  Statistical techniques used in air pollution modelling usually lack the
possibility to understand which predictors affect air pollution in which
functional form; and are not able to regress on exceedances over certain
thresholds imposed by authorities directly. The latter naturally induce
conditional quantiles and reflect the seriousness of particular events. In the
present paper we focus on this important aspect by developing quantile
regression models further. We propose a general Bayesian effect selection
approach for additive quantile regression within a highly interpretable
framework. We place separate normal beta prime spike and slab priors on the
scalar importance parameters of effect parts and implement a fast Gibbs
sampling scheme. Specifically, it enables to study quantile-specific covariate
effects, allows these covariates to be of general functional form using
additive predictors, and facilitates the analysts' decision whether an effect
should be included linearly, non-linearly or not at all in the quantiles of
interest. In a detailed analysis on air pollution data in Madrid (Spain) we
find the added value of modelling extreme nitrogen dioxide (NO2) concentrations
and how thresholds are driven differently by several climatological variables
and traffic as a spatial proxy. Our results underpin the need of enhanced
statistical models to support short-term decisions and enable local authorities
to mitigate or even prevent exceedances of NO2 concentration limits.
