# Data-Driven Approaches for Predicting and Forecasting Air Quality in Urban Areas
## 4 Discussion  
This research aimed to develop an \(AQI\) prediction model based on a unique combination of parameters in the literature, including CO, NO\({}_{2}\), SO\({}_{2}\), PM\({}_{2.5}\), temperature, humidity, wind speed, and vehicular traffic. The model was trained on data corresponding to the period from 1 January 2020 to 31 December 2023, for the Bronx, but it is designed to be universally applicable in other regions with similar data. The exclusion of PM\({}_{10}\) is justified by its partial overlap with PM\({}_{2.5}\), a subcategory of PM\({}_{10}\). This indicator is considered more dangerous to health because it penetrates deeper into the respiratory system. For this reason, numerous studies opt to use only PM\({}_{2.5}\) as a representative indicator of suspended particles. O\({}_{3}\) is a secondary pollutant whose formation depends on complex photochemical reactions between other pollutants (precursors) such as NO\({}_{2}\) and VOCs, under the influence of solar radiation. Due to this complexity, O\({}_{3}\) modeling requires a separate approach, with specific input factors different from those integrated in this research. Moreover, in the Bronx area, O\({}_{3}\) concentrations are influenced by the regional transport of pollutants, making it difficult to directly correlate them with local traffic sources or specific weather conditions. The paper evaluated 19 prediction algorithms, which were hyperparameterized to achieve the best results. In the end, it was determined that RFR achieved the best results. The validation was carried out from 1 March 2024 to 1 July 2024, with an accuracy deviation of 0.58%. The model metrics were an \(R^{2}\) score of 99.59%, an MAE of 0.22%, an MAPE of 0.68%, and an OP score of 95.61%. For a comparative analysis of the results, [PERSON] et al. [97] analyze NO, NO\({}_{2}\), and CO on PM\({}_{2.5}\), which obtained an \(R^{2}\) of 82.76%, while MA-AQI obtained 99.59%. The AQIF model recorded an \(R^{2}\) of 71.62%, demonstrating the ability to anticipate \(AQI\) trends over five days but still leaving room for further improvements. [PERSON] et al. [98] explore the PM\({}_{2.5}\) and PM\({}_{10}\) parameters through time series. The results of this research indicate \(R^{2}\) values of 79% while analyzing two parameters. Unlike this research, AQIF obtained an \(R^{2}\) of 71.62% while simultaneously analyzing eight parameters. In the paper  
Figure 17: Content of the SMS notification message. by [PERSON] et al. [99], two models that used NO\({}_{2}\) as input were investigated, and the results showed \(R^{2}\) values below 80%. These comparative results are summarized in Table 4.  
The models MA-AQI and AQIF were integrated into a dedicated real-time prediction and forecasting application, RTPF. An automated PIP notification system was also implemented, which generates alerts via email or SMS based on the estimated _AQI_ values. The first limitation of this research is that the dataset includes only four years (2020-2023), which may limit the generalizability of the DL models. At the same time, no variation in hyperparameters was analyzed to identify the optimal setup for each model. For this research, the default values for the hyperparameters were employed. This may have impacted the performance of the DL models, combined with the limited number of samples in the dataset. While the MA-AQI model demonstrates high predictive accuracy, the AQIF model exhibits a lower \(R^{2}\) accuracy, indicating certain limitations in its capacity for long-term forecasting. This constraint is particularly evident when attempting to predict more complex, evolving AQ patterns, highlighting areas where the model's precision could be improved. The models were customized based on data from the Bronx, New York, which restricts the generalizability of the findings. Using data from a single location with specific industrial activity levels and environmental characteristics may limit the model's applicability to regions with different pollution profiles or activity types.