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

Area of Science:

• Environmental Health
• Toxicology
• Data Science

Background:

• Conventional airborne polycyclic aromatic hydrocarbons (PAHs) risk assessment relies on carcinogenicity, neglecting broader systemic health impacts.
• Existing methods like toxic equivalency factors (TEFs) do not fully capture the multidimensional hazards of PAHs.

Purpose of the Study:

• To develop the HEDGE index (Hazard and Exposure Index from Data-Driven Generation) for a more comprehensive risk assessment of airborne PAHs.
• To integrate real-world exposure data with multidimensional hazard profiles for improved health risk evaluation.

Main Methods:

• Classified 16 priority PAHs into High-, Medium-, and Low-Risk subgroups using ensemble clustering on 16 toxicological and environmental fate descriptors.
• Employed an interpretable XGBoost machine learning model to identify key structural drivers of PAH hazard.
• Conducted an epidemiological case study in Guangzhou, China, to compare HEDGE index with the traditional toxic equivalency quantity (TEQ) approach.

Main Results:

• The HEDGE index demonstrated a superior model fit for daily nonaccidental mortality compared to the TEQ approach (ΔAIC = 6.2).
• An interquartile range increase in HEDGE-weighted exposure was associated with a 14.7% rise in total mortality, significantly higher than TEQ estimates.
• The observed associations remained robust even after adjusting for copollutants.

Conclusions:

• The HEDGE framework provides a more scientifically robust and health-protective tool for regulatory prioritization of airborne PAHs.
• This data-driven approach accurately captures a broader spectrum of systemic health risks beyond carcinogenicity.
• HEDGE index enhances the assessment of inhalation exposure risks from PAHs.