Network-based sound monitoring systems are deployed in various cities over the world and mobile applications allowing participatory sensing are now common. Nevertheless, the sparseness of the collected measurements, either in space or in time, complicates the production of sound maps. This paper describes the results of a measurement campaign that has been conducted in order to test different spatial interpolation strategies for producing sound maps. Mobile measurements have been performed while walking multiple times in every street of the XIIIth district of Paris. By adaptively constructing a noise map on the basis of these measurements, the role of the density of observations and the performance of four different interpolation strategies is investigated. Ordinary and universal Kriging methods are assessed, as well as the effect of using an alternative definition of the distance between observation locations, which takes the topology of the road network into account. The results show that a high density of observation points is necessary to obtain an interpolated sound map close to the reference map.

Because noise is a major pollution leading to non-negligible socio-economical impacts, many national regulations aim at reducing the population noise exposure. Within the context of the European directive 2002/49/EC, a special attention is paid to the evaluation of the existing noise environment. Nowadays, this assessment is addressed based on simulated noise maps, which however present some limitations due to the simplification of noise generation and propagation phenomena. Smartphone participative measurements are alternatively being developed, offering the high temporal and spatial granularities recommended by the EU directive. However, the existing approaches often lack a quantification of the produced noise maps accuracy, and are rarely user-oriented. In this context, within the framework of the EU project ENERGIC-OD, a Spatial Data Infrastructure (SDI “OnoMap”) has been developed to manage smartphones measurements using a dedicated Android application (“NoiseCapture”) and to produce relevant noise maps. In the present communication, this infrastructure is detailed, with a specific attention to the following major key points: data management and qualification from its production to its dissemination, use of standards for data interoperability, optimization of noise measurements, production of 24h noise maps based on measurements distributed over a day, integration of a confidence index on the produced data.