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Impact of input data uncertainty on environmental exposure assessment models: A case study for electromagnetic field modelling from mobile phone base stations

Authors
Journal
Environmental Research
0013-9351
Publisher
Elsevier
Identifiers
DOI: 10.1016/j.envres.2014.05.038
Keywords
  • Uncertainty Analysis
  • Monte Carlo Simulation
  • Electromagnetic Fields
  • Exposure Assessment
  • Mobile Phone Base Stations
Disciplines
  • Biology
  • Computer Science
  • Ecology
  • Geography
  • Medicine
  • Physics

Abstract

Abstract Background With the increased availability of spatial data and computing power, spatial prediction approaches have become a standard tool for exposure assessment in environmental epidemiology. However, such models are largely dependent on accurate input data. Uncertainties in the input data can therefore have a large effect on model predictions, but are rarely quantified. Methods With Monte Carlo simulation we assessed the effect of input uncertainty on the prediction of radio-frequency electromagnetic fields (RF-EMF) from mobile phone base stations at 252 receptor sites in Amsterdam, The Netherlands. The impact on ranking and classification was determined by computing the Spearman correlations and weighted Cohen׳s Kappas (based on tertiles of the RF-EMF exposure distribution) between modelled values and RF-EMF measurements performed at the receptor sites. Results The uncertainty in modelled RF-EMF levels was large with a median coefficient of variation of 1.5. Uncertainty in receptor site height, building damping and building height contributed most to model output uncertainty. For exposure ranking and classification, the heights of buildings and receptor sites were the most important sources of uncertainty, followed by building damping, antenna- and site location. Uncertainty in antenna power, tilt, height and direction had a smaller impact on model performance. Conclusions We quantified the effect of input data uncertainty on the prediction accuracy of an RF-EMF environmental exposure model, thereby identifying the most important sources of uncertainty and estimating the total uncertainty stemming from potential errors in the input data. This approach can be used to optimize the model and better interpret model output.

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