Long-term effects of outdoor air pollution on mortality and morbidity-prediction using nonlinear autoregressive and artificial neural networks models
ATMOSPHERIC POLLUTION RESEARCH(2021)
Abstract
The daily association between mortality and air pollution is alarming and there is consistent evidence that air pollution has short-term effects on mortality and respiratory morbidity. Accurate predictions of these health effects of air pollution are essential for efficient planning of various sectors related to economic performances as well as strategic management to improve human health and air quality. The main objectives of this research were to determine the long-term effects of air pollution on respiratory morbidity and mortality with the Dickey-Fuller test as well as develop accurate prediction of respiratory morbidity and mortality with nonlinear autoregressive and artificial neural network models. This study examined daily variations in respiratory mortality and morbidity attributed to air pollutants in Ahvaz for 9-yrs period. The results showed that nitrogen monoxide and carbon monoxide have significant effect on total respiratory mortality. The sensitivity analysis and ADF test showed that the other pollutants (NO2, SO2, O-3, PM10) had no significant effect on the total respiratory morbidity and mortality rate. For the nonlinear autoregressive model, topology 2-10-1 with two input, including nitrogen oxide and carbon oxide, ten hidden layers were the best (MSE = 0.1 and R = 0.82) for predicting the total mortality rate. Artificial neural network and nonlinear autoregressive models are very powerful methods for accurate prediction of respiratory mortality and mobility with at least three inputs. These findings strongly support the need for policymakers to set targets to reduce carbon monoxide and nitrogen monoxide concentrations in the environment.
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Key words
Air pollution,Artificial neural networks,Morbidity,Mortality,Respiratory diseases
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