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

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 PM(10), SO(2), O(3), NO(2), 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 PM(10) (R = 0.62). The presence of SO(2) and NO(2) 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 SO(2) and CD (R = 0.4). The annual variation trend of SO(2), NO(2), and CO concentrations was more similar to the variations trend in meteorological parameters. Moreover, the temperature had also been an effective factor in the O(3) variation rate at lag = 0. On the other hand, SO(2) has been the most effective contaminant in CD patient admissions in hospitals (R = 0.45). In the monthly database classification, SO(2) and NO(2) were the most prominent factors in the CD (R = 0.5). The multivariate linear regression model also showed that CO and SO(2) 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, NO(2), O(3), and SO(2), respectively. Thus, the R(all) 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.