## 2 Materials and Methods
### Proposed Deep-Learning Models  
In addition to the architectural differences between the models mentioned in Section 2.1, the authors adopted two distinct approaches regarding data processing and storage, distinguishing between edge and cloud computing implementations. Their indicative design is as follows:  
1. One perspective focuses on the implementation of both the neural network and the previously mentioned GRU model within an edge computing framework. In this scenario, both models are designed to receive identical timesteps and data parameters, ensuring comparable model sizes. These intentionally smaller models are designed to meet the constraints and computational limitations of edge devices. Notably, edge computing is being increasingly adopted in air quality monitoring applications as it enables efficient, low-latency forecasting in resource-constrained environments [48; 49]. Such integration of AI at the edge is especially beneficial for real-time, autonomous air pollution assessment [50]. Furthermore, these models could be integrated into micro-IoT devices or even embedded directly into environmental sensors [51]. Based on their localized measurements, which align with the features used during model training, the models are capable of generating short-term forecasts of Air Quality Index (AQI) values. This approach is particularly suitable for short-term prediction horizons, where timely and on-site decision making is critical.
2. The second approach focuses on large-scale air quality forecasting through cloud computing. In this case, only the GRU-based model is used, with a significantly larger number of GRU cells and layers, in an effort to fully utilize the computational resources and scalability offered by cloud infrastructure. Unlike edge-based implementations, cloud computing is not constrained by memory or processing limitations, enabling the use of deeper architectures and more complex temporal dependencies. This makes it particularly suitable for long-term predictions and the collection of data from multiple sources, such as distributed sensor networks or satellite feeds [52; 53]. Recent studies have demonstrated the efficiency of cloud-based systems in air quality monitoring. For instance, the integration of wireless sensor networks with cloud computing has been shown to facilitate real-time data collection and analysis, enhancing the responsiveness and accuracy of air quality assessments [54]. The cloud model can be trained and deployed using higher dimensional input vectors thanks to the cloud's virtually unlimited computing capacity, which allows it to pick up more subtle patterns in the variations in air quality over time. Because of this, it works especially well for regional forecasting, policy assessment, and assisting with large-scale environmental monitoring systems.  
Figure 1 depicts the entire data processing and model deployment workflow that was implemented in this study. The process began with raw input data, which underwent validation and temporal preprocessing before being used to train the proposed neural network models. Depending on the forecasting scale, computational requirements, and user-specific restrictions, the most appropriate model architecture was chosen, followed by a deployment strategy targeting either edge computing environments or cloud-based platforms. This workflow ensures flexibility, scalability, and optimal utilization of the available resources. Each step of the process, from data preparation and model training to performance evaluation and final deployment, is discussed in detail in Sections 2.2.1 and 2.2.2.