Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air

Abstract Air quality has been the main concern worldwide and Nitrous oxide (NO2) is one of the pollutants that have a significant effect on human health and environment. This study was conducted to compare the regression analysis and neural network model for predicting NO2 pollutants in the air of T...

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Autores principales:	Seyedeh Reyhaneh Shams, Ali Jahani, Saba Kalantary, Mazaher Moeinaddini, Nematollah Khorasani
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/39f2262b5a8142dca50b7a2ad5b04e3a
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spelling	oai:doaj.org-article:39f2262b5a8142dca50b7a2ad5b04e3a2021-12-02T15:23:47ZArtificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air10.1038/s41598-021-81455-62045-2322https://doaj.org/article/39f2262b5a8142dca50b7a2ad5b04e3a2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-81455-6https://doaj.org/toc/2045-2322Abstract Air quality has been the main concern worldwide and Nitrous oxide (NO2) is one of the pollutants that have a significant effect on human health and environment. This study was conducted to compare the regression analysis and neural network model for predicting NO2 pollutants in the air of Tehran metropolis. Data has been collected during a year in the urban area of Tehran and was analyzed using multi-linear regression (MLR) and multilayer perceptron (MLP) neural networks. Meteorological parameters, urban traffic data, urban green space information, and time parameters are applied as input to forecast the daily concentration of NO2 in the air. The results demonstrate that artificial neural network modeling (R2 = 0.89, RMSE = 0.32) results in more accurate predictions than MLR analysis (R2 = 0.81, RMSE = 13.151). According to the result of sensitivity analysis of the model, the value of park area, the average of green space area and one-day time delay are the crucial parameters influencing NO2 concentration of air. Artificial neural network models could be a powerful, effective and suitable tool for analysis and modeling complex and non-linear relation of environmental variables such as ability in forecasting air pollution. Green spaces establishment has a significant role in NO2 reduction even more than traffic volume.Seyedeh Reyhaneh ShamsAli JahaniSaba KalantaryMazaher MoeinaddiniNematollah KhorasaniNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Seyedeh Reyhaneh Shams Ali Jahani Saba Kalantary Mazaher Moeinaddini Nematollah Khorasani Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air
description	Abstract Air quality has been the main concern worldwide and Nitrous oxide (NO2) is one of the pollutants that have a significant effect on human health and environment. This study was conducted to compare the regression analysis and neural network model for predicting NO2 pollutants in the air of Tehran metropolis. Data has been collected during a year in the urban area of Tehran and was analyzed using multi-linear regression (MLR) and multilayer perceptron (MLP) neural networks. Meteorological parameters, urban traffic data, urban green space information, and time parameters are applied as input to forecast the daily concentration of NO2 in the air. The results demonstrate that artificial neural network modeling (R2 = 0.89, RMSE = 0.32) results in more accurate predictions than MLR analysis (R2 = 0.81, RMSE = 13.151). According to the result of sensitivity analysis of the model, the value of park area, the average of green space area and one-day time delay are the crucial parameters influencing NO2 concentration of air. Artificial neural network models could be a powerful, effective and suitable tool for analysis and modeling complex and non-linear relation of environmental variables such as ability in forecasting air pollution. Green spaces establishment has a significant role in NO2 reduction even more than traffic volume.
format	article
author	Seyedeh Reyhaneh Shams Ali Jahani Saba Kalantary Mazaher Moeinaddini Nematollah Khorasani
author_facet	Seyedeh Reyhaneh Shams Ali Jahani Saba Kalantary Mazaher Moeinaddini Nematollah Khorasani
author_sort	Seyedeh Reyhaneh Shams
title	Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air
title_short	Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air
title_full	Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air
title_fullStr	Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air
title_full_unstemmed	Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air
title_sort	artificial intelligence accuracy assessment in no2 concentration forecasting of metropolises air
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/39f2262b5a8142dca50b7a2ad5b04e3a
work_keys_str_mv	AT seyedehreyhanehshams artificialintelligenceaccuracyassessmentinno2concentrationforecastingofmetropolisesair AT alijahani artificialintelligenceaccuracyassessmentinno2concentrationforecastingofmetropolisesair AT sabakalantary artificialintelligenceaccuracyassessmentinno2concentrationforecastingofmetropolisesair AT mazahermoeinaddini artificialintelligenceaccuracyassessmentinno2concentrationforecastingofmetropolisesair AT nematollahkhorasani artificialintelligenceaccuracyassessmentinno2concentrationforecastingofmetropolisesair
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Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air

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