Glossary

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Glossary contains a list of terms that have a particular meaning in the context of environmental health impact assessment.


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Agent is a chemical, biological, or physical entity that contacts a target.

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Analysis is viewed here in a sense of examination of something, together with thoughts and judgments about it for the purpose of a risk assessment and its variable definition (refers not to chemical analysis, i.e. separation of substance into parts). It may include optimizing scenarios.

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<section begin=glossary />

Bayes' theorem (also known as Bayes' rule or Bayes' law) is a result in probability theory that relates conditional probabilities. If A and B denote two events, P(A|B) denotes the conditional probability of A occurring, given that B occurs. The two conditional probabilities P(A|B) and P(B|A) are in general different. Bayes theorem gives a relation between P(A|B) and P(B|A).
An important application of Bayes' theorem is that it gives a rule how to update or revise the strengths of evidence-based beliefs in light of new evidence a posteriori.
As a formal theorem, Bayes' theorem is valid in all interpretations of probability. However, it plays a central role in the debate around the foundations of statistics: frequentist and Bayesian interpretations disagree about the kinds of things to which probabilities should be assigned in applications. Whereas frequentists assign probabilities to random events according to their frequencies of occurrence or to subsets of populations as proportions of the whole, Bayesians assign probabilities to propositions that are uncertain. A consequence is that Bayesians have more frequent occasion to use Bayes' theorem. The articles on Bayesian probability and frequentist probability discuss these debates at greater length.<section end=glossary />

See also

References


Related files

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<section begin=glossary />

Bayesian network (or a Bayesian belief network, BBN) is a probabilistic graphical model that represents a set of variables and their probabilistic independencies. For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. The term "Bayesian networks" was coined by Pearl (1985) to emphasize three aspects:
  1. The often subjective nature of the input information.
  2. The reliance on Bayes's conditioning as the basis for updating information.
  3. The distinction between causal and evidential modes of reasoning, which underscores Thomas Bayes's posthumous paper of 1763.[1]
Formally, Bayesian networks are directed acyclic graphs whose nodes represent variables, and whose arcs encode conditional independencies between the variables. Nodes can represent any kind of variable, be it a measured parameter, a latent variable or a hypothesis. They are not restricted to representing random variables, which represents another "Bayesian" aspect of a Bayesian network. Efficient algorithms exist that perform inference and learning in Bayesian networks. Bayesian networks that model sequences of variables (such as for example speech signals or protein sequences) are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. <section end=glossary />

See also

References

  1. ↑ Thomas Bayes (1763). "An Essay towards solving a Problem in the Doctrine of Chances. By the late Rev. Mr. Bayes, F.R.S., communicated by Mr. Price, in a letter to John Canton, A.M., F.R.S.". Philosophical Transactions of the Royal Society of London 53: 370–418. 


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During the scoping phase a causal diagram should be drawn that describes all variables of the assessment including their causal relationships. It consists of the variables of the assessment. At first all variables are taken into account that seem relevant. While checking for causality it may turn out that more variables are needed and the pictures becomes more complex. Then a simplification process starts and only the relevant links and variables are kept.
The process can be started on a quite general level (e.g. from the viewpoint of a certain stressor or health outcome) or it can be started on a quite detailed level (e.g. from the viewpoint of PM2.5 concentration and its effects on health in Copenhagen).<section end=glossary />


<section begin=glossary />
Causality means that there is a causal influence between two variables (or objects): if the value of the one upstream is changed, the object downstream changes as well. Causal relationships are represented as arrows in causal diagrams. However, the lack of a causal relationship, i.e. a lack of an arrow between two variables is a stronger statement than an arrow. One operationalization of causality is a Bayesian belief network. Causality is also called dependency.
A risk assessment method based on the full-chain approach utilises causality as a major concept. This implies that the assessment products produced in the assessments should be causal network descriptions that cover the relevant phenomena from emissions to exposures to health effects and their impacts in accordance with the chosen endpoints and purpose. However, it should be emphasized that the method does not only describe issues that are associated with the full chain. It describes those issues that cause effects along the full chain, and it describes how the causes and effects are related. This, of course, makes risk assessment a challenging, or even difficult, process. Strict emphasis on causality, however, should be the way to e.g. estimate the impacts of policies on emissions and consequently to health effects. For further details, see Guidance and methods for indicator selection and specification.<section end=glossary />


<section begin=glossary />
Conclusion of a risk assessment refers to an arrangement or agreement that introduces either a changed state of affairs or verifies business as usual (BAU) as a best option for the assessment scope. The (new) verified state of affairs is likely to last for some time.[1]<section end=glossary />

References

  1. ↑ Procter, J. (Editor-in-Chief) (1978). Longman Dictionary of Contemporary English, Longman Group Ltd., UK.
<section begin=glossary />
Data are e.g. population data, mortality background data, biomonitoring data. Data are needed for defining and calculating variables but stem from outside the risk assessment process. Input for a variable derived by a causal linkage to another variable is not called data but is the result of the other variable. Data can be "raw" data or accumulated to different degrees including e.g. the mean and the standard deviation or a distribution.<section end=glossary />

Data fact sheet

The structure of a fact sheet for data is described here. see also biomarkers fact sheet as an example for an already existing fact sheet.

  • Keywords
  • SCOPE OF THE DATA
    • Type of data
    • Source
    • Definitions and comment
    • Exposure duration
  • DATA APPLICATION
    • Population
    • Web site
    • Availability of scientific reports
    • Advantage
    • Disadvantage
  • APPLICATION EXAMPLES
  • REFERENCES

See also

Decision theory is an area of study of discrete mathematics that models human decision-making in science, engineering and indeed all human social activities. It is concerned with how real or ideal decision-makers make or should make decisions, and how optimal decisions can be reached.
Most of decision theory is normative or prescriptive, i.e. it is concerned with identifying the best decision to take, assuming an ideal decision maker who is fully informed, able to compute with perfect accuracy, and fully rational. The practical application of this prescriptive approach (how people should make decisions) is called decision analysis, and aimed at finding tools, methodologies and software to help people make better decisions. The most systematic and comprehensive software tools developed in this way are called decision support systems.
Since it is obvious that people do not typically behave in optimal ways, there is also a related area of study, which is a positive or descriptive discipline, attempting to describe what people will actually do. Since the normative, optimal decision often creates hypotheses for testing against actual behaviour, the two fields are closely linked. Furthermore it is possible to relax the assumptions of perfect information, rationality and so forth in various ways, and produce a series of different prescriptions or predictions about behaviour, allowing for further tests of the kind of decision-making that occurs in practice.<section end=glossary />
<section begin=glossary />
Decision is a special kind of variable: it answer a question like this: "What are different decisions that decision maker X can make in situation Y, and what are different options of each decision?" Decision maker X may be a single individual, a decision making body with several individuals, or a set of separate decision makers, who each can decide about some but not all decisions. In such a situation, an especially interesting part of the related assessment is to look at the interactions of the decisions by different decision makers, none of which can fully control the decision situation.
The decisions have a special role in causal diagrams, as they list such specific structural parts of variables in a causal diagrams that can be modified by a decision maker. Without a decision, the causal diagram describes the business-as-usual (BAU) situation. A decision variable describes how the situation changes if a decision maker chooses an option over another options of a decision.

