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在现场评估一种主动-被动采样 (APS) 技术,用于监测废水污水中的精神活性化合物

Allen Jun Anies¹, Maria Laimou-Geraniou², Maarten Quireyns³

¹ECOSPHERE, Department of Biology, University of Antwerp, Groenenborgerlaan 171, 2020 Antwerp, Belgium.

Environmental pollution (Barking, Essex : 1987)

|March 11, 2026

概括

积极-被动抽样 (APS) 有效地监测废水中的精神活性药物,补充复合方法. 这种方法提供了对生物可用药物成分的洞察力,有助于评估环境命运.

科学领域:

环境化学环境化学
分析化学分析化学
环境科学环境科学

背景情况:

对精神活性药物的废水监测对于了解环境影响至关重要.
被动采样提供了时间整合的数据和对生物可用化合物分数的洞察力.

研究的目的:

评估主动-被动采样 (APS) 方法,用于监测废水中的八种精神活性化合物.
将APS与薄膜 (DGT) 和复合采样方法中的扩散梯度进行比较.
将现场数据与吸附剂与水分区系数的预测建模集成.

主要方法:

在废水中部署使用HLB和MCX吸附剂的APS和DGT采样器.
收集每日复合样本进行比较.
使用机器学习模型预测分区系数 (随机森林,XGBoost).

主要成果:

APS显示了非理想的沉降行为和质量转移限制,而DGT仍然是扩散控制的.
预测的分区系数使得APS数据的可靠解释成为可能.
一般来说,APS和DGT测量是一致的,APS对大多数化合物的度更高.
由于控制的流量,APS显示了增强的合一致性.

结论:

APS是一种强大而通用的工具,用于监测水生系统中的精神活性化合物.
APS为复合采样提供了补充数据,提供了对生物可用性和颗粒结合分量的洞察.
该研究支持对污染物动态和环境命运的定量评估.

关键词:

废水监测废水监测废水监测积极-被动抽样 (APS) 是一种主动-被动抽样.薄膜中的扩散梯度 (DGT)预测建模预测建模精神活性化合物精神活性化合物吸收动力学吸收动力学

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