Using geostatistical simulation to disaggregate air quality model results for individual exposure estimation on GPS tracks
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.
KeywordsUncertainties Exposure PM10 GPS Change of support
The presented research has been funded by the European project UncertWeb (FP7-248488). The views expressed herein are those of the authors and are not necessarily those of the European Commission. We thank J. Hackenberg for data acquisition. Data and scripts used in this paper can be obtained from the authors upon request.
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