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Question

How should decisions be described in open assessment and what should their structure be?

Answer

A typical structure of a data table of a decision.
Decision maker Decision Option Variable Cell Change ... Value Description
The person or group who can decide about this. Typically, one of the stakeholder groups. A group of options that are exclusive and the choice between them is made by one decision making body. An option within one decision. A variable whose value is to be changed by the decision option. A cell whose value is to be changed within the variable data table. The type of change. Possible options are listed below the table. The table may also contain other indices. The functions dealing with decision variables use the default merge functionality when combining objects. The value to be used in the calculations. This row contains descriptions about columns. The next rows contain examples.
City of Kuopio Energy saving education Training to house owners Energy balance in Kuopio Activity: Residential; Fuel: Heat Multiply 0.94 Energy training leads to renovation of houses and leads to heating demand reduction by 6 %.
City of Kuopio Public transport subsidy More money to bus transport Energy balance in Kuopio Activity: Transport; Fuel: Petrochemical products Multiply 0.95 Energy consumption of buses increases diesel use by 2.5 % but the reduced car transport causes a net reduction of 5 % in fuel use.
City of Kuopio Public transport subsidy Much more money to bus transport Energy balance in Kuopio Activity: Transport; Fuel: Petrochemical products Multiply 0.9 Energy consumption of buses increases diesel use by 5 % but the reduced car transport causes a net reduction of 10 % in fuel use.

Possible options for the column Change.

  • Add the value to the existing value.
  • Multiply the value with the existing value.
  • Replace the existing value with this value.
  • Remove those rows from the variable that fulfil the cell criterion.
  • Insert rows to the variable based on data in the decision.
  • Split the existing random sample into two parts from the value. This is especially needed in upstream inference.

OpasnetUtils/CheckDecisions

Rationale

Previously, it was thought that decision variables are an essential starting point of a causal diagram, and that they should be general and applicable to all possible situations where that causal diagram is used. Now the thinking is different. Decisions are seen as descriptions of plausible options decision makers have in a particular situation. Decisions are implemented in an assessment as scenarios (deliberate deviations from the truth, asking: "What would happen if the truth about the decision maker's action was this?"). It was previously also thought that decisions are probability distributions about what a decision maker will decide, and assessment-specific scenarios are used separately to select the interesting options from the decision distribution in a particular assessment. Now the thinking is different: instead of attempting to describe all the decisions explicitly, the core of a causal diagram does not describe any decisions. It just describes the phenomena that are important for understanding a situation, such as activities, emissions, exposures, and health effects. Decisions that may change the future situation are implicitly included in the probability distributions as uncertainties. Then, decisions are added to that for describing how e.g. activities would change if some decision options would be chosen. Mathematically, this means that one or more variables in a model are conditionalised to reflect the impact of decisions. In other words, decision variables are used as scenarios. What are scenarios, anyway?

A scenario is a deliberate deviation from the truth in a description. In the case of decisions, it is a particular value of some variable that is changed if the decision option at hand is implemented. It can also be a set of particular values of several variables changed by the decision. For policy assessments, often several scenarios are defined and then compared to each other, e.g. if the impacts of a certain policy (measure) is assessed. A particular set of scenarios can be saved and used in several risk assessments. A scenario therefore can be a part of an assessment but is not an assessment itself. In a sense, BAU is not a scenario, it is our best estimate about what will actually happen, implicitly including all plausible decisions by all decision makers.

A decision describes changes in values of other variables. These changes must be within the range of values the variable has.R↻

See also

References


Related files

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A dose is the amount of agent that enters a target after crossing an exposure surface. If the exposure surface is an absorption barrier, the dose is an absorbed dose/uptake dose (see uptake); otherwise, it is an intake dose. See also intake.[1]

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References

  1. ↑ IPCS (The WHO International Programme on Chemical Safety).
<section begin=glossary />
Contact between an agent and a target. Mainly used for air pollution. Exposure is usually described as concentration of the agent in the medium around the target during a defined duration (exposure duration).[1]<section end=glossary />

References

  1. ↑ IPCS (The WHO International Programme on Chemical Safety).


<section begin=glossary />
Exposure-response function (ERF) (or exposure-response relationship) is the relationship between the exposure of a given organism, system, or (sub)population to an agent in a specific pattern during a given time and the magnitude of a continuously graded effect to that organism, system, or (sub)population.
This term has several related terms that may have slightly different meaning. Effect and response are interchangeable words. Also the word function is used instead of relationship. In Opasnet, we use the term exposure-response function (or ERF) as the generic term for different kinds of relationships. Often the exposure metric is more specifically defined in an alternative term. Two common examples:
Concentration-effect relationship
Relationship between the exposure, expressed in concentration, of a given organism, system, or (sub)population to an agent in a specific pattern during a given time and the magnitude of a continuously graded effect to that organism, system, or (sub)population. The concentration is measured at a defined site. [1]
Dose-response relationship
Relationship between the amount of an agent administered to, taken up by, or absorbed by an organism, system, or (sub)population and the change developed in that organism, system,or (sub)population in reaction to the agent. [1]<section end=glossary />

Question

What is such a representation for ERF that it fulfills the following criteria?

  • It is widely applicable to all kinds of agents, exposures, and responses.
  • A single ERF is widely applicable, within its domain, to different situations and populations.
  • It is mathematically clear so that impact calculations can be operationalised based on it.

Answer

An ERF is a mathematical construct describing the relationship between a response and an exposure. In the general form, it is described as a probability.

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Rationale

Equations

By nature, a response to an agent occurs at individual level (with some exceptions such as agents affecting herd immunity). Thus, the individual response should be the basic unit for ERF. If individual variation is of no interest, a population ERF can simply be expressed as the average of individual ERFs.

