

Bayesian networks are based on directed, acyclic graphs representing a set of random variables 

(nodes) and their conditional dependences (edges). For the modeling of ENM exposure to the 

environment, a Bayesian network may represent the probabilistic relationship between 

environmental media and the amount of ENM present in the system. Given an amount of ENM 

produced, the network can be used to estimate the amount of ENM in specific environmental 

media based on the likelihoods of material flow through the network. 

The development of a Bayesian network offers two significant advantages: (1) because a 

Bayesian network only connects nodes that are probabilistically related, an enormous 

computational saving can result; and (2) Bayesian networks are extremely adaptable. 

Traditionally, probabilistic models could lead to excessive numbers of potential states to be 

solved, which could require impractical computational efforts. Bayesian networks offer a 

solution to the computational challenges by limiting the possible combinations of states based on 

probabilistic relationships. The adaptability of Bayesian networks lies in the fact that networks 

can be expanded or modified as scientifically relevant information emerges. 

Some have refrained from using BayesNets due to the belief that they will only work well if the 

probabilities upon which they are based are exact. In actuality, approximate probabilities, even 

those based on professional judgment, can provide very useful results. In other words, BayesNets 

are generally robust to imperfect knowledge. Thus, the combination of several strands of 

imperfect knowledge can still allow surprisingly strong conclusions. 

Figure 4-2 displays a network developed by CEINT designed to predict environmental exposure 

to ENMs. Network nodes represent ENM mass residing within the system, and edges represent 

material flows through the system. The nodes may be either: (1) a source of ENM; or (2) an 

environmental compartment in the system where ENMs may reside. Sources of ENMs may 

include: initial production sources of raw ENMs (S); intermediate products containing ENMs (I); 

and final products containing ENMs (P). For the CEINT model, the environmental compartments 

included are atmosphere, wastewater treatment plant (WWTP), storage, landfill, effluent, sludge, 

natural waters, and agricultural land. The directed edges indicate the flow of ENM from one 

compartment (node) to another. In the case of edges from products to environmental 

compartment, this represents the leakage (i.e., release of ENMs into the environment). Leakage 

can be aggregated over environmental compartments or over specific stages of the value chain 

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(NM production, use, and transport as products are produced). For example, the directed edge 

from S into the atmosphere compartment represents the potential loss of ENM from the raw 

ENMs used for production into the immediate atmosphere. A probabilistic relationship for each 

flow path (edge) must be defined for this to be a Bayesian network. The probabilistic relationship 

assigned to each edge designates the fraction of ENM that moves from one node to the next. A 

framework for describing ENM production and incorporation into products as well as leakage to 

the environment can now be explored. 

From this generalization (Figure 4-2), flows in this system can be characterized. Conceptually, 

the description of all flows within this network represents a very high demand for information on 

trends in commercialization, product use, product degradability, and ENM transformation and 

transport. However, the framework provides the ability to aggregate across the value chain or 

across receiving compartments such as wastewater, thereby reducing the number of unknowns at 

the cost of loss of detail. For example, the amount of ENM entering the wastewater compartment 

can be expressed as the product of the ENM source term and the sum of products of coefficients 

representing all pertinent intermediate flows. This aggregation yields a single coefficient that 

captures ENM production and use profiles relevant to the wastewater compartment. Though the 

value of this coefficient may not be known initially, it may be estimated from measurements of 

the quantities of ENMs in wastewater or from commercial projections and assumptions of use of 

these products. Moreover, assumptions regarding the amount of ENMs entering wastewater are 

made explicit through the specification of the coefficient and can be examined in what-if 

scenarios. In this fashion the concentration of NPs that make their way into in wastewater sludge 

and can be estimated by the above network using equations that will be available upon 

completion of the work. Differences in production/usage profiles and the physical-chemical 

characteristics of the ENMs determine their environmental fate with respect to wastewater 

treatment residuals (sludge and treated water). Similar conceptual equations can be developed for 

ENMs entering surface waters, landfills, and the atmosphere. Associated production/usage 

profiles and transfer functions will be generated through the CEINT research. 

The broad variety of materials made into NPs (e.g. metals, oxides, or carbon-based), the 

technical difficulties associated with measuring NPs at low concentrations, and the added 

complexity of detecting particles in the complex media that constitute natural waters, soils, and 

air, present significant challenges to estimating potential exposures to ENMs. The Bayesian 

network approach for ENM exposure assessment described above requires quantitative 

relationships between the amounts of ENMs entering disposal and treatment systems and 

environmental compartments. Thus, transfer functions must be developed for each environmental 

compartment considering properties necessary for transport in the specified media. 

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Figure 4-2. Conceptual network of ENM flows over value chain and into environmental 

compartments. 

73 
5.0 Conclusions 

Chapter 5.0 

Conclusions 

In this section, conclusions are drawn from the state-of-the-science review of models and 

methods relevant to the exposure assessment of ENMs released into the environment. These 

conclusions are presented with respect to the five basic questions that this review was designed 

to answer, for convenience, repeated below. 

1.	 What models and approaches have been used successfully to simulate nanomaterial 

behavior in environmental systems? 

2.	 What models and approaches cannot be used to predict exposures to ENMs in 

ecosystems? 

3.	 What models and approaches can be used in the near term, and what types of 

predictions can be supported by available models? 

4.	 What techniques can be used to address uncertainties and support risk management 

decisions in the near term given obvious gaps in information? 

5.	 What does the state-of-the-science suggest with respect to long-term research goals 

that can be undertaken to improve fate and transport modeling tools for ENMs? 

The remainder of this section discusses each of these science questions with respect to results of 

the state-of-the-science for exposure modeling of ENMs in the environment. 

What models and approaches have been used successfully to simulate nanomaterial 

behavior in environmental systems? 

Fate and transport models that predict the behavior of ENMs in the environment must consider 

particulate transport behaviors, including processes of aggregation and disaggregation, 

attachment and detachment, settling and sedimentation, filtering and enhanced transport in 

porous media, as well as particulate diffusion (see Section 2.3 for further discussion of these 

processes related to ENMs). We identified several models (see Section 4) that account for many 

of these particulate processes, including models developed specifically for ENMs, some 

established regulatory models, and models originally developed for other purposes (e.g., colloid 

transport models). However, even if models incorporate descriptions of key particulate-transport 

behaviors, there remain significant knowledge gaps for ENMs. Reliable parameterization of such 

models will not be possible until sufficient data (laboratory and field) are available. Therefore, 

the utility of these models to predict ENM concentrations in the environment in the near term is 

limited. 

Several alternative approaches to traditional risk assessment that are rooted in risk management 

and decision analysis offer the ability to evaluate ENMs risks in the short term without 

necessarily requiring extensive data collection. Furthermore, these alternative approaches offer 

distinct advantages in terms of integrating different types of information (e.g., expert knowledge 

and scientific judgment) and account for uncertainty and incomplete knowledge. Section 4.3 

provided an introduction to several promising alternative approaches, including adaptive 

evaluation frameworks, MCDA, and Bayesian approaches. Although these alternative 

approaches may not be sufficiently robust to support prescriptive regulatory requirements in the 

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5.0 Conclusions 

near term (e.g., setting allowable concentration limits), they may provide information on relative 

risks, which can be useful in risk management. Risk ranking results could be used to prioritize 

research studies (i.e., characterize those ENMs with the greatest potential risk) as well as to 

prioritize regulatory initiatives such as voluntary agreements with industry to avoid potentially 

high risk practices. 

What models and approaches cannot be used to predict exposures to ENMs in ecosystems? 

Models that do not consider critical particulate-transport behaviors and associated properties 

show little promise for the evaluation of ENM transport. Many established regulatory models do 

not account for key ENM behaviors (see Section 4). In addition, many established fate and 

transport models are based on traditional equilibrium partitioning relationships. Existing 

estimates of partition coefficients (e.g., those based on QSARs) are generally invalid for 

materials at the nanoscale due primarily to the fact that ENMs exhibit properties of particles as 

well as chemicals (see Section 2.3.10). Furthermore, enhanced partitioning models will likely be 

required to predict ENM behaviors reliably (i.e., models that account for the distribution of mass 

between solid, aqueous, as well as particulate phases and potentially nonequilibrium, kinetic 

mass transfer). 

It is important to recognize that environmental risk assessment often relies on low or moderate 

complexity models that describe detailed transport mechanisms using relatively simple model 

constructs (sometimes referred to as lumped parameter, conceptual models; see Box 4-1). Such 

approaches are necessary (and appropriate) to simulate the fate and transport of conventional 

chemicals, even though they are simplifications of natural systems that are variable and complex. 

However, the use of such models involves uncertainty, as emphasized in the silver book, and it is 

critical to characterize and, where possible, quantify the uncertainty in the risk estimates. It is as 

yet unknown whether such lumped-parameter formulations will be appropriate for simulating the 

behavior of ENMs in environmental systems or whether alternative modeling constructs will be 

required. Regardless, lumped parameter models must be grounded in empirical evidence in order 

to make reliable predictions (given their empirical or semi-empirical basis, they cannot make 

reliable predictions a priori). Therefore, even if the underlying model constructs ultimately are 

shown to be appropriate (i.e, model structural analysis), the current applicability of these models 

for predicting ENM behavior is significantly limited by a lack of knowledge and lack of 

available, empirical data to ensure reliable predictions. 

What models and approaches can be used in the near term, and what types of predictions 

are currently supported? 

The alternative approaches described in Section 4.3 can be applied in the near term to evaluate 

risks associated with nanomaterials released to the environment. Some of these approaches result 

in a qualitative (or semi-quantitative) relative ranking of potential risks from specific ENMs. To 

the extent that these approaches produce quantitative results (e.g., predicted environmental 

concentrations), current gaps in our knowledge will create significant and possibly 

unquantifiable uncertainties. Nevertheless, the relative risk results should provide important 

insights for regulatory decision makers in the near term. 

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5.0 Conclusions 

What techniques can be used to address the uncertainties and support risk management 

decisions in the near term given obvious gaps in information? 

The alternative approaches described in Section 4.3 provide promising methods for near term 

evaluation of ENM exposures. Several of these approaches address uncertainties explicitly. For 

example, the BayesNet approach is a probability-based approach that produces probability 

distributions for estimated exposures and effects. Some of the reviewed fate and transport models 

(Section 4.2) show promise for ENMs, and many of these models may be implemented using 

methods developed to represent variability and uncertainty (e.g., Monte Carlo analysis). 

However, the extensive data gaps associated with ENMs limit the utility of fate and transport 

models in the near term. 

What does the state-of-the-science suggest with respect to long-term research goals that can 

be undertaken to improve fate and transport modeling tools for ENMs? 

In concluding this state-of-the-science report, we offer the following to inform the development 

of an integrated research strategy for exposure modeling of ENMs: 

	 As emphasized in this report, there are significant data gaps in the understanding of 

nanomaterial behaviors in the environment. Naturally, research should continue to 

support the development of a basic understanding of the fundamental mechanisms 

controlling fate and transport. This research should include: (1) empirical studies 

(laboratory and field) to characterize ENM transport under a variety of natural conditions 

and to develop parameters in support of ENM modeling; (2) the field testing of existing 

models to develop insight into the magnitude of their current limitations; and (3) given 

the importance of the partitioning approach in multimedia modeling, new modeling 

approaches should be developed to replace or modify the partitioning approach used for 

conventional organic chemicals. 

	 The prevalent data gaps in characterizing ENMs will severely limit the ability to predict 

ENM transport using existing fate and transport models, even if these models account for 

key particulate-transport processes associated with ENMs. It may require years to 

develop sufficient knowledge and modeling expertise that support reliable predictions of 

the environmental behavior of ENMs. Given these challenges, alternative approaches 

(described in Section 4.3) can be used to support science-based risk management, 

explicitly acknowledging uncertainties in the estimation of exposures. Therefore, we 

recommend a parallel research track (along with fundamental fate and transport research) 

that promotes decision analytic and/or adaptive management approaches that, ultimately, 

can be linked to mechanistic fate and transport models/data under development. 

	 We recommend a state-of-the-science evaluation similar to the current report that is 

focused on non-organic ENMs, most notably metals. The focus of the current report was 

on organic-based ENMs, although many of the concepts discussed are more broadly 

applicable. 

	 Multiple references emphasized the lack of consistency in reporting (and measuring) 

ENM properties as well as ambiguity in nomenclature for this emerging field. In addition, 

the large number of ENMs and their highly variable properties and behaviors suggest that 

76 
5.0 Conclusions 

different modeling and parameterization approaches will be required for different types 

of ENMs. Therefore, we suggest a model-based classification system for ENMs that 

captures differences and similarities in environmental behaviors and dependencies. Such 

a classification system would (at a minimum) need to consider the chemical composition 

of the base material (e.g., organic versus metal) as well as the composition of any surface 

modification to the ENM. Development of a standard ENM data model that links fate and 

transport modeling needs to basic research standards on ENM properties would provide a 

more integrated approach to environmental modeling of ENMs. Such a data model would 

support key input parameter requirements for fate and transport models—a core data set 

required for each class of ENM. This data model may in turn lead to characterization and 

reporting recommendations for ENM manufacturers, thereby providing much needed data 

for environmental fate and transport modeling and risk assessment. 

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89 
Appendix A 

Titles Pertaining to the Use of Exposure Models for Nanomaterials 

(references highlighted in blue are reviewed in Appendix B) 

Exposure Science and Model Evaluation 

Title First Author Date Source Co

Meeting Report: Hazard Assessment for Nanoparticles—Report from an Interdisciplinary Workshop 

Balbus, J. 2007 Meeting Report Risk as

Towards a framework for life cycle thinking in the assessment of nanotechnology 

Bauer, C. 2008 Article Re

Understanding risk assessment of nanotechnology Bell, T. 2007 Article Risk as

Nanoparticles and the environment Biswas, P. 2005 Article Re

Where Does the Nano Go? End-of-Life Regulation of Nanotechnologies 

Breggin, L. 2007 Report Re

Nanoparticles: Their potential toxicity, waste and environmental management 

BystrzejewskaPiotrowska, G. 2009 Article Re

Nano risk framework Environmental 

Defense Fund 

2007 Report Risk as

The Appropriateness of Existing Methodologies to Assess the Potential Risks Associated with Engineered and Adventitious Products of Nanotechnologies 

European 

Commission 

SCENIHR 

2006 Report Rev

asse

Redefining risk research priorities for nanomaterials Grieger 2010 Article Rev

asse

Research Strategies for Safety Evaluation of Nanomaterials, Part II: Toxicological and Safety Evaluation of Nanomaterials, Current Challenges and Data Needs 

Holsapple, M. 2005 Article Framew

Uncertainty and precaution in environmental management Krayer von Krauss, 

M. 

