期刊
出版社
MDPI
DOI: 10.3390/ijerph17062025
关键词
ANN; prediction; traffic; sound; acoustic; ozone; nitrogen oxides; ammonia; particulate matter; deep learning
资金
- Deutsche Forschungsgemeinschaft [DFG KL 623/21]
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 (CO2, NH3, NO, NO2, NOx, O-3, PM1, PM2.5, PM10 and PN10) in a street canyon in Munster 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, NO2, NOx, and O-3 reveal very good agreement with observations, whereas predictions for particle concentrations and NH3 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.
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