There are several different functions that may be used. These are defined here. In all equations, these variables are used:

  • RR: the relative risk for unit exposure difference. Note that β = ln(RR).
  • E: exposure level of the individual
  • B: background exposure level that is considered negligible or lowest achievable. Exposures below B are not considered.
  • T: threshold exposure level below which no impact will occur
  • RR' is the relative risk for the actual exposure
  • Imax is the maximal relative impact
  • ED50 is the dose that causes 50 % of the maximal impact.

NOTE! ED50 parameter is given in the Threshold column, and Imax parameter is given in the ERF or Result column.


Approaches relative to background disease risk

Relative risk (RR)
describes the relative risk compared with a reference exposure. The actual number of cases is calculated with the equation below. (E > T) is 1 if E is greater than T and 0 otherwise.

Failed to parse (Missing <code>texvc</code> executable. Please see math/README to configure.): RR' = (e^{ln(RR) (E - max(B, T))} - 1) (E > T) + 1

Relative Hill
Relative Hill means an ERF function derived from Hill's plot:
Failed to parse (Missing <code>texvc</code> executable. Please see math/README to configure.): RR' = \frac{E \times I_{max}}{E + ED_{50}}


Approaches independent of disease risk

Exposure-response slope (ERS)
A linear relationship where ERS defines the slope of the exposure-response line. Typically the intercept is assumed to be 0. Response metric MUST be defined as it varies from one case to another.

Failed to parse (Missing <code>texvc</code> executable. Please see math/README to configure.): I = ERS \times E

Cancer slope factor (CSF)
A linear relationship between constant lifetime exposure (typically in units mg/kg/d) and lifetime probability of cancer.

Failed to parse (Missing <code>texvc</code> executable. Please see math/README to configure.): P(I) = CSF \times E


Step function
Assumes that exposure at certain range is not a hazard, while exposure outside that range is. Threshold and ERF parameters are used to give the lower and upper ends of the range, respectively. The Step function includes tolerable daily intake (TDI), recommended daily intake (RDI), acceptable daily intake (ADI), no-observed-adverse-effect level (NOAEL) and related, often administrative limits. The value is TRUE (or 1) if exposure fails to meet the recommendation and FALSE (or 0) otherwise.

Pages with standardised ERF tables

If a page has a standardised ERF data table (see an example above), the data can be automatically read and used by an R code and combined with other ERF tables. Therefore it is preferable to use the standard format. It makes modelling much easier and also enables an easy way to add more endpoints to assessments, if there are interesting exposures and available ERF tables.

The tables can contain the following columns (obligatory columns are in bold):

  • Obs (automatic)
  • Exposure agent (index)
  • Response (index)
  • Population (index)
  • Age (index)
  • Sex (index)
  • Exposure (index)
  • Exposure unit (index)
  • ER function (index)
  • Exposure metric (index)
  • Scaling (index)
  • Observation (hidden), containing two locations:
    • Threshold
    • ERF
  • Result
  • Description (description, there may be any number of description columns because they are not stored in the database)

Note! Spaces in column names will be replaced with "_" to avoid problems in the code.

Standardised ERF tables that have been combined on this page.
Page Ident Code name Description
ERFs of environmental pollutants Op_en5827 initiate Contains ERFs for radon, PM2.5, noise, chlorinated byproducts in drinking water, arsenic, dampness in buildings, formaldehyde, fluoride, ozone, lead, dioxin, quartz dust, asbestos.
ERF of omega-3 fatty acids Op_en5830 initiate Contains ERFs for Omega3 fatty acids.
ERF of methylmercury Op_en5825 initiate Contains ERFs for MeHg.
ERF of dioxin Op_en5823 initiate Contains ERFs for dioxin TEQ.
ERFs of vitamins Op_en6866 initiate Contains ERF for vitamin D.

Calculations

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Technical issues

  • In Opasnet, the use of term Exposure-response function (ERF) is recommended. The pages about ERFs should be named: "ERF of <agent> on <endpoint> in <population>."
  • ERFs are typically variables, and they should be categorised to Category:Exposure-response functions
  • In the definition of a variable, an ERF data is described as a t2b table under subheading Data. The actual ovariable that is used in models is defined in an "initiate" code under subheading Calculations.

See also

References

  1. ↑ 1.0 1.1 IPCS_WHO
<section begin=glossary />
Falsification is an attempt to falsify a statement. Falsifiability (or refutability or testability) is the logical possibility that an assertion can be shown false by an observation or a physical experiment. That something is "falsifiable" does not mean it is false; rather, it means that it is capable of being criticized by observational reports. Falsifiability is an important concept in science and the philosophy of science.
Some philosophers and scientists, most notably Karl Popper, have asserted that a hypothesis, proposition or theory is scientific only if it is falsifiable.
For example, "all men are mortal" is unfalsifiable, since no finite amount of observation could ever demonstrate its falsehood: that one or more men can live forever. "All men are immortal," by contrast, is falsifiable, by the presentation of just one dead man. However, the unfalsifiable "all men are mortal" can be the logical consequence of a falsifiable theory, such as "all men die before they reach the age of 150 years". Thus, unfalsifiable statements can almost always be put into a falsifiable framework. The falsifiable does not exclude the unfalsifiable, it embraces and exceeds it.
Not all statements that are falsifiable in principle are so in practice. For example, "it will be raining here in one million years" is theoretically falsifiable, but not practically.<section end=glossary />

References


Related files

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<section begin=glossary />
Hazard is 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.[1]<section end=glossary />

References

  1. ↑ IPCS (The WHO International Programme on Chemical Safety).
<section begin=glossary />
Health impact is the overall effect, direct or indirect, of a policy, strategy, programme or project on the health of a population.[1]<section end=glossary />

References

  1. ↑ Gothenburg consensus paper, Dec 1999 by the WHO (World Health Organization) ECHP (European Centre for Health Policy).

<section begin=glossary />

The INTARESE framework comprises all relevant aspects and builds on all relevant methods to provide guidance for a comprehensive integrated environmental health impact assessment.[1] It recognised the concept of the DPSIR, DPSEEA and MEME frameworks but provides a more flexible and comprehensive framework. The key attributes are:
  • the full chain approach, including variables and causal relationships linking the different steps in the chain from source to impact
  • the logical process of assessment (steps involved in the execution of the assessment, tasks and responsibilities of the parties involved)
  • information input and models (data input and processing, applying models, transforming intermediate variables into meaningful indicators and summary indices)
  • appraisal of the information from multiple perspectives

<section end=glossary />


Framework Impact Pathway Approach Full chain DPSIR DPSEEA
Used by whom ExternE Intarese / Heimtsa EEA WHO
Source [2] [3] [4] [5] [6] [7] [8]
Summary The impact pathway approach allows for the determination of impacts (e.g. health effects) and damages (external costs) due to emissions of pollutants. The full chain covers all the aspects from the other frameworks and focuses on comprehensiveness and integration. It acknowledges the importance of multi-causalities, complexity, interdependencies and uncertainty. It is limited to human health. The causal framework for describing the interactions between society and the environment adopted by the European Environment Agency: driving forces, pressures, states, impacts, responses (extension of the PSR model developed by OECD). The DPSEEA (Driving Forces - Pressures - State - Exposure - Effects - Actions) model is useful in designing a system of environmental health indicators within the decision-making context.
 
