  A Bayesian multiple change-point model is proposed to analyse violations of
air quality standards by pollutants such as nitrogen oxides (NO2 and NO) and
carbon monoxide (CO). The model is built on the assumption that the occurrence
of threshold exceedances may be described by a non-homogeneous Poisson process
with a step rate function. Unlike earlier approaches, our model is not
restricted by a predetermined number of change-points, nor does it involve any
covariates. Possible short-range correlations in the exceedance data (e.g., due
to chemical and meteorological factors) are removed via declusterisation. The
unknown rate function is estimated using a reversible jump MCMC sampling
algorithm adapted from Green (1995), which allows for transitions between
parameter subspaces of varying dimension. This technique is applied to the
17-year (1993-2009) daily NO2, NO and CO concentration data in the City of
Leeds, UK. The results are validated by running the MCMC estimator on simulated
data replicated via a posterior estimate of the rate function. The findings are
interpreted and discussed in relation to some known traffic control actions.
The proposed methodology may be useful in the air quality management context by
providing quantitative objective means to measure the efficacy of pollution
control programmes.