2005 Article Model d
A review of carbon nanotube toxicity and assessment of potential occupational and environmental health risks 

Lam, C. 2006 Article Re

Nano-risk and macro-uncertainty: Using probability networks to model the environmental implications of nanotechnology 

Money, E. 2009 Abstract Model d

Do Nanoparticles Present Ecotoxicological Risks for the Health of the Aquatic Environment? 

Moore, M. 2006 Article Rev

asse

Exposure Modeling of Engineered Nanoparticles in the Environment 

Mueller, N. 2008 Article Revie

deve

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Bekhit, H. 2005 Article Model d

Exchange of TiO 2 Nanoparticles between Streams and Streambeds 

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A Stochastic Model for Colloid Transport and Deposition Bradford, S. 2007 Article Model d

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Espinasse, B. 2007 Article Re

Comparative toxicity of nanoparticulate ZnO, bulk ZnO and ZnCl2 to a freshwater microalga (Pseudokirchneriella subcapitata): The importance of particle solubility 

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Deposition and re-entrainment dynamics of microbes and nonbiological colloids during non-perturbed transport in porous media in the presence of an energy barrier to deposition 

Johnson, W. 2007 Article Resear
Experimental study and mathematical model of nanoparticle transport in porous media 

Ju, B. 2009 Article Model de

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Two Dimensional Transport Characteristics of Surface Stabilized Zero-valent Iron Nanoparticles in Porous Media 

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Investigation of the Transport and Deposition of Fullerene (C60) Nanoparticles in Quartz Sands under Varying Flow Conditions 

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Mobility of Multiwalled Carbon Nanotubes in Porous Media Liu, X. 2009 Article Model de

res

Stochastic probability modelling to predict the environmental stability of nanoparticles in aqueous suspension 

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Preliminary 3-D site-scale studies of radioactive colloid transport in the unsaturated zone at Yucca Mountain, Nevada 

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EOS9nT: a TOUGH2 Module for the Simulation of Flow and Solute/Colloid Transport 

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A physiochemical model for colloid exchange between a stream and a sand streambed with bed forms 

Packman, A. 2000 Article Model d

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Photocatalytic decomposition of seawater-soluble crude-oil fractions using high surface area colloid nanoparticles of TiO2 

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Possibilities and limitations of modeling environmental exposure to engineered nanomaterials by probabilistic material flow analysis 

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Comparison of manufactured and black carbon nanoparticle concentrations in aquatic sediments 

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Alternative Approaches and Models 

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Categorization framework to aid exposure assessment of nanomaterials in consumer products 

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A toxicologic review of quantum dots: Toxicity depends on physicochemical and environmental factors 

Hardman, R. 2006 Article Re

Guidelines on the Precautionary Matrix for Synthetic Nanomaterials 

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Classifying Nanomaterial Risks Using Multi-Criteria Decision Analysis 

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Multi-criteria decision analysis and environmental risk assessment for nanomaterials 

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From Comparative Risk Assessment to Multi-Criteria Decision Analysis and Adaptive Management: Recent Developments and Applications. 

Linkov, I. 2006 Article Model de

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Development of a preliminary framework for informing the risk analysis and risk management of nanoparticles 

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DLTR Multi-Criteria Decision Analysis Manual Office of the 

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UK 

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re

Concept of assessing nanoparticle hazards considering nanoparticle dosemetric and chemical/biological response metrics 

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Šimůnek, J. 2006 Article Model d

Precautionary Principle Analyzed Treder, M. 2003 Article Re

Trade-Off Analysis Planning and Procedures Guidebook Yoe, C. 2002 Report Model de

re
Appendix B 

Appendix B 

Exposure Model/Method Summaries for NMs in the Environment 

List of Reviewed Models and Methods 

Surface Water Models................................................................................................................. 101
 

Mackay et al. (2006)................................................................................................................ 101
 

Koelmans et al. (2009) ............................................................................................................ 104
 

Packman et al. (2000).............................................................................................................. 107
 

Subsurface Models...................................................................................................................... 110
 

MNM1D .................................................................................................................................. 110
 

Li et al. (2008)......................................................................................................................... 113
 

Liu et al. (2009)....................................................................................................................... 115
 

Cullen et al. (2009).................................................................................................................. 117
 

TOUGH2................................................................................................................................. 120
 

HYDRUS ................................................................................................................................ 123
 

Multimedia Models..................................................................................................................... 126
 

Boxall et al. (2007).................................................................................................................. 126
 

Blaser et al. (2008) .................................................................................................................. 129
 

Mueller and Nowack (2008) ................................................................................................... 133
 

Gottschalk et al. (2009) ........................................................................................................... 136
 

Alternative Approaches .............................................................................................................. 141
 

Höck et al. (2008).................................................................................................................... 141
 

Hansen et al. (2008) ................................................................................................................ 145
 

Linkov et al. (2007)................................................................................................................. 148
 

Linkov et al. (2009)................................................................................................................. 151
 

99 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix B 

Appendix B provides model reviews that are structured according to the categories discussed in 

Section 4.1. We have provided reviews for models that have been developed for and/or applied 

to ENMs as well as other models that have potential applicability (in their present form) for the 

evaluation of ENM transport in the environment. Some review categories were not applicable to 

all models. In those cases, we note that the categories/questions are not applicable to the model. 

Table B-1 provides a summary categorical overview of the models reviewed within this 

Appendix. 

Table B-1. Summary of Models Reviewed 

Model Reference 

Overview 

Model 

Type 

Peer 

Review 

Availability 

and Usability 

Page Reference 

Natural 

Engineered

Deterministic (D) or 

Probabilistic (P) 

Process-based (P) or

Statistical (S)

Journal Article 

External Peer Review 

User-interface

Use of proprietary 

algorithms

Source code 

available 

Surface Water Models 

Mackay et al. (2006) X P S B-4 

Koelmans et al. (2009) X X D P X B-7 

Packman et al. (2000) X D P X B-10 

Subsurface Models 

MNM1D X D P X X B-13 

Li et al. (2008) X D P X B-16 

Liu et al. (2009) X P P X B-19 

Cullen et al. (2009) X D P X X X B-21 

TOUGH2 X D P X X X X B-24 

HYDRUS X D P X X X X B-27 

Multimedia Models 

Boxall et al. (2007) X D P B-30 

Blasér et al. (2008) X D P X B-33 

Mueller and Nowack (2008) X D S X B-37 

Gottschalk et al. (2009) X P S X B-40 

Alternative Approaches 

Höck et al. (2008) X X D P X X B-45 

Hansen et al. (2008) X D P X X B-49 

Linkov et al. (2007) X D P X B-52 

Linkov et al. (2009) X P S X B-55 

100 
Appendix B 

Surface Water Models 

Mackay et al. (2006) 

Summary: This model provides an approach for predicting whether nanoparticle suspensions in 

aqueous systems will be stable – i.e., whether the particles will aggregate and settle out or 

remain stable in solution. The model is inherently probabilistic and results in a probability 

distribution for the predicted behavior. The model was developed to support the documented 

analysis. No other use, verification, or validation is known. 

KEY REFERENCES: 

Mackay, C., Johns, M., Salatas, J., Bessinger, B., Perri, M. 2006. Stochastic probability 

modelling to predict the environmental stability of nanoparticles in aqueous suspension. 

Integrated Environmental Assessment and Management 2(3):293-298. 

CONTACT/AVAILABILITY INFORMATION: 

Christopher E Mackay (mackayc@exponent.com) 

Exponent, 15375 Southeast 30th Place, Suite 250, Bellevue, Washington 98007, USA 

PURPOSE AND SCOPE: 

What is the model purpose? This model is designed to predict whether nanoparticle suspensions 

will be stable in aqueous systems – i.e., whether the particles will aggregate and settle out or 

remain in a stable solution. 

What processes are simulated? The model considers buoyancy, aggregation, settling of 

nanoparticles in solution. 

What are the primary assumptions? Insufficient information is available to document the model 

assumptions. 

What transport media are considered? Aqueous solutions are considered in this model. 

What spatial and temporal scales does the model consider? The scales are unspecified, however 

it could be any scale at which a nanoparticle aqueous suspension may be present. 

What are the forms of results produced? The primary model result is the probability that a 

nanoparticle suspension will be stable in aqueous solution. 

EVALUATION: 

Background and History 

How extensively has the model been used and applied? To our knowledge, the model has only 

been used for the referenced study. 

101 
Appendix B 

What verification and validation has been conducted? There is no known verification or 

validation of the model with experimental results or environmental data. 

Complexity 

What physical and chemical properties are considered? The model inputs include: fluid column 

height, nanomaterial density, aggregation rate constant, dissociation constant, number of 

particles, probability of aggregation per collision, particle radius, temperature, particle volume, 

and fluid viscosity. 

What is the mathematical representation? The model evaluates the critical buoyancy properties 

using the Botzmann equation and potential aggregate settling using the Stokes-Einstein equation. 

Kinetic aggregation is treated as a chain of binary reactions characterized by an aggregation rate 

constant. The model implements a stochastic Monte Carlo solution and predicts a probability 

distribution of apparent solubilities for nanoparticle suspensions. 

What are the data requirements? The model input parameters are not extensive, however many 

of the parameters (e.g., aggregation rate constant) are likely unavailable currently for most 

nanoparticle solutions, particularly in environmental conditions (e.g., non-ideal aqueous 

mixtures). 

Consideration of Uncertainty 

How does the model account for uncertainty? The model is implemented within a stochastic 

Monte Carlo framework and thus inherently considers uncertainty. However, given that input 

data are unavailable for many nanomaterials and natural conditions, realistic uncertainty 

predictions may not be possible in most cases. 

Availability and Usability – not applicable 

Application to nanomaterial behavior 

Does the model consider relevant chemical and physical properties to capture nanomaterial 

behavior (specific to the media modeled)? This model was developed specifically to evaluate 

nanoparticle specific behavior in aqueous systems. However, the model assumes ideal conditions 

and input parameter knowledge that currently significantly limit its use for evaluating many 

environmental nanomaterial transport problems. 

How does the model address input data gaps associated with many traditional models? The 

model accounts for key processes of nanoparticle stability and allows user-specification of 

associated inputs (e.g., aggregation rate constant). However, the model does not provide any 

guidance on parameterization for different nanomaterials. Also, it is unclear if the model can 

easily be updated for emerging, scientifically relevant information for nanomaterial behavior. 

What kind of interpretations/predictions can be made from this model? This model framework 

may be useful to incorporate into other fate and transport models in order to extend their utility 

to nanomaterials. For example, this modeling approach could predict the stability of nanoparticle 

102 
Appendix B 

suspensions under different environmental conditions (i.e, the effective aqueous concentration 

accounting for the presence of stable particles). An environmental flow model then could predict 

the migration of nanoparticle contaminants at the predicted effective concentration (assuming 

that environmental conditions do not change). 

103 
Appendix B 

Koelmans et al. (2009) 

Summary: This mass balance model was used to compare the concentrations of manufactured 

carbon nanoparticles (MCNPs) to naturally occurring black carbon nanoparticles (BCNPs) in 

aquatic sediments. The model is a relatively simple compartmental model accounting for 

sedimentation, burial, and degradation. The analysis concluded that MCNP concentrations in 

sediments are likely to be negligible relative to concentrations of BCNPs. This conclusion is 

possible even when considering the significant uncertainties in the estimate due to the very 

large magnitude difference in the estimated concentrations. 

KEY REFERENCE: 

Koelmans AA, Nowack B, Wiesner MR. 2009. Comparison of manufactured and black carbon 

nanoparticle concentrations in aquatic sediments. Environ Pollut 157:1110–1116. 

CONTACT/AVAILIBILITY INFORMATION: 

A.A. Koelmans (bart.koelmans@wur.nl, Phone: 31 0 317 483201) 

Aquatic Ecology and Water Quality Management Group. Wageningen University, PO Box 47, 

6700 AA Wageningen, The Netherlands. 

PURPOSE AND SCOPE: 

What is the model purpose? This mass balance model is used to compare the concentrations of 

manufactured carbon nanoparticles (MCNPs) to naturally occurring black carbon nanoparticles 

(BCNPs) in aquatic sediments. This model calculates the steady state concentrations of carbon 

nanoparticles using the parameters of concentration in the sediment, nanoparticle sedimentation 

flux, sediment thickness, accumulation, and a first order removal rate of manufactured carbon 

nanoparticles. 

What processes are simulated? This model simulates the processes of sedimentation, burial, and 

removal due to aggregation and degradation to calculate the concentrations of carbon nanotubes 

within aquatic sediments. 

What are the primary assumptions? The model is characterized by three main assumptions: (1) 

There is a distinct, mixed biologically active layer; (2) MCNPs enter the sediment through 

sedimentation; (3) MCNP removal can be modeled as a first order decay process. 

What transport media are considered? This model considers transport between a water column 

and underlying sediments. 

What spatial and temporal scales does the model consider? The specific spatial and temporal 

scales are not specified. However, the sediment layer is assumed to be 10 cm thick. 

What are the forms of results produced? The model outputs are MCNP concentrations and 

MCNP to BCNP weight ratios. 

104 
Appendix B 

EVALUATION: 

Background and History 

How extensively has the model been used and applied? To our knowledge, the model has only 

been used for the referenced study. 

What verification and validation has been conducted? There is no known verification or 

validation of the model with experimental results or environmental data. 

Complexity 

How many physical and chemical properties are considered? Input parameters include: sediment 

layer thickness, sediment accumulation rate, first-order removal rate constant, sedimentation flux 

of settling solids in the water column, total concentration of MCNPs in the water column, 

concentration of settling solids. 

What is the mathematical representation? The model is a relatively simple mass balance 

compartmental model characterized by mass inflows and internal transformations. 

What are the data requirements? To calculate the concentration of CNPs, this model requires 

data inputs of: (1) sediment thickness; (2) sediment accumulation rate; (3) removal rate constant; 

(4) sedimentation flux; (5) total CNP concentration in the water column; (6) concentration of 

settling solids; and (7) Conditional distribution ratio between MCNP concentration in settling 

particles and MCNP concentration in water. These values are presented in table 1 of the article. 

Consideration of Uncertainty 

How does the model account for uncertainty? The model is deterministic and does not explicitly 

consider uncertainty. 

Availability and Usability – not applicable 

Application to nanomaterial behavior 

Does the model consider relevant chemical and physical properties to capture nanomaterial 

behavior (specific to the media modeled)? The model does not explicitly represent fundamental 

mechanisms of particulate transport (e.g., the model does not rely on DLVO theory). Rather, the 

model aggregates several potential mechanisms into single mass transfer and transformation 

rates (e.g., first order decay, settling rate). This approach allows straightforward predictions to be 

made. The model does not allow any theoretical estimation of behavior and instead relies 

completely on the reliability and accuracy of the user-specified inputs. 

