2017-03-26Zeitschriftenartikel DOI: 10.3390/environments4020026
Modelling of Urban Near-Road Atmospheric PM Concentrations Using an Artificial Neural Network Approach with Acoustic Data Input
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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