Uncertainty, variability
and environmental risk analysis
Linnaeus University Dissertations
No 35/2011
U NCERTAINTY , VARIABILITY
AND ENVIRONMENTAL RISK
ANALYSIS
M ONIKA F ILIPSSON
LINNAEUS UNIVERSITY PRESS
UNCERTAINTY, VARIABILITY AND ENVIRONMENTAL RISK ANALYSIS.
Doctoral dissertation, School of Natural Sciences, Linnaeus University
2011.
ISBN: 978-91-86491-63-5
Printed by: Intellecta Infolog, Gothenburg
Abstract
Filipsson, Monika. (2011). Uncertainty, variability and environmental risk
analysis. Linnaeus University Dissertations No 35/2011. ISBN: 978-91-8649163-5. Written in English.
The negative effects of hazardous substances and possible measures that can be
taken are evaluated in the environmental risk analysis process, consisting of risk
assessment, risk communication and risk management. Uncertainty due to lack
of knowledge and natural variability are always present in this process. The aim
of this thesis is to evaluate some tools as well as discuss the management of
uncertainty and variability, as it is necessary to treat them both in a reliable and
transparent way to gain regulatory acceptance in decision making.
The catalytic effects of various metals on the formation of chlorinated
aromatic compounds during the heating of fly ash were investigated (paper I).
Copper showed a positive catalytic effect, while cobalt, chromium and
vanadium showed a catalytic effect for degradation. Knowledge of the catalytic
effects may facilitate the choice and design of combustion processes to decrease
emissions, but it also provides valuable information to identify and characterize
the hazard.
Exposure factors of importance in risk assessment (physiological
parameters, time use factors and food consumption) were collected and
evaluated (paper II). Interindividual variability was characterized by mean,
standard deviation, skewness, kurtosis and multiple percentiles, while
uncertainty in these parameters was estimated with confidence intervals.
How these statistical parameters can be applied was shown in two exposure
assessments (papers III and IV). Probability bounds analysis was used as a
probabilistic approach, which enables separate propagation of uncertainty and
variability even in cases where the availability of data is limited. In paper III it
was determined that the exposure cannot be expected to cause any negative
health effects for recreational users of a public bathing place. Paper IV
concluded that the uncertainty interval in the estimated exposure increased
when accounting for possible changes in climate-sensitive model variables. Risk
managers often need to rely on precaution and an increased uncertainty may
therefore have implications for risk management decisions.
Paper V focuses on risk management and a questionnaire was sent to
employees at all Swedish County Administrative Boards working with
contaminated land. It was concluded that the gender, age and work experience
of the employees, as well as the funding source of the risk assessment, all have
an impact on the reviewing of risk assessments. Gender was the most
significant factor, and it also affected the perception of knowledge.
Keywords: Contaminated land, exposure assessment, exposure factors, risk
analysis, risk perception, uncertainty, variability, probabilistic risk assessment
Sammanfattning
Negativa effekter orsakade av skadliga ämnen och möjliga åtgärder bedöms och
utvärderas i en miljöriskanalys, som kan delas i riskbedömning,
riskkommunikation och riskhantering. Osäkerhet som beror på kunskapsbrist
samt naturlig variabilitet finns alltid närvarande i denna process. Syftet med
avhandlingen är att utvärdera några tillvägagångssätt samt diskutera hur
osäkerhet och variabilitet hanteras då det är nödvändigt att båda hanteras
trovärdigt och transparent för att riskbedömningen ska vara användbar för
beslutsfattande.
Metallers katalytiska effekt på bildning av klorerade aromatiska ämnen under
upphettning av flygaska undersöktes (artikel I). Koppar visade en positiv
katalytisk effekt medan kobolt, krom och vanadin istället katalyserade
nedbrytningen. Kunskap om katalytisk potential för bildning av skadliga ämnen
är viktigt vid val och design av förbränningsprocesser för att minska utsläppen,
men det är också ett exempel på hur en fara kan identifieras och karaktäriseras.
Information om exponeringsfaktorer som är viktiga i riskbedömning
(fysiologiska parametrar, tidsanvändning och livsmedelskonsumtion) samlades
in och analyserades (artikel II). Interindividuell variabilitet karaktäriserades av
medel, standardavvikelse, skevhet, kurtosis (toppighet) och multipla percentiler
medan osäkerhet i dessa parametrar skattades med konfidensintervall.
Hur dessa statistiska parametrar kan tillämpas i exponeringsbedömningar visas i
artikel III och IV. Probability bounds analysis användes som probabilistisk
metod, vilket gör det möjligt att separera osäkerhet och variabilitet i
bedömningen även när tillgången på data är begränsad.
Exponeringsbedömningen i artikel III visade att vid nu rådande
föroreningshalter i sediment i en badsjö så medför inte bad någon hälsofara. I
artikel IV visades att osäkerhetsintervallet i den skattade exponeringen ökar när
hänsyn tas till förändringar i klimatkänsliga modellvariabler. Riskhanterare
måste ta hänsyn till försiktighetsprincipen och en ökad osäkerhet kan därmed
få konsekvenser för riskhanteringsbesluten.
Artikel V fokuserar på riskhantering och en enkät skickades till alla anställda
som arbetar med förorenad mark på länsstyrelserna i Sverige. Det konstaterades
att anställdas kön, ålder och erfarenhet har en inverkan på
granskningsprocessen av riskbedömningar. Kön var den mest signifikanta
variabeln, vilken också påverkade perceptionen av kunskap. Skillnader i de
anställdas svar kunde också ses beroende på om riskbedömningen finansierades
av statliga bidrag eller av en ansvarig verksamhetsutövare.
TABLE OF CONTENTS
LIST OF PAPERS................................................................................................. 3
ABBREVIATIONS ............................................................................................... 4
INTRODUCTION ............................................................................................... 5
The risk analysis process .................................................................................... 5
Uncertainty and variability ................................................................................. 7
Characterization of uncertainty and variability in risk assessments ................... 9
Aims of the included papers in a risk-analysis context .................................... 11
METHODS ......................................................................................................... 13
Catalytic effects of metal oxides....................................................................... 13
Variability and uncertainty in exposure factors ................................................ 14
Exposure assessments ...................................................................................... 16
Risk management of contaminated land ......................................................... 20
RESULTS AND DISCUSSION ........................................................................ 22
Catalytic effects of metal oxides....................................................................... 22
Variability and uncertainty in exposure factors ................................................ 22
Exposure assessments ...................................................................................... 24
Risk management of contaminated land ......................................................... 30
CONCLUSIONS................................................................................................. 32
ACKNOWLEDGEMENTS .............................................................................. 34
REFERENCES.................................................................................................... 35
APPENDIX.......................................................................................................... 43
LIST OF PAPERS
The following papers, referred to in the text by roman numerals, form the
basis of this thesis. Published papers are reprinted with permission from
Elsevier (papers I and III) and John Wiley and Sons (paper II).
I.
Öberg, T., Bergbäck, B., Filipsson, M. 2008. Catalytic effects by metal
oxides on the formation and degradation of chlorinated aromatic
compounds in fly ash. Chemosphere, 71, 1135–1143.
II.
Filipsson, M., Öberg, T., Bergbäck, B. 2011. Variability and
uncertainty in Swedish exposure factors for use in quantitative exposure
assessments. Risk Analysis, 31, 108-119.
III.
Filipsson, M., Lindström, M., Peltola, P., Öberg, T. 2009. Exposure to
contaminated sediments during recreational activities at a public
bathing place. Journal of Hazardous Materials, 171, 200-207.
IV.
Augustsson, A., Filipsson, M., Öberg, T., Bergbäck, B. Climate change
– an uncertainty factor in risk analysis of contaminated land. Submitted.
V.
Filipsson, M., Ljunggren, L., Öberg, T. Gender differences in risk
management of contaminated land. Manuscript.