How does the model address input data gaps associated with many traditional models? The 

authors have been explicit in the basis for input parameters and have utilized several documented 

estimation approaches – e.g., estimates of water concentrations based on Mueller and Nowack 

(2008). However, the authors readily state that the results are highly uncertain due to lack of 

knowledge regarding nanomaterials in the environment. The model is relatively simple and 

should be easily updated to account for emerging, scientifically relevant information describing 

nanomaterial behavior. 

105 
Appendix B 

What kind of interpretations/predictions can be made from this model? A major conclusion of 

the analysis was that MCNPs are unlikely to be present in aquatic sediments at concentrations 

significant relative to concentrations of BCNPs. 

106 
Appendix B 

Packman et al. (2000) 

Summary: The model simulates mass exchange of colloids between a stream and a streambed. 

The model considers key behaviors associated with particulates, including settling and porous 

media filtering. The model has been applied successfully by Boncagni et al. (2009) to evaluate 

an experimental study of titanium dioxide nanoparticle transport. 

KEY REFERENCES: 

Packman, A., Brooks, N., Morgan, J. 2000. A physiochemical model for colloid exchange 

between a stream and a sand streambed with bed forms. Water Resources Research. 36: 8 2351 – 

2361. 

Packman, A. I., N.H. Brooks, and J. J. Morgan, Kaolinite exchange between a stream and s 

treambed: Laboratory experiments and validation of a colloid transport model, Water Resources 

Research 36: 8. 

CONTACT/AVAILABILITY INFORMATION: 

Aaron I. Packman (a-packman@northwestern.edu, Phone: 847-491-9902) 

Department of Civil and Environmental Engineering, Northwestern University, Evanston, IL. 

PURPOSE AND SCOPE: 

What is the model purpose? The model simulates the mass exchange of colloids between a 

stream and a streambed. 

What processes are simulated? The model considers bed form driven advective pore water flow 

driven by stream flow over the bed forms. The advective pore water flow along with 

sedimentation and filtering in the porous medium resulted in pumping exchange of suspended 

sediment between the water column and the sediments. 

What are the primary assumptions? Assumptions of the model documented in the reference 

included: the only effect of the bedform on the flow field in the bed is to produce a sinusoidal 

pressure distribution at the bed surface (no erosion or resuspension of particulates); 

homogeneous porous medium; suspension and bedform properties do not change with time; 

What transport media are considered? The model simulates mass exchange between a stream 

water column and underlying sediments. 

What spatial and temporal scales does the model consider? The model formulation is in terms of 

dimensionless parameters, which theoretically should allow the approach to be used at a wide 

range of spatial scales. 

What are the forms of results produced? The primary results of the model are predicted 

concentrations in the water column and in the bed sediments as well as associated mass fluxes. 

107 
Appendix B 

EVALUATION: 

Background and History 

How extensively has the model been used and applied? The model used in the primary reference 

as well as by Boncagni et al. (2009) to evaluate the transport of titanium dioxide nanoparticles. 

Section 4.2.2.1 of the report describes the Boncagni (2009) modeling effort. We are not aware of 

uses of the model other than these two academic studies. 

What verification and validation has been conducted? The initial paper documenting the model 

(Packman et al., 2000) was published along with a companion paper documenting a model 

verification effort. 

Complexity 

What physical and chemical properties are considered? The model input parameters include: 

particle diameter, particle settling velocity, dune wavelength, bedform height, filtration 

coefficient, porosity of bed sediment, stream width, average stream velocity, dispersion 

coefficient, depth of sand bed, stream depth, concentration in stream. 

What is the mathematical representation? The model is developed using a series of equations 

characterizing advective puming theory, colloid filtration, and settling. The formulation results in 

an analytical expression. 

What are the data requirements? There is an extensive list at the end of the document which 

labels all the input parameters needed for the model. 

Consideration of Uncertainty 

How does the model account for uncertainty? The model does not explicitly consider 

uncertainty. 

Availability and Usability – not applicable 

Application to nanomaterial behavior 

Does the model consider relevant chemical and physical properties to capture nanomaterial 

behavior (specific to the media modeled)? The model considers key behaviors of particulate 

transport including settling and porous media filtering. In addition, the model has been 

successfully applied in a study of titanium dioxide nanoparticles (Boncagni et al., 2009). 

How does the model address input data gaps associated with many traditional models? The 

Boncagni et al. (2009) study involved calibration of the model to experimental data. The model 

does not provide the ability to estimate input parameters for nanomaterials without calibration. 

The model is also set up to be easily updated to account for emerging, scientifically relevant 

information regarding nanomaterial behavior. 

108 
Appendix B 

What kind of interpretations/predictions can be made from this model? The primary model 

results include predicted concentrations within surface water and underlying sediments as well as 

the mass flux rate between these media. 

109 
Appendix B 

Subsurface Models 

MNM1D 

Summary: The model simulates one-dimensional transport of nanoparticles in porous media. 

The model accounts for key nanoparticle behaviors, including attachment, detachment, and 

blocking, as well as transient ionic strength effects. The authors have developed a unique 

empirical relationship for attachment, detachment, and blocking coefficients as a function of 

ionic strength. 

KEY REFERENCES: 

Tosco T., Sethi R. (2009). MNM1D: A Numerical Code for Colloid Transport in Porous Media. 

Implementation and Validation. American Journal of Environmental Science 4: 516-524. 

CONTACT/AVAILABILITY INFORMATION: 

Tiziana Tosco (tiziana.tosco@polito.it, Phone: +39 011-564-7670) 

DITAG-Department of Land, Environment and Geotechnologies, Politecnico di Torino, C.so 

Duca Degli Abruzzi 24, 10129, Torino, Italy 

PURPOSE AND SCOPE: 

What is the model purpose? This model was developed to simulate the one-dimensional transport 

of nanoparticles in porous media. 

What processes are simulated? The model considers potentially transient ionic strength 

conditions, which can impact the stability of nanoparticle suspensions. The model accounts for 

particle attachment and detachment using one or two linear and/or langmuirian sorption sites and 

first-order kinetic attachment coefficients. The model also considers potential blocking 

phenomena, whereby all sorption sites become occupied. 

What are the primary assumptions? Information regarding model assumptions is not provided in 

the reference. 

What transport media are considered? Transport in saturated porous media is considered for this 

model. 

What spatial and temporal scales does the model consider? The potential spatial scale for the 

model is not explicitly provided, however the example problems used for verification involve 

small scale problems (0.1 m, similar to a laboratory column) over relatively short time periods 

(minutes). 

What are the forms of results produced? The model predicts nanoparticle concentrations as a 

function of time and one-dimensional distance from the source. 

110 
Appendix B 

EVALUATION: 

Background and History 

How extensively has the model been used and applied? To our knowledge, the model has only 

been used for the referenced study. 

What verification and validation has been conducted? In this paper, the authors verified the 

model algorithms through comparisons with existing groundwater transport models (HYDRUS1D and STANMOD). 

Complexity 

What physical and chemical properties are considered? Input parameters include: inlet colloid 

concentration, solid bulk density, dispersion coefficient, Darcy velocity, porosity, attachment and 

detachment coefficients, maximum attached particle concentration (for blocking), inlet salt 

concentration (for ionic strength effects). 

What is the mathematical representation? The model is formulated using an advectiondispersion equation along with the relevant source/sink and transformation terms describing 

particulate transport processes along with the coupled transport of a conservative tracer (salt, for 

transient ionic strength effects). The model solution is implemented in Matlab using a finite 

difference solution to the partial differential equations. 

What are the data requirements? The data requirements for each equation presented in the model 

are listed after the equations. 

Consideration of Uncertainty 

How does the model account for uncertainty? It does not consider uncertainty. 

Availability and Usability – not applicable 

Application to nanomaterial behavior 

Does the model consider relevant chemical and physical properties to capture nanomaterial 

behavior (specific to the media modeled)? This model was developed specifically for the 

simulation of nanparticle transport in porous media. The model does account for some of the key 

processes for nanomaterial behavior (attachment/detachment, blocking, and ionic strength 

effects). 

How does the model address input data gaps associated with many traditional models? The 

model relies on an empirical relationship developed by the authors based on laboratory 

experiments and theoretical considerations, which estimates attachment, detachment, and 

blocking coefficients as a function of ionic strength. Also, the model is implemented in Matlab 

and likely can readily be updated as emerging, scientifically relevant information becomes 

available. 

What kind of interpretations/predictions can be made from this model? Several of the input 

parameters are difficult and potentially impossible to predict a priori (e.g., attachment and 

111 
Appendix B 

detachment rates for multiple types of sorption sites). This limitation does not necessarily 

prevent the use of this model to evaluate environmental transport problems. However, it does 

indicate that the model must be calibrated to laboratory and/or environmental data in order to 

develop reasonable input parameter ranges. 

112 
Appendix B 

Li et al. (2008) 

Summary: The one-dimensional model of nanoparticle transport in porous media accounted for 

attachment and site blocking. The model was used to interpret experimental fullerene (c60) 

transport. The experimental results and the calibrated model results were utilized to develop 

correlations relating the maximum retention capacity to the flow velocity, nanoparticle size, and 

mean grain size of the porous medium. The authors also estimated collision efficiency factors 

based on their experimental and modeling results and compared them with theoretical 

predictions using DLVO theory. The fitted values were more than one order of magnitude 

greater than the theoretically predicted efficiency factors. The authors attribute this to surface 

heterogeneities and suggest that clean bed filtration theory may need to be modified (to consider 

surface heterogeneity) in order to accurately simulate nC60 transport in porous media. 

KEY REFERENCES: 

Li, Y. S., Y. G. Wang, K. D. Pennell, and L. M. Abriola. 2008. Investigation of the Transport 

and Deposition of Fullerene (C60) Nanoparticles in Quartz Sands under Varying Flow 

Conditions, Environmental Science & Technology, 42(19), 7174-7180. 

CONTACT/AVAILABILITY INFORMATION: 

Linda M. Abriola (linda.abriola@tufts.edu, Phone: 617-627-3237) 

Department of Civil and Environmental Engineering, Tufts, University, 200 College Avenue, 

Medford, Massachusetts 

PURPOSE AND SCOPE: 

What is the model purpose? The model simulates the one-dimensional transport of nanoparticles 

in porous media, considering particle attachment and maximum retention (blocking). The model 

was utilized in the referenced study to interpret laboratory results from a study of nC60 transport 

in porous media. 

What processes are simulated? The model considers particle advection, dispersion, attachment, 

and maximum retention (site blocking). 

What are the primary assumptions? Model assumptions are not documented. 

What transport media are considered? The model evaluates transport in saturated porous media. 

What spatial and temporal scales does the model consider? The model has been utilized to 

evaluate laboratory data at a scale of approximately 16 cm (a laboratory column). The time scale 

was not specified. 

What are the forms of results produced? The primary model predictions are concentrations as a 

function of space and time. 

113 
Appendix B 

EVALUATION: 

Background and History 

How extensively has the model been used and applied? To our knowledge, the model has only 

been used for the referenced study. 

What verification and validation has been conducted? Model results were compared successfully 

with results measured in laboratory experiments. 

Complexity 

What physical and chemical properties are considered? Input parameters include: inlet 

concentration, soil bulk density, porosity, dispersion coefficient, pore-water velocity, particle 

attachment rate, and the particle retention capacity. 

What is the mathematical representation? The model partial differential equations are solved 

using a finite difference numerical approach. 

What are the data requirements? Table 1 of the document outlines the data requirements for this 

model. 

Consideration of Uncertainty 

How does the model account for uncertainty? The model does not explicitly consider 

uncertainty. 

Availability and Usability – not applicable 

Application to nanomaterial behavior 

Does the model consider relevant chemical and physical properties to capture nanomaterial 

behavior (specific to the media modeled)? The model considers several key processes for 

particulates in porous media, including attachment and blocking. 

How does the model address input data gaps associated with many traditional models? The 

experimental results and the calibrated model results were utilized to develop correlations 

relating the maximum retention capacity to the flow velocity, nanoparticle size, and mean grain 

size of the porous medium. It is unknown if the model can be easily updated to account for 

emerging, scientifically relevant information regarding nanomaterial behavior. 

What kind of interpretations/predictions can be made from this model? The authors estimated 

collision efficiency factors based on their experimental and modeling results and compared them 

with theoretical predictions using DLVO theory. The fitted values were more than one order of 

magnitude greater than the theoretically predicted efficiency factors. The authors attribute this to 

surface heterogeneities and suggest that clean bed filtration theory may need to be modified (to 

consider surface heterogeneity) in order to accurately simulate nC60 transport in porous media. 

114 
Appendix B 

Liu et al. (2009) 

Summary: The model was used to interpret experimental multiwalled carbon nanotube 

(MWCNT) transport data. The model simulates one-dimensional transport of MWCNTs in 

porous media. The model is based on colloid filtration theory (attachment) with an added siteblocking term. The model successfully reproduced the experimental results. Results showed 

that MWCNTs were relatively mobile under the higher flow rates evaluated in this study. 

Because these flow rate conditions are similar to conditions in a drinking water treatment 

system sand filter, the results suggest that augmented treatment technologies may be necessary 

to remove MWCNTs from drinking water. 

KEY REFERENCES: 

Liu, X. Y., D. M. O'Carroll, E. J. Petersen, Q. G. Huang, and C. L. Anderson. 2009. Mobility of 

Multiwalled Carbon Nanotubes in Porous Media. Environmental Science & Technology, 25 

43(21), 8153-8158. 

CONTACT/AVAILABILITY INFORMATION: 

Denis M. O’Carroll (docarroll@eng.uwo.ca, Phone: 519-661-2193) 

Department of Civil and Environmental Engineering, University of Western Ontario, London, 

ON, Canada N6A 5B9 

PURPOSE AND SCOPE: 

What is the model purpose? The model was used to interpret experimental multiwalled carbon 

nanotube (MWCNT) transport data. 

What processes are simulated? The model simulates one-dimensional transport of MWCNTs in 

porous media. The model is based on colloid filtration theory (attachment) with an added siteblocking term. 

What are the primary assumptions? A complete set of model assumptions is not provided in the 

reference. 

What transport media are considered? Saturated porous media 

What spatial and temporal scales does the model consider? The model has been used to evaluate 

laboratory scale column experiments. 

What are the forms of results produced? The primary model results are concentrations as a 

function of time and space. 

EVALUATION: 

Background and History 

How extensively has the model been used and applied? To our knowledge, the model has not 

been used beyond the referenced study. 

115 
Appendix B 

What verification and validation has been conducted? The model was successfully used to 

simulate measured data from laboratory experiments. 

Complexity 

What physical and chemical properties are considered? Input parameters include: inlet 

concentration, soil bulk density, porosity, dispersion coefficient, pore-water velocity, particle 

attachment rate, and the particle retention capacity. 

What is the mathematical representation? The model is based on a finite-element solution to the 

underlying partial differential equations. 

