**1. Introduction**

Particulate matter (*PM*) is an air pollutant comes from vehicular traffic, industrial activities and street dust, or from the atmosphere, by transformation of the gaseous emissions. In recent years the interest in the health effects of this pollutant have increased, since high concentration levels in urban area have been measured.

Several studies suggest an association between fine particulate air pollution and the increase of the mortality rate [1]. In particular, *PM* up to 10 micrometers in size (*PM*10) could cause negative health effects such as respiratory illness or cardiovascular problems. Hence, the analysis of temporal evolution of this pollutant could be useful in decision-making process for environmental policy.

Typically, in time series analysis, the Box-Jenkins methodology is widely applied and the autocorrelation function (*ACF*) is used as a standard exploratory tool to identify the model structure [3, 4]. In this context, the use of geostatistical techniques could also be convenient, nevertheless these techniques are usually applied to analyze, through the variogram, spatial relationships among sample data measured at some locations in a domain and to predict the corresponding spatial phenomena [6, 18, 22, 29]. In particular, the variogram could represent a complementary exploratory tool for assessing stationarity in time series [2, 19] and it has the considerable advantage that it is defined in much wider circumstances than the autocovariance and the autocorrelation. Moreover, this analytical tool is appropriate to identify trends and periodicity exhibited by the data and to obtain kriging predictions of the variable under study, either for temporal intervals with missing values (interpolation mode) and in time points after the last available data (extrapolation mode).

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Different studies have suggested the use of geostatistical methods in time domain [7, 19]. In particular, De Iaco et al. [12] illustrated the role of variogram in this context for different purposes.

The aim of this paper is to analyze *PM*<sup>10</sup> air pollution in an area of South Italy characterized by high levels of industrial emissions and vehicular traffic, through geostatistical techniques.

Thus, after a brief review on stochastic processes and geostatistical methods in time series analysis, the temporal evolution of *PM*<sup>10</sup> daily concentrations, for the period 2010-2013 has been assessed. After the identification of trend and periodicity, the reconstruction of the analyzed time series by estimation of missing values has been discussed, and predictions of *PM*<sup>10</sup> daily concentrations at some unsampled points have been produced. Moreover, the probability distributions of the variable under study have been estimated for future time points.

For interpolation and prediction purposes, a modified version of *GSLib* kriging routine has been used.
