Using geostatistical simulation to disaggregate air quality model results for individual exposure estimation on GPS tracks

Original Paper

Abstract

In this work, we address the mismatch in spatio-temporal resolution between individual, point-location based exposure and grid cell based air quality model predictions by disaggregating the grid model results. Variability of PM10 point measurements was modelled within each grid cell by the exponential variogram, using point support concentration measurements. Variogram parameters were estimated over the study area globally using constant estimates, and locally by multiple regression models using traffic, weather and land use data. Model predictions of spatio-temporal variability were used for geostatistical unconditional simulation, estimating the deviation of point values from grid cell averages on GPS tracks. The distribution of deviations can be used as an estimate of uncertainty for individual exposure. Results showed a relevant impact of the disaggregation uncertainties compared to other uncertainty sources, dependent of the model used for spatio-temporal variability. Depending on individual behaviour and variability of the pollutant, these uncertainties average out again over time.

Keywords

Uncertainties Exposure PM10 GPS Change of support 

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Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  1. 1.Institute for Geoinformatics, University of MuensterMünsterGermany

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