# Data-Driven Approaches for Predicting and Forecasting Air Quality in Urban Areas
## 1 Introduction
### AI Used in Air Quality Measurement  
Measures to combat pollution have increasingly incorporated artificial intelligence (AI), smart devices, and the Internet of Things (IoT) to enhance monitoring, prediction, and control of environmental pollutants, considering compatibility concepts between technologies [22]. In recent years, data-driven AQ models have garnered attention due to their capacity to utilize large datasets and advanced computational methods for predicting pollution levels. These models harness concepts such as machine learning (ML) and more restrictive deep learning (DL) techniques to increase prediction and forecasting accuracy [23]. The data-driven AQ modeling methodology includes applying ML and DL techniques. For example, Long Short-Term Memory (LSTM) networks, a recurrent neural network component (RNN), are used for forecasting time-series of AQ parameters. The LSTM networks capture temporal dependencies in AQ data, resulting in better predictive performance than traditional statistical methods [24; 25]. The hybrid models produce promising results by incorporating multiple data sources, including meteorological and geographical information, to enhance AQ forecasts [26]. [PERSON] et al. [27] present the uncertainty quantification in AQ forecasting through the probabilistic DL methods, such as those utilizing Gaussian processes and quantile regression decision-making in environmental management. Multiple datasets, including Automatic Identification System (AIS) data for maritime emissions, were studied in refining AQ models. Incorporating AIS data allows researchers to estimate emissions from shipping activities and their impact on local AQ, offering a holistic perspective on pollution sources [28]. Many geospatial datasets use regression models to enhance air pollution mapping across urban environments, enabling targeted intervention strategies [29]. Recent innovations in model architectures, such as diffusion convolutional recurrent neural networks, have shown superior performance in regional predictions of pollutants like O\({}_{3}\) and PM [30]. These models leverage graph-based techniques to capture spatial relationships between monitoring stations. ML has also emerged as a powerful tool for AQ prediction [31]. It leverages complex datasets to enhance predictive accuracy beyond traditional statistical methods. One notable approach is ensemble methods, such as Random Forests, which have demonstrated improved prediction accuracy by incorporating atmospheric circulation patterns and key meteorological factors like wind speed, temperature, and humidity [32; 33]. For example, [PERSON] et al. [32] mention that integrating a vertical gradient between neighboring pressure fields enhances PM\({}_{10}\) forecasts and that atmospheric dynamics influence AQ predictions. In the paper by [PERSON] and [PERSON] [34], a LightGBM model was developed that achieved a root mean square error (RMSE) of approximately 1.0 for PM\({}_{2.5}\) concentration forecasts, showcasing the effectiveness of tree-based models in this context. [PERSON] and [PERSON] [35] applied the LSTM networks to forecast air pollution parameters, utilizing historical weather data to enhance prediction accuracy. There is growing interest in hybrid DL frameworks, combining multiple neural network architectures to improve performance in AQ forecasting tasks [36]. The advancements in DL have led to the introduction of a Diffusion Convolutional Recurrent Neural Network, which models the spatial and temporal dependencies of air pollutants [30]. This model outperformed traditional approaches by utilizing graph neural networks to represent the complex relationships present in environmental data. [PERSON] and [PERSON] [37] show the advantages of combining convolutional neural networks (CNNs) with LSTMs for PM forecasting. [PERSON] et al. [38] highlight the importance of integrating meteorological factors with real-time industrial emissions data to improve AQ forecasts' accuracy. This view is supported by [PERSON] et al.