3
ABBREVIATIONS
ABS
AF
ANOVA
AT
BaP equivalents
BW
CAB
Cd
CF
CI
CR
CS
CW
ED
EF
ET
IR
Kd
MLR
PAH
PBA
P-boxes
PCA
PCDD
PCDF
PLSR
RME
SA
SD
TRV
US EPA
WHO
4
Absorption factor (no unit)
Sediment-to-skin adherence factor (mg/cm2)
Analysis of variance
Period over which exposure is averaged (days)
Benzo[a]pyrene equivalents
Body weight (kg)
County Administrative Board (Länsstyrelsen)
Cadmium
Conversion factor (10-6 kg/mg)
Confidence interval
Contact rate, i.e. the amount of water swallowed while
swimming (L/h)
Chemical concentration in sediment (mg/kg)
Chemical concentration in water (mg/L)
Exposure duration (year)
Exposure frequency (days/year)
Exposure time, i.e. time spent in water (h/day)
Ingestion rate, i.e. intake of sediment from the
contaminated source (mg/day)
Soil-water distribution coefficient
Multiple linear regression
Polycyclic aromatic hydrocarbons
Probability bounds analysis
Probability boxes
Principal component analysis
Polychlorinated dibenzo-p-dioxins
Polychlorinated dibenzofurans
Partial least squares regression
Reasonable maximum exposure
Skin surface area available for contact (cm2/day)
Standard deviation
Toxicological reference value
United States Environmental Protection Agency
World Health Organization
INTRODUCTION
The release of hazardous substances may have negative effects on humans and
the environment. These effects, as well as possible measures taken, are
evaluated in a risk analysis process. Uncertainty and variability are inevitably
included in this process. Knowledge and reliable methods to deal with
uncertainty and variability are essential for transparency and trust in the risk
analysis process, which is necessary in order to gain regulatory acceptance in
decision making. This thesis aims to evaluate some of the tools available and
provide insight into how knowledge about uncertainty and variability in the
environmental risk analysis process can be managed.
The risk analysis process
The risk analysis process can be divided into the following three sub-processes:
risk assessment, risk communication and risk management (figure 1) (WHO
2004). The overall process can be seen as an iterative procedure, since previous
assessments may be altered and re-evaluated, and new hazards are also
continuously identified.
Risk assessment
Hazard identification
Hazard characterization
Exposure assessment
Risk characterization
Risk management
Risk
communication
Risk evaluation
Risk reduction
Risk monitoring
Figure 1. The risk analysis process.
The risk assessment itself can be further divided into different stages: hazard
identification, hazard characterization (dose-response assessment), exposure
assessment and risk characterization (figure 1) (National Research Council
1983, WHO 2004).
In the first step, hazard identification, potentially harmful factors are
identified. The question in the hazard identification is whether a factor can
cause negative effects in a system, (sub)population or an organism. A hazard
can be defined as a source with potential to cause harm, which can be
5
differentiated from the risk, which is the probability to harm or injure and can
thereby be expressed as a combination of probability and consequences
(Kaplan and Garrick 1981). According to the IPCS (International Programme
on Chemical Safety) risk assessment terminology a hazard is defined as an
“inherent property of an agent or situation having the potential to cause
adverse effects when an organism, system, or (sub)population is exposed to
that agent” and a risk as “the probability of an adverse effect in an organism,
system, or (sub)population caused under specified circumstances by exposure
to an agent” (WHO 2004).
The aim of the second step, hazard characterization, is to further describe
and characterize the hazard. It aims to determine possible adverse effects
caused by the identified hazard, qualitative or if possible, quantitative. This
can be done by determining the relationship between the dose of a substance
and the negative effects (dose-response assessment). These first two steps in
the risk assessment, hazard identification and hazard characterization, can also
be summarized and called hazard assessment.
Even though exposure is often quantified in an exposure assessment, the next
step of the risk assessment, it is necessary to bear in mind that this is an
assessment that aims to approximate an exposure scenario, not an exact
calculation of reality. These assessments are often based on models including
input variables that are associated with varying degrees of uncertainty and
variability. Questions to be answered in the exposure assessment are, for
example: which are the exposure pathways and who is exposed, how is the
pollutant transferred, and what is the magnitude of the exposure?
In the fourth and final stage of a risk assessment, risk characterization, the
previous stages are pulled together and conclusions are drawn regarding the
risk to the environment or the exposed population or organism. This can be
done qualitatively, or when possible quantitatively. Risk characterization forms
the final and conclusive part of a risk assessment.
Risk communication is the part of the risk analysis process where the
outcome of the risk assessment is transmitted to the parties concerned. It is an
interactive process which includes the exchange of information between risk
assessors, risk managers (decision makers), the media, stakeholders and the
public. This stage does not necessarily need to occur only after the risk
assessment, but can also be integrated into the assessment, as well as in the
next part of the risk analysis, risk management.
Factors such as knowledge and expertise, openness and honesty as well as
concern and care increase the perception of trust and credibility, which affect
the risk communication (Peters et al. 1997). Risk communication is also
closely linked to risk perception. Trust and confidence has shown to reduce
the risk perceived (Siegrist et al. 2005). Risk perception is affected by other
factors; for example if the risk is unknown (e.g. not possible to observe,
delayed effect, new risks), and if the risk is uncontrollable, global, catastrophic
or may be high for future generations (dread risk) (Slovic 1987). Several
6
studies point out gender as a factor that influences not only risk perception,
but also risk judgments (Davidson and Freudenburg 1996, Slovic 1999).
Risk management is the decision making process where political, social,
economic and technical factors are considered together with the outcome of
the risk assessment (WHO 2004). Transparency is an important factor in the
risk management process and has the potential to affect the perceived risk
(Sparrevik et al. 2011). In risk management, the risk is evaluated and if
necessary reduced and monitored in three different stages (figure 1). In risk
evaluation, the assessed risk is evaluated by being contrasted to the positive
outcome of a reduced risk, including for example economical values, lives
saved or better quality of life, and an improved environment, which then serve
as the basis for decision making. Risk reduction includes different measures
aiming to reduce, prevent and control risks. This element has also been
referred to as emission and exposure control (WHO 2004). The last part, risk
monitoring, aims to follow-up the development of risks.
Uncertainty and variability
Uncertainty and variability, both often referred to as uncertainties, are present
in and affect every risk assessment and need, therefore, to be considered.
Mathematical models are often used in risk assessment, and are associated
with a varying degree of uncertainty, both in the choice of model and in
parameters. Sources of uncertainty in empirical quantities can, for example,
include measurement errors, systematic errors, natural variation, inherent
randomness and subjective judgments (Granger Morgan et al. 1990, Regan et
al. 2002a). There are also uncertainties in language and uncertainties due to
disagreement between experts (Carey and Burgman 2008, Granger Morgan
and Keith 1995). Risk assessment is inherently subjective as it includes both
science and judgments (Slovic 1999).
Various taxonomies of uncertainties have been suggested (Cullen and Frey
1999, Granger Morgan et al. 1990, Rowe 1994). However, uncertainty that is
due to a lack of knowledge is often separated from natural variation
(variability) (Cullen and Frey 1999, Ferson and Ginzburg 1996, Hoffman and
Hammonds 1994), as has also been recommended by the United Stated
Environmental Protection Agency (US EPA 1995). In this thesis, uncertainty
refers to the uncertainty that arises from a lack of knowledge and variability
denotes natural variation.
Uncertainty that is due to incomplete information has, for example, been
referred to as epistemic uncertainty, subjective uncertainty, lack-of-knowledge
uncertainty, incertitude or ignorance (Cullen and Frey 1999, Ferson and
Ginzburg 1996). This uncertainty can be reduced by further investigations.
7
The other type of uncertainty is variability and it arises from natural
heterogeneity or stochasticity. It cannot be reduced, which differentiates it
from uncertainty that is due to lack of knowledge. However, it can be better
described, and the uncertainty in our knowledge of variability is thereby
reduced (Jager et al. 2001). Different terms used for variability include
stochastic uncertainty and aleatory uncertainty (Cullen and Frey 1999, PatéCornell 1996).
The following section outlines various types of uncertainty and variability,
beginning with uncertainty (Cullen and Frey 1999, ECHA 2008, US EPA
1992, US EPA 2001, WHO 2008):
Model uncertainty exists since models are never exact representations
of reality, but rather simplifications. Sources of model uncertainty can
be extrapolation, dependencies, assumptions and when a model is
used out of its applicability domain. Further, the complexity of a
model also contributes to uncertainty. Although model uncertainty
may decrease with the increasing number of model variables, the
estimation error will increase with the increasing number of variables
in the model, since there is uncertainty in the parameters included in
the model.
Parameter uncertainty is uncertainty in different types of quantities.
These can be both empirical quantities (measurable) and defined
constants. Sources of this uncertainty can, for example, be
measurement errors, the use of default data and sample uncertainty,
that is to say the representativeness of the data set and uncertainty in
the choice of statistical distributions.
Scenario uncertainty is uncertainty in assumptions about different
scenarios made in risk assessments; for example in the choice of
exposure pathways or in the description of the source and release of a
chemical. It arises from a lack of information on present conditions or
future scenarios.
Natural variation, or variability, can be of various types; here interindividual,
spatial and temporal variability are described (Cullen and Frey 1999, ECHA
2008):
8
Interindividual variability is variation between individuals, such as
differences in physiological parameters, lifestyle and consumption
rates. There is also variability within an individual: intraindividual
variability. This type of variability can also be mentioned as inter- and
intraspecies variability.
Spatial variability is variation in space, for example differences in the
distance between the source of the contamination and the exposed
individuals. In air and water, the concentration of the contaminant
can vary rapidly. Contaminations in soil also differ depending on
geographical variation.
