This paper investigates the jointly use of local polynomials and feedforward neural networks for estimating the probability of exceedance of the daily average for in the presence of missing data. In contrast to other approaches focusing on some assumption on the distribution of , the reconstruction of the unobserved time series is obtained by using a procedure involving two nonparametric steps based on the estimation of the trend-cycle and of the superimposed nonlinear stochastic component of the series. By using Neural Network Sieve Bootstrap, the probability to overcross the limit established by the European Union for is evaluated at the dates where time series shows missing values. An application to real data is also presented and discussed.