Assessing the Environmental Impacts of Plastic Materials: A Step Towards Sustainable Materials and Eco-Friendly Products

The unprecedented surge in disposable face mask consumption during the COVID-19 pandemic has led to the accumulation of substantial plastic waste, posing significant environmental and ecological challenges, particularly due to the reliance on non-biodegradable polypropylene (PP) materials. However, the long-term environmental behavior and degradation pathways of these masks remain poorly understood, comparative evaluations of biodegradable alternatives such as polylactic acid (PLA) are still limited, and effective strategies for identifying and implementing sustainable material alternatives are still lacking. This dissertation presents a comprehensive, multi-phase investigation into the environmental impact, available material alternatives, and predictive evaluation frameworks for single-use face masks, facilitating the transition toward sustainable personal protective equipment (PPE) solutions. The degradation behavior of PP-based masks in simulated landfill leachate environments was studied. The results revealed that PP masks experience rapid structural deterioration and release quantities of microplastics (MPs) and metal elements, raising concerns about long-term ecological toxicity. In addition, to address these issues, biodegradable polymer PLA was proposed as a sustainable alternative to be further investigated. A full life cycle assessment (LCA) comparing PP and PLA masks was systematically conducted, encompassing raw material extraction, production, packaging, transportation, and end-of-life management. Results indicate that PLA masks produce approximately 37% lower greenhouse gas emissions and generally outperform PP masks in several key environmental impact categories, particularly global warming potential and fossil resource depletion. To further validate the environmental suitability of PLA, laboratory-scale degradation experiments were carried out under diverse environmental conditions, including exposure to ultraviolet (UV) radiation, landfill leachate, seawater, and enzymatic environments. These results revealed that PLA degrades primarily through hydrolysis, with significant differences in degradation rates across environments and material layers. PLA masks released significantly fewer microparticles than PP masks, suggesting a significantly reduced risk of persistent pollution. Building on these findings, the final phase of this study introduces a novel predictive screening framework that integrates multi-criteria decision analysis (MCDA) and machine learning (ML). The model uses material-level properties—such as molecular weight and functional group composition—to predict degradation performance and rank candidate materials in terms of environmental suitability. The framework enables rapid, early-stage screening of novel materials without requiring extensive laboratory experimentation, thus providing a scalable tool for sustainable material development and selection. In summary, this dissertation bridges environmental risk assessment, life cycle-based decision-making, and data-driven prediction to provide a holistic strategy for assessing and guiding the transition toward usage and development of sustainable raw materials for mask production.