Temporal variability is variation over time. For example, the pollutant
concentration in ambient air can vary dramatically over time,
depending on wind velocity and temperature, which can also be
mentioned as variability in environmental characteristics. The
breathing rate varies over a 24-hour period; food consumption varies
depending on the season (home-grown vegetables) and so forth. How
these factors are to be handled depends on, for example, the time
aspect of the risk assessment; whether it is a short-term risk
assessment (acute effects) or a long-term one.
There is also linguistic uncertainty in risk analysis (Carey and Burgman
2008), which means uncertainty in language that arises since natural language
often is vague, ambiguous, context dependent, not specific enough or exhibits
theoretical indeterminacies (Regan et al. 2002a).
Characterization of uncertainty and variability in
risk assessments
An appropriate treatment of variability and uncertainty in risk assessment is
essential as a basis for decision making (Aven and Zio 2011). The various
methods used in quantitative risk assessments as well as the flexibility in the
different methods imply that the inputs in a quantitative risk assessment, and
thus the characterization of variability and uncertainty, can take different
shapes: point estimates, probability distributions, probability boxes (p-boxes),
as well as fuzzy arithmetic including intervals (Darbra et al. 2008, Ferson
2002, Hammonds et al. 1994).
Deterministic risk assessment
In a deterministic risk assessment, each input parameter is given by a point
estimate. Variability and uncertainty can be taken into account when choosing
the input; however, variability and uncertainty are not controlled or evaluated
in the calculations. The traditional way of handling uncertainty and variability
has been to incorporate safety factors or use conservative assumptions, which
can lead to unrealistically high estimations which are neither transparent nor
efficient when further testing or measures might be necessary (Jager et al.
2001). Conversely, it is also possible to underestimate the exposure for
sensitive populations (Bonomo et al. 2000). Deterministic estimations cannot
elucidate the number of individuals that might be exposed to a dose over a
9
reference value or the probability of a certain exposure. Furthermore,
deterministic estimations are given with a precision that does not reflect the
uncertainty and variability that is inevitable in such assessments.
Probabilistic risk assessment
Probabilistic risk assessment is an alternative to deterministic risk assessment.
The interest in and attention given to probabilistic methods in the chemical
and environmental field have increased during the past years (Bogen et al.
2009, Jager et al. 2001, Lester et al. 2007, Mekel and Fehr 2001, Öberg and
Bergbäck 2005), although it has been used before in the nuclear field (US
NRC 1975).
Probability is the likelihood of a certain outcome and is expressed as a
number from 0 to 1, where 0 indicates that the outcome is impossible and 1
indicates that the outcome is sure to happen. Even though there is not one
single definition of probability, the definition “the extent to which an event is
likely to occur” is according to the ISO (the International Organization for
Standardization)/IEC (the International Electrotechnical Commission) Guide
73. In order to make a probabilistic evaluation of risk, it is consequently
necessary to account for uncertainty and variability. Thus, in probabilistic
methods, variability and uncertainty are characterized to obtain a more
transparent and better basis for decision making.
Probabilistic risk assessments can be performed in various ways, for
instance using interval calculations, Monte Carlo analysis or probability
bounds analysis (PBA) (Ferson 2002, Hammonds et al. 1994, US EPA
1997b). But probabilistic approaches require more work, quality assurance and
awareness of limitations. However, one procedure does not exclude the other;
point estimates can be used initially, and thereafter complemented by
probabilistic methods in cases where risks cannot be fully excluded.
Interval calculation is a simple method to evaluate variability and
uncertainty and it is similar to the point estimate but complemented with a
second value. It can be a minimum and maximum value or a best estimate and
a reasonable maximum exposure (RME).
Describing inputs as parametric or empirical distributions is a common
approach in probabilistic risk assessment. An empirical distribution is based on
data, while a parametric has a functional form and is defined by a few
parameters (Cullen and Frey 1999). Moments are used to describe the shape
of a probability distribution and thereby also the variability. The first, second,
third and fourth moments are mean, variance, skewness and kurtosis,
respectively. Kurtosis is the peakedness of a distribution. Skewness describes
the asymmetry of a distribution; thus, a symmetric distribution has a skewness
of zero.
In a Monte Carlo simulation, probability distributions are used to
characterize variability and values are randomly selected from these
distributions in a large number of iterations (US EPA 1997b). Monte Carlo
10
analyses can be performed one-dimensionally with only single distributions.
The distributions can also be complemented with estimations of uncertainty
and is thereby two-dimensional, and variability and uncertainty are then
propagated separately (WHO 2008). A separation of variability and
uncertainty is important since it will increase the accountability and
transparency of a risk assessment (US EPA 1997b).
While probability theory has been accepted as a suitable tool to describe
variability, there have been discussions on the proper way to describe
uncertainty; other methods such as interval analyses and fuzzy logic have been
suggested (Aven 2010, Darbra et al. 2008, Ferson and Ginzburg 1996). Thus,
probability distributions are an optimal way to describe variability when much
information is available. But it is usually not possible to give an exact
description of variability in the form of a distribution. The choice of a
distribution is associated with uncertainty and this step is therefore of great
importance and should be documented (Burmaster and Anderson 1994, Haas
1997, US EPA 1997b, US EPA 2001). In cases where the precise distribution
or the parameters used to define a distribution cannot be given, probability
boxes (p-boxes) may serve as an alternative since the exact distribution does
not need to be specified.
In a probability bounds analysis (PBA), probability boxes are defined by
combining probability theory with interval arithmetic, describing variability
and uncertainty, respectively (Tucker and Ferson 2003). Specific distribution
does not need to be chosen although it is possible. P-boxes do not represent a
single distribution, but rather a class of distributions (Tucker and Ferson
2003). P-boxes can be defined using different inputs depending on the
information available; for example distributions or a variety of different
statistical parameters, such as mean, standard deviation (SD) and percentiles.
This information on variability can be combined with uncertainty intervals
such as confidence intervals. A wide variety of statistics can be used in
different combinations to define the p-boxes. A further description and
examples of p-boxes are given in the method section as well as in the
appendix.
Aims of the included papers in a risk-analysis context
The research in this thesis is focused on different parts of the risk analysis
process and the overall goal is to provide information that aims to facilitate the
performance of transparent, comparable and trustworthy risk analyses.
In paper I, the aim was to investigate varying catalyzing potency of
different metals in the formation process of chlorinated aromatic compounds
during heating of fly ash. Chlorinated aromatic compounds are toxic,
11
persistent and unwanted by-products and a description of these emissions is
useful information in a hazard identification and characterization.
The main aim of paper II was to provide data on Swedish exposure factors
(physiological parameters, time use factors and food consumption) including
measures of variability and uncertainty. Exposure factors data that are welldescribed will facilitate the performance of risk assessments and contribute to
more transparent and comparable risk assessments. The statistics provided in
paper II can be used in deterministic risk assessments as well as in probabilistic
ones, as shown in paper III and IV where PBA was used as a probabilistic
approach.
In paper III, the exposure to a number of metals and polycyclic aromatic
hydrocarbons (PAHs) at a public bathing place was assessed. Previous
measurements in sediments from the deeper parts of the lake showed
contamination levels of concern (Sternbeck et al. 2003), and the aim was
therefore to investigate whether recreational activities such as swimming may
cause any significant adverse health effects.
PAHs are persistent and metals are not biodegradable; both of them have
the potential to be toxic for future generations as well. The aim of paper IV
was to exemplify how future climate changes may affect cadmium exposure for
4-year-old children at a highly contaminated iron and steel works site in
southeast Sweden. Of the 39 model variables six were assessed to be sensitive
to a change in climate, and thus changed in two different climate change
scenarios and compared to a present scenario.
While papers I to IV mainly concern risk assessment, paper V is about risk
management. These processes are equally important since the information
about the risk needs to be managed in order to lead to improvement and
thereby good health and environment. A questionnaire was sent to employees
at all the Swedish County Administrative Boards (Länsstyrelsen) working
with contaminated land. The aim was to investigate if the employees’ gender,
age and experience affect their reviewing of risk assessments and their
perception of knowledge gained from the research programme Sustainable
Remediation. The funding source of the contaminated land project was also
considered: projects financed by the government or by a legally responsible
operator.
