# The Quest for the Best Explanation: Comparing Models and XAI Methods in Air Quality Modeling Tasks
## 3 Results
### XGBoost Model Feature Importance vs. XAI Methods  
To investigate the agreement between the model-internal and explainability method's explanations, we computed the normalized average importance of each feature using both XGBoost's gain-based importance and SHAP values, aggregated across all stations. The results, presented in Figure 3, illustrate the relative contribution of each feature to the estimation of PM\({}_{2.5}\) concentrations. Both methods consistently identify PM\({}_{10}\) and CO as dominant predictors. However, SHAP reveals a more distributed importance profile, attributing significant weight to features such as TEMP, month, O\({}_{3}\), and notably DEWP (dew point). In contrast, the model's internal importance ranks TEMP, month, O\({}_{3}\), and DEWP near zero, suggesting they play an almost negligible role. This discrepancy indicates that SHAP is capturing deeper relationships between an interaction-based effect that the model was unable to adjust even though it had nearly the same performance across all stations (highest reduction in R\({}^{2}\) -1%). This disparity is especially relevant in cases like DEWP, where the feature may have low standalone importance but nonetheless has inherent influence in interaction with other variables. More generally, SHAP appears to assign moderate importance to a wider range of contextual and meteorological variables, whereas XGBoost's native importance tends to favor only the strongest predictors that could lead to unnecessary biases in future steps. This comparison highlights the added explanatory depth that SHAP offers in uncovering meaningful but model-suppressed signals. SHAP provides more detailed explanations, making it especially useful for understanding overall feature importance and how features interact--things that gain-based methods often miss. That said, a potential pitfall of overvaluing raw gain importance is the possibility of making extremely oversimplified interpretations of the dynamics of processes in an environment and potentially undervaluing predictive signals, particularly for predictors--features that have high interaction exhibit low prediction gain (as measured by XGBoost's main attribution factor) [8], as is the case with dew point. Since air quality  
Figure 3: XGBoost gain-based feature importance vs. SHAP normalized importance averaged for all stations. dynamics are inherently complex, nonlinear, and operate in multidimensional spaces, XAI methods that allow for feature decomposition are crucial for implementation in practice. As a next step, both LIME and SHAP results are used to identify features and feature value ranges that contribute to PM\({}_{2.5}\) nowcasting. For this reason, in Figure 4, a comparison of LIME and SHAP values is presented, taking into account different conditions (features and feature value ranges) used in a predictive model, therefore identifying how each feature contributes to the model's output. The y-axis lists specific conditions or value ranges for our environmental features (e.g., SO\({}_{2}\), PM\({}_{10}\), CO, and TEMP), while the x-axis shows their corresponding influence values: blue bars represent LIME weights, and red bars represent mean SHAP values across all stations on average. Several key features show strong agreement between LIME and SHAP, both in direction (positive or negative) and approximate magnitude. For example, \"TEMP \(\leq\) 3.65\", \"TEMP \(\leq\) 3.10\", and \"TEMP \(\leq\) 3.00\" have consistently high positive values in both LIME and SHAP, indicating that low temperature conditions are strongly associated with increased PM\({}_{2.5}\) concentration estimations both locally (individual cases) and globally (on average across the dataset). Similarly, high PM\({}_{10}\) levels (e.g., \"38.00 \(<\) PM\({}_{10}\)\(\leq\) 86.00\", \"39.00 \(<\) PM\({}_{10}\)\(\leq\) 91.00\") also show a strong positive influence in both methods, marking them as significant predictors. Figure 4: SHAP averaged values in the top conditions derived from the LIME application for all stations.