Sensitivity of pollutant concentrations in urban streets to asphalt and traffic-related emissions.

The higher concentrations of atmospheric particles, such as black carbon (BC) and organic matter (OM), detected in streets compared to the urban background are predominantly attributed to road traffic. The integration of this source of pollutant in air quality models nevertheless entails a high degree of uncertainty and some other sources may be missing. Through sensitivity scenarios, the impacts on pollutant concentrations of sensitivities related to traffic and road-asphalt emissions are evaluated. The 3D Eulerian model Polair3D and the street network model MUNICH are applied to simulate various scenarios and their impacts at the regional and local scales. They are coupled with the modular box model SSH-aerosol to represent formation and aging of primary and secondary gas and particles. Traffic emissions are calculated with the COPERT methodology. Using recent volatile organic compound speciations for light vehicles with more detailed information pertaining to intermediate, semi- and low-volatile organic compounds (I/S/LVOCs) leads to limited reductions of OM concentrations (10% in streets). Changing the method of estimating I/S/LVOC emissions leads to an average reduction of 60% at emission and a decrease of the OM concentrations of 27% at the local scale. An increase in 219% of BC emissions from tire wear, consistent with the uncertainties found in the literature, doubles the BC concentrations at the local scale, which remain underestimated compared to observations. I/S/LVOC emissions are several orders of magnitude higher when considering emissions from road asphalt due to pavement heating and exposure to sunlight. However, simulated concentrations of PM at the local scale remain within acceptable ranges compared to observations. These results suggest that more information is needed on I/S/LVOCs and non-exhaust sources (tire, brake and road abrasion) that impact the particle concentration. Furthermore, currently unconsidered emission sources such as road asphalt may have non-negligible impacts on pollutant concentrations in streets.

An intestinal surgery simulator: real-time collision processing and visualization.

This research work is aimed toward the development of a VR-based trainer for colon cancer removal. It enables the surgeons to interactively view and manipulate the concerned virtual organs as during a real surgery. First, we present a method for animating the small intestine and the mesentery (the tissue that connects it to the main vessels) in real-time, thus enabling user interaction through virtual surgical tools during the simulation. We present a stochastic approach for fast collision detection in highly deformable, self-colliding objects. A simple and efficient response to collisions is also introduced in order to reduce the overall animation complexity. Second, we describe a new method based on generalized cylinders for fast rendering of the intestine. An efficient curvature detection method, along with an adaptive sampling algorithm, is presented. This approach, while providing improved tessellation without the classical self-intersection problem, also allows for high-performance rendering thanks to the new 3D skinning feature available in recent GPUs. The rendering algorithm is also designed to ensure a guaranteed frame rate. Finally, we present the quantitative results of the simulations and describe the qualitative feedback obtained from the surgeons.