2020
DOI: 10.3390/ijerph17062025
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Modelling of Urban Air Pollutant Concentrations with Artificial Neural Networks Using Novel Input Variables

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 measurement… Show more

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Cited by 23 publications
(13 citation statements)
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“…To select the best model, the majority of the authors selected different benchmark models and, applying the same validation metrics to all models, chose the outperformed model. Only a few authors, such as Goulier et al [31] and Zhang et al [32] have focused on the importance to test whether the model performs well enough, acceptable or not. It is important to follow up on evaluation studies to ensure that the evaluation procedure is correct.…”
Section: Resultsmentioning
confidence: 99%
“…To select the best model, the majority of the authors selected different benchmark models and, applying the same validation metrics to all models, chose the outperformed model. Only a few authors, such as Goulier et al [31] and Zhang et al [32] have focused on the importance to test whether the model performs well enough, acceptable or not. It is important to follow up on evaluation studies to ensure that the evaluation procedure is correct.…”
Section: Resultsmentioning
confidence: 99%
“…Based on the literature review, the study of [45] predicts the concentration of air pollutants at canyon street. Still, our study predicts the level of air pollutants at the roadside, which is an open space.…”
Section: Discussionmentioning
confidence: 99%
“…There are some studies used three datasets, namely, air quality, meteorological, and traffic datasets. [45]. [46] proposed an ANN model to predict CO concentration at Subang Jaya Toll plaza, Selangor, Malaysia.…”
Section: Related Workmentioning
confidence: 99%
“…For 6 % of the recorded license plates, no vehicle characteristics were available either because they were from a foreign country or because the ANPR was faulty. Additionally, a microphone recorded the sound volume in the street canyon to test sound as a traffic indicator (Goulier et al 2020).…”
Section: Accepted Manuscriptmentioning
confidence: 99%