Predictive Accuracy of Polytopic Vector Analysis in Environmental Forensics: Sensitivity to Seeding Methods, Random Noise, and Sample Size

Although the sensitivity of receptor models to seeding methods, random noise, and sample size are frequently discussed in environmental forensics, a rigorous evaluation of these factors has been lacking. We generated 435,600 unique datasets with between two and seven sources of PCBs or PFAS that attempt to approximate real-world environmental datasets. Evaluation of these simulated datasets with PVA shows that EXRAWC and NNDSVD seeding methods converge more often and are typically more accurate when sources are similar to each other or are present in small proportions. The results also show that increasing sample sizes up to 25 samples can improve predictive accuracy.尽管受体模型（receptor models）对种子初始化方法、随机噪声以及样本量的敏感性在环境取证领域已被广泛探讨，但目前仍缺乏针对上述因素的严谨评估。本研究生成了435600个唯一数据集，每个数据集包含2至7个多氯联苯（PCBs）或全氟和多氟烷基物质（PFAS）污染源，旨在模拟真实世界的环境数据集。通过PVA对这些模拟数据集开展评估后发现，当污染源彼此相似或占比较低时，EXRAWC与NNDSVD种子初始化方法的收敛频率更高，且通常具备更优的预测精度。研究结果同时表明，将样本量提升至25个时，可有效改善预测准确性。