Using particle dimensionality-based modeling to estimate lung carcinogenicity of 3D printer emissions

Area of Science:

• Occupational Health
• Environmental Science
• Toxicology

Background:

• 3D printing use is increasing in industry and consumer settings.
• Methods to assess lung cancer risk from 3D printer emissions are underdeveloped.
• Understanding emission risks is crucial for worker safety and public health.

Purpose of the Study:

• To demonstrate a methodology for modeling lung cancer risk from 3D printer emissions.
• To correlate emission levels with specific exposure scenarios.
• To evaluate risks associated with various filament types (ABS, PLA, PETG).

Main Methods:

• Assessed emissions from 15 filaments under 23 conditions in a cleanroom (ANSI/CAN/UL 2904).
• Utilized three risk estimation approaches: PM concentrations, pulmonary deposition, and particle aerodynamic diameter.
• Modeled carcinogenic potential of various particle types using aerodynamic diameter data.

Main Results:

• Average PM2.5 concentration was 25.21 μg/m³; mass median aerodynamic diameter (MMAD) was 0.35 μm.
• Modeled carcinogenic potential showed high correlation (0.999) with particle characteristics.
• Estimated lung cancer risk at 14.74 cases per 10,000 workers for a typical exposure scenario.

Conclusions:

• The study provides a novel methodology for quantifying 3D printer emission-related lung cancer risk.
• PLA filaments presented the lowest risk, while PETG filaments showed the highest.
• Findings are critical for developing safety guidelines and mitigating health risks associated with 3D printing technologies.