Statistical models to assess the health effects and to forecast ground-level ozone

TLDR: There is urgent demand for forecasting episodes of high ozone that may help susceptible persons to avoid high exposure, and respiratory symptoms are associated with the daily maximum of the 8-h average ozone concentration, which in turn is best predicted by means of non-linear statistical models.

Abstract: By means of statistical approaches we attempt to bridge both aspects of the ground-level ozone problem: assessment of health effects and forecasting and warning. Disagreement has been highlighted in the literature recently regarding the adverse health effects of tropospheric ozone pollution. Based on a panel study of children in Leipzig we identified a non-linear (quadratic) concentration-response relationship between ozone and respiratory symptoms. Our results indicate that using ozone as a linear covariate might be a misspecification of the model, which might explain non-uniform results of several field studies in health effects of ozone. We conclude that there is urgent demand for forecasting episodes of high ozone that may help susceptible persons to avoid high exposure. Novel approaches to statistical modelling and data mining are helpful tools in operational smog forecasting. We present a rigorous assessment of the performance of 15 different statistical techniques in an inter-comparison study based on data sets from 10 European regions. To evaluate the results of the inter-comparison exercise we suggest an integrated assessment procedure, which takes the unbalanced study design into consideration. This procedure is based on estimating a statistical model for the performance indices depending on predefined factors, such as site, forecasting technique, forecasting horizon, etc. We find that the best predictions can be achieved for sites located in rural and suburban areas in Central Europe. For application in operational air pollution forecasting we may recommend neural network and generalised additive models, which can handle non-linear associations between atmospheric variables. As an example we demonstrate the application of a Generalised Additive Model (GAM). GAMs are based on smoothing splines for the covariates, i.e., meteorological parameters and concentrations of other pollutants. Finally, it transpired that respiratory symptoms are associated with the daily maximum of the 8-h average ozone concentration, which in turn is best predicted by means of non-linear statistical models. The new air quality directive of the European Commission (Directive 2002/3/EC) accounts for the special relevance of the 8h mean ozone concentration.