Geographic location and extent: Difference between revisions

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If the aim is to relate impacts to sources (e.g. by a means of source attribution), the study area also needs to include all the main source contributors, including those that are remote from the target population.  This means that, where far-travelled pollutants are concerned, a large study area may need to be identified for the purposes of modelling emissions and transport of the pollutants; within this, however, attention may focus on a more narrowly defined target area or exposed population.
If the aim is to relate impacts to sources (e.g. by a means of source attribution), the study area also needs to include all the main source contributors, including those that are remote from the target population.  This means that, where far-travelled pollutants are concerned, a large study area may need to be identified for the purposes of modelling emissions and transport of the pollutants; within this, however, attention may focus on a more narrowly defined target area or exposed population.
==See also==
* [[Geographic variation]]
** [[Geographic location and extent]]
** [[Spatial resolution]]
** [[Zone design]]
{{IEHIAS}}

Latest revision as of 18:45, 14 October 2014

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

The most obvious implication of geographic variation is that everywhere is not the same. Where an assessment is done therefore influences the apparent impacts; assessments done in one place do not necessarily translate to a different location. It is often not possible to do an assessment for an entire area or population, either because the necessary data are not available, or because of limits of time and resources. Many assessments are therefore conducted on smaller, supposedly representative samples or study areas. Ensuring that these are representative of the wider area and population is vital, for otherwise the results may be highly biased. This is best done by comparing the study population and area with the wider population and area it is meant to represent in terms of its vital characteristics. Populations should be equivalent, for example, in terms of age, gender and socio-economic status; the environments should be similar in terms of the average level, and range, of the hazards (or benefits) under consideration - e.g. the type and abundance of emission sources, the levels and composition of the emissions, the air pollution mix and concentrations. Where the location of an assessment is predefined (e.g. by the users who commissioned it), then the results should not be extrapolated to other areas, unless these can be shown to be comparable in terms of these environmental and population characteristics.

Equally, results of the assessment depend on how large an area is considered. Like the location, the geographic boundary of the assessment is often predetermined (e.g. by administrative responsibilities). In these cases, the potential biases that may arise need to be recognised. If the area is too small, for example, the overall impacts will be under-estimated in absolute terms (e.g. the total burden of disease), because some affected people (e.g. those who are exposed while working within the study area but live outside it) will have been excluded. If it is too large, the effects will be diluted in relative terms (e.g. as a proportion of the overall population), by the inclusion of people who are not at risk. Where possible, therefore, the geographic extent of the assessment should be adjusted to ensure that it includes:

  • all the areas where the people of interest spend their time; and
  • all the people who might be affected by events within the study area of interest.

If the aim is to relate impacts to sources (e.g. by a means of source attribution), the study area also needs to include all the main source contributors, including those that are remote from the target population. This means that, where far-travelled pollutants are concerned, a large study area may need to be identified for the purposes of modelling emissions and transport of the pollutants; within this, however, attention may focus on a more narrowly defined target area or exposed population.

See also

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
Models and methods Ind. select · Mindmap · Diagr. tools · Scen. constr. · Focal sum · Land use · Visual. toolbox · SIENA: Simulator Data Description · Mass balance · Matrix · Princ. comp. · ADMS · CAR · CHIMERE · EcoSenseWeb · H2O Quality · EMF loss · Geomorf · UVR models · INDEX · RISK IAQ · CalTOX · PANGEA · dynamiCROP · IndusChemFate · Transport · PBPK Cd · PBTK dioxin · Exp. Response · Impact calc. · Aguila · Protocol elic. · Info value · DST metadata · E & H: Monitoring Frameworks · Integrated monitoring: Concepts Framework Methods Needs
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