Scenarios Scenario development / description: comparison of (a) policy option(s) to a reference scenario Either consider the status quo or investigate scenarios
Driving forces Activities
Activities that lead to emissions, e.g. driving a car, producing energy, using hairspray 		Activities
Activities that lead to emissions, e.g. driving a car, producing energy, using hairspray, natural activities like volcanoe eruptions 		Driving forces
Areas in public life that exert pressure on the environment, e.g. economic sectors, households. 		Driving forces
The driving forces refer to the factors that motivate and push the environmental processes involved.
Pressures, e.g. emissions Emissions
Emissions into air, water and soil, depending on activities and emission factors; can be reduced by applying mitigation measures 		Sources: emissions, releases
Due to activities and processes (natural and anthropogenic) 		Pressures
Resulting environmental burden, e.g. due to waste and built-up areas 		Pressures
This result is the generation of pressures on the environment.
State of the environmental media Concentrations / Depositions
Changes in the state of the environmental media leading to impacts 		Quality of environmental media: concentration
After dispersion and transformation 		State
State of an environmental compartment that is exposed to the burden, e.g. changes in atmosphere and lithosphere 		State
In response to the pressures, the state of the environment is often modified.
Exposure Concentrations / Depositions / Intake/Uptake
Concentrations that effect the population intake via ingestion.
Sensible area that is exposed to deposition
Material that is exposed to depostion 		Exposure settings: Exposure
Depending on population behaviour, e.g. time-activity pattern, product use, diet 		  Exposure
Deterioration in the state of the environment, however, poses risks to human well-being only when there is interplay between people and the hazards in the environment. Exposure is therefore rarely an automatic consequence of the existence of a hazard: it requires that people are present both at the place and at the time that the hazard occurs. Exposure to environmental hazards, in turn, leads to a wide spectrum of health effects, which may be acute or chronic. The concept of exposure is best developed in relation to pollutants in environmental media. The amount of the pollutant absorbed, i.e. the "dose", depends on the duration and intensity of the exposure.
Impacts / Effects, e.g. health effects Impacts
Impacts on the receptors, e.g. human health effects, adverse effects on crops, materials and ecosystems 		Human body: dose, health effects
After inhalation, dermal exposure, ingestion Pathophysiological processes lead from a dose to a health effect 		Impacts
Specific impact due to the environmental burden, e.g. greenhouse effect, soil pollution 		Effects
Some hazards may have a rapid effect following exposure, whereas others may require a long time to produce an adverse health effect.
Damages Damages
External costs of the impacts due to the emissions. Thus, the impacts are made comparable; and a cost-benefit-analysis can be conducted. 		Social, cultural, political, economical and judicial settings: Impacts
Taking place of valuation and weighing; risk characterisation; e.g. policy deficits, disease burden, societal (external) costs, perceptions 		   
Answers of society / Actions   Responses
Social reaction to the burden, e.g. research and laws 		Actions
In face of the environmental problems and consequent health effects, society attempts to adopt and implement a range of actions. These may take many forms and be targeted at different points within the environment-health continuum. Actions may be taken to reduce or control the hazards concerned, such as by limiting emissions of pollutants or introducing flood control measures. The most effective long-term actions, however, are those that are preventive in approach, aimed at eliminating or reducing the forces that drive the system.

References

  1. ↑ D5 Intarese Conceptual Model of Assessment Framework (May 2007)
  2. ↑ http://www.externe.info/
  3. ↑ http://www.intarese.org
  4. ↑ Briggs, D. (2008). "A framework for integrated environmental health impact assessment of systemic risks." Environmental Health 7:61. doi:10.1186/1476-069X-7-61
  5. ↑ http://glossary.eea.europa.eu/EEAGlossary/D/DPSIR
  6. ↑ http://en.wikipedia.org/wiki/DPSIR
  7. ↑ http://de.wikipedia.org/wiki/DPSIR
  8. ↑ http://www.euro.who.int/EHindicators/Indicators/20030527_2


<section begin=glossary />
Impact assessment is a combination of procedures, methods and tools by which a policy, program or project may be judged as to its potential effects on the health of a population, and the distribution of those effects within the population. Includes benefits in addition to risks. Contrary to risk assessment impact assessment includes damages which are certain, i.e. have a probability of 1.[1]

<section end=glossary />

Scope

Definition

←1: The whole IA Tools handbook should be copied and reorganised in Opasnet. This is possible due to the licence that makes it possible to use material as long as it is properly referenced.[2] --Jouni 11:26, 18 June 2009 (EEST)

Result

Procedure

See also

References

  1. ↑ Gothenburg consensus paper, Dec 1999 by the WHO (World Health Organization) ECHP (European Centre for Health Policy).
  2. ↑ Legal notice


<section begin=glossary />
An indicator comprises a characteristic or condition which can be described or measured in a way which provides information about some other characteristic or condition which is, itself, not amenable to direct observation or measurement. Environmental Health Indicators are usually numbers that represent a certain state of the environment, exposure, health state and/ or policy actions. An indicator is a variable that is of a particular interest, for example those that are reported in the risk assessment report. See also variable.<section end=glossary /><section begin=glossary />
Intake is the amount (mass) of an agent crossing an outer exposure surface of a target without passing an absorption barrier, i.e., through ingestion or inhalation. See also dose.[1]<section end=glossary />


<section begin=glossary />

Intake fraction (also iF) is the fraction of emission that is eventually inhaled or ingested by someone in the target population (population of interest) - integrated over time and space.
Intake fraction can be estimated with different methods, such as
  1. iF based on measured concentration fields
  2. iF based on exposure monitoring
  3. iF based on shortcuts

<section end=glossary />

The text on this page is taken from an equivalent page of the IEHIAS-project.

Scope

Purpose

The purpose of intake fraction is to provide a representation of the emissions-to-intake relationship. This is a significant part of the risk assessment of chemicals. Quantification of this relationship provides an indication of the potential impact of emissions on exposed populations and allows for the determination of the effect of source control on health outcomes. Thus, an important tool for risk assessment is the derivation of a value that relates emissions to exposure in an efficient manner for both screening level assessments and policy comparisons. A useful attribute of iF is that it can be applied under conditions of very limited data, so long as the underlying principles are known. It is thus especially useful at the screening stage in impact assessments.

