Modelling of Urban Air Pollutant Concentrations with Artificial Neural Networks Using Novel Input Variables

Laura Goulier, Bastian Paas, Laura Ehrnsperger and Otto Klemm
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Laura Goulier: Climatology Research Group, University of Münster, Heisenbergstraße 2, 48149 Münster, Germany
Bastian Paas: Climatology Research Group, University of Münster, Heisenbergstraße 2, 48149 Münster, Germany
Laura Ehrnsperger: Climatology Research Group, University of Münster, Heisenbergstraße 2, 48149 Münster, Germany
Otto Klemm: Climatology Research Group, University of Münster, Heisenbergstraße 2, 48149 Münster, Germany

IJERPH, 2020, vol. 17, issue 6, 1-22

Abstract: Since operating urban air quality stations is not only time consuming but also costly, and because air pollutants can cause serious health problems, this paper presents the hourly prediction of ten air pollutant concentrations (CO 2 , NH 3 , NO, NO 2 , NO x , O 3 , PM 1 , PM 2.5 , PM 10 and PN 10 ) in a street canyon in Münster using an artificial neural network (ANN) approach. Special attention was paid to comparing three predictor options representing the traffic volume: we included acoustic sound measurements (sound), the total number of vehicles (traffic), and the hour of the day and the day of the week (time) as input variables and then compared their prediction powers. The models were trained, validated and tested to evaluate their performance. Results showed that the predictions of the gaseous air pollutants NO, NO 2 , NO x , and O 3 reveal very good agreement with observations, whereas predictions for particle concentrations and NH 3 were less successful, indicating that these models can be improved. All three input variable options (sound, traffic and time) proved to be suitable and showed distinct strengths for modelling various air pollutant concentrations.

Keywords: ANN; prediction; traffic; sound; acoustic; ozone; nitrogen oxides; ammonia; particulate matter; deep learning (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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