What are the data requirements? The data requirements are the same as the physical and 

chemical properties used in this model. 

Consideration of Uncertainty 

How does the model account for uncertainty? The model does not explicitly account for 

uncertainty. 

Availability and Usability – not applicable 

Application to nanomaterial behavior 

Does the model consider relevant chemical and physical properties to capture nanomaterial 

behavior (specific to the media modeled)? The model considers several key processes for 

particulates in porous media, including attachment and blocking. 

How does the model address input data gaps associated with many traditional models? The 

model was used successfully to simulate experimental data. Beyond the conditions of the 

experiments, however, the model does not provide an approach or recommendations for 

parameterization for other nanomaterials. It is unknown if the model can be easily updated to 

account for emerging, scientifically relevant information regarding nanomaterial behavior. 

What kind of interpretations/predictions can be made from this model? The MWCNTs were 

quite mobile at the relatively high flow rates associated with the study (similar conditions that 

exist in sand filtration drinking water treatment systems). This results suggests that traditional 

filtration systems that do not incorporate additional treatment steps such as coagulation may not 

adequately remove MWCNTs. Under natural subsurface conditions, where pore water velocities 

would be in the lower range of those used in this study, the MWCNTs are substantially less 

mobile. The MWCNTs employed here were specifically engineered to be stable in aqueous 

solutions. 

116 
Appendix B 

Cullen et al. (2009) 

Summary: This model of nanoparticle transport in porous media is based on colloid filtration 

theory and associated porous media filtration mechanisms. In addition, the model allows 

specification of a maximum particulate retention (blocking). The model was developed to 

support an academic study and has not been widely used or tested. The model was developed 

using the COMSOL Multiphysics modeling system. Key conclusions of the analysis included 

that carbon nanotubes were more mobile than fullerenes and that the particulate transport 

behavior was strongly dependent on collision efficiency and blocking factors. 

KEY REFERENCES: 

Cullen, E., O'Carroll, D., Yanful, E.K., Sleep, B., 2010. Simulation of the Subsurface Mobility of 

Carbon Nanoparticles at the Field Scale, Advances in Water Resources. 33: 361–371. 

CONTACT/AVAILABILITY INFORMATION: 

Denis M. O’Carroll (docarroll@eng.uwo.ca, Phone: 519-661-2193) 

Department of Civil and Environmental Engineering, University of Western Ontario, London, 

ON, Canada N6A 5B9 

PURPOSE AND SCOPE: 

What is the model purpose? The model was developed to support an academic study of nanofullerenes (nC60) and multi-walled carbon nanotubes (MWCNTs) and their transport in porous 

media. 

What processes are simulated? The model simulated nanoparticle advection, dispersion, 

straining, attachment, and blocking in heterogeneous porous media. The model does not address 

straining due to increased particle aggregation (i.e., temporal changes in the particulate 

suspension). 

What are the primary assumptions? Removal of the nanoparticles from the aqueous phase was 

assumed to adhere to colloid filtration theory and associated mechanisms (e.g., deposition, 

interception and sedimentation). The aquifer was assumed to be completely saturated and under 

steady-state flow conditions. The vertical to horizontal permeability ratio was assumed to be 0.5 

to account for anisotropy. The top and bottom domain boundaries were subject to Type II 

(Neumann) no flow boundary conditions and the right and left side boundaries were subject to 

Type I (Dirichlet) constant head boundary conditions. No nanoparticle flux occurred across the 

top and bottom boundaries. 

What transport media are considered?The model simulates transport in saturated porous media. 

What spatial and temporal scales does the model consider? The model considered a twodimensional domain of approximately 10 square meters and time scales of several days. 

What are the forms of results produced? The primary results of the model include particulate 

concentrations with respect to time and space. 

117 
Appendix B 

EVALUATION: 

Background and History 

How extensively has the model been used and applied? To our knowledge, the model has only 

been used for the referenced study. 

What verification and validation has been conducted? The reference does not document any 

verification or validation analysis associated with this model. However, the model was 

implemented using the COMSOL multiphysics system, which has been subject to extensive 

verification and validation. 

Complexity 

What physical and chemical properties are considered? Input requirements for the model 

include: source concentration, hydraulic gradient, longitudinal and transverse dispersion, 

porosity, collision efficiency factor, collector removal efficiency, and the particle retention 

capacity. 

What is the mathematical representation? The model is implemented within the COMSOL 

multiphysics system, which provides a finite element solution to the underlying partial 

differential equations. 

What are the data requirements? Table 1 of the document outlines the input parameters needed 

for this model. 

Consideration of Uncertainty 

How does the model account for uncertainty? The model does not consider uncertainty. 

Availability and Usability 

Is a user-friendly interface available? The model is implemented within the COMSOL 

Multiphysics system, which does provide a user interface. 

Does the model rely on proprietary algorithms and/or use interfaces? Yes, COMSOL 

Multiphysics is aproprietary modeling system. 

Is the documentation complete and transparent? Yes, the model is well documented within the 

provided reference, and the COMSOL Multiphysics system is well documented. 

Application to nanomaterial behavior 

Does the model consider relevant chemical and physical properties to capture nanomaterial 

behavior (specific to the media modeled)? Yes, the model incorporates key processes associated 

with nanoparticle transport in porous media, including porous media filtering and blocking. 

How does the model address input data gaps associated with many traditional models? The 

model accounts for particle transport processes and allows user specification of the associated 

118 
Appendix B 

inputs. However, no guidance is provided for estimating these inputs. It is unknown if the model 

can be easily updated to account for emerging, scientifically relevant information regarding 

nanomaterial behavior. 

What kind of interpretations/predictions can be made from this model? Nanoparticle transport 

and maximum concentrations are very sensitive to collision efficiency factors and blocking 

factors. At present, accurate methods to predict these factors a priori from soil and nanoparticle 

characteristics have not been developed. For the conditions evaluated the carbon nanotubes are 

much more mobile than nC60 due to the smaller collector efficiency associated with carbon 

nanotubes. However, the mobility of nC60 increased significantly when a maximum retention 

capacity term was included in the model. Model results also demonstrate that, for the systems 

examined, nanoparticles were predicted to be less mobile in heterogeneous systems compared to 

the homogeneous systems with the same average hydraulic properties. 

119 
Appendix B 

TOUGH2
 

Summary: The TOUGH2 simulators are recognized for their powerful simulation capabilities 

involving complex fluid flow and heat transfer in porous and fractured media. The TOUGH2 

codes have been applied to problems ranging from Yucca mountain groundwater flow to multicomponent environmental remediation. A module has been developed to support modeling of 

the transport of colloids in porous media. The model simulates the potential filtering of colloids 

through linear kinetic rate constants characterizing attachment and detachment. 

KEY REFERENCES: 

Pruess, K., C. Oldenburg and G. Moridis. TOUGH2 User's Guide, Version 2.0, Lawrence 

Berkeley National Laboratory Report LBNL-43134, Berkeley, CA, November 1999. (2.2 

Megabytes) 

Pruess, K. TOUGH2 - A General Purpose Numerical Simulator for Multiphase Fluid and Heat 

Flow, Lawrence Berkeley Laboratory Report LBL-29400, Lawrence Berkeley Laboratory, 

Berkeley, CA, May 1991. 

Moridis, G. J., Y. S. Wu, and K. Pruess, EOS3nT: A TOUGH2 Module for the Simulation of 

Nonisothermal Fluid Flow and Solute/Colloid Transport in the Subsurface; LBNL Report No. 

44260, August 1999 

CONTACT/AVAILABILITY INFORMATION: 

Karsten Pruess (K_Pruess@lbl.gov, Phone: 510-486-6732) 

Senior Scientist, Earth Sciences Division, Lawrence Berkely National Laboratory 

PURPOSE AND SCOPE: 

What is the model purpose? TOUGH2 is a general-purpose numerical simulation program for 

multi-dimensional fluid and heat flows of multiphase, multicomponent fluid mixtures in porous 

and fractured media. Chief application areas are in geothermal reservoir engineering, nuclear 

waste isolation studies, environmental assessment and remediation, and flow and transport in 

variably saturated media and aquifers. Although primarily designed for geothermal reservoir 

studies and high-level nuclear waste isolation, TOUGH2 can be applied to a wider range of 

problems in heat and moisture transfer, and in the drying of porous materials. The TOUGH2 

simulator was developed for problems involving strongly heat-driven flow. To describe these 

phenomena a multi-phase approach to fluid and heat flow is used, which fully accounts for the 

movement of gaseous and liquid phases, their transport of latent and sensible heat, and phase 

transitions between liquid and vapor. TOUGH2 takes account of fluid flow in both liquid and 

gaseous phases occurring under pressure, viscous, and gravity forces according to Darcy's law. 

Interference between the phases is represented by means of relative permeability functions. The 

code includes Klinkenberg effects and binary diffusion in the gas phase, and capillary and phase 

adsorption effects for the liquid phase. Heat transport occurs by means of conduction (with 

thermal conductivity dependent on water saturation), convection, and binary diffusion, which 

includes both sensible and latent heat. 

120 
Appendix B 

What processes are simulated? Processes simulated include advection, diffusion, dispersion, 

equilibrium, kinetic, or combined sorption following linear, Langmuir, and/or Freundlich 

isotherms, radioactive decay including daughter products, linear chemical reactions, colloid 

filtration, and colloid-assisted solute transport. 

What are the primary assumptions? Some of the assumptions include: water flow is isothermal; 

concentration are at a trace level without an effect on flow properties; the gas phase pressure 

does not deviate from the reference pressure of the system; there is no phase change. 

What transport media are considered? Porous and fractured media 

What spatial and temporal scales does the model consider? The spatial and temporal scales 

considered are flexible. The model can evaluate small or large-scale problems at variable time 

scales. 

What are the forms of results produced? The primary results are predicted material flows and 

concentrations as well as and fluxes associated with the simulated system. 

EVALUATION: 

Background and History 

How extensively has the model been used and applied? Since its introduction in 1991, TOUGH2 

has been extensively utilized and applied in the academic, regulatory, and industry realms. The 

model has been utilized by approximately 300 organizations in over 30 countries. 

What verification and validation has been conducted? The model has been associated with many 

verification and validation analyses. 

Complexity 

What physical and chemical properties are considered? The model requires many input 

parameters. The reader is referred to the user’s guide for additional information. 

What is the mathematical representation? TOUGH2 uses an integral finite difference method for 

space discretization, and first-order fully implicit time differencing. A choice of either a sparse 

direct solver or a various preconditioned conjugate gradient algorithms is available for linear 

equation solution. Thermophysical properties of water are represented, within experimental 

accuracy, by steam table equations provided by the International Formulation Committee. The 

program provides options for specifying injection or withdrawal of heat and fluids. Doubleporosity, dual-permeability, and multiple interacting continua (MINC) methods are available for 

modeling flow in fractured porous media 

What are the data requirements? Section 5 of the Moridis (199) User Guide document outlines 

the input parameters needed for the model. 

121 
Appendix B 

Consideration of Uncertainty 

How does the model account for uncertainty? Although the model is deterministic and does not 

explicitly account for uncertainty, several studies have ealuated uncertainty using the model. 

Availability and Usability 

Is a user-friendly interface available? No GUI is available inthe public domain, however several 

utility programs supporting use of the model can be found here: 

http://esd.lbl.gov/TOUGH2/PROGRAMS/FREEPROGRAMS.html. In addtion, a proprietary 

user interface named PetraSim is available from Rockware, Inc. Golden, Colorado. 

Does the model rely on proprietary algorithms and/or use interfaces? No, the model is in the 

public domain. However, a proprietary user interface named PetraSim isavailable. 

Is the documentation complete and transparent? Yes. Also the source code for TOUGH2, 

written in standard FORTRAN77, is available from the Energy Science and Technology 

Software Center (ESTSC) of the U.S. Department of Energy. The LBNL group, headed by 

Karsten Pruess, serves as custodians of the code, and provides limited technical support. 

Application to nanomaterial behavior 

Does the model consider relevant chemical and physical properties to capture nanomaterial 

behavior (specific to the media modeled)? Yes, the model includes a module (EOS9nT) that 

implements aspects of colloid filtration theory including attachment/detachment behavior. 

Although the model accounts for some critical processes for particulate transport in porous 

media, it cannot provide guidance for parameterizing the model for nanomaterials. Also, the 

model has been revised numerous times since its inception, demonstrating that enhancements can 

be made to account for emerging, scientifically relevant information. 

What kind of interpretations/predictions can be made from this model? The primary model 

predictions include concentrations and flow rates over time as well as boundary fluid and 

contaminant mass fluxes. 

122 
 

Appendix B 

HYDRUS 

Summary: HYDRUS simulates the movement of water, heat, multiple solutes, and particulates 

in variably saturated porous media (unsaturated and saturated zones). In addition to key 

processes relevant to transport in porous media, the model utilizes colloid filtration theory to 

describe the attachment/detachment behavior of particulates in porous media systems. The 

model has been extensively used, verified, and peer reviewed. A graphical user interface is 

available. The 1D version of the model is public domain, however the 2D and 3D versions are 

proprietary. 

KEY REFERENCES: 

Šimůnek, J., Changming He, J. L. Pang, and S. A. Bradford, Colloid-facilitated transport in 

variably-saturated porous media: Numerical model and experimental verification, Vadose Zone 

Journal, 5, 1035-1047, 2006. 

Šimůnek, J., and M. Th. van Genuchten, Using the Hydrus-1D and Hydrus-2D codes for 

estimating unsaturated soil hydraulic and solute transport parameters, in van Genuchten, M. Th., 

F. J. Leij, and L. Wu (eds.) Characterization and Measurement of the Hydraulic Properties of 

Unsaturated Porous Media, University of California, Riverside, CA, 1523-1536, 1999. 

CONTACT/AVAILABILITY INFORMATION: 

Jirka SimunekJirka Simunek (Jiri.Simunek@ucr.edu, Phone: 951-827-7854) 

Professor and Hydrologist, Department of Environmental Sciences, University of California 

Riverside, Riverside, CA 92521, USA 

PURPOSE AND SCOPE: 

What is the model purpose? HYDRUS simulates the movement of water, heat, multiple solutes, 

and particulates in variably saturated porous media (unsaturated and saturated zones). 

What processes are simulated? HYDRUS considers advection, diffusion and dispersion, 

sorption, and degradation of up to 15 solutes. The model also simulates diffusive transport in the 

gas phase. In addition, HYDRUS can evaluate non-equilibrium mass transfer through tworegion, dual porosity sorption formulation, which considers mobile and immobile regions of the 

pore space. Filtration theory is used to describe attachment/detachment behavior of particulates 

(viruses, colloids, or bacteria). 

What are the primary assumptions? The technical reference and user guide do not provide a 

listing of assumptions. 