Boundaries

Intake fraction is subject to the same uncertainties associated with any modelling assessment (e.g. parameter uncertainty, model specification uncertainty). There are, therefore, several assumptions and limitations to be aware of when using intake fraction.

  1. There is an assumption that the relationship between emissions and concentration (and intake) is linear. Intake fraction has been less frequently applied for reactive or secondary pollutants.
  2. With an aggregate measure such as iF, one must be careful to include changes over time in the model.
  3. Difficulties arise in how to deal with multiple exposures - i.e. repeated intake of the same pollutant entity. On the one hand, one needs to be careful not to double-count exposure:
    • long-lived substances, especially, may recycle though the environment and be available for multiple intake;
    • some substances (e.g. dioxins) may be passed between mother and child.

On the other hand, such recycling is still a part of the impact of the emissions that perhaps should not be ignored.

Method description

Input

Intake fraction requires two types of inputs, both of which can be derived from measurements, modelling, or a combination of the two. Since iF is a fraction, one input is the numerator, which is the population or individual intake value, generally in units of mass. The other input is the denominator, which is the emissions (in mass) from the source(s). The units may also be in rates (e.g. mass/time). The intake and emissions must be of the same units.

Output

The output comprises a dimensionless number that summarises, for every unit emission of a pollutant from a source or source type, the fraction that is taken in by the exposed population: e.g. for every tonne of benzene emitted from motor vehicles in a given city, 1 gram is inhaled by the exposed population in that city.

Method

Intake fraction (iF) estimates how much of a unit emission of a pollutant is taken up by an exposed population. In other words, iF is the integrated incremental intake of a pollutant released from a source or source category (such as mobile sources, power plants, or refineries) summed over all exposed individuals during a given exposure time, per unit of emitted pollutant (Bennett et al. 2002a).

(1) Failed to parse (Missing <code>texvc</code> executable. Please see math/README to configure.): iF=\frac{ \sum_{people, time} \text{mass intake of pollutant by an individual}}{\text{mass release into the environment}}

Practically speaking, this is usually quantified as

(2) Failed to parse (Missing <code>texvc</code> executable. Please see math/README to configure.): iF=\frac{\text{Concentration}_{\text{Source}} \times \text{Population} \times \text{Intake rate}}{\text{Emissions}}

An intake fraction of 10-6 can be interpreted as for every kg of pollutant released into the environment 1 μg of the pollution will be taken up by the exposed population (Bennett et al. 2002a).

Three intake routes are included in the concept: intake through ingestion or inhalation, and dermal uptake. The different routes are related to the total intake fraction according to the following relationship for all exposure pathways (Bennett et al. 2000b).

iF(total) = iF(inhalation) + iF(ingestion) + iF(dermal) [3]

Bennett and co-workers suggest the following notation: iF(route, media, subpopulation), where route refers to ingestion, inhalation, dermal uptake or total, media refers to release to air, water and soil, and subpopulation refers to exposed group - e. g. workers, residents or all exposed. While population iF is useful in determining large-scale impacts of a pollutant, the evaluation of the distribution of individual intake fractions throughout a population space can also provide useful information. The total intake fraction can then be calculated as the sum of all of the individual intake fractions (iFi) for an exposed population (Bennett et al. 2002a).

As exposures to pollution are rarely evenly distributed, the effectiveness of control policies should account for the factors that lead to particularly high exposures. The distribution of individual intake fractions across time and space and various activities and microenvironments can provide such information. Generally iF should be applied for situations with a fairly long time frame. Calculations can be made for short periods also, but these are less useful, at least for screening level purposes. Most commonly, iF is calculated as an annual average or for a lifetime. The numerator of iF requires an estimation of population or individual intake, derived from multiplying the media concentration or exposure of a pollutant (e.g. benzene in ambient or microenvironment air) by the appropriate intake rate (e.g. breathing rate). The denominator requires some estimation of total emissions over a specified time period or emission rate. This may be derived from inventories or emissions models. Both measured or modelled values may be used in the numerator and denominator; however, one must be careful to note that measured values for the exposure concentration may include contributions from sources in addition to the source under investigation (e.g. the benzene concentration in urban air includes several source types, such as vehicles, industry, long range transport). Modelled values (e.g. from dispersion models) are more able to provide just the source contribution to exposure.

The numerator of iF requires an estimation of population or individual intake, derived from multiplying the media concentration or exposure of a pollutant (e.g. benzene in ambient or microenvironment air) by the appropriate intake rate (e.g. breathing rate). The denominator requires some estimation of total emissions over a specified time period or emission rate. This may be derived from inventories or emissions models. Both measured or modelled values may be used in the numerator and denominator; however, one must be careful to note that measured values for the exposure concentration may include contributions from sources other than the source under investigation (e.g. the benzene concentration in urban air includes several source types, such as vehicles, industry, long range transport). Modelled values (e.g. from dispersion models) are more able to specify only the source contribution to exposure.

Further details on applying the intake fraction methodology are given in the document on Exposure-intake models (see link below).

Rationale

An appealing aspect of iF is that it allows for an estimate of population exposure to a substance for which no exposure data are available, even new substances, as long as certain basic characteristics are known. Intake fraction tends to be relatively consistent and comparable across exposure pathways and source categories.

For example, studies have found the following general ranges for pathways:

  • Inhalation dominant: range 1E-09 – 1E-05
  • For primary PM2.5 typically 1E-06 – 1E-05
  • Multipathway: range 1E-07 – 1E-05
  • Ingestion dominant: range 1E-06 – 1E-04

Proximity of population to source and population density are also influencing factors, as the nearer the population to the source and the higher the density, the higher the intake fraction. For example, iFs of power plants are generally lower than those for vehicular emissions of primary particulate matter.

There are several assumptions and limitations to be aware of when using intake fraction. Intake fraction is subject to the same uncertainties associated with any modelling assessment (e.g. parameter uncertainty, model specification uncertainty). There is an assumption that the relationship between emissions and concentration (and intake) is linear. Intake fraction has been less frequently applied for reactive or secondary pollutants. Also, with an aggregate measure such as iF, one must be careful to include changes over time in the model. Equally, one needs to be careful not to double-count exposure as, for long-lived substances, the substance may recycle though the environment or, in the dioxins case, between mother and child. On the other hand, such recycling is still a part of the impact of the emissions that perhaps should not be ignored.

