To safeguard the quality of river water, a comprehensive approach is required within the European Water Framework Directive. It is vital to conduct non-target screening of the complete chemical fingerprint of the aquatic ecosystem, as this will help to identify chemicals of emerging concern and uncover their unusual dynamic patterns in river water. Achieving this goal calls for an advanced combination of two measurement paradigms: tracing the potential pollution path through the river network and detecting the numerous compounds that constitute the chemical composition, both known and unknown. To address this challenge, we propose an integrated approach that combines the preprocessing of ongoing Gas Chromatography Mass Spectrometry (GC-MS) measurements at nine sites along the Rhine using PARAllel FActor Analysis2 (PARAFAC2) for non-target screening, with spatiotemporal modelling of these sites within the river network using a statistical path modelling algorithm called Process Partial Least Squares (Process PLS). With an average explained variance of 97.0%, PARAFAC2 extracted mass spectra, elution, and concentration profiles of known and unknown chemicals. On average, 76.8% of the chemical variability captured by the PARAFAC2 concentration profiles was extracted by Process PLS. The integrated approach enabled us to track chemicals through the Rhine catchment, and tentatively identify known and as-yet unknown potential pollutants, including methyl tert-butyl ether and 1,3-cyclopentadiene, based on non-target screening and spatiotemporal behaviour.

为了保障河水质量，欧洲水框架指令需要采取综合方法。对水生生态系统的完整化学指纹进行非目标筛选至关重要，因为这将有助于识别新出现的令人关注的化学物质并揭示它们在河水中不寻常的动态模式。实现这一目标需要两种测量范式的高级组合：追踪河流网络中的潜在污染路径，并检测构成化学成分的多种已知和未知化合物。为了应对这一挑战，我们提出了一种综合方法，该方法结合了使用 PARAllel FActor Analysis2 (PARAFAC2) 进行非目标筛选的莱茵河沿岸 9 个地点正在进行的气相色谱质谱 (GC-MS) 测量的预处理，以及对河网内这些地点的时空建模使用称为过程偏最小二乘法（Process PLS）的统计路径建模算法。PARAFAC2 提取已知和未知化学物质的质谱、洗脱和浓度分布，平均解释方差为 97.0%。平均而言，PARAFAC2 浓度曲线捕获的化学变异性的 76.8% 是通过 Process PLS 提取的。综合方法使我们能够跟踪莱茵河流域的化学物质，并初步识别已知和未知的潜在污染物，