What transport media are considered? Partially saturated and/or fully saturated porous media 

may be simulated. 

What spatial and temporal scales does the model consider? The spatial and temporal scales that 

may be considered are flexible. The finite element mesh may be set up to conform to irregular 

boundaries that encompass very small or very large systems (e.g., a few centimeters or miles). 

123 
Appendix B 

What are the forms of results produced? The primary model predictions include concentrations 

and flow rates over time as well as boundary water and contaminant mass fluxes. 

EVALUATION: 

Background and History 

How extensively has the model been used and applied? From its introduction in the mid 1990’s, 

HYDRUS has been used very extensively, primarily (though not exclusively in the 

environmental field to evaluate potential contaminant migration under various scenarios. A web 

page listing HYDRUS-related references includes well over 100 citations (http://www.pcprogress.com/en/Default.aspx?h3d-references). 

What verification and validation has been conducted? The HYDRUS technical manual (2006) 

provides a series of comparisons with other models and with laboratory and field data. Several of 

the references on the HYDRUS website (http://www.pc-progress.com/en/Default.aspx?h3dreferences) include model verification and validation analyses. 

Complexity 

What physical and chemical properties are considered? The model includes a relatively 

complex, spatially explicit representation of porous media. The model input includes a large 

number of potential parameters for a series of optional modules simulating a range of different 

processes (e.g., constructed wetlands design, root water uptake). The reader is referred to the 

user manual (2007) for additional information about the model input parameters. 

What is the mathematical representation? The flow model is based on a finite element solution 

to Richard’s equation for variably saturated flow. The transport model is based on a finite 

element solution to the advection-dispersion equation. 

What are the data requirements? The model input includes a large number of potential 

parameters. The reader is referred to the user manual (2007) for additional information about the 

model input parameters. 

Is the model linked with any corresponding input databases? We are not aware of explicit 

database linkages with HYDRUS, however we have not performed a comprehensive search of 

the extensive HYDRUS documentation and references. Given its extensive use, it would not be 

surprising if the model had been incorporated into a data-driven modeling system. 

Consideration of Uncertainty 

How does the model account for uncertainty? The model is deterministic and the graphical user 

interface does not appear to support uncertainty analysis. As with any deterministic model, 

HYDRUS could be implemented for a probability-based analysis using Monte Carlo techniques. 

Availability and Usability 

Is a user-friendly interface available? Yes, a user interface is available at the product website 

(http://www.pc-progress.com/). 

124 
Appendix B 

Does the model rely on proprietary algorithms and/or use interfaces? The 1D version of the 

model and an associated user interface are in the public domain. The 2D and 3D versions of the 

model are proprietary. 

Is the documentation complete and transparent? Yes. Also, the source code is available for the 

1D code. The code for the 2D and 3D versions may not be available, because the model is 

proprietary. 

Application to nanomaterial behavior 

Does the model consider relevant chemical and physical properties to capture nanomaterial 

behavior (specific to the media modeled)? Yes, the model relies on colloid filtration theory and 

accounts for processes of particulate attachment and detachment. The model does not appear to 

account for potential blocking or enhanced transport processes. The model also does not consider 

potentially changing aqueous chemical conditions (e.g., pH, ionic strength) which can have a 

profound impact on particulate dispersions. 

How does the model address input data gaps associated with many traditional models? Although 

the model accounts for some critical processes for particulate transport in porous media, it cannot 

provide guidance for parameterizing the model for nanomaterials. Also, the model has been 

revised numerous times since its inception, demonstrating that enhancements can be made for 

emerging, scientifically relevant information. 

What kind of interpretations/predictions can be made from this model? The primary model 

predictions include concentrations and flow rates over time as well as boundary water and 

contaminant mass fluxes. 

125 
Appendix B 

Multimedia Models 

Boxall et al. (2007) 

Summary: This model develops a series of algorithms to calculate predicted environmental 

concentrations (PECs) of engineered nanoparticles in air, soil, and water that arise from the use 

of a single product containing nanoparticles. The algorithms combine to form equations to 

calculate PECs based on the concentration of nanoparticles within the product combined with 

hypothetical daily usage of the product. This simplistic approach is applied to a limited range of 

products, environmental compartments, and life cycle stages, thus limiting the information that 

can be used. The parameter values are also entered as point estimates, addressing uncertainty at 

a minimum thus requiring high confidence in the data used to populate the model. 

KEY REFERENCES: 

Boxall, A., Chaudhry, Q., Sinclair, C., Jones, A., Aitken, R., Jefferson, B., Watts, C. 2007. 

Current and Predicted Environmental Exposure to Engineered Nanoparticles. Central Science 

Laboratory, York. 

CONTACT INFORMATION: 

Alistair B A Boxall (abab500@york.ac.uk, Phone: 01904 434791) 

Senior lecturer, Environment Department, University of York, Heslington, York, YO10 5DD 

PURPOSE AND SCOPE: 

What is the model purpose? This model is designed to calculate the PECs of engineered 

nanoparticles in air, soil, and water arising from a range of applications of products containing 

nanoparticles. In order to calculate the PECs, the authors develop a series of algorithms to 

determine master equations for PECs of air, water, and soil. 

What processes are simulated? The modeled processes are broken down into the specific media 

they are simulated for. The model simulates dispersion and emissions for transport in air, dilution 

and application processes for water, and application processes for soil. 

What are the primary assumptions? This model assumes that the system is in a steady state. 

What transport media are considered? The model considers the media of air, water, sludge, and 

soil. 

What spatial and temporal scales does the model consider? Spatial and temporal scales are 

neglected in this model. 

What are the forms of results produced? This model produces point estimates for the PECs of 

nanomaterials in the specified environmental compartment as a result of a specific consumer 

product. 

126 
Appendix B 

EVALUATION: 

Background and History 

How extensively has the model been used and applied? To our knowledge, the model has only 

been used for the referenced study. 

What verification and validation has been conducted? There is no known verification or 

validation of the model with experimental results or environmental data. 

Complexity 

What physical and chemical properties are considered? There are no considerations of specific 

physical and chemical properties of the nanomaterials. Most parameter values considering 

nanomaterials are estimated, such as concentration levels within the product and application 

rates. 

What is the mathematical representation? PECs within the air compartment are calculated by 

computational fluid dynamic models to capture the dispersion after emissions from personal 

hygiene products, skin care products, traffic, and industrial stack sources. The model also 

includes wind speeds and dilution factors in the calculations. 

To calculate the PECs for water, the model uses dilution equations for direct application of 

nanoparticles to the surface water, application via runoff and spray drift, and application via the 

sewage system dependent upon the amount of wastewater produced per capita per day. These 

three equations combine to determine the PECs for surface water. The dilution factor is specific 

to the receiving water. 

Soil PECs are calculated based on the processes for direct application (through remediation 

technologies, plant protection products, excretion of nanomedicines used in veterinary products, 

and from aerial deposition) and the application of sewage sludge. To calculate the PECs as a 

result of sludge application, the authors first calculate the PECs of the sewage sludge based on 

parameters for concentration of the nanoparticles within the product, daily usage, percent 

removed via sewage treatment, market penetration, and sludge production. 

What are the data requirements? The data requirements for calculations of PECs are presented in 

the table below. Along with the data characteristic are a description of the data needed and the 

type of form the data is inputted as (e.g. probability distribution, point estimate, ranking, etc.). 

The model input parameters are not extensive, however many of the parameters such as 

application rates, concentrations, and removal fractions are likely unavailable for most 

nanoparticle containing products. 

127 
 

Appendix B 

Data Characteristic Type of Input Description 

Application rate(s) Point estimate This is the rate at which nanoparticles are applied to the 

environmental source. Application can be directly, via runoff, via 

spray drift, or via sludge. 

Dimensions Point estimate Length, width, depth, and density of the environmental media. 

Runoff Point estimate Fraction of the nanoparticle applied via runoff. 

Spray drift Point estimate Fraction of the nanoparticle release in spray drift. 

Nanoparticle Point estimate Percentage of nanoparticle contained in the product being evaluated. 

concentration 

Daily usage Point estimate This represents the amount of nanoparticle emitted due to usage of 

the product per day. 

Market penetration Point estimate The amount of the population using the product. 

Removal percentage Point estimate Fraction of nanoparticle removed by sewage treatment 

Wastewater amount Point estimate The amount of wastewater produced and applied to the 

environmental compartment of concern per capita per day 

Dilution factor Point estimate The dilution factor in the receiving water 

Sludge production Point estimate The amount of sludge produced and applied to the environmental 

compartment of concern per capita per day 

Table. Data Requirements. 

Consideration of Uncertainty 

How does the model account for uncertainty? The model does not allow for parameters to be 

inputted in distributional form, but rather as point estimates. The authors consider uncertainty by 

calculating PECs for the different concentrations of nanoparticles within the product generating a 

three-scenario analysis. 

Availability and Usability – not applicable 

Application to nanomaterial behavior 

Does the model consider relevant chemical and physical properties to capture nanomaterial 

behavior (specific to the media modeled)? This model was developed specifically for predicting 

environmental exposure of nanomaterials, however does not model the chemical and physical 

properties specific to the behaviors of nanomaterials. 

How does the model address input data gaps associated with many traditional models? The data 

gaps associated with traditional models are not addressed in this model. The model does not 

include parameters for the chemical and physical properties pertinent to nanomaterial behavior 

so it cannot be updated with this emerging information. 

What kind of interpretations/predictions can be made from this model? Because this model is a 

simplistic approach based on a limited range of products, life-cycle processes, and environmental 

compartments and processes, it is difficult to accurately calculate the PECs of air, soil, and water 

for engineered nanoparticles without sufficient data. 

128 
Appendix B 

Blaser et al. (2008) 

Summary: This multimedia model incorporates the use of the Rhine river model in estimating 

the predicted environmental concentrations (PECs) of nanomaterials in environmental 

compartments of an aquatic setting (sewage treatment plants, freshwaters, and freshwater 

sediments). However, the model has many limiting assumptions pertaining to emissions of 

nanomaterials, most likely generating underestimates of the actual concentrations in the 

environmental compartments. 

KEY REFERENCES: 

Blaser, S.A., Scheringer, M., MacLeod, M., Hungerbuhler, K., 2008. Estimation of cumulative 

aquatic exposure and risk due to silver: contribution of nanofunctionalized plastics and textiles. 

Science of the Total Environment 390, 396–409. 

CONTACT/AVAILABILITY INFORMATION: 

Martin Scheringer (scheringer@chem.ethz.ch, Phone: +41 (44) 632 30 62) 

Senior Scientist, Safety and Environmental Technology Group, Institute for Chemical and 

Bioengineering, ETH Zürich, Wolfgang-Pauli-Str. 10, Room HCI G 127, CH-8093 Zürich, 

Switzerland 

PURPOSE AND SCOPE: 

What is the model purpose? The model is designed to ultimately characterize the risk of aquatic 

exposure of silver through 4 stages: (1) Conduct mass flow analysis of silver and emissions 

estimates; (2) Estimate the PEC of silver in a river system; (3) Estimate PNEC of silver; and (4) 

Quantify the risk. 

The development of a 13 compartment mass-balance model is used to determine the flow of 

silver within a freshwater system, utilizing the Rhine river model to assess the flow within the 

river system. The model then aims to quantify the PECs of silver within the environmental 

compartments of STPs, freshwater, and freshwater sediments. Once the PECs are calculated, the 

model calculates risk quotients (PEC/PNEC) by developing PNEC estimates from the literature. 

What processes are simulated? The model simulates mass flow of silver into the environment 

based on emission scenarios and mass balance models for correspond transport media. The 

processes include sedimentation, exchange, diffusion, water flow, burial, and bed load shift. 

What are the primary assumptions? This model considers the emissions of engineered 

nanomaterials for nano-silver as the only source of silver into the river stream. This assumption 

neglects sources such as particulate emissions, production of silver-containing plastics and 

textiles, aerial deposition, and leachates. The model bases its assessment on estimated silver use 

in the year 2010. 

What transport media are considered? This model considers transport media of natural 

freshwaters and freshwater sediments. 

129 
Appendix B 

What spatial and temporal scales does the model consider? The spatial scale is selected to be the 

year of 2010 and the spatial domain is the 25 member countries of the European Union. 

What are the forms of results produced? The results are in the form of PECs and ultimately risk 

quotients. These results are point estimates for each of the scenario (low, intermediate, and high) 

EVALUATION: 

Background and History 

How extensively has the model been used and applied? To our knowledge, the model has only 

been used for the referenced study. 

What verification and validation has been conducted? The model compares its outputs to those 

of empirical data from 16 different sources relating to the environmental concentrations in STP, 

dry sewage sludge, river water, and river sediments. This comparison is presented in figure 5 of 

the article. 

Complexity 

What physical and chemical properties are considered? It is not clear what physical and 

chemical properties are considered in the model. 

What is the mathematical representation? The emission of silver into the aquatic environment is 

determined by first calculating the amount of silver released into wastewaters from biocidal 

products (equation 1 in the article). The authors then use this output in equation 2 to calculate the 

amount of silver input into the natural water system. Values are calculated for all 3 emission 

scenarios. 

PECs are estimated utilizing the Rhine river compartment model which includes moving water, 

stagnant water, and the top layer of the equatic sediment and displayed in figure 3 of the article. 

Permanent sediment is included in the representation, but is not modeled. This model 

incorporates the processes of water flow, bed load shift, sedimentation, burial, exchange, and 

diffusion. The parameter values 

What are the data requirements? The data requirements for calculations of PECs are presented in 

the table below. Along with the data characteristic are descriptions of the data needed and the 

type of form the data is inputted as (e.g. range, point estimate, ranking, etc.). Model input 

parameters for the Rhine river model were more extensive, utilizing a range of values with a 

confidence factor. 

130 
 

 

Appendix B 

Data Characteristic Type of Input Description 

Silver emissions for 

into the environment 

Point estimates There were 17 different input parameters for silver emissions into 

the environment. These input parameters were characterized by the 

media in which silver was emitted (wastewater, STP, natural water, 

sewage sludge, solid waste, landfills, slag, fly ashes, and the 

atmosphere) as well as the form from which the silver came 

(biocidal products, other sources). These parameters were given 3 

different values pertaining to the emission scenarios of minimum, 

intermediate, and maximum scenarios. 

Emission scenario 

parameters 

Point estimates There were 14 input parameters pertaining to the emission scenarios 

which quantified the population, silver release rate, amount of silver, 

wastewater produced, and fraction removed during different stages 

of the silver life cycle. 

Mass balance 

parameters 

Range There were 7 input parameters to model the mass balance model 

once the silver had entered the aquatic environment. These 

parameters included: (1) water flow velocity; (2) Concentration of 

suspended particulate matter (SPM); (3) Sediment density; (4) 

Porosity of sediment; (5) Settling velocity of SPM; (6) Resuspension 

rate; and (7) SPM-water partition coefficient. 

