# The Quest for the Best Explanation: Comparing Models and XAI Methods in Air Quality Modeling Tasks
## 4 Discussion
### Interpretability  
To better understand how individual features influence predictions--and how they interact--we used SHAP dependence plots aggregated across all stations. These plots visualize the relationship between a feature's raw value and its SHAP value, while using color to represent the value of the most interacting feature. This dual encoding is one of the most compelling strengths of SHAP: it can identify nonlinear interactions between features that traditional feature-importance metrics or even LIME often miss [10]. The three most important features identified via SHAP co-dependence analysis were PM\({}_{10}\), CO, and O\({}_{3}\). These were selected based on their high Spearman's or Pearson's correlation with the model's output across all stations. In the PM\({}_{10}\) subplot, we observed a clear linear trend: as PM\({}_{10}\) levels increased, SHAP values also rose, indicating that higher PM\({}_{10}\) levels consistently contributed to higher PM\({}_{2.5}\) predictions. This is expected as PM\({}_{2.5}\) is a fraction of PM\({}_{10}\), and therefore they are strongly correlated. The color gradient, reflecting CO values, revealed an important interaction effect--CO appeared to amplify the PM\({}_{10}\) contribution, especially at higher pollution levels. This means that with increasing PM concentrations, CO becomes the prominent predictor of PM\({}_{2.5}\) rise, giving insight into the PM main emission sources (the same that contribute to CO high levels) in Beijing during the examined period. This kind of conditional influence would be difficult to uncover using traditional univariate analysis. The CO subplot (Figure 2) showed a different pattern: its relationship with SHAP values was nonlinear and saturated at higher concentrations. This saturation indicates that while CO plays a significant role in modeling PM\({}_{2.5}\), its marginal contribution diminishes beyond a certain point--a pattern often seen in environmental phenomena dominated by threshold-related approaches [19]. Interestingly, the CO plot's color gradient again featured PM\({}_{10}\) as the interacting variable, further reinforcing the importance of this relationship. This result may suggest that CO-related emission sources (i.e., engine combustions) play an important role in growing PM\({}_{2.5}\) concentrations, nevertheless when these concentrations are elevated, then the incremental impact of CO on PM\({}_{2.5}\) diminishes. This suggests additional emission sources (like household heating or long-range transport) as the main parameters shaping high PM\({}_{2.5}\) values. Such two-sided interactions (PM\({}_{10}\) affecting CO's impact and vice versa) emphasize the need for methods like SHAP that can account for feature synergy rather than treating predictors independently. The O\({}_{3}\) subplot exhibited a weaker and more variable trend. There was a modest negative relationship between ozone levels and SHAP values, suggesting that in some contexts, higher ozone levels may slightly reduce PM\({}_{2.5}\) predictions, probably suggesting the suppression of household heating as an emission mechanism during the warmer period of the year. However, the color encoding again pointed to PM\({}_{10}\) as a key modulating factor. These more nuanced effects highlight the model's capacity to detect context-dependent behaviors and demonstrate how SHAP can uncover relationships that may be both subtle and dynamic in real-world environmental systems [5, 11]. Another contribution of this study is the comparison between SHAP values and XGBoost's internal gain-based feature importance. The results show substantial agreement in the top predictors--PM\({}_{10}\) and CO appear at the top of both rankings--but diverge significantly in mid- and lower-ranked features. SHAP assigned moderate importance to variables like dew point (DEWP) and month (relevant to seasons of the year), which were almost entirely ignored by XGBoost's gain scores.