Gwenaël Guillaume

Noise mapping based on participative measurements

Abstract The high temporal and spatial granularities recommended by the European regulation for the purpose of environmental noise mapping leads to consider new alternatives to simulations for reaching such information. While more and more European cities deploy urban environmental observatories, the ceaseless rising number of citizens equipped with both a geographical positioning system and environmental sensors through their smartphones legitimates the design of outsourced systems that promote citizen participatory sensing. In this context, the OnoM@p system aims at offering a framework for capitalizing on crowd noise data recorded by inexperienced individuals by means of an especially designed mobile phone application. The system fully rests upon open source tools and interoperability standards defined by the Open Geospatial Consortium. Moreover, the implementation of the Spatial Data Infrastructure principle enables to break up as services the various business modules for acquiring, analysing and mapping sound levels. The proposed architecture rests on outsourced processes able to filter outlier sensors and untrustworthy data, to cross- reference geolocalised noise measurements with both geographical and statistical data in order to provide higherlevel indicators, and to map the collected and processed data based on web services.

Optimized transmission line matrix model implementation for graphics processing units computing in built-up environment

The increase in computational capabilities has made time-domain methods applicable for long-range sound propagation modelling. However, such approaches remain very demanding in terms of computational resources. Most current computers are supplied with a powerful device which is still little exploited: the graphics processing unit (GPU). The paper describes an implementation of a transmission line matrix model which allows parallel calculations on heterogeneous systems. A voxelization algorithm used to generate the computational domain is presented. A splitting process is also expounded which makes feasible performing huge domains simulations by accurately dividing the computational domain into subdomains. Each subdomain is enlarged by introducing extra cells containing neighbours subdomains data in order to run several computational iterations on a graphic device without data exchange with the system memory. The influence of the ghost layer depth and the speeding up of computation time with GPU are then illustrated in a realistic built-up area.

Noise mapping based on participative measurements with a smartphone

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 m...