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

Laura Goulier1, Bastian Paas1, Laura Ehrnsperger1

  • 1Climatology Research Group, University of Münster, Heisenbergstraße 2, 48149 Münster, Germany.

International Journal of Environmental Research and Public Health
|March 25, 2020
PubMed
Summary

This study used artificial neural networks (ANNs) to predict hourly air pollutant concentrations in Münster. Traffic data, including sound, vehicle counts, and time, were compared as input variables for improved urban air quality monitoring.

Keywords:
ANNacousticammoniadeep learningnitrogen oxidesozoneparticulate matterpredictionsoundtraffic

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Area of Science:

  • Environmental Science
  • Atmospheric Chemistry
  • Artificial Intelligence

Background:

  • Urban air quality monitoring is costly and time-consuming.
  • Air pollutants pose significant risks to public health.
  • Street canyons present unique challenges for air pollution modeling.

Purpose of the Study:

  • To develop an hourly prediction model for ten air pollutants (CO2, NH3, NO, NO2, NOx, O3, PM1, PM2.5, PM10, PN10) in a street canyon.
  • To compare the effectiveness of traffic volume predictors: acoustic sound, vehicle counts, and time-based data.
  • To evaluate the performance of artificial neural network (ANN) models for air quality forecasting.

Main Methods:

  • Utilized an artificial neural network (ANN) approach for hourly air pollutant concentration prediction.
  • Compared three sets of input variables: acoustic sound measurements, total vehicle counts, and time (hour/day of week).
  • Trained, validated, and tested ANN models to assess prediction accuracy.

Main Results:

  • ANN models demonstrated very good agreement for predicting gaseous pollutants (NO, NO2, NOx, O3).
  • Predictions for particulate matter (PM1, PM2.5, PM10, PN10) and ammonia (NH3) were less accurate, suggesting areas for model improvement.
  • Acoustic sound, vehicle counts, and time-based data were all suitable predictors, each showing unique strengths for different pollutants.

Conclusions:

  • ANNs offer a viable approach for hourly air quality prediction in urban street canyons.
  • Traffic-related data, including sound and vehicle counts, are valuable inputs for air pollution modeling.
  • Further refinement of models is needed for accurate prediction of particulate matter and ammonia concentrations.