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A Bayesian multiple change-point model is proposed to analyse violations ofair quality standards by pollutants such as nitrogen oxides (NO2 and NO) andcarbon monoxide (CO). The model is built on the assumption that the occurrenceof threshold exceedances may be described by a non-homogeneous Poisson processwith a step rate function. Unlike earlier approaches, our model is notrestricted by a predetermined number of change-points, nor does it involve anycovariates. Possible short-range correlations in the exceedance data (e.g., dueto chemical and meteorological factors) are removed via declusterisation. Theunknown rate function is estimated using a reversible jump MCMC samplingalgorithm adapted from Green (1995), which allows for transitions betweenparameter subspaces of varying dimension. This technique is applied to the17-year (1993-2009) daily NO2, NO and CO concentration data in the City ofLeeds, UK. The results are validated by running the MCMC estimator on simulateddata replicated via a posterior estimate of the rate function. The findings areinterpreted and discussed in relation to some known traffic control actions.The proposed methodology may be useful in the air quality management context byproviding quantitative objective means to measure the efficacy of pollutioncontrol programmes.

Multiple change-point Poisson model for threshold exceedances of air pollution concentrations