Examining the distribution of individual iFs or the spatial distribution might provide more information on the variability in a population or geographical area of the intake relative to the source emissions and could be useful in examining equity issues. Another issue that should be considered when estimating iFs is the background level as well as the dose-response of the substance.

It is important when using measured values to subtract out background values, which are not due to the source of interest. Also, the concept of iF is most useful with linear dose-response curves. For threshold or non-linear functions risks, iF may be poorly applicable for estimating population risks.

References

See also

Further information:

Examples of application:

Integrated Environmental Health Impact Assessment System
IEHIAS is a website developed by two large EU-funded projects Intarese and Heimtsa. The content from the original website was moved to Opasnet.
Topic Pages
Toolkit
Data

Boundaries · Population: age+sex 100m LAU2 Totals Age and gender · ExpoPlatform · Agriculture emissions · Climate · Soil: Degredation · Atlases: Geochemical Urban · SoDa · PVGIS · CORINE 2000 · Biomarkers: AP As BPA BFRs Cd Dioxins DBPs Fluorinated surfactants Pb Organochlorine insecticides OPs Parabens Phthalates PAHs PCBs · Health: Effects Statistics · CARE · IRTAD · Functions: Impact Exposure-response · Monetary values · Morbidity · Mortality: Database

Examples and case studies Defining question: Agriculture Waste Water · Defining stakeholders: Agriculture Waste Water · Engaging stakeholders: Water · Scenarios: Agriculture Crop CAP Crop allocation Energy crop · Scenario examples: Transport Waste SRES-population UVR and Cancer
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Listings Health impacts of agricultural land use change · Health impacts of regulative policies on use of DBP in consumer products
Guidance System
The concept
Issue framing Formulating scenarios · Scenarios: Prescriptive Descriptive Predictive Probabilistic · Scoping · Building a conceptual model · Causal chain · Other frameworks · Selecting indicators
Design Learning · Accuracy · Complex exposures · Matching exposure and health · Info needs · Vulnerable groups · Values · Variation · Location · Resolution · Zone design · Timeframes · Justice · Screening · Estimation · Elicitation · Delphi · Extrapolation · Transferring results · Temporal extrapolation · Spatial extrapolation · Triangulation · Rapid modelling · Intake fraction · iF reading · Piloting · Example · Piloting data · Protocol development
Execution Causal chain · Contaminant sources · Disaggregation · Contaminant release · Transport and fate · Source attribution · Multimedia models · Exposure · Exposure modelling · Intake fraction · Exposure-to-intake · Internal dose · Exposure-response · Impact analysis · Monetisation · Monetary values · Uncertainty
Appraisal
<section begin=glossary />
Intake rate is the rate (mass / time unit) at which an argent crosses the outer exposure surface of a target (humans) during ingestion or inhalation.[1]<section end=glossary />

References

  1. ↑ 1.0 1.1 IPCS (The WHO International Programme on Chemical Safety).

<section begin=glossary />

Intended user is a potential user of an assessment belonging to a key target group, as defined by the participants of the assessment.<section end=glossary />


<section begin=glossary />
Integrated assessment is a multidisciplinary process of synthesizing knowledge across scientific disciplines with the purpose of providing all relevant information to decision makers to help to make decisions.
The integration takes place:
  • integration across causal chains
  • integration of aggregated indicators
  • integration of outcomes: disease, money perception
  • integration of many pollutants
  • integration across many risk assessments
  • integration across policy studies/areas
  • integration across scientific disciplines
  • integration across sources
  • integration across pollutants/stressors
  • integration across impacts/receptors
  • integration across environmental media
  • integration across scales [1]<section end=glossary />
  • A multidisciplinary process of synthesizing knowledge across scientific disciplines with the purpose of providing all relevant information to support policy decision making
  • "What does it mean for assessment to be integrated? Again, the answer is highly context-specific. The only general answer is that to be integrated is to present a broader set of information than standard research activity, more than typical good research from a single discipline. (...) This guide defines integrated assessment broadly. The two defining characteristics are a) that it seeks to provide information of use to some significant decision-maker rather than merely advancing understanding for its own sake; and b) that it brings together a broader set of areas, methods, styles of study, or degrees of certainty, than would typically characterize a study of the same issue within the bounds of a single research discipline." (Consortium for International Earth Science Information Network (CIESIN) at http://sedac.ciesin.org/mva/iamcc.tg/TGHP.html)
  • "Probably the most succinct definition is that IEA (integrated environmental assessment) is an interdisciplinary and policy-oriented synthesis of scientific information (first part) with some qualifications (second part). While there is a considerable degree of agreement on the first part: IEA is interdisciplinary and aims at producing policy-relevant results, significant disagreements start when one adds qualifications." (Ferenc L. Toth and Eva Hizsnyik: Integrated environmental assessment methods: Evolution and applications. Potsdam Institute for Climate Impact Research (PIK). Environmental Modeling and Assessment 3. 1998. p. 193–207)
  • 1st Year Report:
    The earliest published formulation of the concept of integrated assessment was in the 1960s in the context of climate change, and since then it has become a relatively well established approach in the areas of environmental change and environmental policy. It underpins, for example, the work of the European Environment Agency. In this context, it has been defined as follows:
    Integrated assessment (IA) is a reflective and iterative participatory process that links knowledge (science) and action (policy) regarding complex global change issues such as acidification and climate change. IA can be defined as an interdisciplinary process of combining, interpreting and communicating knowledge from diverse scientific disciplines in such a way that the whole cause–effect chain of a problem can be evaluated from a synoptic perspective with two characteristics: (i) it should have added value compared to single disciplinary assessment; and (ii) it should provide useful information to decision makers….. The cause effect chains that IA aims to evaluate start with socio-economic drivers, leading to economic activity and other practices, leading to emissions and other pressure on the environment, leading to environmental changes, leading to physical impacts on societies and ecosystems, leading to socioeconomic impacts, eventually returning to change the socioeconomic drivers. Therefore, IA needs to integrate insights from a multitude of disciplines to arrive at a synoptic view on the problem at hand. (from J.P. van der Sluijs in Encyclopaedia of global environmental change)


References

  1. ↑ D5 Intarese Conceptual Model of Assessment Framework (May 2007)


<section begin=glossary />
Integrated risk assessment is the assessment of risks to human health from environmental stressors based on a whole system approach. It thus endeavours to take account of all the main factors, links, effects and impacts relating to a defined issue or problem.[1]<section end=glossary />

