Model design and description of each model.

Air pollution, driven by seasonal and meteorological variations, poses a significant threat to public health and urban sustainability. Despite numerous forecasting approaches, the influence of seasonal patterns on air pollutant levels remains underexplored. This study presents a computational framework utilizing the Nonlinear Autoregressive network with Exogenous inputs (NARX) model to predict concentrations of key pollutants SO₂, PM₁₀, NO, NOX, and O₃ in Esenyurt, one of the most industrialized districts in Istanbul, Turkey. Through systematic feature selection techniques, the study determines the most influential seasonal factors for each pollutant, reducing model complexity while improving predictive accuracy. The developed framework exhibits substantial improvements in predictive performance, with the optimal models achieving high determination coefficients (up to R² = 0.965 for O₃) and low error metrics across training and validation datasets. Particularly, the inclusion of seasonal variables considerably improved prediction accuracy for NO, NO₂, and PM₁₀, while SO₂ predictions performed best when utilizing comprehensive seasonal indicators. These results demonstrate that seasonal dynamics play a crucial role in governing pollutant behavior and highlight the importance of incorporating such variables in forecasting models. This research contributes significantly to the field by advancing methodological approaches in air quality prediction while providing an adaptable model for policymakers and environmental agencies to implement in proactive pollution management strategies. Through examination of seasonal dependencies in air pollutant patterns, the study delivers a practical tool for urban planning and public health applications in rapidly expanding metropolitan regions.

受季节与气象变化驱动的大气污染，对公众健康与城市可持续发展构成严重威胁。尽管现有大气污染预测方法层出不穷，但季节模式对空气污染物浓度的影响仍未得到充分探索。本研究提出一种计算框架，采用带外生输入的非线性自回归网络（Nonlinear Autoregressive network with Exogenous inputs, NARX）模型，对土耳其伊斯坦布尔工业化程度最高的区域之一埃森尤尔特（Esenyurt）的关键污染物——二氧化硫（SO₂）、可吸入颗粒物（PM₁₀）、一氧化氮（NO）、氮氧化物（NOX）及臭氧（O₃）的浓度进行预测。本研究通过系统性特征选择技术，确定了每种污染物最具影响力的季节影响因子，在降低模型复杂度的同时提升了预测精度。所构建的计算框架在预测性能上实现了显著提升，最优模型在训练与验证数据集上均取得了较高的决定系数（臭氧（O₃）的决定系数最高可达R²=0.965）与较低的误差指标。尤为关键的是，引入季节变量可显著提升一氧化氮（NO）、二氧化氮（NO₂）及可吸入颗粒物（PM₁₀）的预测精度，而二氧化硫（SO₂）的预测则在采用综合季节指标时表现最优。上述研究结果表明，季节动态在支配污染物变化规律中发挥着至关重要的作用，同时也凸显了在预测模型中纳入此类变量的重要性。本研究通过改进空气质量预测领域的方法论路径，为政策制定者与环境管理机构提供了可适配的模型工具，助力其主动实施污染管控策略，对该领域做出了重要贡献。通过解析空气污染物分布的季节依赖性，本研究为快速扩张的大都市区域的城市规划与公共健康应用提供了实用工具。