12
METHODS
Catalytic effects of metal oxides
Chlorinated aromatic compounds, such as dibenzo-p-dioxins (PCDD) and
dibenzofurans (PCDF) are toxic, persistent and unwanted by-products of the
combustion process. To investigate the variability in catalytic effects of
different metal oxides on the formation and degradation of chlorinated
aromatic compounds, fly ash was mixed in glass tubes with metal oxides in
various combinations (paper I). The fly ash originated from an electrostatic
precipitator at a biofuel incinerator, where the fuels consisted of household
and industrial wastes (paper and textiles). The content of fly ash had been
investigated previously, and it includes a wide variety of different elements and
chlorinated aromatics (Öberg et al. 2007b). The glass tubes were sealed with
permeable polyurethane foam plugs and heated. The test tubes with fly ash
and foam plugs were thereafter analysed at an accredited laboratory as a
composite sample. The samples were analysed for chlorinated benzenes,
polychlorinated dibenzo-p-dioxins (PCDD) and dibenzofurans (PCDF).
Factorial experiment
The catalytic effects of metal oxides (magnesium, yttrium, titanium,
vanadium, niobium, chromium, molybdenum, tungsten, manganese, iron,
cobalt, nickel, copper, zinc and tin) were statistically evaluated by a factorial
experiment. The aim with an experimental design like this is to analyse each
factor separately, without confounding with the other factors.
Fifteen factors (the metal oxides) were investigated initially by a resolution
III fractional factorial, including 16 runs and two replicates. This experiment
was not enough for separating main effects from two-factor interactions, since
the number of significant factors was too high. The resolution III experiments
were therefore complemented by a fold-over, giving a resolution IV fractional
factorial, with a total of 32 runs and four replicates. The main effects were
thereby separated from the two-factor interactions. These two experimental
batches were performed with a time interval of six weeks. As control two
replicates without any metal oxides were included in each batch.
Data analysis
The data analysis was conducted by linear multiple linear regression (MLR),
analysis of variance (ANOVA) and partial least squares regression (PLSR).
13
The software programs Design-Expert v6.0 (Stat-Ease Inc.) and Unscrambler
v9.1 (CAMO software A/S) were used for the data analysis.
Multiple linear regression (MLR) is an approach to analyse one response
variable, and it was used to fit linear polynomial models to the data. The
statistical significance of these models was assessed with an ANOVA.
Partial least squares regression (PLSR) is a multivariate approach, meaning
that several variables for each observation can be analysed simultaneously. In
this case correlations between different congeners of chlorinated benzenes,
PCDD and PCDF were analysed.
Variability and uncertainty in exposure factors
Information on relevant non-chemical specific exposure factors was compiled
and evaluated in paper II. With the help of literature searches and direct
contact with researchers and other external information contributors,
information about exposure factors, such as body weight, time use, food
consumption, was collected from official statistics, technical reports and the
scientific literature. Primary data were obtained from external studies (Becker
and Pearson 2002, Berg et al. 2009, Boldeman et al. 2004, Boldemann et al.
2006, Boström 2006, Pettersson and Rasmussen 1999, Sepp et al. 2002,
Västernorrland County Council n.d.) and were used for further statistical
evaluations with the kind permission of the data owner. The statistical
evaluations included characterizations of interindividual variability as well as
uncertainty. Interindividual variability was described by arithmetic mean,
standard deviation (SD), skewness, kurtosis and multiple percentiles. The
calculated percentiles were 1, 5, 10, 25, 50 (median), 75, 90, 95 and 99;
however, the 1st and 99th percentiles were not always calculated depending on
sample size. Calculations were made with SPSS 15.0 for Windows (SPSS
Inc., Chicago, IL).
All the above-mentioned measurements of interindividual variability are
more or less uncertain. To describe uncertainty, 95% confidence intervals (CI)
were estimated with bootstrapping. The Excel add-in module Crystal Ball
v7.2.2 (Decisioneering Inc., Denver, CO) was used with 10 000 bootstrap
replications for the calculations.
Bootstrapping
Bootstrapping, or “resampling of data with replacement”, is a technique used
for statistical inference, such as estimating confidence intervals, to assess the
accuracy of different parameters (Efron and Tibshirani 1993). The term refers
to the idiomatic expression “to pull oneself up by one’s bootstraps”, in this case
meaning that only primary data are used for the statistical analyses, i.e. the
14
resampling is non-parametric (empirical distributions are used). Therefore, no
underlying assumption about distribution of data is needed, unlike many other
statistical analyses. This is an advantage since assumptions about underlying
distribution are uncertain (Binkowitz and Wartenberg 2001, Sander et al.
2006). Furthermore, bootstrapping can provide confidence intervals where
analytical mathematical solutions are missing (Frey and Burmaster 1999).
But the primary data limits the bootstrap analysis and thus this analysis
only addresses the uncertainty that comes from the error due to a finite sample
size. Other sources of uncertainty, such as analytical error, biased sampling
designs, bias in self-reporting or differences between the population in the
survey and the population of concern in a risk assessment are not considered.
Some of these sources of uncertainty are discussed in paper II.
Bootstrapping was used to generate confidence intervals (95%) for all
parameters that describe interindividual variability: mean, SD, skewness,
kurtosis and multiple percentiles.
Bootstrapping works as follows (Efron and Tibshirani 1993):
Values from the dataset are randomly selected. Each sample is
replaced after selection and the random selection continues until
as many values have been selected and replaced as there are values
in the data set. If a dataset contains 841 values (as in the case for
girls’ body weight and skin surface area in table 1 under Results
and discussion), 841 values are randomly selected and replaced.
This means that some values might be selected several times and
others not at all.
Parameters (mean, SD, skewness, kurtosis and multiple
percentiles) are calculated in the new dataset.
This process is repeated a large number of times, in this case
10 000 so-called bootstrap replications. These 10 000 replications
each contain 841 values drawn from the original primary data set.
This will generate 10 000 different estimations of each parameter,
from which confidence intervals can be calculated from the
percentiles. The 95th percentile is then given by the 2.5th and 97.5th
percentile.
Since confidence intervals for high and low percentiles were calculated,
10 000 replications were chosen. A large number of bootstrap replications are
then needed to provide a stable estimate since empirical data are fewer in the
tails of the distribution.
15
Exposure assessments
Deterministic and probabilistic risk assessments have been performed both in
papers III and IV. Paper III concerns exposure to metal- and PAHcontaminated sediments at a public bathing place, and paper IV concerns
exposures of cadmium at a highly polluted iron and steel works site and the
possible impacts of uncertainty due to climate changes.
Exposure to contaminated sediments at a public bathing place
The exposure to contaminants in sediment and lake water during recreational
activities, such as swimming and playing in water at a public bathing place was
assessed at Lake Trekanten, Stockholm, Sweden. Best estimates (average) and
reasonable maximum exposures (RME) (95th percentile) were assessed for a
number of metals and PAHs, which were complemented by a probabilistic
assessment performed by probability bounds analysis (PBA).
A multiple pathway model, which in this case is a mathematical
description of the exposure process, was used for the exposure assessment. The
exposure pathways considered were oral intake of contaminants from lake
water, oral intake of contaminants from sediment and dermal uptake of
contaminants from sediment. The exposure model conforms to the US EPA
multiple pathway model for Superfund risk assessments (US EPA 1989).
Oral intake of contaminants from lake water while bathing, mg/kg-day (Iw)
is determined by chemical concentration in water, mg/L (CW), amount of
water swallowed, L/h (CR), exposure time, i.e. time spent in water, h/day
(ET), exposure frequency, i.e. days spent at the site, days/year (EF), exposure
duration, years (ED), body weight, kg (BW) and the period over which
exposure is averaged, days (AT):
Iw =
CW × CR × ET × EF × ED
BW × AT
(Eq. 1)
Oral intake of contaminants from sediment, mg/kg-day (Is) is determined by
chemical concentration in sediment, mg/kg (CS), ingestion rate, i.e. intake of
sediment from the contaminated source, mg/day (IR), exposure frequency,
days/year (EF), exposure duration, years (ED), body weight, kg (BW) and the
period over which exposure is averaged, days (AT). A conversion factor of 10-6
kg/mg is also included:
Is =
16
CS × IR × CF × EF × ED
BW × AT
(Eq. 2)
Dermal uptake of contaminants from sediment, mg/kg-day (Idu) is determined
by chemical concentration in sediment, mg/kg (CS), skin surface area available
for contact, cm2/day (SA), sediment-to-skin adherence factor, mg/cm2 (AF),
absorption factor, no unit (ABS), exposure frequency, days/year (EF),
exposure duration, years (ED), body weight, kg (BW) and the period over
which exposure is averaged, days (AT). A conversion factor of 10-6 kg/mg is
included:
I du =
CS × CF × SA × AF × ABS × EF × ED
BW × AT
(Eq. 3)
When calculating with imprecise figures, such as intervals or probability boxes,
repeated parameters can result in greater uncertainty than necessary (Ferson
2002). To avoid repetition of exposure factors in the probabilistic calculation,
the exposure pathways (Eq. 1-3) were reorganized and summarized as follows:
I tot =
EF × ED
× ((CW × CR × ET) + (CS × CF × (IR + (SA × AF × ABS))))
BW × AT
(Eq. 4)
To estimate the contaminant uptake as accurately as possible, the exposure
factors in equations 1-4 should be characterized. Behaviour (exposure time
and exposure frequency) was investigated on site at the bathing place
(Lindström 2006). Sediment and water samples were collected at the bathing
site (Peltola 2006, Peltola 2007).
