The technology development, population growth, development of metropolises and subsequent pollution are serious threats to the environment and public health. Therefore, monitoring and evaluation of various emissions and their sources, and also providing practical strategies of pollution reduction, are necessary to solve these problems. In this regard, the use of modern methods to predict the concentration of pollutants can improve decision-making and provide appropriate solutions. Tehran has been ranked as one of the most polluted cities in Iran. In this study, the meteorological monthly data were employed to achieve potent models based on a Box-Jenkins method for the modelling of concentration level of five major air pollutants in Tehran such as NO₂, PM₁₀, O₃, SO₂, CO, and Pollutant Standard Index. The best models were selected using goodness of fit criteria such as Akaike Information Criterion (AIC) and Schwartz Bayesian Criterion (SBC) and least prediction error. Prediction of concentrations of those pollutants can be a powerful tool in order to take preventive measures, such as the reduction of emissions and alerting the affected population. The results indicated that the concentration of pollutants in each period was influenced by their level and shocks they received during previous periods, which is mainly explained by special climatic and geographic conditions of Tehran that accumulates the pollution over time.

技术发展、人口增长、大都市的发展以及随之而来的污染对环境和公众健康构成了严重威胁。因此，需要对各种排放物及其来源进行监测和评估，并提供切实可行的污染减排策略，以解决这些问题。在这方面，使用现代方法来预测污染物的浓度可以改进决策并提供适当的解决方案。德黑兰被列为伊朗污染最严重的城市之一。在这项研究中，气象月数据被用于建立基于 Box-Jenkins 方法的有效模型，用于模拟德黑兰五种主要空气污染物（如 NO ₂、PM ₁₀、O _{3 ）}的浓度水平、SO ₂、CO 和污染物标准指数。使用拟合优度标准（例如 Akaike 信息准则 (AIC) 和施瓦茨贝叶斯准则 (SBC) 和最小预测误差）选择最佳模型。预测这些污染物的浓度可以成为采取预防措施的有力工具，例如减少排放和提醒受影响的人群。结果表明，每个时期的污染物浓度都受到其水平和之前时期所受到的冲击的影响，这主要是由于德黑兰特殊的气候和地理条件，随着时间的推移而积累了污染。