# The Quest for the Best Explanation: Comparing Models and XAI Methods in Air Quality Modeling Tasks
## 4 Discussion
### Limitations  
Despite the promising results, this study has several limitations. First, it focuses exclusively on XGBoost, an ensemble tree-based algorithm. While this model is widely used and respected for its performance, future research should explore how well the findings generalize to other model families, such as deep neural networks or hybrid ensemble methods, i.e., autoencoding models or multilayer perception models [24]. Neural networks, for example, may require different interpretability strategies, and the behavior of SHAP and LIME may differ in those contexts [7; 17]. Secondly, the study is geographically restricted to Beijing, which has a distinct pollution profile shaped by its climate, topography, and regulatory landscape. Applying these models and interpretability strategies to other cities--especially those in coastal or mountainous regions--might yield different patterns. Air quality systems are heavily shaped by local conditions, and interaction effects observed in one environment may not hold in another [19]. Expanding the study to other geographies is a necessary next step for ensuring robustness and transferability. While the use of SHAP co-dependence plots allowed us to visualize complex feature interactions, the analysis could benefit from including higher-order interactions, potentially using more advanced techniques like SHAP interaction values or counterfactual explanations [24]. These methods could help uncover multiway dependencies that are not visible in pairwise comparisons alone. Finally, while SHAP and LIME provide useful explanations, they are still post hoc methods and can be vulnerable to adversarial manipulation or instability [25; 26]. Developing models that are interpretable by design--not just via post hoc analysis--remains a long-term goal for the field. Our findings complement and extend previous work in explainable environmental modeling. For example, ref. [27] demonstrated that ensemble models often perform well for air quality prediction but highlighted the difficulty of interpreting them without specialized tools. By contrast, our use of SHAP in a tree-based model provides a balance between performance and interpretability, without requiring more computationally expensive ensemble approaches. More recent studies have also explored XAI in environmental contexts using attention-based deep learning models [28] or graph-based models for spatiotemporal dependencies [5], but these approaches can be opaque and computationally intensive. Our study suggests that interpretable models like XGBoost, enhanced with SHAP explanations, offer a scalable and accurate alternative for modeling tasks--especially when deployed across multiple stations with varying data completeness. Furthermore, unlike conventional methods or black-box optimization techniques [24], SHAP operates natively within the model's architecture and data structure, requiring no custom loss functions or sampling strategies. This makes it more suitable for real-world applications, where model transparency and ease of deployment are key considerations. In terms of real-world application, our findings have direct relevance to both environmental agencies and public health stakeholders. As cities increasingly rely on data-driven systems to guide air quality management, the ability to explain not only _what_ the model predicts but also _why_ is essential for building trust. For instance, if a model signals an upcoming PM\({}_{2.5}\) spike, city planners can refer to SHAP outputs to identify which other pollutant precursors (e.g., CO) are driving the change. This in turn enables more targeted and informed interventions, such as traffic control, industrial regulation, or public alerts [1; 2]. Equally important is the potential for these models to support long-term environmental planning. Understanding how variables like seasonality, wind direction, or urban heating contribute to pollution can guide infrastructure investments and zoning decisions.