Other exposure factors data were compiled from various sources. Body
weight and skin surface area are according to paper II. Accidental water
ingestion while swimming was investigated by the US EPA in a pilot study
and a full field sampling study (Dufour et al. 2006, Evans et al. 2006). Primary
data from the full field sampling study (Evans et al. 2006) were used to
characterize variability and uncertainty with the same approach as in paper II.
Children’s intake of sediments was estimated according to the Exposure
Factors Handbook and the Child-Specific Exposure Factors Handbook (US
EPA 1997a, US EPA 2002). A conservative estimate of the mean value was
chosen as a best estimate since it is reasonable that the exposure to sand is
higher at a sandy bathing place. A sediment-to-skin adherence factor for
children was estimated by combining sediment-to-skin adherence data (Shoaf
et al. 2005) with the information on the percentage of skin surface area per
body part (US EPA 1985).
The Risk Assessment Guidance for Superfund Volume I: Human Health
Evaluation Manual (Part E, Supplemental Guidance for Dermal Risk
17
Assessment) recommends that, due to a lack of further information, the same
absorption factor as for soil is used for sediment (US EPA 2004). In the same
report, an absorption factor of 13% has been recommended specifically for
PAHs, based on a study by Wester et al. (1990). This value is likely an
overestimation for the exposure scenario at Lake Trekanten, since the contact
time was 24 hours in the survey. A number of additional studies also indicate
that 13% for PAHs is an overestimate (Abdel-Rahman et al. 2002, Kao 1989,
Roy and Singh 2001, Sartorelli et al. 2001, Yang et al. 1989). Further, the
absorption has been shown to decrease when the contaminants are mixed with
the soil for a longer period of time (Abdel-Rahman et al. 2002, Roy and Singh
2001). For these reasons, half the value was used as the best estimate.
Climate change – an uncertainty factor in risk analysis
The exposure assessment in paper IV considers climate change by comparing a
present-day exposure scenario with two possible scenarios in which climatesensitive variables were altered. The investigated site was Kallinge Bruk, a
heavily contaminated former iron and steel works site in southeastern Sweden
(Sander and Öberg 2006).
In the two climate change scenario the climate-sensitive variables were
changed in an order of 5-10% and 15-20% respectively. The variables that
were assessed, by a literature review, as climate-sensitive were soil moisture,
groundwater recharge, hydraulic conductivity, soil-water distribution
coefficient (Kd) and bioconcentration factor for root respective stem. A further
explanation with motivation for the choice of climate sensitive variables is
given in paper IV.
A similar multiple pathway model to that used in paper III was also
applied for this exposure assessment. The model used in this case was
developed by the Swedish Environmental Protection Agency for use in
contaminated land risk assessments (Swedish EPA 2009). The exposure
pathways considered were oral intake of contaminants from ground water and
vegetables, as well as direct soil contact (inhalation of dust, ingestion of soil
and dermal contact). Inhalation of volatile compounds is considered in the
original model but was not included in this assessment as Cd is not volatile.
The exposure model and input variables are thoroughly described in paper IV
with its appendix and the exposure assessment is therefore not further
exemplified and described here.
Probability bounds analysis (PBA)
The probabilistic approach applied in paper III and IV was probability bounds
analysis (PBA) using the software program RAMAS Risk Calc v4.0 (Applied
Biomathematics, Setauket, NY) (Ferson 2002). The method was originally
developed in the 1980s (Frank et al. 1987, Williamson and Downs 1990) and
18
the approach has thereafter been applied in different context in the
environmental and technical field (Karanki et al. 2009, Kriegler and Held
2005, Regan et al. 2002b), including assessment of contaminated land (Regan
et al. 2002c).
In a PBA, variability and uncertainty are propagated separately by letting
the input variables be described by probability boxes (p-boxes) and the result is
thus also presented as p-boxes. P-boxes (examples are shown in figure 2 and
the appendix) are delimited by two cumulative distribution functions.
Variability is then represented by the probability distributions and uncertainty
by interval bounds (the horizontal distance between the two distribution
functions).
1
BW3
P-boxes are delimited by:
min, max and uncertainty
intervals of mean and SD
0.5
BW2
min, max and uncertainty
intervals of mean, SD and
multiple percentiles
BW1
normal distribution specified
with uncertainty intervals of
mean and SD
0
0
10
20
30
40
Figure 2. P-boxes, describing body weight (kg) of girls (age 4), delimited by different parameters
but using the same data from table 1. The data originates from paper II. The y-axis shows
cumulative probability from 0 to 1.
Parametric distributions, for example normal or log-normal, with or
without uncertainty bounds on the parameters that specify the distribution can
be used to define p-boxes. Figure 2 shows three different p-boxes. The
19
narrowest is defined with a normal distribution specified with uncertainty
intervals (95% confidence intervals) of the mean and standard deviation (SD).
However, assumptions about a specific distribution are uncertain, as
previously highlighted (Binkowitz and Wartenberg 2001, Filipsson et al. 2008,
Sander et al. 2006). But p-boxes can also be constructed using available
information without making any further assumptions about distributions.
In paper II, interindividual variability was described by the different
statistical parameters mean, standard deviation (SD), skewness, kurtosis and
multiple percentiles. Confidence intervals (95%) were used as estimates of
uncertainty of the parameters, which can be used to define p-boxes as in
papers III and IV. Figure 2 also shows a p-box defined by minimum,
maximum and uncertainty intervals of the mean, SD and multiple percentiles.
If little information about the input variable is known, the p-box can be
delimited by less information. This means that available data can be fully
applied (Ferson 2002).
A wider p-box delimited by minimum, maximum and uncertainty intervals
of the mean and SD is also shown in figure 2. Less information will generate a
wider p-box along the horizontal axis. PBA thus consider the uncertainty in
the selection of input distributions (Ferson 2002, Ferson and Ginzburg 1996),
since a certain p-box encloses all possible distributions that are in agreement
with the specified constraints. The bounds will be tighter if more data is
specified and the p-box will then gradually move closer to the true
distribution. In the exposure assessments in paper III and IV, one parametric
distribution, the lognormal, was only used to define the p-box in the case of
cadmium soil concentration in paper IV as more data were available.
Risk management of contaminated land
The questionnaire
A questionnaire was sent to employees working with contaminated land at all
Swedish County Administrative Boards (CAB), a government authority in
each of the 21 Swedish counties. The aim was to investigate if the personal
factors gender, age and experience affect the reviewing of risk assessments.
The funding source of the contaminated land project was also considered by
asking the same questions for projects financed by governmental grants and by
the legally responsible operator, respectively. Further, the questionnaire
included questions and statements about perception and the application of
knowledge gained from the research programme Sustainable Remediation,
which involved around 50 projects during the period 2003-2009 (Svensson et
al. 2009). All questions and statements are given in paper V. The respondents
rated answers from 1 to 5, where 1 is “do not agree at all” and 5 is “totally
20
agree”, with some exceptions where the answer had to be given as text,
numbers or yes/no.
Data analysis
The respondents’ answers with respect to their gender, age and experience
were analysed with two multivariate methods, principal component analysis
(PCA) and partial least squares regression (PLSR). Both PCA and PLSR are
based on linear transformations to a limited number of orthogonal factors. In
PCA the covariance between the original variables are maximized (Jackson
1991). Patterns in the whole data set can thereby be identified initially. This
analysis showed that gender was the most important factor and the correlation
between respondents’ gender and their answers was therefore analyzed with a
discriminant analysis using PLSR. In PLSR the covariance between a linear
combination of the independent predictor variables and a linear combination
of the dependent response variables are maximized (Martens and Næs 1989).
Jack-knifing was used to assess the statistical significance (Westad and
Martens 2000). The lengths of employment and working experience were logtransformed. Gender, job description and yes/no questions were coded into
dummy variables. The variables were standardized to zero mean and unit
variance before the data analysis. The analysis was performed using the
software program Unscrambler v9.1 (CAMO Process, Oslo, Norway).
Additional t-tests, with significance level of α=0.05, were performed using
PASW Statistics 18 (SPSS Inc., Chicago, IL, USA).
21
RESULTS AND DISCUSSION
Catalytic effects of metal oxides
The catalytic activity of copper in the formation process of chlorinated
aromatic compounds has been shown in several studies (Hatanaka et al. 2003,
Takayuki et al. 2007, Taylor and Lenoir 2001, Öberg and Öhrström 2003).
