Ambient air pollution and cardiovascular disease rate an ANN modeling: Yazd-Central of Iran

Abstract This study was aimed to investigate the air pollutants impact on heart patient's hospital admission rates in Yazd for the first time. Modeling was done by time series, multivariate linear regression, and artificial neural network (ANN). During 5 years, the mean concentrations of PM10,...

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Autores principales:	Mahrokh Jalili, Mohammad Hassan Ehrampoush, Mehdi Mokhtari, Ali Asghar Ebrahimi, Faezeh Mazidi, Fariba Abbasi, Hossein Karimi
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Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e3e52c8797cf49d4a588335b2583093b
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spelling	oai:doaj.org-article:e3e52c8797cf49d4a588335b2583093b2021-12-02T16:45:53ZAmbient air pollution and cardiovascular disease rate an ANN modeling: Yazd-Central of Iran10.1038/s41598-021-94925-82045-2322https://doaj.org/article/e3e52c8797cf49d4a588335b2583093b2021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94925-8https://doaj.org/toc/2045-2322Abstract This study was aimed to investigate the air pollutants impact on heart patient's hospital admission rates in Yazd for the first time. Modeling was done by time series, multivariate linear regression, and artificial neural network (ANN). During 5 years, the mean concentrations of PM10, SO2, O3, NO2, and CO were 98.48 μg m−3, 8.57 ppm, 19.66 ppm, 18.14 ppm, and 4.07 ppm, respectively. The total number of cardiovascular disease (CD) patients was 12,491, of which 57% and 43% were related to men and women, respectively. The maximum correlation of air pollutants was observed between CO and PM10 (R = 0.62). The presence of SO2 and NO2 can be dependent on meteorological parameters (R = 0.48). Despite there was a positive correlation between age and CD (p = 0.001), the highest correlation was detected between SO2 and CD (R = 0.4). The annual variation trend of SO2, NO2, and CO concentrations was more similar to the variations trend in meteorological parameters. Moreover, the temperature had also been an effective factor in the O3 variation rate at lag = 0. On the other hand, SO2 has been the most effective contaminant in CD patient admissions in hospitals (R = 0.45). In the monthly database classification, SO2 and NO2 were the most prominent factors in the CD (R = 0.5). The multivariate linear regression model also showed that CO and SO2 were significant contaminants in the number of hospital admissions (R = 0.46, p = 0.001) that both pollutants were a function of air temperature (p = 0.002). In the ANN nonlinear model, the 14, 12, 10, and 13 neurons in the hidden layer were formed the best structure for PM, NO2, O3, and SO2, respectively. Thus, the Rall rate for these structures was 0.78–0.83. In these structures, according to the autocorrelation of error in lag = 0, the series are stationary, which makes it possible to predict using this model. According to the results, the artificial neural network had a good ability to predict the relationship between the effect of air pollutants on the CD in a 5 years' time series.Mahrokh JaliliMohammad Hassan EhrampoushMehdi MokhtariAli Asghar EbrahimiFaezeh MazidiFariba AbbasiHossein KarimiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mahrokh Jalili Mohammad Hassan Ehrampoush Mehdi Mokhtari Ali Asghar Ebrahimi Faezeh Mazidi Fariba Abbasi Hossein Karimi Ambient air pollution and cardiovascular disease rate an ANN modeling: Yazd-Central of Iran
description	Abstract This study was aimed to investigate the air pollutants impact on heart patient's hospital admission rates in Yazd for the first time. Modeling was done by time series, multivariate linear regression, and artificial neural network (ANN). During 5 years, the mean concentrations of PM10, SO2, O3, NO2, and CO were 98.48 μg m−3, 8.57 ppm, 19.66 ppm, 18.14 ppm, and 4.07 ppm, respectively. The total number of cardiovascular disease (CD) patients was 12,491, of which 57% and 43% were related to men and women, respectively. The maximum correlation of air pollutants was observed between CO and PM10 (R = 0.62). The presence of SO2 and NO2 can be dependent on meteorological parameters (R = 0.48). Despite there was a positive correlation between age and CD (p = 0.001), the highest correlation was detected between SO2 and CD (R = 0.4). The annual variation trend of SO2, NO2, and CO concentrations was more similar to the variations trend in meteorological parameters. Moreover, the temperature had also been an effective factor in the O3 variation rate at lag = 0. On the other hand, SO2 has been the most effective contaminant in CD patient admissions in hospitals (R = 0.45). In the monthly database classification, SO2 and NO2 were the most prominent factors in the CD (R = 0.5). The multivariate linear regression model also showed that CO and SO2 were significant contaminants in the number of hospital admissions (R = 0.46, p = 0.001) that both pollutants were a function of air temperature (p = 0.002). In the ANN nonlinear model, the 14, 12, 10, and 13 neurons in the hidden layer were formed the best structure for PM, NO2, O3, and SO2, respectively. Thus, the Rall rate for these structures was 0.78–0.83. In these structures, according to the autocorrelation of error in lag = 0, the series are stationary, which makes it possible to predict using this model. According to the results, the artificial neural network had a good ability to predict the relationship between the effect of air pollutants on the CD in a 5 years' time series.
format	article
author	Mahrokh Jalili Mohammad Hassan Ehrampoush Mehdi Mokhtari Ali Asghar Ebrahimi Faezeh Mazidi Fariba Abbasi Hossein Karimi
author_facet	Mahrokh Jalili Mohammad Hassan Ehrampoush Mehdi Mokhtari Ali Asghar Ebrahimi Faezeh Mazidi Fariba Abbasi Hossein Karimi
author_sort	Mahrokh Jalili
title	Ambient air pollution and cardiovascular disease rate an ANN modeling: Yazd-Central of Iran
title_short	Ambient air pollution and cardiovascular disease rate an ANN modeling: Yazd-Central of Iran
title_full	Ambient air pollution and cardiovascular disease rate an ANN modeling: Yazd-Central of Iran
title_fullStr	Ambient air pollution and cardiovascular disease rate an ANN modeling: Yazd-Central of Iran
title_full_unstemmed	Ambient air pollution and cardiovascular disease rate an ANN modeling: Yazd-Central of Iran
title_sort	ambient air pollution and cardiovascular disease rate an ann modeling: yazd-central of iran
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/e3e52c8797cf49d4a588335b2583093b
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Ambient air pollution and cardiovascular disease rate an ANN modeling: Yazd-Central of Iran

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