The analysis of mixtures of contaminants remains a challenging task in many fields, including water quality and waste management. For example, the degradation of industrial waste such as plastics, leads to complex mixtures with hundreds of organic contaminants and often non-referenced analytes. In such cases, non-targeted or effects-based analyses provide complementary information to classical targeted-analyses, regarding contaminants nature or properties (molecular mass, lability, toxicity). In this study, a novel analytical method is proposed to characterise mixtures of unknown organic contaminants, with a focus on the lipophilicity of solutes. The proposed process, named “P“ (Partition of Aqueous Waste), aims at the quantification of octanol-water partition coefficients (P) of mixed organic analytes. The process is based on sequential liquid-liquid partition equilibria. The output result is a lipophilicity histogram of the solutes, screened according to the chosen detection method. The process quantifies the distribution of analytes as a function of their octanol-water partition coefficients, without requiring any identification or prior knowledge. The P process is applicable with various detectors (UV–Visible, total carbon, liquid scintillation, etc.) allowing to focus on specific families of contaminants (e.g. organic solutes, colloids, C-bearing, etc.). Experimental proofs of concept are proposed, illustrating process implementation and possible fields of application. The first example deals with purity analysis of synthetic radiolabeled compounds. The second example aims the monitoring of cellulose degradation and quantification of the lipophilicity of degradation products. The P analytical process seems especially useful for characterisation of mixtures containing both hydrophilic and lipophilic compounds, e.g. neutral and ionizable organic contaminants, hardly characterisable simultaneously by chromatographic methods. It could be complementary to more detailed targeted or screening analysis of samples and effluents. For example it may help assessing the composition and environmental fate of mixtures of unknown analytes, thus facilitating waste management or mitigation strategies.

中文翻译：

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“PAW”是一种评估混合物中溶质亲脂性的智能分析过程


在许多领域，包括水质和废物管理，污染物混合物的分析仍然是一项具有挑战性的任务。例如，塑料等工业废物的降解会产生含有数百种有机污染物和通常非参考分析物的复杂混合物。在这种情况下，非靶向或基于效应的分析为传统的靶向分析提供了关于污染物性质或特性（分子质量、不稳定性、毒性）的补充信息。在这项研究中，提出了一种新的分析方法来表征未知有机污染物的混合物，重点关注溶质的亲脂性。所提出的过程名为“P”（水性废物的分配），旨在量化混合有机分析物的辛醇-水分配系数（P）。该过程基于顺序液-液分配平衡。输出结果是根据所选检测方法进行筛选的溶质亲脂性直方图。该过程将分析物的分布量化为其辛醇-水分配系数的函数，无需任何识别或先验知识。 P 过程适用于各种检测器（紫外-可见检测器、总碳检测器、液体闪烁检测器等），可以重点关注特定系列的污染物（例如有机溶质、胶体、含碳等）。提出了概念的实验证明，说明了流程的实施和可能的应用领域。第一个示例涉及合成放射性标记化合物的纯度分析。第二个例子旨在监测纤维素降解和量化降解产物的亲脂性。 P 分析过程似乎对于表征同时含有亲水性和亲脂性化合物的混合物特别有用，例如中性和可电离的有机污染物，很难通过色谱方法同时表征。它可以补充对样品和废水进行更详细的目标或筛选分析。例如，它可能有助于评估未知分析物混合物的成分和环境命运，从而促进废物管理或缓解策略。