Identifying and removing heterogeneities between monitoring networks

There is an increased interest in merging observations from local networks into larger national and international databases. However, the observations from different networks have typically been made using different equipment and applying different post-processing of the values. These heterogeneities in recorded values between networks can lead to inconsistencies between different networks, and to discontinuities at the borders between regions if the observations are used as a source for interpolated maps of the process. Such discontinuities are undesirable, and could create difficulties in interpreting the maps by decision makers. In this paper, we present two variants of a method that can be used to identify and quantify differences between networks. The first variant deals with networks sharing the same region (usually multiple networks within a country) while the second variant deals with networks in neighbouring regions (usually networks in different countries). The estimated differences can be used to estimate individual biases for each network, which can be subtracted as a harmonization procedure. The method was applied to European gamma dose rate (GDR) measurements from May 2008 from the European Radiological Data Exchange Platform (EURDEP) database. Data from the Slovenian GDR network are used for an application of the first variant of the method whereas the complete dataset is used to illustrate the second variant. The results indicate that these two variants are able to identify and quantify biases reliably, and the interpolated maps after subtraction of the estimated biases appear more reliable than maps created on the basis of the recorded dat

Data harmonization with geostatistical tools: a Bayesian extension

Mapping at an international scale may suffer from biasedness due to systematic differences in measurement devices and procedures. Biases show up when interpolating the target variable across borders. Harmonization of multinational datasets is therefore important and becomes a compulsory preprocessing step prior to the geostatistical mapping and analysis. This paper explores the possibility of automatic removal of systematic biases between different measurement networks. We extend the method proposed by Baume et al. (2008) to a Bayesian setting. Sources of heterogeneities in the data ¿ measurement device types, data handling methods and site criteria for instance ¿ are taken into account in a global setting using a linear regression Kriging model. The model introduces both measurement and state variables, incorporating in the trend bias factors as well as natural drifts. Analytical solutions are given of posterior distribution of the biases in the Gaussian case and applied to a case study. In the harmonization context, prior information on network biases may be more specifically treated in two ways. Best is to rely on expert knowledge but in the case of missing information an alternative local estimation method introduced by Skøien et al. (2008) is used. An example is taken from mapping radioactivity exposure across European countries. Results show that significant bias is present in the radioactivity data and can successfully be removed with the networks presented

Network optimization algorithms and scenarios in the context of automatic mapping

Many different algorithms can be used to optimize the design of spatial measurement networks. For the spatial interpolation of environmental variables in routine and emergency situations, computation time and interpolation accuracy are important criteria to evaluate and compare algorithms. In many practical situations networks are not designed from scratch but instead the objective is to modify an existing network. The goal then is to add new measuring stations optimally or to withdraw existing stations with as little damage done as possible. The objective of this work is to compare the performance of different optimization algorithms for both computation time and accuracy criteria. We describe four algorithms and apply these to three datasets. In all scenarios the mean universal kriging variance (MUKV) is taken as the interpolation accuracy measure. Results show that greedy algorithms that minimize the information entropy perform best, both in computing time and optimality criterion

Geostatistical modeling of sound propagation: Principles and a field application experiment

The assessment of noise sources for environmental purposes requires reliable methods for mapping. Numerical models are well adapted for sophisticated simulations and sensitivity analyses; however, real-time mapping of large frequency bands must be based on fast and acceptable computations and honor in situ measurements. In this paper, a real-time mapping procedure of noise exposure is proposed. The procedure is based on geostatistical modeling of spatial variations and applied to a case study taken from an experimental campaign, where a point source was placed on a flat meadow. An analytical approximation of the acoustic field was first computed with the Embleton model. The difference between this approximation and the actual measurements (L-eq15 min 1/3-octave bands samples from 19 microphones spread over the meadow) showed spatial structure, which has been modeled with a variogram. Finally, the geostatistical technique of kriging with external drift provided an optimal interpolation of the acoustic field data while encapsulating the first approximation from the Embleton model. Systematic geostatistical inference and real-time mapping with the proposed procedure can be envisaged in simple cases

Optimizing the spatial pattern of networks for monitoring radioactive releases

This study presents a method to optimize the sampling design of environmental monitoring networks in a multi-objective setting. We optimize the permanent network of radiation monitoring stations in the Netherlands and parts of Germany as an example. The optimization method proposed combines minimization of prediction error under routine conditions with maximizing calamity detection capability in emergency cases. To calculate calamity detection capability, an atmospheric dispersion model was used to simulate potentially harmful radioactive releases. For each candidate monitoring network, we determined if the releases were detected within one, two and three hours. Four types of accidents were simulated: small and large nuclear power plant accidents, deliberate radioactive releases using explosive devices, and accidents involving the transport of radioactive materials. Spatial simulated annealing (SSA) was used to search for the optimal monitoring design. SSA was implemented by iteratively moving stations around and accepting all designs that improved a weighted sum of average spatial prediction error and calamity detection capability. Designs that worsened the multi-objective criterion were accepted with a certain probability, which decreased to zero as iterations proceeded. Results were promising and the method should prove useful for assessing the efficacy of environmental monitoring networks designed to monitor both routine and emergency conditions in other applications as well