# The Quest for the Best Explanation: Comparing Models and XAI Methods in Air Quality Modeling Tasks
## 1 Introduction  
Air quality (AQ) nowcasting--predicting pollution levels in real time--is becoming increasingly important as cities around the world struggle with increasing air pollution. Fine particulate matter, especially PM\({}_{2.5}\) (i.e., particles with a mean aerodynamic diameter of less than 2.5 um), is considered one of the most harmful pollutants due to its ability to penetrate deep into the lungs and bloodstream. Exposure to high levels of PM\({}_{2.5}\) is linked to a wide range of health issues, including respiratory problems, cardiovascular disease, andeven premature death [1]. In rapidly growing urban environments such as Beijing, where pollution levels are dangerous and populations are very dense, it creates the potential for serious problems, which makes real-time air quality forecasting crucially important for both public health policy and planning [2]. Traditional approaches to modeling AQ include parametric models (e.g., autoregressive integrated moving average (ARIMA) and linear regression) based on time series, chemical transport models (CTMs) based on atmospheric chemistry and numerical modeling, and dispersion models using Gaussian plumes and puffs to statistically model the dispersion of pollutants. Parametric AQ models tend to be good for transparency and are relatively easy to interpret but struggle when it comes to adapting to sudden changes in weather and/or pollution sources and are usually poor at predicting rapidly changing conditions in the real world [3; 4; 5]. Although CTMs use differential equations to explicitly model physical and chemical processes taking place in the atmosphere, their execution is computationally expensive, and the spatial resolution is rather coarse (\(\sim\)km). Dispersion models require extensive databases that rely on emission sources. These databases are often outdated and hard to acquire for the larger part of urban environments. In recent years, ML has gathered great attention because it relies on data alone and is adaptive to missing data sources. Among ML methods, tree-based models like XGBoost have gained popularity for air quality tasks because they offer high predictive accuracy and handle many input features efficiently. Meanwhile, deep learning (DL) models such as CNNs, RNNs, and LSTMs have shown strong performance in capturing patterns over time [5] while on the other hand tending to be more computationally expensive and less transparent. More recently, models like temporal convolutional networks (TCNs) and transformer-based architectures have also been applied successfully to AQ forecasting problems [4; 5; 6]. However, these advanced models often function like \"black boxes\", making it difficult to understand how specific predictions are made or which variables are most important. This lack of transparency can be a major drawback when these tools are used for public health or environmental policy decisions [7; 8]. To solve the interpretability problem, researchers have introduced Explainable AI (XAI) tools such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). These tools aim to explain the reasoning behind ML predictions, either by showing the average effect of each feature across all predictions (SHAP) or by breaking down the logic of single instances (predictions) as LIME [9; 10]. SHAP has its origins in game theory and estimates how much each input feature contributes to the final prediction. In simple terms, it simulates a theoretical game where each feature represents a player and tries to estimate the assigned contribution. On the other hand, LIME fits a simple model (i.e., linear regressor) around a single data point to explain the outcome locally. These methods have been widely used in areas like healthcare, finance, and increasingly in environmental modeling [6; 11]. One major benefit of using SHAP and LIME is that they do not just make the models more understandable--they can also help improve performance and increase utility for better models.