Extreme value analysis of Munich airpollution data

We present three different approaches to model extreme values of
daily air pollution data. We fitted a generalized extreme value
distribution to the monthly maxima of daily concentration measures.
For the exceedances of a high threshold depending on the data the
parameters of the generalized Pareto distribution were estimated.
Accounting for autocorrelation clusters of exceedances were used.
To get information about the relationship of the exceedance of the
air quality standard and possible predictors we applied logistic
regression. Results and their interpretation are given for daily
average concentrations of ozone and of nitrogendioxid at two
monitoring sites within the city of Munich.