Predicted No-Effect Point estimates These values were taken from the literature and used to calculate the 

Concentrations risk quotient in the environmental compartments of STP, freshwater, 

and freshwater sediments 

Table. Data Requirements 

Consideration of Uncertainty 

How does the model account for uncertainty? The model accounts for uncertainty by conducting 

a three scenario analysis labeled minimum-, intermediate-, and maximum emission scenarios. 

The intermediate scenario considers the most probable assumptions, while the minimum and 

maximum scenarios consider the assumptions that lead to lower or elevated PECs. The model 

performs a first-order error propagation to assess the uncertainty in the model outputs. 

Confidence factors in some of the model inputs are used to address the uncertainty of model 

inputs such as those used in the mass balance models within the aquatic environment. 

Availability and Usability – not applicable 

Application to nanomaterial behavior 

Does the model consider relevant chemical and physical properties to capture nanomaterial 

behavior (specific to the media modeled)? The chemical and physical properties for this model 

only pertain to the behavior of the nanomaterials within the aquatic system. Dispersivity 

properties (as suggested in section 2.2 of this report when considering nanomaterial behavior in 

aquatic systems) do not appear to be considered in the Rhine river model, so all relevant 

nanospecific properties are not considered in this model. 

How does the model address input data gaps associated with many traditional models? 

Parameter values can be altered in the Rhine river model as information is made available. 

However, many of the values pertinent to nanomaterial behavior may not be included. 

131 
Appendix B 

What kind of interpretations/predictions can be made from this model? The model is intended to 

calculate PECs and ultimately the risk characterization of silver within different environmental 

compartments (STPs, freshwater, and freshwater sediment). However, the model only considers 

nano-silver in biocidal products as the exclusive source of silver emission, neglecting other 

sources such as particulate emissions. Therefore, the PECs are most likely underestimates of the 

actual concentrations. 

132 
Appendix B 

Mueller and Nowack (2008) 

Summary: This model was the first attempt at simulating the flow of nanomaterials through the 

environment based on a life cycle assessment. Though much of the data required to model this 

flow is uncertain and estimated on best guesses or worst case scenarios, the model sets up a 

framework to calculate predicted environmental conditions (PECs) for environmental 

compartments such as air, soil, sewage treatment plants (STP), and surface waters. This 

framework can be built upon as scientifically relevant information on nanomaterials becomes 

available. 

KEY REFERENCES: 

Mueller, N. Nowack,. B.2008. Exposure Modeling of Engineered Nanoparticles in the 

Environment. Environ. Sci. Technol. 42, 4447—4453. 

CONTACT/AVAILIBILITY INFORMATION: 

Bernd Nowack (nowack@empa.ch, Phone: +41 71 2747 692) 

Empa-Swiss Federal Laboratories for Materials Testing and Research, Technology & Society 

Laboratory, Environmental Risk Assessment and Management Group, Lerchenfeldstrasse 5, CH9014 St. Gallen 

PURPOSE AND SCOPE: 

What is the model purpose? This model was designed to calculate the quantities of engineered 

nanoparticles in different compartments of the environment based on a life cycle assessment. Per 

this assessment, the model is set up to utilize material flow analysis based on the parameters of 

estimated worldwide production volume, allocation of the production of nanomaterial into 

product categories, particle release from products, and flow coefficients between environmental 

compartments. Ultimately the model is intended to calculate PECs such that risk quotients can be 

determined based on PNECs located in the literature. 

What processes are simulated? The processes simulated are substance flows based on life cycle 

assessments and emissions into the environment based on production and product use. 

What are the primary assumptions? The primary assumptions of the model were: (1) Primary 

compartments of air, soil, and water were considered to be homogeneous; (2) For simplicity the 

system was considered to be in a steady-state; (3) In the absence of real data, substance flow 

rates between environmental compartments were the same for all nanoparticles; and (4) 

Secondary compartments (i.e. sediments and groundwater) were not considered. 

What transport media are considered? The transport media of air, soil, and water compartments 

are considered in this model. 

What spatial and temporal scales does the model consider? The model is spatially representative 

of Switzerland and bases its parameters for flow coefficients on tons/year. However, the system 

is assumed to be in a steady state such that transfer coefficients can be used to calculate 

concentrations. 

133 
Appendix B 

What are the forms of results produced? The model produces PEC for the three types of 

nanoparticles studied. These calculations are then compared to predicted no effect concentrations 

(PNEC) derived from the literature to estimate a possible risk quotient (PEC/PNEC). Typically, 

risk quotients are categorized by a threshold of 1. 

EVALUATION: 

Background and History 

How extensively has the model been used and applied? To our knowledge, the model has only 

been used for the referenced study. 

What verification and validation has been conducted? There is no known verification or 

validation of the model with experimental results or environmental data. 

Complexity 

What physical and chemical properties are considered? The model is based on material flow 

analysis and used flow coefficients between environmental compartments to determine PECs 

within each compartment. It is not clear if physical and chemical properties are considered 

within these compartments.

 What is the mathematical representation? The calculation of PEC for specific environmental 

compartments is determined by substance flow through waste incineration plants (WIPs) 

landfills, and/or sewage treatment plants (STPs). This produced point estimates for PECs 

dependent upon the input values into the system. 

What are the data requirements? The data requirements for calculations of PECs are presented in 

the table below. Along with the data characteristic are descriptions of the data needed and the 

type of form the data is inputted as (e.g. range, point estimate, ranking, etc.). The model input 

parameters are not extensive, however many of the parameters such as application rates, 

concentrations, and removal fractions are likely unavailable for most nanoparticle containing 

products. Thus, the input requirements were based on worst-case scenarios in the absence of 

sufficient data. 

134 
 

 

 

Appendix B 

Data Characteristic Type of Input Description 

Worldwide production 

volumes 

Point estimates Production volumes were taken at a best guess based on the 

production volumes of 10 companies in Switzerland. 

Allocation of the 

production volumes to 

product categories 

Point estimates 

for weighting 

factors 

Different product categories were assigned to a specific nanoparticle 

if present in that product category. Weight factors allocated how 

much of the total production volume was contained in that product 

category. 

Particle release from 

products 

Point estimates Each nanospecific product category had 2 to 6 different release 

points during the life-cycle. A percentage of nanomaterial release 

was given to each of these release points. The compartment to which 

the nanomaterial was released was also specified. 

Flow coefficients Point estimates Flow coefficients between different compartments in the model were 

quantified by tons/year. 

Table. Data Requirements. 

Consideration of Uncertainty 

How does the model account for uncertainty? The model accounts for uncertainty by developing 

a realistic and high exposure scenario (RE- and HE scenario). The HE scenario relied on worstcase scenario estimations, which lead to higher concentrations in the environment. This twoscenario analysis was the extent of uncertainty analysis conducted. 

Availability and Usability – not applicable 

Application to nanomaterial behavior 

Does the model consider relevant chemical and physical properties to capture nanomaterial 

behavior (specific to the media modeled)? This model was developed to estimate PECs of 

nanomaterials with the lack of data concerning relevant chemical and physical properties specific 

to them. 

How does the model address input data gaps associated with many traditional models? The 

model addresses data gaps associated with many traditional models by assigning flow 

coefficients between environmental compartments, rather than calculating the flow within each 

compartment, and modeling a flow-in, flow-out process. However, calculation of the flow 

coefficients may not include all the necessary parameters. The model can be updated as 

information regarding percentage of particle release from products is quantified, as well as more 

data that can increase the certainty of flow coefficients. 

What kind of interpretations/predictions can be made from this model? This model is a 

simplified approach to quantifying PECs of nanomaterial based on a life cycle assessment. The 

framework seems applicable, but requires more certainty in the parameter values before this 

assessment can be used in traditional risk assessments. 

135 
Appendix B 

Gottschalk et al. (2009) 

Summary: This multimedia environmental model was designed to calculate the predicted 

environmental concentrations (PECs) to be used towards a quantitative assessment of the risks 

of nanomaterials in the environment. Because of the current data gaps surrounding 

nanomaterials, this model was developed to model basically any substance with a distinct lack 

of data concerning environmental fate, exposure, emission and transmission characteristics. 

Thus, by combining methods of sensitivity and uncertainty analysis, Monte Carlo simulation, 

and Markov Chain Monte Carlo modeling, the proposed model can realistically calculate PECs 

when facing significant data gaps. 

KEY REFERENCES: 

Gottschalk, F., Scholz, R.W., Nowack, B., 2009. Probablilistic material flow modeling for 

assessing the environmental exposure to compound: Methodology and an application to 

engineered nano-TiO2 particles. Environmental Modelling & Software 25 (2010) 320-332. 

Gottschalk, F., Sonderer, T., Scholz, R., & Nowack, B.. 2010. Possibilities and limitations of 

modeling environmental exposure to engineered nanomaterials by probabilistic material flow 

analysis. Environmental Toxicology and Chemistry. 29, 5, 1036–1048. 

Gottschalk, F., Sonderer, T., Scholz, R., & Nowack, B.. 2009. Modeled Environmental 

Concentrations of Engineered Nanomaterials (TiO2, ZnO, Ag, CNT, Fullerenes) for Different 

Regions. Environmental Science and Technology. 43, 24, 9216–9222. 

CONTACT/AVAILIBILITY INFORMATION: 

Fadri Gottschalk (fadri.gottschalk@env.ethz.ch, Phone: +41 44 632 63 25) 

ETH Zurich, Institute for Environmental Decisions (IED), CHN J 73.2, Universitätstrasse 22, 

8092 Zürich 

PURPOSE AND SCOPE: 

What is the model purpose? The proposed exposure assessment develops its methods from a 

material flow analysis (MFA) modeling approach and is accomplished via the steps: 

(1) characterize source and production volumes of material (compounds or chemicals); 

(2) estimate the emissions of material to environmental compartments (air, sediment, soil, 

surface water, etc.); (3) specify the fate in the environment; and (4) derive distributions of 

predicted environmental concentrations (PECs) for the studied material. 

The proposed PEC modeling approach combines sensitivity and uncertainty analysis, Monte 

Carlo (MC) simulation, Bayesian and Markov chain modeling, and is intended for cases 

characterized by a distinct lack of data concerning environmental fate, exposure, emission, and 

transmission characteristics. The simulated PECs for materials in the desired environmental 

compartments provide the basis for the quantitative exposure assessment and are derived from 

the results of the probabilistic material flow analysis (PMFA). 

136 
Appendix B 

What processes are simulated? The model simulates the material flow of substance through 11 

compartments consisting of life-cycle (production, manufacturing, and consumption), recycling, 

waste incineration plants, sewage treatment plants, untreated sewage, landfills, atmosphere, soil, 

sediments, groundwater, and surface water. 

What are the primary assumptions? The model assumptions are presented in section 2.2 of the 

article title system analysis. The section presents the system flow chart to explain all of the inputs 

and outputs between each environmental compartment. 

What transport media are considered? This model considers the transport media of air, surface 

water, subsurfaces, sewage treatment plants, and waste incineration plants. 

What spatial and temporal scales does the model consider? The model considered the flow of 

engineered nanoparticles in Switzerland as a spatial scale. 

What are the forms of results produced? The results of the model give distributions of PECs for 

different environmental compartments. 

EVALUATION: 

Background and History 

How extensively has the model been used and applied? This method and approach has been 

utilized in several recent publications (Gottschalk et al. 2010, Gottschalk et al. 2009). The former 

is a recent follow-up in which the author addresses both the possibilities and limitations of using 

this particular method for modeling environmental fate and transport. Within this article, the 

author discusses the other models currently used to model PECs with regards to nanomaterials. 

Comparatively, the author justifies why this model is more useful than the models currently 

developed. As of yet, there have been no new authors that have used this model in their own 

nanomaterial risk assessment. 

What verification and validation has been conducted? Due to the novelty of its design and 

limitations of other research in this area, this model cannot be verified using similar models. 

Complexity 

What physical and chemical properties are considered? The model is based on material flow 

analysis and used flow coefficients between environmental compartments to determine PECs 

within each compartment. It is not clear if physical and chemical properties are considered 

within these compartments. 

What is the mathematical representation? A flow chart (Figure 1) of the mathematical 

methodology displays the steps necessary in deriving the most informative probability 

distributions of PECs for any material using the PMFA modeling framework. The remainder of 

this summary will discuss this process and the methods presented by Gottschalk which follow 

the steps: (1) develop a system model with a corresponding general system of linear equations to 

describe the flow model; (2) use a probabilistic Monte Carlo method to determine a probability 

distribution of the PECs in selected environmental compartments; (3) utilize Markov chain 

137 
Appendix B 

Monte Carlo algorithms based on the previous findings to more accurately determine the 

probability distribution of the PECs; and (4) perform sensitivity on the PMFA to give the risk 

manager more insight on the model. 

Steps 1 through 3 of the methodology conceptualization are followed to develop a system design 

of the desired mass balance compartment flow model which specifies the 

compartments/processes in which the material will flow through and the directional flows 

associated with them. Often, a flow chart is prepared to aid in the visual understanding of the 

model as well as the mathematical formulation. The compartments or processes represent the 

different stages of material flow and directional flows represent the transfer coefficients of 

material. Transfer coefficients imply the transport of material between and within the 

compartments. 

If all transfer coefficients of the material are known (or estimated by point values), the flow of 

the material in the system can be determined mathematically through a stationary input-output 

model of a set of n linear equations containing n unknowns. Matrix algebra can be used in this 

case and solutions for PECs can be found deterministically by computing inverse matrices. 

However, if transfer coefficients are not known, a probabilistic approach must be used to capture 

the uncertainty of these values. Density functions that represent the uncertainty of transfer 

coefficient values will be used in the probabilistic methods used to determine PECs for the 

desired environmental compartments. 

Before the probabilistic methods are employed, a system of linear equations must be defined 

which calculates flows to and deposition within the examined compartments of the system. This 

system follows the balance principle that the mass of all inputs into a process equals the mass of 

all outputs of the process and includes accumulation or depletion of mass within the 

compartment. The balance principle is used to define the transfer coefficients between each 

compartment. Matrix algebra, input values of transfer coefficients, and values of periodic input 

of material to specified compartments are then combined to define a matrix equation that can 

solve the steady-state of the desired system of linear equations. As mentioned, point estimates of 

the input parameters can be used to solve the system straightforwardly and may be done for 

model validation (MFA standard). 

