Modelling untraviolet radiation exposures in Europe

From Opasnet
Jump to: navigation, search
The text on this page is taken from an equivalent page of the IEHIAS-project.

As part of the EU-funded INTARESE project, which contributed to the development of this Toolbox, a case study was carried out to assess health impacts associated with exposure to ultraviolet radiation (UVR) under different climate change scenarios.

The case study focused on melanoma and non-melanoma skin cancer, accounting for the changes in stratospheric ozone (due to reduced emission of ozone depleting substances and increasing emissions of CH4 and N2O). Exposures and health impacts were assessed for two IPCC scenarios: SRES B1 and A2 (IPCC 2000), using a radiative transfer model.

Explanation of the method

Exposure simulations using radiative transfer modelling

All simulations were done using the libRadtran software (Mayer and Kylling, 2005). This model has been extensively validated in the past both with measurements (Mayer et al., 1997) and against other models (Van Weele et al., 2000). In general, the model provides good agreement with measurements under cloudless skies (within 5%).

Exposure indicator

The annual erythemal doses and average daily erythemal doses were selected as exposure indicators, in order to match the metrics used in published exposure-response functions. The erythemal dose is based on the action spectrum for erythema (sunburn). (The software can be modified to handle other dose types as well.)

Exposure distribution

Measurements of ambient UVR only give an indication of 'possible' UVR exposure of a population; the relationship between an outcome and the risk factor occurs at an individual level. However, ambient UVR does not easily translate to actual population exposure distribution, because of differences in local UV levels, individual time-activity patterns and dress behaviour. Detailed information on personal exposure and exposure distribution was not applied here; ambient UVR is thus used as the best available proxy of population exposure, and is also in accord with the available ERFs.

Current day exposure data

Data on current daily exposure were required both to define baseline conditions, and as a means of validating the models.

For this purpose, daily erythemal doses for years 1997-2003 were estimated from the TOMS satellites. [ Daily total ozone data] were likewise obtained from TOMS for the same period.

Modification factors needed to be estimated to allow for the effects of non-clear sky conditions: for erythemal doses, the application of modification factors has been validated (e.g. Schwander et al. 2002). Daily clear-sky erythemal doses were therefore estimated for the total ozone columns, assuming cloudless conditions and a black non-reflecting surface. Cloud/aerosol/albedo modification factors were then estimated by:

  • adding all the matching daily erythemal doses from the TOMS satellite data;
  • likewise adding all the matching clear-sky daily erythemal doses;
  • taking the ratio of the two cumulated doses over the seven year period (1997-2003).

Provisionally, modification factors were assumed to remain constant for the next century.

Future UV predictions

The same approach was used to estimate future clear sky erythemal doses, for the years 2000, 2030, 2050 under the IPCC 2001 scenarios A2 and B1. Future global ozone fields for these conditions were derived from the Oslo Chemical Transport Model (Rummukainen et al., 1999). The clear sky erythemal daily doses were then rescaled, using the fixed modification factors to obtain realistic estimates of ambient UV exposure.


  • IPCC 2000 Special report on emission scenarios. Cambridge: Cambridge University Press.
  • IPCC 2005 Special report on safeguarding the ozone layer and the global climate system: issues related to hydrofluorocarbons and perfluorocarbons. Cambridge: Cambridge University Press.
  • Mayer, B., Seckmeyer, G. and Kylling, A. 1997 Systematic long-term comparison of spectral UV measurements and UVSPEC modeling results. Journal of Geophysical Research 102(D7):8755-8767.
  • Mayer, B. and Kylling, A. 2005 Technical note: the libRadtran software package for radiative transfer calculations: Description and examples of use. Atmospheric Chemistry and Physics 5, 1855-1877.
  • Rummukainen, M., Isaksen, I., Rognerud, B. and Stordal, F. 1999 A global model tool for three-dimensional multiyear stratospheric chemistry simulations: Model description and first results, Journal of Geophysical Research 104(D21), 26437-26456.
  • Schwander, H., Koepke, P., Kaifel, A. and Seckmeyer, G. 2002 Modification of spectral UV irradiance by clouds, Journal of Geophysical Research 107(D16), 4296, doi:10.1029/2001JD001297.
  • van Weele, M., Martin, T.J., Blumthaler, M., Brogniez, C., den Outer, P.N., Engelsen, O., Lenoble, J., Pfister, G., Ruggaber, A., Walravens, B., Weihs, P., Dieter, H., Gardiner, B.G., Gillotay, D., Kylling, A., Mayer, B., Seckmeyer, G. and Wauben, W. 2000 From model intercomparisons towards benchmark UV spectra for six real atmospheric cases. Journal of Geophysical Research 105(D4), 4915-4925.

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

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