Optimal spatial design for air quality measurement surveys. Issue 1 (16th January 2014)

• Record Type: Journal Article
• Authors: Romary, Thomas; Malherbe, Laure; de, Chantal
• Is Part Of: Environmetrics. Volume 25:Issue 1(2014:Feb.)
• Page Start: 16
• Page End: 28
• Publication Date: 2014-01-16
• Subjects: Environmental sciences -- Statistical methods -- Periodicals; 550.72
• DOI: 10.1002/env.2253
• Languages: English
• ISSNs: 1180-4009

Abstract

<abstract abstract-type="main" id="env2253-abs-0001"> <title> <x xml:space="preserve">Abstract</x> </title> <p id="env2253-para-0001">Measurement surveys using passive diffusion tubes are regularly carried 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 relations 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 <italic>a priori</italic> 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 performed on available data leads to choose the most relevant auxiliary variables and to determine an <italic>a priori</italic> variogram model for the residual. An <italic>a priori</italic> 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 agglomeration 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 <italic>a priori</italic> 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. Copyright © 2014 John Wiley & Sons, Ltd.</p> </abstract>