## 4 Discussion
As seen in Table 6, the performance improved progressively with broader output ranges, suggesting that slideNN benefits from inductive learning over extended targets. However, it also suffered from slower convergence, higher susceptibility to overfitting, and limited temporal abstraction capabilities, requiring a large number of training epochs to achieve modest reductions in loss. In contrast, the GRU-based model exhibited superior temporal modeling capacity and training efficiency. As detailed in Table 7, the GRU configurations consistently outperformed their slideNN counterparts, achieving significantly lower RMSEs (e.g., 0.233 vs. 0.596 for the 512 \(\rightarrow\) 16 configuration) and larger effect sizes (up to d = 1.90). Notably, the GRU models required only 50 epochs with early stopping to achieve these results, indicating faster and more robust convergence. For identical input-output sizes and training conditions, the GRU models offered 25-80% lower loss, demonstrating a much stronger capacity for long-term temporal pattern extraction. Combining these architectures in a unified GRU-NN model enhances their strengths: GRUs serve as temporal feature extractors, while subsequent dense layers support non-linear mappings and noise smoothing. This hybridization improves both predictive accuracy and training stability, as verified in Scenario II (cloud case detailed in Section 3.4),where GRU-NN consistently outperformed GRU-only and NN-only baselines across all configurations. These findings validate the architectural synergy and justify the hybrid design beyond simple stacking, supporting the added value of combining recurrent and dense mechanisms in air quality forecasting tasks. Several recent studies have explored deep learning models for predicting the Air Quality Index (AQI), providing valuable benchmarks to assess the performance of this model. For instance, the LSTM-based model presented in a conference paper by [70] achieved an 8-hour AQI prediction with a best root mean square error (RMSE) of 12.38 using PM\({}_{2.5}\), PM\({}_{10}\), O\({}_{3}\), CO, temperature, and relative humidity as input variables. Our GRU model achieved a comparable 8-hour RMSE of 0.212 (equivalent to 12.41 when destandardized). While its accuracy is similar to that of the LSTM model, our GRU model is more computationally efficient because of its lighter architecture. Additionally, our model incorporates extra meteorological variables, such as wind speed and direction, which were not included in this study. This integration could enhance the model's generalizability and robustness across various environmental contexts. Additionally, the hybrid LSTM-GRU model presented in the study published in Environmental Pollution [71] examined a broader spectrum of pollutants, such as O\({}_{3}\), CO, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Configuration** & **RMSE** & **Parameters** & **Memory** & **Insights** \\ \hline Cloud GRU-NN & 128 \(\rightarrow\) 4 & 0.334 & \(\sim\)37 M & \(\sim\)141 MB & Best RMSE gain (34.1\%) over edge, ideal for mid-range forecasts \\ Edge GRU & 256 \(\rightarrow\) 8 & 0.317 & \(\sim\)44 K & \(\sim\)171 KB & Best edge trade-off for moderate horizon with minimal resources \\ Edge GRU & 512 \(\rightarrow\) 16 & 0.233 & \(\sim\)175 K & \(\sim\)690 KB & Most efficient: achieves 80\% of cloud accuracy with just 0.5\% of the parameters \\ Cloud GRU-NN & 512 \(\rightarrow\) 16 & 0.201 & \(\sim\)37 M & \(\sim\)141 MB & Highest accuracy, but 200\(\times\) larger in terms of size \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Key GRU model comparisons based on accuracy, efficiency, and edge vs. cloud deployment. NO\({}_{2}\), SO\({}_{2}\), PM\({}_{2.5}\), and PM\({}_{10}\), in addition to various meteorological factors, like temperature, wind speed, and relative humidity. Nonetheless, despite this expansive dataset, their model produced RMSE values ranging from 57.77 to 51.36, which is significantly higher than the RMSE of 0.334 in this study (corresponding to a 19.47 destandard RMSE).