References

  1. ↑ Intarese guidance and methods for indicator selection and specification


<section begin=glossary />

Issue farming is the process of defining the issue that will be assessed. Its aim is to specify the scope and key elements and boundaries of the issue to be considered, and to provide the explicit rationale for the assessment. Issue framing should specify:
  • the purpose of the assessment (why it is being done, for whom)
  • the scope and boundaries of the issue (what is included and what is not)
  • the main factors and links to be considered in the assessment, variables, indicators and causality
  • the target area, time period and population (including specific age, gender or social groups)
  • key assumptions (e.g. value judgements and stakeholder interests that have shaped the specification of the issue
  • the process by which the issue was defined and agreed (who was involved, what consultation methods were used).<section end=glossary />


References

"David's paper of May 2006 on scoping - some new developments in SP1"<section begin=glossary />

Medium is a material (e.g., air, water, soil, food, consumer products) surrounding or containing an agent. The place for events to occur and and manifest themselves.[1]<section end=glossary />


References

  1. ↑ IPCS (The WHO International Programme on Chemical Safety).
<section begin=glossary />
Monetarization is a transformation of results of risk assessments for all risk categories (e.g. mortality, morbidity, acidification, global warming, ...) into monetary values, allowing to compare and add all kinds of risks. The monetary values per unit risk are in principle derived from stated or revealed preferences of the affected population.[1]<section end=glossary />


References

  1. ↑ USTUTT (Stuttgart University)
<section begin=glossary />
Name attribute is the identifier of a variable or an assessment. The variable names should be chosen so that they are descriptive, unambiguous and not easily confused with other variables. An example of a good variable name is: daily average of PM2.5 concentration in Helsinki. See main articles variable and assessment.<section end=glossary />

<section begin=glossary />

A person that participates in an assessment. Participants are defined as a subattribute of the assessment. See also Defining the users of an assessment.<section end=glossary />

There are different kinds of participants:

  • Owner has the final say in the assessment scope.
  • Assessor takes the responsibility for finalising (a part of) the assessment in time with available resources.
  • Contributor brings information, values, or comments to the assessment.
  • Reader reads the non-finished assessment report and possibly comments it verbally but does not contribute in the writing of it.


<section begin=glossary />
A phase is a certain stage in an assessment process.<section end=glossary />

<section begin=glossary />

PSSP is a general methodology for organising information and process descriptions. It offers a uniform and systematic information structure for all systems, whether small details or large integrated models. The four attributes of PSSP (Purpose, Structure, State, Performance) enable hierarchical descriptions where the same attributes are used in all levels.<section end=glossary />

<section begin=glossary />

Answer answers this question: What is the answer to the research question?

It is an expression of the state of the part of reality that the object describes. It is the outcome of the contents under the definition attribute. In open assessment it is an attribute of information objects. <section end=glossary />

Previously, answer was called result. Now, result has a narrower meaning as

  • a sub-attribute of answer in assessments, containing the numerical results of the assessment and creating a basis for the conclusion (another sub-attribute of answer in assessment); or
  • the column(s) of an ovariable that contains the actual observations of the ovariable (in contrast to other, explanatory columns).

See also

Materials and examples for training in Opasnet and open assessment
Help pages Wiki editing • How to edit wikipages • Quick reference for wiki editing • Drawing graphs • Opasnet policies • Watching pages • Writing formulae • Word to Wiki • Wiki editing Advanced skills
Training assessment (examples of different objects) Training assessment • Training exposure • Training health impact • Training costs • Climate change policies and health in Kuopio • Climate change policies in Kuopio
Methods and concepts Assessment • Variable • Method • Question • Answer • Rationale • Attribute • Decision • Result • Object-oriented programming in Opasnet • Universal object • Study • Formula • OpasnetBaseUtils • Open assessment • PSSP
Terms with changed use Scope • Definition • Result • Tool


<section begin=glossary />
Risk assessment is a systematic process for describing and quantifying the risks associated with processes, projects, policies, actions or events. In the context of environmental public health risks, risk assessment is the process of quantifying the probability of a harmful effect to individuals or the frequency of a harmful event to the population caused by exposure to one or several agents, which is again caused by projects, processes a.s.o. (causal chain). Risk assessment includes an uncertainty estimate.[1]<section end=glossary />

See also

References

  1. ↑ Covello, V.T. and Merkhofer, M.W. 1993. Risk assessment methods, Approaches for assessing health and environmental risks. Plenum Press, New York and London, p. 3.


<section begin=glossary />

Risk characterization is the qualitative and, wherever possible, quantitative determination, including attendant uncertainties, of the probability of occurrence of known and potential adverse effects of an agent in a given organism, system or (sub)population, under defined exposure conditions.[1] Typically, risk characterisation is a transformation of risks for the same risk category (e.g. mortality, morbidity, acidification, global warming, ...) into values with a common unit, so that results can be directly compared. Example: transformation of health risks into DALY's (disability adjusted life years).[2]

<section end=glossary />

References

  1. ↑ WHO Report
  2. ↑ USTUTT (Stuttgart University)

<section begin=glossary />

Scenario is a set of assessment-specific deliberate deviations from the results of one or more variables in the assessment. It is noteworthy that the result of a variable is the current best estimate of the truth; therefore, scenarios are deliberate deviations from the truth because they serve the functionality of learning what would happen if this was the situation (compared with a baseline, business-as-usual, or other scenarios). There are also alternative definitions that are slightly different from the one used in the Intarese framework or in open assessment:
  1. Archetypal descriptions of alternative images of the future, created from mental maps or models that reflect different perspectives on past, present and future developments. [1]
  2. A coherent, internally consistent and plausible description of a possible future state of the world. [2]
  3. Variation in the assumptions used to create models. [3]
  4. A synthetic description of an event or series of actions and events. [4]<section end=glossary />

Question

What are scenarios and how should they be used in open assessment?

Answer

Definition
A scenario is a set of assessment-specific deliberate deviations from the results of one or more variables in the assessment.

There are two main ways of using scenarios in open assessment: 1) as decision options and 2) for excluding unwanted parts of results of variables.


Scenarios as decision options

Scenarios can describe possible worlds were a set of decision options are selected over some other options. Using several scenarios, it is possible to perform an assessment where all interesting combinations of decisions are explicitly evaluated against each other. For more details, see Decision, and for an example, see Climate change policies in Kuopio.


Scenarios for excluding unwanted parts of variables

The world is very rich and diverse, and often this richness would make assessments very complex or computationally large if everything that is related would be included. In some cases, it is useful to limit the assessment by either excluding whole variables, some locations of selected indices, or some values from the probability distribution of the result. Excluding such parts of an assessment may increase the usability and acceptability of the resulting simpler assessment although the assessment may not be as calibrated description of the truth as the full assessment would be.

