In this paper, we present an optimization methodology for reducing the number of sensors in an existing monitoring network. These sensors measure the concentration of pollutant gas in the air, in order to estimate the position and intensity of a pollutant source. Two statistical methods were used and compared. The first method is based on Hierarchical Agglomerative Clustering (HAC), and the second one is Self-Organized Maps (SOM). The aim is to regroup sensors of the same behavior, based on similarity measure; then, we keep only one sensor of each cluster. The methodology was tested on synthetic data, with Bayesian inference and Monte Carlo Markov Chain (MCMC) algorithm to identify the pollutant source position and intensity. Of 88 sensors in the initial network, the number was reduced to 21 by HAC and 27 by SOM. As for the identification, both methods had close estimation of the source position, however the SOM had better results in the estimation of the source intensity in general.