Once the system of linear equations has been determined, the study employs a probabilistic 

determination of the unknown output variables (storage of material within the processes) via 

Monte Carlo (MC) methods. The MC methods in this study follow two steps: (1) model the 

probability distributions of all model input parameters (transfer coefficients); and (2) a repeated 

computation of a proposed linear equation system to determine the output variables. The first 

step determines a sequence of random variables for each model input parameter following a 

desired probability distribution (picked to represent the probability that the input parameter falls 

within a particular interval). The second step solves the new system of linear equations that result 

from these input parameters. At the conclusion of the MC method, this algorithm has solved 

thousands of systems of linear equations, generating a probability distribution of the unknown 

output variables. This is a crucial step in the modeling procedure, because it allows the modeler 

to gain insight on the PECs of the studied material without a full understanding of all input 

138 
Appendix B 

parameters. By simulating this method enough (Meerschaert, 2007), we can develop a smooth 

probability density curve of the output variables. 

To more accurately describe the probability distribution of the unknown output variables, 

Markov chain Monte Carlo (MCMC) methods were employed. Markov chains are discrete 

random processes with the Markov property (Ross, 2003). A discrete random process is defined 

as a system that can be in various states and changes randomly in discrete steps. The Markov 

property distinguishes that the probability distribution of the next step is determined only by the 

current state of the system, and not by the previous states of the system. The state of the system 

changes according to the transition probabilities of the chain. Over time, regardless of the 

starting state of the system, this discrete process converges to an equilibrium distribution. 

MCMC methods are a class of algorithms that sample from probability distributions based on 

constructing a Markov chain that has a desired distribution as its equilibrium distribution 

(stationary). 

Gottschalk uses Bayesian inferences to provide posterior distributions to define what is known 

about unobservable model input parameters given measured or simulated data. The simulated 

data refers to the probability distributions of the output variables found by the MC methods 

above. This posterior distribution is used in the MCMC algorithms to improve upon the 

probability distributions of the PECs. Bayesian techniques require a proposal distribution and 

posterior distribution. In particular, Gottschalk utilized Metropolis algorithms (Albert, 2007) 

with a symmetric proposal distribution and the above posterior distribution as the stationary 

distribution of the Markov chain to approach the optimal input parameter values. The MCMC 

method is repeated until the Markov chain is considered well mixed, or close to its steady state 

solution. 

System Model 

and System of 

Linear Equations 

Probability 

distributions of 

PECs 

Improved PEC 

probability 

distributions 

Interpretation, 

assessment of 

PECs 

Definition of 

goods, products, 

processes and 

functions 

Probabilistic 

modeling of the 

input parameters 

Markov Chain 

Monte Carlo 

(MCMC) modeling 

Sensitivity and 

uncertainty analysis 

Figure 1. Basic flow chart of the PMFA methodology. 

139 
Appendix B 

What are the data requirements? Table 3 of the Gottschalk et al. (2009) reference 30 modeling 

parameters needed for the model. Each parameter is inputted as a probability density function. 

Is the model linked with any corresponding input databases? Currently, the model is not linked 

with any input databases, but potentially could be developed to utilize an input database. 

Consideration of Uncertainty 

How does the model account for uncertainty? A probabilistic approach is employed capture the 

uncertainty of transfer coefficient values. Uncertainty in the model is addressed by assigning 

probability density functions to determine transfer coefficient values. The (drawn) values are 

used in the probabilistic methods detailed above to calculate PECs for the desired environmental 

compartments. 

Sensitivity analysis is conducted on the model to determine which input parameters have the 

greatest influence on output variables. This analysis allows the risk manager more information of 

the model at hand. Identifying the most influential parameter inputs will help the risk manager 

prioritize which inputs should be studied in order to reduce the uncertainty of the model and 

improve the overall exposure modeling process. 

Availability and Usability – not applicable 

Application to nanomaterial behavior 

Does the model consider relevant chemical and physical properties to capture nanomaterial 

behavior (specific to the media modeled)? This model was designed to specifically model 

nanomaterials. Though the model does not explicitly model the relevant physical and chemical 

properties needed to model nanomaterial behavior, it does employ probabilistic and Bayesain 

approaches to capture these behaviors. 

How does the model address input data gaps associated with many traditional models? The 

model addresses an overall lack of data by utilizing a probabilistic and Bayesian approach to 

develop a probabilistic material flow analysis to quantify the output variables. The model 

parameters can be updated to account for relevant information regarding nanomaterial behavior, 

thus more accurately describing the steady state of the system. 

What kind of interpretations/predictions can be made from this model? This model allows for a 

calculation of PECs in different environmental compartments in a steady-state system. From this 

assessment, risk managers would be able to calculate risk quotients of nanomaterials by 

obtaining predicted no effect concentrations PNECs. 

140 
Appendix B 

Alternative Approaches 

Höck et al. (2008) 

Summary: The precautionary matrix serves as a guide to determine the potential health concern 

of nanorelavent materials in the absence of extensive data. Though this safety matrix in no way 

replaces the traditional risk assessment methods, it does allow a pre-characterization of 

potential risks and non-risk nanomaterials. 

KEY REFERENCES: 

Höck J., Hofmann H., Hörner K., Krug H., Lorenz C., Limbach L., Nowack B., Riediker M., 

Schirmer K., Som C., Stark W., Studer C., von Götz N., Wengert S., Wick P.: Guidelines on the 

Precautionary Matrix for Synthetic Nanomaterials. Federal Office for Public Health and Federal 

Office for the Environment, Berne 2008. 

The PDF version of the guidelines can be downloaded from 

http://www.bag.admin.ch/themen/chemikalien/00228/00510/05626/index.html?lang=en. 

CONTACT/AVAILABILITY INFORMATION: 

Jürgen Höck (juergen.hoeck@temas.ch, Phone: +41 71 446 50 30) 

Federal Office of Public Health FOPH, CH-3003 Bern 

PURPOSE AND SCOPE: 

What is the model purpose? The precautionary matrix is a safety matrix developed for the Swiss 

action plan for synthetic materials. The safety matrix is intended to estimate “nanospecific 

precautionary need” of synthetic nanomaterials and their applications for employees, consumers, 

and the environment. The model is intended to facilitate communication between all interest 

groups, increasing responsibility in the development of nanotechnology. However, it should be 

noted that the safety matrix does not substitute for an actual risk assessment. Rather than 

evaluating the dangers and risks associated with specific nanoparticles, this model should only be 

used to identify key areas for action (which may include an extensive risk assessment). 

What processes are simulated? This model calculates values for nanorelevance, specific 

framework conditions, potential effects of the nanomaterials, and potential human and 

environmental exposure to nanomaterials. 

What are the primary assumptions? The precautionary matrix operates under two assumptions: 

(1) Treat all nanomaterials as if no investigations have been carried out for specific cases to 

allow consistently objective evaluations; and (2) In the case of data gaps, use the worst case 

scenario. 

What transport media are considered? The precautionary matrix does not model the transport of 

substance through media, rather estimates the potential risk of input into the environment. 

141 
Appendix B 

What spatial and temporal scales does the model consider? The precautionary matrix includes 

the life cycle of the nanomaterial based on user inputs. The spatial and temporal scales are 

specific to each nanomaterial, as well as the input of the user. 

What are the forms of results produced? The precautionary matrix produces a risk value based 

on a function of the nanorelevance, specific framework conditions, potential effect of the 

nanomaterial, and potential human and environmental exposure of the nanomaterial. This risk 

value is then classified as a low rating of need for nanospecific action or a need for nanospecific 

action. 

EVALUATION: 

Background and History 

How extensively has the model been used and applied? To our knowledge, the model has only 

been used for the referenced study. 

What verification and validation has been conducted? This novel approach has not been verified 

by experimental or empirical data. Also, no other models have validated this construction. 

Complexity 

What physical and chemical properties are considered? The model considers redox activity, 

catalytic activity and stability of the nanomaterial. However, these values are quantified as low, 

medium or high and are not actually modeled. 

What is the mathematical representation? The risk value of a given (nano)material is based on a 

function of the four modeled processes given above. This estimation of precautionary need is 

presented in section 5.2 of the guideline document where section 5.1 explains the subcalculations needed for this overall estimation. Section 5.3 then classifies the estimation of 

precautionary need into two categories: (1) A score of 0-20 rates the nanospecific need for action 

as low; and (2) A score >20 suggests a need for nanospecific action (existing measures should be 

reviewed, further clarification undertaken, and measures to reduce the risk associated with 

manufacturing, use and disposal implemented in the interests of precaution). 

What are the data requirements? The data requirements for calculations of nano-specific risk are 

presented in the table below. Along with the data characteristic are a description of the data 

needed and the type of form the data is inputted as (e.g. probability distribution, point estimate, 

ranking, etc.). The model input parameters are not extensive and, in the case of uncertainty, are 

based on a worst case scenario. 

142 
 

 

Appendix B 

Data Characteristic Type of Input Description 

Nanorelevance Yes/No given 

values of 1 or 0 

Specific framework Yes/Partly/No 

characteristics given values of 

0,3, or 5 

Potential effect	 Low/Medium/High 

given values of 1, 

5, and 9 

Potential Human and Various rankings; 

Environmental {0 or 1}, or {1, 5, 

Exposure or 9} 

There are 5 parameters that determine the nanorelevance of a 

material. These parameters are based on particle and agglomerate 

sizes (e.g. does the material form agglomerate > 500nm?) 

There are 4 parameters that quantify the risk surrounding specific 

framework characteristics. These parameters quantify the data gaps 

in the current knowledge about the material (e.g. Is the origin of the 

starting materials known?) 

There are 3 parameter values for the potential effect of a material 

and they are based on the redox activity, catalytic activity, and the 

stability of the material. For the given nanomaterial a score of 

low(1), medium(5), or high(9) is given for each characteristic 

above. 

There are 9 parameter values to determine the potential human and 

environmental exposure and depend on: (a) the physical 

surroundings in production or application; (b) the contact per day; 

(c) potential input into the environment. An example parameter is 

frequency with which a consumer uses the nanomaterial product. It 

is given corresponding values to monthly(1), weekly(5), or 

daily(10). 

Table. Data Requirements. 

Consideration of Uncertainty 

How does the model account for uncertainty? The model accounts for uncertainty addressing the 

data gaps in a separate variable in the overall risk calculation: specific framework conditions. If 

information is not known about the nanomaterial, the risk value is increased. If it is determined 

that the material is nanorelevant, and there is no knowledge about the material, then the 

mathematical representation will generate a value that is classified as a “need for nanospecific 

action” based solely on the specific framework conditions category. 

Availability and Usability 

Is a user-friendly interface available? The precautionary matrix excel form is downloadable 

from the website. The cells are programmed to calculate the individual category rankings as well 

as the overall risk value. 

Is the documentation complete and transparent? Yes. Also, the embedded functions in the excel 

sheet are accessible such that they can be evaluated to determine if the source code is consistent 

with the documentation. 

Application to nanomaterial behavior 

Does the model consider relevant chemical and physical properties to capture nanomaterial 

behavior (specific to the media modeled)? The precautionary matrix considers redox activity, 

catalytic activity, and stability of nanomaterials. However, it does not model these properties, nor 

simulate transport in the environment. 

143 
Appendix B 

How does the model address input data gaps associated with many traditional models? This 

model is used to quantify a precautionary need when developing nanomaterials. Thus, in the 

absence of extensive data, the precautionary matrix can serve as a tool to determine which 

nanomaterials may need precautionary action in development. The ability to store information 

and values in a precautionary matrix allows for an easy update of the matrix in the presence of 

emerging, scientifically relevant information. 

What kind of interpretations/predictions can be made from this model? The precautionary matrix 

cannot be used to predict the risks of nanomaterials, however it can be used to evaluate whether 

or not more information should be gathered on a particular nanomaterial and the process in 

which it is used. 

144 
Appendix B 

Hansen et al. (2008) 

Summary: This adaptive framework model permits classification of products containing 

nanomaterials into categories of expected, possible, and no expected exposure, based on 

information not typically used for risk assessment but more accessible. Using (a) information 

from the Woodrow Wilson International Center pertaining to the location and concentration of 

nanoparticles within consumer products and (b) best estimates available or worst-case 

assumptions, the model estimates consumer exposure to selected nanomaterials. Though this 

model is not designed specifically for the environmental modeling of nanomaterials, a similar 

framework could be employed to generate preliminary estimates of environmental exposure. 

KEY REFERENCES: 

Hansen SF, Michelson ES, Kamper A, Borling P, Stuer Lauridsen F, Baun A (2008) 

Categorization framework to aid exposure assessment of nanomaterials in consumer products. 

Ecotoxicology 17:438–447. 

CONTACT/AVAILABILITY INFORMATION: 

Steffen Foss Hansen (sfha@env.dtu.dk, Phone +45 45251593) 

Postdoc, Department of Environmental Engineering, Technical University of Denmark, DTU 

Bygningstorvet, Building 113, room 072, 2800 Kgs. Lyngby, Denmark 

PURPOSE AND SCOPE: 

What is the model purpose? This model proposes a framework to aid in exposure assessment of 

consumer products containing nanomaterials in the absence of traditional risk assessments. The 

model is designed to group nanomaterial-containing consumer products into three separate 

categories of: (1) expected to cause exposure; (2) may cause exposure; and (3) no expected 

exposure to the consumer. The model is also designed to categorize nanomaterial-containing 

products into groups pertaining to the: (a) chemical composition of the nanomaterial within the 

product; (b) location of the nanomaterial within the product; and (c) the type or class of 

nanomaterial within the product. 

What processes are simulated? The only process simulated in this model is an exposure 

assessment which is based on default values and equations taken from the Technical Guidance 

Document (TGD) on risk assessment for existing substance (European Commission JRC 2003). 

What are the primary assumptions? The primary assumption of this model is that exposure 

assessment of products based on nanotechnology must take into account the location of the 

nanomaterial in the product. 

What transport media are considered? No transport media are considered in this model because 

it is designed to predict exposure from the use of consumer products. 

What spatial and temporal scales does the model consider? Spatial and temporal scales are not 

included in this model. However, different life cycle stages of the consumer products are 

145 
Appendix B 

considered in the model to conduct exposure assessment (e.g. paint containing nanomaterials is 

evaluated for when it is in liquid form and in dried form). 

What are the forms of results produced? This model gives results of exposure as point estimates 

in terms of mg kg-1 bw d-1. 

EVALUATION: 

Background and History 

How extensively has the model been used and applied? To our knowledge, the model has only 

been used for the referenced study. 

What verification and validation has been conducted? There is no known verification or 

validation of the model with experimental results or environmental data. 

Complexity 

What physical and chemical properties are considered? The only physical properties considered 

are concentration of the nanomaterial within the consumer product. However, the model uses the 

TGD on risk assessment for existing substances from the European Commission JRC (2003), so 

it is possible that more properties are considered. 

What is the mathematical representation? The model calculates exposure assessments by 

estimating the quantity of active nanomaterial either: (a) on the skin per application of product 

(lotions); or (b) inhaled per application of the product. This value is given as a point estimate 

based on parameters such as chemical make-up of the nanomaterial, concentration of 

nanomaterial in the product, location of the nanomaterial in the product, and type of 

nanomaterial class. This value is then used to classify the product as: (1) expected to cause 

exposure; (2) may cause exposure; and (3) no expected exposure to the consumer. The authors 

stress that the estimated values should not be used as the basis for a risk assessment because 

many of the information needed for the exposure assessment is unavailable. 