Other metals also seem to affect the catalytic formation or degradation (Öberg
et al. 2007a, Öberg et al. 2007b).
In line with previous studies, copper showed a positive catalytic effect on
the formation of chlorinated aromatic compounds in paper I. A weak positive
effect could also be observed for zinc. Conversely, chromium, cobalt and
vanadium showed catalytic effects for degradation of chlorinated aromatic
compounds. A weak negative effect was observed for iron. The other metals
did not yield any significant effects.
These results found contribute to the understanding of formation and
degradation of chlorinated aromatic compounds in thermal processes
(combustion, metal industries, etc.) and can provide valuable information for
use in risk assessment involving these compounds. The information can also
lead to improved risk management strategies to reduce the emissions. The
statistical experiment design was a useful approach to screen the effects of 15
metals at the same time and thereby identify and characterize hazards.
Variability and uncertainty in exposure factors
Variability has been presented previously in a number of secondary sources
(ECETOC 2001, US EPA 1997a, US EPA 2008, US EPA 2009, Vuori et al.
2006), but information about uncertainty is mostly lacking. In paper II, data
on Swedish exposure factors including variability and uncertainty are
presented under the following categories:
Physiological parameters (body weight and skin surface area).
Time use (time outdoors during stay in preschools).
Intake of food and water (vegetables, fruit and berries and tap water).
The data analyses showed that there is a major interindividual variability
inherent in these exposure factors, which must be considered in risk
assessments. However, there is not only variability between individuals within
the same gender and age group, but also between different age groups and
22
between females and males. The exposure factor parameters are also uncertain
and here these uncertainties are represented by 95% confidence intervals. The
statistics provided can be used as point estimates, as well as in probabilistic
assessment, as exemplified in paper III and IV.
As example of the data, table 1 shows girls’ body weight and total skin
surface area. Additional statistics are given in paper II and in a more extensive
report in Swedish (Filipsson et al. 2008).
Table 1. Girls’ (age 4)1 body weight and total skin surface area calculated using data from the
database Epibarn (Västernorrland County Council n.d.).
N
Mean
SD
Skewness
Kurtosis
Body weight (kg)
Total skin
surface area (m2)
841
841
18.2
(18.0-18.4)
2.8
(2.5-3.0)
1.5
(0.8-2.1)
8.7
(4.1-13.4)
0.75
(0.74-0.75)
0.07
(0.06-0.07)
0.98
(0.49-1.49)
6.19
(3.53-9.34)
Confidence interval
(95%) within brackets
representing uncertainty
Percentiles
12.8
0.61
(12.3-13.8)
(0.59-0.63)
14.6
0.65
5
Variability
(14.3-14.8)
(0.64-0.66)
15.2
0.67
10
(15.0-15.4)
(0.66-0.67)
16.4
0.70
25
(16.2-16.6)
(0.70-0.71)
17.8
0.74
50
(17.6-18.0)
(0.73-0.74)
19.6
0.78
75
(19.2-19.8)
(0.77-0.79)
21.6
0.83
90
(21.0-22.0)
(0.82-0.84)
23.4
0.87
95
(22.6-24.2)
(0.85-0.89)
27.1
0.94
99
(24.8-28.2)
(0.91-0.97)
1
Mean age was 4.1 years. Most children were aged between 4.0 and 4.9: only 7% were aged
3 years and only 1.5% 5 or 6 years.
1
There is still a need for more supporting data, both on uncertainty and
variability. Data must be collected, evaluated and made available for risk
23
assessors. There is a lack of information for some factors, in particular the
consumption of home-grown vegetables, tap-water consumption (from private
wells) and time-use patterns. Several factors were not evaluated in this study,
such as chemical specific exposure factors. Dependencies between exposure
factors were not investigated, but should preferably be characterized. There is
also an ongoing need for collection and evaluation of exposure factors data
since new studies are published constantly. During the data compilation
presented in paper II, new surveys without published results were found, for
example on time spent outdoors1 and time spent by children in day care
centers2. Furthermore, some surveys recur over time, for example Health on
Equal Terms was performed in 2010 for the seventh time (FHI 2010). Body
weight, which has increased over time (Berg et al. 2005, Portier et al. 2007,
Rasmussen et al. 1999), is included in this survey. Other factors, such as food
consumption, also change over time (Becker and Pearson 2002), which
motivates a re-evaluation with time.
Exposure assessments
The results from the exposure assessments performed in papers III and IV are
presented and discussed below under the headings “Exposure to contaminated
sediments at a public bathing place” and “Climate change – an uncertainty
factor in risk analysis”, respectively.
Exposure to contaminated sediments at a public bathing place
PAHs are a group of ubiquitous environmental contaminants which include
more than 100 substances that humans are exposed to, most often as mixtures
rather than individual compounds (Ramesh et al. 2004). The exposure
assessment for PAHs for children aged 1 to 6 years are shown below. Other
exposure durations have been evaluated and are presented in paper III. The
assessment is also presented in a more extensive report in Swedish (Filipsson
and Öberg 2008).
Deterministic exposure assessment
The contaminant intakes for recreational users of Lake Trekanten were
estimated using the exposure model (equations 1-4), the measured chemical
1
Adonix (Adult onset asthma and nitric oxide) at Occupational and Environmental Medicine, the
Sahlgrenska Academy, University of Gothenburg, Sweden. Publication is in preparation (personal
communication, Kristina Wass, January 24, 2011).
2
Daghemsmiljöns betydelse för astma och allergiska besvär hos barn at SP Technical Research
Institute of Sweden. Publication is in preparation (personal communication, Thorbjörn Gustavsson,
January 14, 2011).
24
concentrations in sediment and water, and the estimated exposure factors
shown in table 2 for the deterministic assessment and in the appendix for the
probabilistic assessment. Both best estimates and reasonable maximum
exposures were calculated using point estimates. Since some PAHs are
genotoxic and carcinogenic, the calculations are averaged over a lifetime (80
years), which means that the exposure is averaged over 29 200 days (365 days
× 80 years). The toxic equivalency factors suggested by Nisbet and LaGoy
were adopted, and the chemical concentrations of individuals PAHs were
recalculated to benzo[a]pyrene equivalents (BaP equivalents). With this
procedure individual PAHs were multiplied with weighting factors and
summarized (Nisbet and LaGoy 1992).
Table 2. Parameters used in the deterministic exposure calculation for PAHs in the case of 4-yearold children.
Best
RME
estimate
Parameter Definition
ABS
Absorption factor (no unit)
0.65
0.13
AF
Sediment-to-skin adherence factor (mg/cm )
0.70
1.17
BW
18.2
14.6
0.054
0.16
CS
Body weight (kg)
Contact rate (water swallowed while swimming)
(L/h)
Chemical concentration in sediment (mg/kg)
0.018
0.074
CW
Chemical concentration in water (mg/L)
1.0E-5
1.5E-5
EF
Exposure frequency (days/year)
32
70
ET
Exposure time, i.e. time spent in water (h/day)
1
Intake of sediment from the contaminated source
200
(mg/day)
Skin surface area available for contact (cm2/day) 7500
CR
IR
SA
2
4
400
6500
The RME daily intake is calculated as follows (Eq. 1-3):
I tot = I w + Is + I du =
0.000015 × 0.16 × 4 × 70 × 6 0.074 × 400 × 0.000001 × 70 × 6
+
+
14.6 × 29200
14.6 × 29200
0.074 × 0.000001 × 6500 × 1.17 × 0.13 × 70 × 6
=
14.6 × 29200
9.5E - 9 + 2.9E - 8 + 7.2E - 8 = 1.1E - 7 mg/kg - day
25
This calculation shows that the most important exposure pathway for BaP
equivalents is skin absorption. However, skin absorption is likely a highly
uncertain pathway since several studies indicate that the absorption factor of
13% is overestimated (Abdel-Rahman et al. 2002, Kao 1989, Roy and Singh
2001, Sartorelli et al. 2001, Yang et al. 1989). By replacing the exposure factors
by best estimates (table 2), a best estimate of the exposure can be calculated.
This calculation does not consider differences in bioavailability by oral
intake. These differences increase the overall uncertainty in the intake
estimates. However, sufficient data were not available to characterize this
additional uncertainty.
Depending on the characteristics of some contaminants, the estimated
daily intake can be averaged over a year rather than a life-time, which is
further explained in paper III. In practice it means that exposure duration
(ED) can be eliminated and the period over which exposure is averaged (AT)
can be set to 365 days, since when ED is X years, depending on the assumed
exposure scenario, AT will be X × 365 days and the number of years is
therefore not relevant.
