Modeling the Impact of Air Quality on Animal and Human Health using SHAP and AI through the One Health Lens

Environmental pollutants have long been observed to negatively affect ecological and human health systems, affecting the biological mechanisms of organisms. Although poor air quality and its isolated impacts have been researched, a robust unified approach across ecosystems has not been developed. Highly Pathogenic Avian Influenza (HPAI) outbreaks in wild birds and regional disparities in U.S. cancer rates both exhibit spatial differences linked to air quality. However, existing models fail to consider human and wildlife health together as a whole, facing limitations because of the lack of explainable feature extraction, temporal misalignment, and class imbalance. After processing quantitative and geographic data on annual AQI, HPAI outbreaks, and cancer incidence rate, we developed a multi-model machine learning framework, using TabNet, ensemble, XGBoost, and Random Forest models to predict disease rates from air quality data points. To account for imbalance in training data, SMOTE/ADASYN oversampling, polynomial features, and log-transformations were implemented. SHAP added post-hoc explainability to the approach. Considering the delayed effects of pollution exposure, time-lagged features were engineered from the data. Our ensemble and TabNet classifiers were highly predictive for HPAI outbreaks (ROC AUC > 0.85), while SHAP-enhanced regression revealed the key features (e.g., PM2.5, NO2) driving cancer incidence patterns (R² ≈ 0.70). These results show air quality is a reliable predictor of ecological health risks for both humans and wildlife. This integrated approach offers a scalable, interpretable predictive model for environmental public health forecasting, supporting cross-species environmental policy planning.