Unveiling Hidden Health Risks: Machine Learning Enhanced Modeling of Plastic Additive Release Kinetics in Fresh Produce Packaging

作者：Baojun Ding, Fangfang Yang, Wenjing Han, Z. J. Xiao, Qing Xie, Huaijun Xie, Jingwen Chen · 发表于：Environmental Science & Technology · 年份：2025 · DOI：10.1021/acs.est.5c02821 · 被引用次数：6 · 研究领域：Microplastics and Plastic Pollution、Recycling and Waste Management Techniques、Effects and risks of endocrine disrupting chemicals

Fresh produce packaging (FPP) plays a critical role in protecting fruits and vegetables from various environmental factors. However, the presence, migration, and human health risks of additives in FPP have received limited attention. This study investigated 73 commonly used additives across six categories of FPP samples collected in China. A Weibull model combined with machine learning techniques was used to assess the migration of these additives into fruits and vegetables. A total of 43 additives were identified in the FPP samples, with concentrations ranging from 1.52 × 10 3 to 2.51 × 10 6 ng/g. Notably, non-phthalate plasticizers (NPPs) were found to be the most prevalent additive group. The migration ratio of additives varied from 10.5% to complete migration, influenced by factors including the molecular structure of the additives, FPP material composition, and temperature. Additives in foamed packaging exhibited the fastest migration rates and the highest migration ratios. Estimates of daily intake indicated that 2-ethylhexanoic acid (EHA) and triethyl phosphate (TEP) migrating from the FPP can pose significant health risks. These findings highlight a crucial source of health risks to humans and underscore the urgent need for the controlled and scientifically informed incorporation of additives in plastic products in the future.