Probabilistic exposure assessment
As an example, an exposure assessment is shown for someone living in the
vicinity between 1 and 6 years of age. Equation 4 was used for the probability
bounds analysis (PBA). Information about how the probability boxes were
entered into Risk Calc is given in the appendix. The result of the probability
bounds analysis can be viewed both numerically and graphically as p-boxes.
Uncertainty intervals in median and 95th percentile average lifetime daily
intake of BaP equivalents are shown in table 3, and the corresponding p-boxes
in figures 3 and 4.
There are often correlations between exposure factors. Dependencies could
not be excluded for some factors in this PBA, which results in wider
uncertainty intervals. Partial dependencies were assumed between some
factors: body weight and the intake estimates, sediment intake and skin
surface area, and sediment-to-skin adherence factor and absorption factor.
Table 3. Uncertainty intervals in daily intakes of BaP equivalents during age 1 to 6 years on a
lifetime basis, assuming independence or partial dependencies between exposure factors (mg/kgday).
Median
95th percentile
[1.1E-11, 1.9E-08]
[1.5E-10, 1.8E-07]
Partial dependencies [3.0E-12, 7.2E-08]
[2.1E-11, 5.6E-07]
Independence
26
1
0.5
Intake1_6_independence
0
0
1e-06
2e-06
3e-06
Figure 3. P-box. Intakes of BaP equivalents during age 1 to 6 years on a lifetime basis (mg/kgday), assuming independence of exposure factors. The y-axis shows cumulative probability from 0
to 1.
1
0.5
Intake1_6_dependence
0
0
1e-06
2e-06
3e-06
Figure 4. P-box. Intake of BaP equivalents during age 1 to 6 years on a lifetime basis (mg/kgday), assuming partial dependencies between exposure factors. The y-axis shows cumulative
probability from 0 to 1.
27
The best estimate and RME assessed deterministically are found within
the interval of the probabilistic uncertainty interval for the median and 95th
percentile respectively. The upper uncertainty bound of the 95th percentile is
higher and more conservative than the point estimate for the RME.
Risk characterization
The estimated lifetime exposure for BaP equivalents was compared to a riskbased daily intake of 8.3E-7 mg/kg-dag, which corresponds to a lifetime
cancer risk of 1E-6 (Swedish EPA 2009). The estimated BaP-equivalents
exposure during age 1 to 6 years was thus lower than the risk-based daily
intake in both the deterministic and the probabilistic estimations. As shown in
paper III, the risk-based daily intake was only slightly exceeded in some cases
when longer exposure duration was assumed, together with assumptions about
partial dependencies between some exposure factors. The overall conclusion of
the risk assessment is that exposure to toxic substances while bathing in Lake
Trekanten does not lead to any significant negative health effects in the
population.
This assessment is valid for the current levels measured and changes in
conditions were not considered. Consumption of fish and crayfish from the
lake could also be a possible exposure pathway but was not considered in this
assessment.
Climate change – an uncertainty factor in risk analysis
The exposure assessment in paper IV concerns cadmium (Cd), which is a toxic
metal that occurs in our environment, both naturally and anthropogenically
(Järup and Åkesson 2009). Metals are not biodegradable and thus have the
potential to be toxic for future generations as well. At the same time, human
activities affect the global climate (Houghton 2005). A change in climate will
likely also alter different input variables in an exposure model.
In this exposure scenario, the exposure pathways were somewhat different
from the scenario at Lake Trekanten. But the principle and approach are
similar. The exposure pathways considered were ingestion of contaminated
soil, groundwater and vegetables grown on the site, as well as dermal uptake
and inhalation of dust. The assessment considers exposure to Cd for a 4-year
old child and the objective was to compare present conditions with two
possible climate change scenarios, referred to as scenario I and II, at a highly
contaminated iron and steel works site in southeastern Sweden.
It was assessed that six of the 39 model variables could possibly change
with a changing climate. As example, the climate sensitive variables and the
expected change in scenario II are shown in table 4. It is uncertain how future
28
climate change will affect these variables, but an estimation of the change was
made using a literature search presented in paper IV.
Table 4. Model variables estimated to likely change with a change in climate and the parameters
at present scenario compared to the expected change in climate change scenario II.
Climate sensitive
variables
Soil moisture, dm3/dm3
Groundwater recharge,
m/yr
Hydraulic conductivity,
m/s
Soil-water distribution
coefficient (Kd), dm3/kg
Bioconcentration factor
root, (mg/kg)/(mg/kg)
Bioconcentration factor
stem, (mg/kg)/(mg/kg)
Defining
Present
parameters in PBA scenario
min
max
mode
interval
0.05
0.50
0.30
[0.2;0.4]
min
max
mode
min
max
mode
mean
stddev
mean
stddev
6.0E-6
2.0E-5
1.3E-5
576
1440
1080
0.35
0.33
0.85
0.61
Expected change in
climate scenario
0.04 (-20%)
0.40 (-20%)
0.24 (-20%)
[0.17;0.34]
(-15%, -15%)
4.8E-6 (-20%)
2.4E-5 (+20%)
1.3E-5 (±0%)
461 (-20%)
1728 (+20%)
1080 (±0%)
0.42 (+20%)
0.40 (+20%)
1.02 (+20%)
0.73 (+20%)
The total exposure for the present condition was compared to the exposure
after a change in climate sensitive variables. Even though only six of model
variables were estimated to be affected by a change in climate, a clear effect
was seen on the exposure scenario. An increase in the uncertainty interval
could be seen in general in the PBA, that is to say the p-box representing the
total exposure got wider both in the lower and upper end along the horizontal
x-axis. The comparison of the scenarios shown in table 4 for the 95th
percentile showed that the lower bound of the uncertainty interval decreased
with 11% while the upper bound increased with 18%. The Kd value was the
variable with the strongest influence on the result
As already mentioned, risk assessment includes subjective judgments, as
does risk management. Thus, this comparison is not an exact truth. However,
it shows that a considerable uncertainty is associated with exposure assessment
accounting for possible future climate change. Exposure levels that are close to
the toxicological reference value (TRV) under present conditions may in the
future exceed the TRV, and thus may also have implications for the risk
management process which often needs to rely on precaution.
29
Risk management of contaminated land
Risk management is the decision making process where the risk assessment is
evaluated in a social, economic and technical perspective (WHO 2004). If
necessary, the risk is also reduced and monitored.
Contaminated land risk management is difficult; as pointed out in papers
II to IV, uncertainty and variability are present in the risk assessment process
and need to be characterized and considered. At the same time, the costs
involved in remediation as well as the consequences if no actions are taken
may be high.
Uncertainty and variability do not only affect the risk assessment process,
but also risk communication and risk management. For example, as already
mentioned, there might be uncertainty in language which could be vague or
ambiguous (Regan et al. 2002a). Furthermore, hazards and risks are perceived
and evaluated differently by different individuals (Byrnes et al. 1999, Davidson
and Freudenburg 1996, Gutteling and Wiegman 1993, Slovic 1999, Slovic et
al. 1997, Steger and Witt 1989).
Of the questionnaire that was sent to employees at the Swedish County
Administrative Boards (CAB) working with contaminated land, 56
questionnaires were returned, which was about 80% of the survey population.
However, the response rate varied depending on the part of the questionnaire.
The questionnaire revealed that there are significant differences in the
reviewing of contaminated land risk assessments depending on the employees’
gender, age and work experience where gender was the most important.
A larger number of women were of the opinion that there were not
sufficient guidance documents compared to men. Women agreed to lesser
degree that there is enough time to review risk assessments. Women reported
that they did not ask the consultants to complement a risk assessment before a
decision was taken as often as men do. These differences may indicate a lower
degree of confidence by women.
Not surprisingly, employees with less experience were also more likely to
indicate that there were not sufficient guidance documents compared to their
more experienced colleagues. Similarly, younger employees agreed to a greater
extent that their assessments mainly depended on guidance documents,
templates or similar, as compared to the older colleagues.
In total, gender was the most significant factor in the reviewing of risk
assessments. Gender also affected the perception of knowledge. Compared to
women, men estimated themselves to be more informed about the results from
the research project Sustainable Remediation. The level of actual knowledge
was, however, not measured. Consequently, the knowledge reported here
reflects perceived confidence and is not necessarily the same as actual
knowledge. In prior research, women had perceived themselves to be less
knowledgeable, but at the same time exhibited a higher level of knowledge
(McCright 2010).
30
The risk assessments can be financed by different sources; governmental
grants or operators that are legally obligated to do so. There are most likely
greater monetary resources available in projects financed by governmental
grants, compared to when a legally responsible operator is the financier. This
may also explain why respondents found risk assessments financed by grants to
be of higher quality and why these risk assessments carried greater weight in
decisions compared to risk assessments financed by a legally responsible
operator. The employees estimated that they had more time to review a risk
assessment financed by grants and that they co-operated with the companies
that performed the risk assessment to a greater extent as compared to when
the risk assessments were financed by a legally responsible operator.
