Total Air Pollution And Space-Time Modelling
Two general problems occur in the analysis of air pollution data; multiple contaminants and a dependence on both spatial location and time of observation. Principal Component Analysis (PCA) provides a tool for removing the interdependence of the contaminant concentrations, in addition an analysis of the principal components, eigenvectors and eigenvalues provides additional insight into the dispersion and occurrence of the pollution plume. New models for space-time variograms and techniques for modelling them have been introduced by De laco, Myers and Posa.
Hourly average concentrations for nitric oxide (NO), nitrogen dioxide (NO 2) and carbon monoxide (CO) measured at 30 stations in 1999 in the Milan district, Italy, were used for the analysis. These were converted to daily averages and PCA was applied to each of the 365 data sets (3 contaminants and 30 stations). The eigenvectors of the correlation matrices were used to generate principal components, which can be considered as measures of Total Air Pollution (TAP) in lieu of the separate contaminant concentrations. These components were treated as samples from unobserved variates defined over space and time. Space-time variograms were fitted to these new variates using the product sum model.
Although linked in these analyses, the principal components and their associated eigenvectors as well as the scores for each station vs the space-time variogram models provide two different pictures of the spatial and temporal dispersion of the contaminants as well as their interaction at different times of the year.
KeywordsDioxide Covariance Carbon Monoxide Kriging Cokriging
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