Debiasing physico-chemical models in air quality monitoring by combining different pollutant concentration measurements

Abstract

Air quality monitoring requires to produce accurate estimation of nitrogen dioxide or fine particulate matter concentration maps, at different moments. A typical strategy is to combine different types of data. On the one hand, concentration maps produced by deterministic physicochemical models at urban scale, and on the other hand, concentration measurements made at different points, different moments, and by different devices. These measurements are provided first by a small number of reference stations, which give reliable measurements of the concentration, and second by a larger number of micro-sensors, which give biased and noisier measurements. The proposed approach consists in modeling the bias of the physicochemical model and estimating the parameters of this bias using all the available concentration measurements. Our model relies on a partition of the geographical space of interest into different zones within which the bias is assumed to be modeled by a singleaffine transformation of the actual concentration. Our approach allows to improve the concentration maps provided by the deterministic models but also to understand the behavior of micro-sensors and their contribution in improving air quality monitoring.We introduce the model, detail its implementation and experiment it through numerical results using datasets collected in Grenoble (France).

Affiliated Institutions

• Instituts Universitaires de Technologie FR
• Département Mathématiques et Informatique Appliquées FR
• Laboratoire de Mathématiques de l'INSA de Rouen
• Université de Rouen Normandie FR
• Université Paris-Saclay FR
• Laboratoire de Mathématiques d'Orsay FR
• Mathématiques et Informatique Appliquées
• AgroParisTech FR
• Laboratoire de Mathématiques FR
• Institut National des Sciences Appliquées Rouen Normandie FR
• Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement FR

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