<p>In recent years, the integration of environmental considerations into supply chain management has become essential due to the rising impacts of air pollution on operational efficiency and public health. While previous research in sustainable supply chains has largely focused on minimizing greenhouse gas (GHG) emissions, the direct and indirect effects of airborne particulate matter (PM₁₀ and PM_2.5) on supply chain performance remain underexplored. This study proposes a novel tri-objective supply chain network design model that explicitly incorporates PM exposure into the decision-making process under uncertainty. The model optimizes (1) the total supply chain cost, which increases with higher PM levels due to transportation delays, equipment wear, and maintenance; (2) CO₂ emissions, aligned with global sustainability targets; and (3) overall PM exposure, serving as a proxy for operational risk and environmental resilience. To capture real-world variability in pollution and demand, the model employs a robust optimization framework with multi-scenario analysis, enabling reliable decisions under both average and worst-case conditions. A numerical case study demonstrates that applying a PM cost penalty can reduce expected exposure by 30% at the expense of an 11% increase in total cost, revealing a clear trade-off between economic efficiency and environmental risk mitigation. Sensitivity and Pareto analyses highlight the nonlinear behavior of the system under different penalty coefficients, providing actionable insights for both supply chain managers and policymakers. This research contributes to the emerging field of environmentally resilient supply chains by integrating air pollution risk into network design, and it offers a foundation for future studies on dynamic adaptation, multi-pollutant modeling, and digital twin-based decision support.</p>