Noise from traffic, industry and neighborhood is a prominent feature in urban environments. In these environments, sound reaches receiver points through reflections and diffractions. Real-time auralization of outdoor scenarios is a common goal for presenting sound characteristics in a realistic and intuitive fashion. Challenges in this attempt can be identified on many levels, however the most prominent part is sound propagation simulation. Geometrical acoustics has become the de-facto standard for the prediction of acoustic propagation in a virtual scenario. A considerable difficulty is the determination of the diffracted sound field component, because it is a wave effect that must be be explicitly integrated into the search algorithm of valid propagation paths. A deterministic solution to this problem is implemented that establishes propagation paths with an arbitrary constellation of far-field interactions at geometrical boundaries, i.e. reflecting surfaces and diffracting edges in large distance to each other. The result is an open-source code algorithm for propagation paths that follows the wave front normal and assembles metadata required for further acoustic modelling, such as incoming and outgoing angles, reflection material and geometrical details for the construction of the diffracting wedge. Calculation times are outlined and a proof of concept is presented that describes the employment of the propagation algorithm as well as the determination of an acoustic transfer function based on the input of the intermediate path representation. Future research will focus on prioritization of path contributions according to physical and psychoacoustical culling schemes.

Air quality assessment is an important task for local authorities due to several adverse health effects that are associated with exposure to e.g., urban particle concentrations throughout the world. Based on the consumption of costs and time related to the experimental works required for standardized measurements of particle concentration in the atmosphere, other methods such as modelling arise as integrative options, on condition that model performance reaches certain quality standards. This study presents an Artificial Neural Network (ANN) approach to predict atmospheric concentrations of particle mass considering particles with an aerodynamic diameter of 0.25-1 μm (PM(0.25-1)), 0.25-2.5 μm (PM(0.25-2.5)), 0.25-10 μm (PM(0.25-10)) as well as particle number concentrations of particles with an aerodynamic diameter of 0.25-2.5 μm (PNC(0.25-2.5)). ANN model input variables were defined using data of local sound measurements, concentrations of background particle transport and standard meteorological data. A methodology including input variable selection, data splitting and an evaluation of their performance is proposed. The ANN models were developed and tested by the use of a data set that was collected in a street canyon. The ANN models were applied furthermore to a research site featuring an inner-city park to test the ability of the approach to gather spatial information of aerosol concentrations. It was observed that ANN model predictions of PM(0.25-10) and PNC(0.25-2.5) within the street canyon case as well as predictions of PM(0.25-2.5), PM(0.25-10) and PNC(0.25-2.5) within the case study of the park area show good agreement to observations and meet quality standards proposed by the European Commission regarding mean value prediction. Results indicate that the ANN models proposed can be a fairly accurate tool for assessment in predicting particle concentrations not only in time but also in space.

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