Example of a common case study

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

Example Common Case Study

Aim of the Common Case Study

In addition to the development and enhancement of single methods one focus of HEIMTSA/INTARESE is to test the integrated environmental health impact assessment system (IEHIAS) developed in INTARESE WP 4.2 and HEIMTSA WP 5.2 and to apply the INTARESE/HEIMTSA methodology to realistic policy scenarios, in order to (i) test the usability of the full chain methodology of IEHIA and the IEHIAS as a whole; (ii) identify any important gaps; (iii) generate results; and (iv) discuss and evaluate both the methodology (for its reliability and completeness) and the results (for their plausibility and practical use).

The so-called Common Case Study is the means to fulfil these objectives. Its aims are (i) to assess environmental health impacts of high-level, cross-cutting policy issues at EU level and (ii) to provide a full example of an integrated environmental health impact assessment according to INTARESE and HEIMTSA recommendation.

Questions

Policies and measures for mitigation of and adaption to climate change are nearly always chosen with a focus on the reduction of CO2-eq and the cost of the measures. However, there may be relevant side benefits or damages, e.g. decreases or increases in health impacts. Those should also be taken into account during the decision process.

To inform decision makers about these side effects, the Common Case Study answered the following questions:

What are the (negative or positive) health impacts of climate change policies in Europe for the years 2020, 2030, and 2050? Specifically,

a      How do EU climate mitigation policies, i.e. policies with the primary purpose of reducing the emissions of greenhouse gases (policies and resulting measures), affect environmental health impacts in Europe, e.g. increased use of biomass as energy source?

b      How do EU climate adaptation options and policies, i.e. policies that reduce negative climate change impacts, affect environmental health impacts in Europe?

Scope

Spatial boundaries:

The case study looked at the European scale (EU29). The spatial resolution differed for sectors and pollutants as appropriate. For air pollutants, e.g. it was based on 50x50 km2 Emep[1] grid cells for regional effects and on a smaller grid for local effects (e.g. traffic in cities). For indoor air pollution parameters were use on a country level including probability distributions. For pesticide modelling a spatially not explicit model that, however, includes trade between EU and non-EU countries was applied.

Temporal boundaries:

The case study looked at a base year (for emission scenario modelling, 2005) and developed scenarios for the future years 2020, 2030 and 2050, i.e. described the state of the system (policies, physical parameters…) and emissions of pollutants in these years. While scenarios for 2020 and 2030 could be developed with a higher degree of certainty, i.e. the possible ranges of parameters were large but still limited; scenarios for 2050 had to be based on less certain assumptions and, thus, bear higher uncertainty. Nevertheless, a quantitative scenario description proved to be helpful in exploring the possible effects of policies including emission mitigation measures.

Effects of emissions might be observed only later than the time of emissions (e.g. exposure is delayed due to a slow dispersion of stressors in the environment; or health impacts can occur only years after exposure) but are attributed to the year of emission – in this case they were discounted to the year of emission to reflect the time preference people give to effects in the future.

Population:

Receptor for the exposure was the European population. According to needs it was stratified by age groups and gender for each 50x50 km2 Emep grid cell. Its growth was also projected to the years 2020, 2030, and 2050 separately for each age group.

Age/sub groups in the PM2.5 exposure modelling were: 0-14, 15-64 and 65+ years of age. The group 15-64 was split by working and non-working people. All groups were stratified by gender.

Age groups for health effect estimation (impact functions) differed from the personal exposure modelling groups. They were 0-1, 0-3, 5-14, 5-16, 0-16, 0-18, 15-64, 18-64, 20+, 25+, 27+, 30+, 65+, 18+ and all ages.

The diversity of subgroups was dealt with in such a way that the age groups of the impact functions were generally used when applying the impact functions to exposure (e.g. concentration or intake fraction). Only for personal exposure modelling to PM2.5 the age groups had to be mapped to fit each other, i.e. age groups were split even finer, the impact functions were applied and the finer age groups aggregated once again to the exposure modelling subgroups.

The reason of working with different age groups during the application of impact functions is that the impact functions were either derived in studies looking at those subgroups (e.g. when looking especially on children) or are only applicable to those subgroups (e.g. susceptibility to ozone for elderly, or infant mortality for infants).

The reason of working with different sub groups during the personal exposure modelling to PM2.5 is to facilitate the comparison of different sub groups, i.e. to explore the impact of different mitigation measures and scenarios on the sub groups.

Stressors:

• Outdoor air pollutants: emissions of primary PM10 and primary PM2.5 including compounds, NO2, NOx, SO2, NMVOCs, NH3

• Indoor air pollutants: PM2.5 and PM10, mould/dampness, CH2O (formaldehyde), environmental tobacco smoke (ETS), radon

• Persistent organic pollutants (POPs): PCB-153 and 2,3,4,7,8-PeCDF

• Noise due to road traffic/transport

• Pesticides: 14 fungicides, 49 herbicides, 7 insecticides, 3 plant growth regulators

• Greenhouse gases (GHGs): CO2, CH4, N2O They are only taken into account for assessing the reduction of GHGs, i.e. how many CO2-equivalents a policy scenario or mitigation measure reduces. No health effects are implied by GHGs.

• Heat: It was explored how measures in urban development like shading impacts on heat exposure of the population.

Health effects:

Impact functions were given as European average. For some functions, due to differences in background rates of disease, impact functions were additionally given for the regions Western, Eastern, Northern and Southern Europe.

• PM10: cardiovascular hospital admissions, respiratory hospital admissions, asthma medication usage (children and adults), lower respiratory symptoms including cough (children and adults)

• PM2.5: mortality, work loss days, minor restricted activity days, restricted activity days

• Ozone: mortality, respiratory hospital admissions, asthma medication usage (children and adults), lower respiratory symptoms excluding cough (children), cough (children), minor restricted activity days

• ETS: coronary heart disease hospitalisation, lung cancer, sudden infant deaths (SIDs), lower respiratory illness symptom days and hospitalisation (children), cough (children), wheeze (children), asthma induction

• Radon: lung cancer

• Formaldehyde: asthma

• Naphthalene: cancer

• Mould/dampness: wheeze (children and adults), asthma development (children and adults)

• Noise: % highly annoyed, % highly sleep disturbed, myocardial infarction

• Pesticides: generic cancer (It is acknowledged that it is likely that other health effects like neurotoxic effects occur, however, no dose response functions could be found.)

• POPs: generic cancer

• Heat: summer mortality

http://www.eea.europa.eu/data-and-maps/data/emep-grids-reprojected-by-eea