The process leading to the remediation of contaminated land, including
risk assessment, risk communication and risk management, inevitable involves
uncertainties and judgments. These differences affect the risk management
process and may therefore also influence the outcome of a contaminated land
project.
31
CONCLUSIONS
The focus of this thesis is environmental risk analysis, and the uncertainty and
variability in this process, as investigated and reported in five different papers.
The experiments in paper I showed that the catalytic effects of metal
oxides on the formation process of chlorinated aromatic compounds during
heating of fly ash vary. The results corroborate previous investigations; copper
showed a positive catalytic effect on the formation of chlorinated aromatic
compounds. In addition, chromium, cobalt and vanadium decreased the
formation. Knowledge of the catalytic effects of various metals may facilitate
the choice and design of combustion processes in order to decrease emissions,
but it also provides valuable information in the risk assessment process,
especially in hazard assessment.
Exposure factors of importance in exposure assessments, such as
physiological parameters, time use factors and consumption rates, were
compiled and evaluated in paper II. Variability and uncertainty in exposure
factors are of major importance and need to be considered in exposure
assessments. Variability was described by mean, SD, skewness, kurtosis and
multiple percentiles, while uncertainty was characterized by 95% confidence
interval of these parameters. Exposure factors change over time and there is a
lack of data for some exposure factors. New surveys are continuously published
and there is therefore an ongoing need to review and evaluate exposure factors
data. The compilation and evaluation of exposure factors data enhance the
accessibility of information and thus facilitate the performance of transparent
and comparable risk assessments.
The exposure factor parameters from paper II were used in exposure
assessments in papers III and IV. Uncertainty and variability in the two
exposure assessments were described by probability boxes in a probability
bounds analysis. An advantage with this method is that no assumptions about
specific distribution are needed. Probability boxes were instead constructed
from available data. In this way, a great deal of data is not necessary but the
available data can be fully applied. It is also an advantage that variability and
uncertainty can be described separately. Probability bounds analysis is a rapid
and flexible approach for risk assessment and provides a useful alternative to
Monte Carlo simulations.
The risk assessment in paper III concludes that even though elevated levels
of contaminants had previously been found in the sediment in the deeper part
of the lake, bathing was not considered to cause any harm.
The exposure assessment in paper IV accounted for possible changes due
to a change in climate. Both an increase and decrease in exposure were seen
32
after altering the six variables that were assessed to be sensitive to a change in
climate. This means that exposure levels that are close to the toxicological
reference value (TRV) at present condition may in the future exceed the TRV,
and thereby also have implications for the risk management process which
often needs to rely on precaution. However, large uncertainties are associated
with exposure assessments accounting for possible future climate change.
In paper V, it was concluded that the gender, age and work experience of
the employees, as well as the funding source of the risk assessment, have an
impact on the reviewing of risk assessments, with gender being the most
significant factor. Gender also affected the perception of knowledge; however,
this perceived knowledge does not necessarily correspond to actual knowledge.
These differences can affect the risk management process and may thus also
have implications for the outcome of a contaminated land project.
In the papers presented in this thesis different aspects of uncertainty and
variability have been highlighted and demonstrated. This is a continuously
ongoing work in progress as the risk analysis process is iterative and conditions
change. Exposure factors also change with time and are in continuous need of
being updated. Knowledge on how to treat uncertainty and variability in the
risk analysis process is essential in order to create transparent and reliable risk
assessments that can gain regulatory acceptance.
33
ACKNOWLEDGEMENTS
I thank my supervisor Tomas Öberg and co-supervisor Bo Bergbäck for their
guidance, support and belief in me. To my co-authors – Anna Augustsson, Bo
Bergbäck, Marianne Lindström, Lill Ljunggren, Pasi Peltola and Tomas
Öberg – it has been a pleasure working with you.
During this time I have received invaluable information from many people.
I would particularly like to gratefully acknowledge those who kindly gave
advice and permission to use primary data from their surveys:
Otis Evans and Alfred P. Dufour (US EPA) for data on water
ingestion while swimming.
Gunnel Boström and the survey Health on Equal Terms (Swedish
National Institute of Public Health), Elisabeth Strandhagen and the
Intergene survey (University of Gothenburg), and Anna Bostedt and
Kicki Wickberg and the Epibarn database (Västernorrland County
Council) for body weight and height data.
Cecilia Boldemann (Stockholm County Council and Karolinska
institute) for data on time spent outdoors by children.
Wulf Becker and Riksmaten 1997-98 (Swedish National Food
Administration) and Hanna Sepp (Uppsala University) for food
consumption data.
Rolf Pettersson (Central Hospital, Skövde) for data on children’s tap
water intake.
I would like to acknowledge the external funding sources. The exposure
factors survey was funded by the Swedish Environmental Protection Agency
(Naturvårdsverket) and the Sustainable Remediation (Hållbar Sanering)
research program. The risk assessment of Lake Trekanten was funded by the
City of Stockholm and the District Administration of Liljeholmen.
For lots of fun and pleasant discussions, I would like to thank my
colleagues at the School of Natural Sciences, Linnaeus University. In
particular I would like to thank Stina Alriksson, Ullrika Sahlin and Ymke
Mulder for comments on the manuscript. My greatest thanks and love go to
David, my family, relatives and friends for always being there and for your
endless support.
34
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42
APPENDIX
In this appendix, the inscriptions in RAMAS Risk Calc 4.0 Software (Ferson
2002) are shown for the exposure assessment example shown for paper III.
Probability boxes (p-boxes) are presented as they appear in Risk Calc. All
figures show cumulative probability from 0 to 1 on the y-axis and the exposure
factor unit on the x-axis.
Exposure model
When exposure factors are assumed to be independent, the inscription of
equation 4 (paper III) in Risk Calc looks as follows:
Intake1_6_indep=((EF|*|6)|/|(BW|*|AT))|*|((CW|*|CR|*|ET)|+|((CS|*|CF
)|*|(IR|+|(SA|*|AF|*|ABS))))
The same procedure except that some exposure factors are assumed to be
dependent on each other (independency assumptions are removed by
removing “|”):
Intake1_6_dep=((EF|*|6)|/|(BW|*|AT))*((CW|*|CR|*|ET)|+|((CS|*|CF)|*|
(IR+(SA|*|AF*ABS))))
43
Exposure factors
In cases where multiple percentiles are available the p-boxes were delimited by
minimum and maximum values, and uncertainty intervals around the mean,
SD and percentiles. This was the case for body weight (BW), skin surface area
(SA) and water intake while swimming (CR). Here, the body weight of 4year-old children is shown as an example. The uncertainty intervals of mean,
SD and multiple percentiles are found in table 1. The inscriptions in Risk
Calc can be made as follows and the output as a p-box is shown in figure A:
BW=imp(imp(minmaxpercentile(10,30,95%,[22.6,24.2]),minmaxpercentile(1
0,30,99%,[24.8,28.2])),imp(imp(imp(posmeanstddev([18,18.4],[2.5,3]),minm
axpercentile(10,30,1%,[12.3,13.8])),imp(minmaxpercentile(10,30,5%,[14.3,14.
8]),minmaxpercentile(10,30,10%,[15.0,15.4]))),imp(imp(minmaxpercentile(10
,30,25%,[16.2,16.6]),minmaxpercentile(10,30,50%,[17.6,18.0])),imp(minmax
percentile(10,30,75%,[19.2,19.8]),minmaxpercentile(10,30,90%,[21.0,22.0])))
))
1
BW
0.5
0
0
10
20
Figure A. P-box. Body weight (kg) of girls aged 4 years.
44
30
40
Information on other exposure factors was somewhat more limited.
Contaminant concentrations in water (CW) and sediment (CS) were
described by minimum, mean and SD. PAH (as BaP equivalents)
concentration in sediment is shown as an example:
CW=minmeanstddev(6.98E-06, 1.02E-05, 3.46E-06)
1
CW
0.5
0
0
1e-05
2e-05
3e-05
4e-05
5e-05
6e-05
Figure B. P-box. BaP-equivalent concentration in water (mg/L).
45
Minimum, maximum and mean values, without uncertainty intervals, were
used for exposure time (ET), exposure frequency (EF) and intake of sediment
(IR), while the uncertainty interval of the mean was used for adherence factor
(AF):
AF=minmaxmean(0.1,2,[0.36,1.17])
1
AF
0.5
0
0
1
2
3
2
Figure C. P-box. Sediment-to-skin adherence factor (mg/cm ).
Conversion factor (CF) and period over which exposure is averaged (AT) were
entered as point estimates. For absorption factor (ABS), minimum and
maximum values were used without uncertainty intervals.
46