What are the data requirements? The data requirements for the categorization framework are 

presented in the table below. Along with the data characteristic are a description of the data 

needed and the type of form the data is inputted as (e.g. categorical, point estimate, ranking, 

etc.). The model input parameters are not extensive, however many of the parameters such as 

concentrations of active substance within the product are likely unavailable for most nanoparticle 

containing products so are estimated to generate different scenarios. 

146 
 

 

 

Appendix B 

Data Characteristic Type of Input Description 

Chemical identity Categorical The nanomaetials are classified by the chemical composition of the 

nanomaterial (e.g. silver, silica, etc.) 

Product Categories Categorical The products are categorized into 8 different types: appliances, food 

and beverages, health and fitness, home and garden, automotive, 

cross-cutting, electronics and computers, and goods for children 

Nanomaterial location Categorical The 3 categories to characterize the distribution of nanomaterials 

within the products are: bulk, surface, and particles. 

Nanomaterial type Categorical The type of nanomaterials included one- or multiphase materials, 

patterned- or unpatterned films, and particles that are surface bound, 

suspended in liquids, suspended in solids, or free airborne particles. 

Exposure assessment 

parameters 

Point estimates These values include amount of product used per application, 

respiratory rates, body weight, body area, and other parameters 

applicable to the use of the product in calculation of exposure. 

Table. Data Requirements. 

Consideration of Uncertainty 

How does the model account for uncertainty? The main approach for uncertainty is to use worstcase assumptions or best estimates in the case of data gaps. 

Availability and Usability – not applicable 

Application to nanomaterial behavior 

Does the model consider relevant chemical and physical properties to capture nanomaterial 

behavior (specific to the media modeled)? The model does not consider many nanomaterial 

properties. The only physical properties considered are the chemical composition of the actual 

nanomaterial and the type of nanomaterial, which is presented in the data requirements section. 

How does the model address input data gaps associated with many traditional models? The 

model attempts to bypass traditional exposure assessment by using non-traditional parameters 

such as location of the nanomaterial in the product and concentration of nanomaterial within the 

product. However, some of this quantification was uncertain as well. The model design is 

intended to be updated as more information concerning nanomaterial location and nanomaterial 

concentration becomes available. However, this particular model is not intended for 

environmental exposure, so does not account for transport properties of nanomaterials. 

What kind of interpretations/predictions can be made from this model? Currently, the model is 

intended for exposure rates to humans. However, it is feasible that the model could be modified 

to calculate the expected exposure to the environment based on the parameters presented above. 

Once the estimated levels are calculated, the results could be used with models for transport 

within the environment. This might require an extensive amount of data though. 

147 
Appendix B 

Linkov et al. (2007) 

Summary: The multicriteria decision analysis (MCDA) presented below develops a method in 

which to prioritize several nanomaterials according to specified management objectives by 

evaluating nanomaterials based on the criteria of potential health and ecological impacts, 

societal importance, and stakeholder preference. By assigning relative importance weights to 

the above criteria and ranking different nanomaterials according to these criteria, decision 

makers can determine which nanomaterials to pursue (whether in production or research) based 

on multiple avenues of evaluation. 

KEY REFERENCES: 

Linkov, I, Satterstrom, F.K., Steevens, J., Ferguson, E., Pleus, R. 2007. Multi-criteria decision 

analysis and environmental risk assessment for nanomaterials. Journal of Nanoparticle Research 

9: 543-554. 

CONTACT/AVAILABILITY INFORMATION: 

Igor Linkov (Igor.Linkov@usace.army.mil, Phone: 617-225-0812) 

Research Scientist, US Army Engineer Research and Development Center, Concord, MA 

PURPOSE AND SCOPE: 

What is the model purpose? This model illustrated an example of MCDA application to the 

problem of prioritizing several nanomaterials according to specified management objectives. 

Three hypothetical alternative nanomaterials were considered, each with different social and 

economic values as well as environmental properties and associated risks and benefits. 

What processes are simulated? The processes simulated for this model are health and ecological 

effects based on public health effects, occupational exposure-related effects, and environmental 

effects, societal importance based on potential uses of the nanomaterials in manufacturing, 

potential uses in consumer products, and availability of alternatives, and stakeholder preference 

based on political, public, and scientific preferences. 

What are the primary assumptions? The main assumptions of the model inherently deal with the 

associated risks of corresponding model criteria. 

What transport media are considered? Transport media were not considered in this model. 

What spatial and temporal scales does the model consider? The model does not incorporate 

spatial and temporal scales. 

What are the forms of results produced? Based on their example, Nanomaterial #1 had the most 

benefits for industry, while Nanomaterial #2 and Nanomaterial #3 were more environmentally 

friendly, although the knowledge on the potential environmental risks and benefits of these 

materials was very uncertain. In their example, Nanomaterial #2 scored the highest and was thus 

the preferred alternative. Although Nanomaterial #1 might have been better economically and 

148 
Appendix B 

preferred socially, the decision process showed that Nanomaterial #2 was better overall because 

of its favorable environmental effects. 

EVALUATION: 

Background and History 

How extensively has the model been used and applied? To our knowledge, the model has only 

been used for the referenced study. However, MCDA has been used for multiple applications. 

What verification and validation has been conducted? There is no known verification or 

validation of the model with experimental results or environmental data. 

Complexity 

What physical and chemical properties are considered? No properties are considered for this 

alternative approach; however there are 9 sub-criteria evaluated for the calculation of the overall 

score. 

What is the mathematical representation? This model utilized the Analytic Hierarchy Process 

(AHP), one of the most widely used tools to help decision makers assign weights. In AHP, the 

category weightings were derived from a series of relative judgments in the form of a weightings 

ratio. The original AHP algorithms require assignments of the value on the scale from 1 to 9, 

while recent AHP adaptation allows incorporation of different scales including experimental and 

measurement values (Figueira et al., 2005). In their example, they assigned the weighting 

themselves. 

Based on their relative weightings, AHP derived normalized weightings for the criteria. Subcriteria and measures were compared and weighted in a pairwise manner similar to that for the 

main criteria. Once relative weightings were given for each of the sub-criteria, normalized 

weightings were calculated for use in scoring different alternatives (see breakdown in Table 2). 

The goal of the weighting process was to set absolute weights that reflect as closely as possible 

the relative ratings input by the user. In AHP procedures, weightings are calculated by finding 

the eigenvector corresponding to the highest eigenvalue of the weightings matrix. Other MCDA 

methods may use different procedures to elicit/calculate weights. 

What are the data requirements? The data requirements for quantification of the above simulated 

processes values for the 9 sub-criteria needed to calculate the scores for each main criteria, 

followed by an overall risk score. The model input parameters are not extensive and require 

assignment from the decision maker. Input parameters were also assigned relative weighting to 

aid in the quantification of the main criteria. 

Consideration of Uncertainty 

How does the model account for uncertainty? The model does not consider uncertainty because 

the decision makers assigned all the weights and evaluation criteria values as point estimates. 

149 
Appendix B 

Availability and Usability – not applicable 

Application to nanomaterial behavior 

Does the model consider relevant chemical and physical properties to capture nanomaterial 

behavior (specific to the media modeled)? The properties of nanomaterials were not included in 

the model. 

How does the model address input data gaps associated with many traditional models? The data 

gaps posed by many traditional models were addressed by developing a ranking system for 

nanomaterials based on the criteria of health effects, social importance, and stakeholder 

preference. However, the model cannot be updated to account for emerging, scientifically 

relevant information unless it changes the ranking values for each nanomaterial as they pertain to 

health impacts, social importance, and stakeholder preference. For instance, if information 

suggesting that nanomaterial #1 was extremely harmful to the environment, the ranking assigned 

to that nanomaterial would be lowered. 

What kind of interpretations/predictions can be made from this model? The model is designed to 

rank the different nanomaterials across all evaluation criteria. From this ranking, risk managers 

can make decisions on which nanomaterials to produce based on the relative health impacts, 

social acceptance, and stakeholder preference. The risk manager can also rerun the model with 

different weights to generate different scenarios, such that all angles of the development of 

nanotechnology can be examined. 

150 
Appendix B 

Linkov et al. (2009) 

Summary: This multicriteria decision analysis (MCDA) application addresses the problem of 

prioritizing several nanomaterials according to the potential risk to the environment. The 

MCDA attempts to incorporate several biological processes (bioaccumulation, bioavailability, 

toxic effects) as well as physical and chemical properties of nanomaterials (agglomeration, size, 

etc.) to categorize nanomaterials into groups of extreme-, high-, medium-, low-, and very low 

risk. By employing Monte Carlo methods, the MCDA explores all feasible values for criteria 

measurements and weights so that the robustness of nanomaterial categorization can be 

assessed. 

KEY REFERENCES: 

Linkov I, Steevens J, Chappell M, Tervonen T, Figuera JR, Merad M. (2009) Classifying 

Nanomaterial Risks Using Multi-Criteria Decision Analysis. In: Linkov I, Steevens JA (eds) 

Nanomaterials: risks and benefits. Springer, Dordrecht, pp 179—191. 

CONTACT/AVAILABILITY INFORMATION: 

Igor Linkov (Igor.Linkov@usace.army.mil, Phone: 617-225-0812) 

Research Scientist, US Army Engineer Research and Development Center, Concord, MA 

PURPOSE AND SCOPE: 

What is the model purpose? This model is designed to guide developers in nanomaterial research 

and application as well as promote the safe use and handling of nanomaterials by employing the 

use of a MCDA support system. The model uses performance metrics that define: (a) toxicity 

and physico-chemical characteristics of nanomaterials; and (b) expected environmental impacts 

through the nanomaterial-containing product life cycle. This model was explored because 

traditional risk assessment models for chemical and biological materials may not be applicable to 

nanomaterials for some time. 

This model uses a stochastic multicriteria acceptability analysis (SMAA-TRI) to explore all 

reasonable values for all model criteria parameters so as to group each product into specific risk 

categories. The SMAA-TRI was chosen because of its ability to address uncertainty by utilizing 

Monte Carlo simulations such that all possible parameter values and criteria weights could be 

assessed for the decision making tool. The goal of this MCDA was to rank the alternatives rather 

than select a single best alternative. In this case, the alternatives are the different nanomaterials 

and are ranked to prioritize the materials that need further study based on risk potentials. 

What processes are simulated? The model simulates biological processes based on 

bioavailability potential, bioaccumulation potential, and toxic potential are given subjective 

probabilities based on the nanomaterial characteristics/properties. 

What are the primary assumptions? The main assumptions of the model inherently deal with the 

associated risks of corresponding model criteria. For instance, a smaller-sized nanoparticle 

represents higher risk. 

151 
Appendix B 

What transport media are considered? Actual transport is not considered in this model but 

bioaccumulation potential considers the accumulation of particles absorbed from all sources of 

soil, water, air, and food. 

What spatial and temporal scales does the model consider? The model does not incorporate 

spatial and temporal scales. 

What are the forms of results produced? The results of this MCDA group nanomaterials into a 

risk categorization of: extreme-, high-, medium-, low-, and very low risk. The analysis also 

quantifies the percentage of scenarios that a particular nanomaterial fell into the relevant risk 

category (figure 2 of the article). 

EVALUATION: 

Background and History 

How extensively has the model been used and applied? To our knowledge, the model has only 

been used for the referenced study. However, MCDA has been used for multiple applications. 

What verification and validation has been conducted? There is no known verification or 

validation of the model with experimental results or environmental data. 

Complexity 

What physical and chemical properties are considered? The model includes five properties of 

nanomaterials relevant to fate and transport: (1) agglomeration; (2) reactivity/charge; (3) Critical 

functional groups; (4) contaminant dissociation; and (5) particle size. 

What is the mathematical representation? The intention of the model is to classify the alternative 

nanomaterials into the categories of: (1) extreme risk; (2) high risk; (3) medium risk; (4) low 

risk; or (5) very low risk. The results are shown in section 5 of the article. 

What are the data requirements? The data requirements for the MCDA are presented in the table 

below. Along with the data characteristic are a description of the data needed and the type of 

form the data is inputted as (e.g. categorical, point estimate, ranking, etc.). The model input 

parameters are not extensive, however many of the parameters such as bioavailability potential 

are likely unavailable for most nanoparticle containing products so are estimated to generate 

different scenarios 

152 
Appendix B 

Data Characteristic Type of Input Description 

Extrinsic nanomaterial Rates, 

characteristics categorizations, 

and estimates in 

distributional 

form. 

Biological processes Subjective 

probabilities 

Criteria weights Probability 

distributions 

The model includes data estimations for agglomeration rates, 

reactivity/charge, critical function groups, contaminant dissociation 

rates, and size. These values are in distributional form to capture the 

uncertainty surrounding the knowledge gaps concerning these 

characteristics 

Bioavailabilty potential, bioaccumulation potential, and toxicity 

potential are based on the nanomaterial characteristics given above. 

The above inputs are given weights to develop multiple scenarios for 

a given nanomaterial. These weights are in distributional form to 

capture the uncertainty surrounding the importance of the above 

characteristics. 

Table. Data Requirements. 

Consideration of Uncertainty 

How does the model account for uncertainty? The model accounts for uncertainty by conducting 

numerical simulations by comparing the effect of changing parameter values and criteria 

evaluations (weighting) on the modeling outcomes. Monte Carlo simulations quantify parameter 

imprecision by drawing parameter values from specified probability distributions. This method 

explores all feasible values for criteria measurements so that the robustness of nanomaterial 

categorization can be assessed. 

Availability and Usability – not applicable 

Application to nanomaterial behavior 

Does the model consider relevant chemical and physical properties to capture nanomaterial 

behavior (specific to the media modeled)? The evaluation criteria are aimed to quantify the 

relevant chemical and physical properties relevant to environmental transport of nanomaterials. 

Although the transport of material is not modeled, the potential of transport is estimated based on 

this quantification. 

How does the model address input data gaps associated with many traditional models? This 

model bypasses the data gaps associated with many traditional models not quantifying the actual 

values of necessary data. Rather, the model allows the decision maker to rank the importance of 

these data gaps. As emerging, scientifically relevant information becomes available, the decision 

maker will be able to alter the uncertainty levels of the model, as well as assign appropriate 

weights to the evaluation criteria. Thus, the decision makers will be able to make more accurate 

decisions about which nanomaterials need more research pertaining to risk assessment. 

What kind of interpretations/predictions can be made from this model? This model is designed to 

rank the nanomaterials into different categories of potential risk. By categorizing them, risk 

managers can prioritize the research needs for current nanotechnology. 

153 
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