Optimal spatial design for air quality measurement surveys
Résumé
Measurement surveys using passive diffusion tubes are regularly car-ried out to elaborate atmospheric concentration maps over various areas. Sampling schemes must be designed to characterize both contaminant concentrations (of benzene or nitrogen dioxide for example) and their re-lations to environmental variables so as to obtain pollution maps as precise as possible. Here, a spatial statistical methodology to design benzene air concentration measurement surveys on the urban scale is exposed. In a first step, an a priori modeling is conducted that is based on the analysis of data coming from previous campaigns on two different agglomerations. More precisely, we retain a modeling with an external drift which consists of a drift plus a spatially correlated residual. The statistical analysis per-formed on available data leads to choose the most relevant auxiliary vari-ables and to determine an a priori variogram model for the residual. An a priori distribution is also defined for the variogram parameters, whose values appear to vary from a campaign to another. In a second step, we optimize the positioning of the measuring devices on a third agglomera-tion according to a Bayesian criterion. Practically, we aim at finding the design that minimizes the mean over the urban domain of the universal kriging variance, whose parameters are based on the a priori modeling, while accounting for the prior distribution over the variogram parameters. Two global optimization algorithms are compared: simulated annealing and a particle filter based algorithm.
Domaines
Statistiques [math.ST]
Origine : Fichiers produits par l'(les) auteur(s)
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