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, O₃, 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.

死亡率和空气污染之间的日常关联令人震惊，并且有一致的证据表明，空气污染对死亡率和呼吸道疾病具有短期影响。准确预测空气污染对健康的影响，对于有效规划与经济表现相关的各个部门以及改善人类健康和空气质量的战略管理至关重要。这项研究的主要目的是通过Dickey-Fuller检验确定空气污染对呼吸道发病率和死亡率的长期影响，以及使用非线性自回归和人工神经网络模型开发对呼吸道发病率和死亡率的准确预测。这项研究调查了9年期间因阿瓦士（Ahvaz）空气污染物引起的呼吸道疾病死亡率和发病率的每日变化。结果表明，一氧化氮和一氧化碳对总呼吸道死亡率有显着影响。灵敏度分析和ADF测试表明，其他污染物（NO₂，SO ₂，O ₃，PM ₁₀）对总呼吸系统发病率和死亡率没有显着影响。对于非线性自回归模型，拓扑2-10-1具有两个输入，包括一氧化氮和一氧化碳，十个隐藏层是最好的（MSE = 0.1和R = 0.82），可用来预测总死亡率。人工神经网络和非线性自回归模型是使用至少三个输入来准确预测呼吸道死亡率和活动性的非常有效的方法。这些发现强烈支持决策者制定降低环境中一氧化碳和一氧化氮浓度的目标。