This makes it possible for the assessor to choose a belief system that deviates from the belief system of the open community without violating the rules of open assessment. However, all deviations become explicit in this way, which makes it possible for others to evaluate the results of the assessment against their own belief systems. This is one answer to the very common "Who decides" question asked by almost all audiences not familiar with the concepts of open assessment: the assessor may decide what is included in his or her particular assessment, but he or she may not decide what descriptions are used in general or in other assessments.

See also

References

  1. ↑ Rotmans, J. (1998). Methods for IA: The challenges and opportunities ahead. Environmental modelling and Assessment 3(3), 155.
  2. ↑ Parry, M. and Carter, T. (1998). Climate impact and Adaptation Assessment. Earthscan Publications Ltd., London, UK.
  3. ↑ Peterson G.D., Cumming G.S., Carpenter S.R. (2003). Scenario Planning: a Tool for Conservation in an Uncertain World. Conservation Biology 17(2), 358-366.
  4. ↑ Wikipedia definition on scenario
<section begin=glossary />
Scoping represents the planning phase of integrated risk assessment. Its purpose is to define the issue to be assessed and the ‘rules’ to be followed in the assessment process. By the same token, scoping provides a checklist on the assessment process, and helps to ensure that all the key factors have been considered during that process. It also provides a framework for discussion with the stakeholders during the assessment process and for reporting results of the risk assessment.
Key elements:
  • issue framing (developing a ‘model’ of the issue to assess)
  • indicator selection and specification (identifying the indicators to be used for the assessment)
  • definition of variables (structuring the assessment in variables and defining a causal diagram/scoping diagram)
  • protocol development (delimiting the assessment methods and data to be used in the assessment process).<section end=glossary />

References


David's paper of May 2006 on scoping - some new developments in SP1.


<section begin=glossary />
Sensitivity analysis is a study performed to find out how the variation in the output of a model (numerical or otherwise) can be apportioned, qualitatively or quantitatively, to different sources of variation.[1]<section end=glossary />


What is sensitivity analysis (SA)?

Sensitivity analysis is a means of analysing the ways in which assessment outputs vary according to variation in assessment inputs. "Sensitivity" is the property of an assessment output that changes when the value or structure of an input is varied. Assessment output variability can be apportioned, qualitatively or quantitatively, to different sources of variation in the inputs (within variables or their location in the causal diagram etc.)

How can SA be used in integrated environmental health impact assessment?

Sensitivity analysis can be used in integrated environmental health impact assessment to determine which variables or steps in the full chain have the greatest effect (quantitatively or qualitatively) on the variation in the assessment's output. This allows the analyst to see clearly which aspects of a causal diagram are most liable to be affected by change in individual variables, in the structure of that model, and in the assumptions used to frame the assessment in the scoping phase.[2] SA, then, serves as a step in the analysis of uncertainty crucial to generating robust and acceptable assessment results.

Sensitivity analysis can be used in dealing with:

  1. Investigating the degree to which an assessment causal diagram resembles the process being modelled;
  2. Judging the quality of model definition;
  3. Establishing which factors contribute most significantly to variability in the output;
  4. Interactions between factors

Resources

There are a number of online resources available in terms of both information on SA (including papers and tutorials), as well as software that can be used to carry out sensitivity analysis on integrated environmental health impact assessment models.

Links

Software, tutorials and presentations

References

  1. ↑ Wikipedia definition on sensitivity analysis
  2. ↑ A forum on sensitivity analysis. Joint Research Centre. Page accessed 13/11/2007

<section begin=glossary />

Step is a certain stage in the assessment causal chain.[1]

<section end=glossary />

References

  1. ↑ Intarese general method.



<section begin=glossary />

Threshold is a dose or an exposure concentration of an agent below which a stated effect is not observed or expected to occur.[1]

<section end=glossary />

References

  1. ↑ WHO Report
<section begin=glossary />
A tool is an entity that helps in the assessment process and includes some kind of calculation or representation, e.g. a visualisation tool, an assessment design guidance tool, a tool for defining and categorising uncertainties, a tool for calculating emissions or for applying dose-response-relationships.<section end=glossary />


<section begin=glossary />
Uncertainty refers not only to statistical uncertainty. The typology used in INTARESE builds on an adapted version of the Walker & Harremoës framework.
One dimension of uncertainties is the location of uncertainties (where the uncertainty is located). For most models it is applicable to distinguish between:
  • context
  • model structure
  • inputs
  • parameters
  • model outcome (result)
For the other dimension of uncertainties it is distinguished between three levels of uncertainties:
  • statistical uncertainty (known outcomes, known probabilities)
  • scenario uncertainty (known outcomes, unknown probabilities)
  • identified ignorance (unknown outcomes, unknown probabilities).<section end=glossary />

References

The uncertainty report by WP 1.5.<section begin=glossary />

Uptake is the process by which an agent crosses an absorption barrier. See also dose. <section end=glossary /><section begin=glossary />
  1. Valuation is a step in the full chain. It provides the means for comparing the outcomes of the models, e.g. health effects or, more generic, impacts. The outcomes are translated into a compound measure, e.g. DALYs, euros. Methods applicable are e.g. burden of desease (DALYs) or monetary valuation.
  2. Valuation only means monetary valuation.<section end=glossary /><section begin=glossary />
A value is used in relation to a value judgement, i.e. an opinion of an entity. It can be used concerning the valuation step but also for all other kinds of variables.<section end=glossary />

<section begin=glossary />

Value judgment is a preferenceD↷ for a certain state of the world, expressed by an individual or by a society. Value judgments - in contrast to valuation - include all kinds of statements of preferences, not only monetary valuation.
Assessment is about estimating impacts that may have either positive or negative value judgments attached to themselves or to the factors causally affecting them or to the factors causally affected by them. These values must be acknowledged in the process of making the assessments, not only in the decision making phase, otherwise there is a risk of compromising the relevance of the assessment. Combining phenomena of physical reality with the value judgments related to them requires methods to distinguish these two things from each other and bringing the value judgments to explicit scrutiny within an assessment.<section end=glossary />

Assessments typically consider complex networks of natural and societal phenomena that are of, at least potential, interest to many organizations and individuals with different perspectives to the issues at stake. These organizations and individuals are not only possible sources of relevant knowledge, but also, and perhaps in particular, societal actors with plural values regarding the issues of assessment. Due consideration of these values is crucial in assessments, in particular if assessments are considered as processes of invoking social learning upon societally important matters. Also value judgments, like factual statements based on evidence, can be subjects of systematic scrutiny in assessments.

See also

References


Comments

<discussion />

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