## 1 Introduction
This is why many scientists have focused on and made notable progress, which is due to the significance of the remaining and ongoing challenges, in this matter. A critical limitation stems from incomplete data dimensionality in most existing models. Traditional approaches frequently treat meteorological parameters (e.g., temperature, humidity, and wind patterns) and par ticulate matter concentrations (PM\({}_{1}\), PM\({}_{2.5}\), and PM\({}_{10}\)) as independent variables, failing to capture their complex synergistic interactions [33]. This simplification neglects their intricate relationship, particularly during pollution events. As indicated by [PERSON] et al. [34], humidity-driven PM\({}_{2.5}\) hygroscopic growth exhibits strong nonlinear relationships that substantially impact prediction accuracy when ignored. Models that fail to account for these interactions exhibit significantly higher errors during high-humidity conditions. The predominance of short-term forecasting approaches presents additional challenges. Although short-term forecasts typically yield lower prediction errors, long-term PM\({}_{2.5}\) forecasting is essential for adequate public health protection and air quality management. It is important to mention that, during winter, biomass burning causes pollution levels to rise sharply and unpredictably due to domestic heating activities [35]. Furthermore, meteorological variability, particularly temperature and wind speed, can significantly impact particulate matter concentrations, increasing uncertainty in the predictability of deep learning models [36]. Given these findings, this study incorporated both PM\({}_{1}\) and PM\({}_{4}\) into its input variables alongside the more standard PM\({}_{2.5}\) and PM\({}_{10}\), aiming to provide a more comprehensive representation of airborne particulate pollution concerning localized meteorological conditions and forecasted Air Quality Index (AQI) levels. Moreover, in recent years, several models based on Bi-Directional Long Short-Term Memory (Bi-LSTM) architectures have been proposed for air quality prediction due to their ability to capture temporal dependencies for both past and future data trends. While, in theory, this type of model shows promise, in practice, it has revealed some crucial flaws [37; 38; 39]. For instance, in a comparative study of PM2.5 prediction models across Seoul, Daejeon, and Busan [40], Bi-LSTM models demonstrated high accuracy for short-term forecasts (within 24 h). However, they showed a significant drop in performance for longer-term predictions, with \(R^{2}\) values decreasing to 0.6, indicating challenges in maintaining accuracy over extended periods. Furthermore, the computational complexity of Bi-LSTM models can make them less convenient and practical for real-time applications as it can lead to increased training times and resource consumption [41]. To address these challenges, this study introduced a framework that acknowledges the correlation between the particulate matter and meteorological data and shows long-term accurate forecasting results consisting of two distinct architectures:  
1. A sliding-window feedforward model (slideNN) that includes four neural sub-model strands of varying sizes, each designed to process different input lengths efficiently and offer short-term accuracy and efficient training performance. 2. And a set of Gated Recurrent Unit (GRU)-based networks [42; 43], which excel in learning temporal dependencies and trends, as well as provide fast inference results (concerning LSTM models), making it particularly effective for multi-day ahead forecasting tasks. Compared to other existing deep learning (DL) solutions, the GRU model outperforms and demonstrates significant improvements in predictive accuracy, fast inference, generalization, and robustness to noise. These refinements are especially evident in industrial urban regions, where air quality patterns are highly nonlinear and affected by multiple environmental factors.