Environmental monitoring studies based on target analysis capture only a small fraction of contaminants of emerging concern (CECs) and miss pollutants potentially harmful to wildlife. Environmental specimen banks, with their archived samples, provide opportunities to identify new CECs by temporal trend analysis and non-target screening. In this study, archived white-tailed sea eagle (Haliaeetus albicilla) muscle tissue was analysed by non-targeted high-resolution mass spectrometry. Univariate statistical tests (Mann-Kendall and Spearman rank) for temporal trend analysis were applied as prioritisation methods. A workflow for non-target data was developed and validated using an artificial time series spiked at five levels with gradient concentrations of selected CECs (n=243). Pooled eagle muscle tissues collected 1965-2017 were then investigated with an eight-point time series using the validated screening workflow. Following peak detection, peak alignment, and blank subtraction, 14 409 features were considered for statistical analysis. Prioritisation by time-trend analysis detected 207 features with increasing trends. Following unequivocal molecular formula assignment to prioritised features and further elucidation with MetFrag and EU Massbank, 13 compounds were tentatively identified, of which four were of anthropogenic origin. These results show that it is possible to prioritise new CECs in archived biological samples using univariate statistical approaches.

基于目标分析的环境监测研究仅捕获一小部分新兴关注的污染物 (CEC)，而遗漏了可能对野生动物有害的污染物。环境样本库及其存档样本提供了通过时间趋势分析和非目标筛选来识别新 CEC 的机会。在这项研究中，通过非靶向高分辨率质谱分析了存档的白尾海雕 ( Haliaeetus albicilla ) 肌肉组织。用于时间趋势分析的单变量统计检验（Mann-Kendall 和 Spearman 等级）被用作优先级排序方法。使用人工时间序列开发并验证了非目标数据的工作流程，该时间序列在五个水平上加标，具有选定 CEC 的梯度浓度（n=243)。然后使用经过验证的筛选工作流程对 1965-2017 年收集的汇集鹰肌肉组织进行了八点时间序列研究。在峰值检测、峰值对齐和空白减法之后，考虑了 14 409 个特征进行统计分析。通过时间趋势分析的优先级检测到 207 个具有增加趋势的特征。在明确将分子式分配给优先特征并使用 MetFrag 和 EU Massbank 进一步阐明后，初步鉴定了 13 种化合物，其中 4 种是人为来源的。这些结果表明，可以使用单变量统计方法在存档的生物样本中优先考虑新的 CEC。