Development of a spatial stochastic multimedia exposure model to assess population exposure at a regional scale.

Analyzing the relationship between the environment and health has become a major focus of public health efforts in France, as evidenced by the national action plans for health and the environment. These plans have identified the following two priorities: - identify and manage geographic areas where hotspot exposures are a potential risk to human health; and - reduce exposure inequalities. The aim of this study is to develop a spatial stochastic multimedia exposure model for detecting vulnerable populations and analyzing exposure determinants at a fine resolution and regional scale. A multimedia exposure model was developed by INERIS to assess the transfer of substances from the environment to humans through inhalation and ingestion pathways. The RESPIR project adds a spatial dimension by linking GIS (Geographic Information System) to the model. Tools are developed using modeling, spatial analysis and geostatistic methods to build and discretize interesting variables and indicators from different supports and resolutions on a 1-km(2) regular grid. We applied this model to the risk assessment of exposure to metals (cadmium, lead and nickel) using data from a region in France (Nord-Pas-de-Calais). The considered exposure pathways include the atmospheric contaminant inhalation and ingestion of soil, vegetation, meat, egg, milk, fish and drinking water. Exposure scenarios are defined for different reference groups (age, dietary properties, and the fraction of food produced locally). The two largest risks correspond to an ancient industrial site (Metaleurop) and the Lille agglomeration. In these areas, cadmium, vegetation ingestion and soil contamination are the principal determinants of the computed risk.

Time series modeling by a regression approach based on a latent process.

Time series are used in many domains including finance, engineering, economics and bioinformatics generally to represent the change of a measurement over time. Modeling techniques may then be used to give a synthetic representation of such data. A new approach for time series modeling is proposed in this paper. It consists of a regression model incorporating a discrete hidden logistic process allowing for activating smoothly or abruptly different polynomial regression models. The model parameters are estimated by the maximum likelihood method performed by a dedicated Expectation Maximization (EM) algorithm. The M step of the EM algorithm uses a multi-class Iterative Reweighted Least-Squares (IRLS) algorithm to estimate the hidden process parameters. To evaluate the proposed approach, an experimental study on simulated data and real world data was performed using two alternative approaches: a heteroskedastic piecewise regression model using a global optimization algorithm based on dynamic programming, and a Hidden Markov Regression Model whose parameters are estimated by the Baum-Welch algorithm. Finally, in the context of the remote monitoring of components of the French railway infrastructure, and more particularly the switch mechanism, the proposed approach has been applied to modeling and classifying time series representing the condition measurements acquired during switch operations.