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A composite theory-guided framework for robust feature attribution in PM₂.₅ ionic composition modeling
1Faculty of Data Science, Musashino University, 3-3-3 Ariake Koto-ku, Tokyo, 135-8181, Japan.
This study introduces a robust method for analyzing PM₂.₅ ionic species, improving upon existing models by combining feature clustering and correlation analysis for reliable variable importance. This approach ensures stable and interpretable results, enhancing air quality research.
Area of Science:
- Environmental Science
- Data Science
- Atmospheric Chemistry
Background:
- Accurate mapping of PM₂.₅ ionic species is crucial for understanding air quality and health impacts.
- Existing methods using extreme gradient boosting and SHAP have limitations in interpretability due to model-specific biases.
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