3D Voxelisation for Enhanced Environmental Modelling Applications

Monitoring and managing environmental problems, particularly those impacting human health such as noise and air pollution, are essential. However, the current implementation has certain limitations that need improvement. In the case of noise pollution, accurately computing noise levels requires considering traffic noise propagating in all directions, necessitating the involvement of a 3D building model. Existing methods using raster cells and noise contours are insufficient in achieving high accuracy. To overcome this, we propose integrating a voxelisation approach and 3D kriging, enabling the depiction of traffic noise values for each voxel. In the context of air pollution, wind movement plays a significant role in the dispersion of contaminants. The current practice involves a random selection procedure for wind simulation within the model discretisation. However, we suggest replacing this randomness with a voxel-based model, which not only improves accuracy but also reduces computing time. Thus, the voxel-based model represents the building model in a wind computation environment, facilitating more realistic wind simulation results. This study demonstrates the applicability of the voxelisation technique in two different environmental modeling contexts using the building model of the city building modeling standard. The level of detail (LoD) in the represented building model differs between these approaches. For traffic noise, a low LoD (LoD1) is sufficient to depict exterior buildings accurately. However, for wind simulation, a higher LoD (LoD2) is necessary to accommodate the complexity of buildings and determine appropriate voxel sizes. In conclusion, the proposed improvements in the form of voxel-based modeling techniques offer enhanced accuracy and efficiency in environmental monitoring. The findings of this study have implications for improving the management and reduction of environmental problems, ultimately benefiting human health and well-being.