HEDGE Index: A Data-Driven Framework Overcoming the Limitations of TEFs for Prioritizing Polycyclic Aromatic Hydrocarbon Risks

Conventional risk assessment of airborne polycyclic aromatic hydrocarbons (PAHs), based on carcinogenicity-focused toxic equivalency factors (TEFs), largely overlooks a broader spectrum of systemic health risks from inhalation exposure. To address this limitation, we developed the HEDGE index (Hazard and Exposure Index from Data-Driven Generation), a data-driven framework integrating real-world exposure with multidimensional hazard profiles. We classified 16 priority PAHs into three distinct hazard subgroups (High-, Medium-, Low-Risk) using an ensemble clustering approach on 16 toxicological and environmental fate descriptors. Subsequently, an interpretable XGBoost machine learning model was trained to identify the key structural drivers, such as density, underlying these classifications. Epidemiological case study in Guangzhou, China, demonstrated that the HEDGE index exhibited improved model fit for daily nonaccidental mortality compared to the traditional toxic equivalency quantity (TEQ) approach (ΔAIC = 6.2). An interquartile range increase in HEDGE-weighted exposure corresponded to a 14.7% (95% CI: 12.5%, 17.0%) rise in total mortality, a risk estimate substantially greater than that from TEQ. The association was robust after adjusting for copollutants. By accurately capturing a broader spectrum of systemic risks, the HEDGE framework provides a more scientifically robust and health-protective tool for regulatory prioritization.