Towards a better understanding of sorption of persistent and mobile contaminants to activated carbon: Applying data analysis techniques with experimental datasets of limited size

IF 12.4 1区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Water Research Pub Date : 2024-12-21 DOI:10.1016/j.watres.2024.123032

Navid Saeidi , Laura Lotteraner , Gabriel Sigmund , Thilo Hofmann , Martin Krauss , Katrin Mackenzie , Anett Georgi

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引用次数: 0

Abstract

The complex sorption mechanisms of carbon adsorbents for the diverse group of persistent, mobile, and potentially toxic contaminants (PMs or PMTs) present significant challenges in understanding and predicting adsorption behavior. While the development of quantitative predictive tools for adsorbent design often relies on extensive training data, there is a notable lack of experimental sorption data for PMs accompanied by detailed sorbent characterization. Rather than focusing on predictive tool development, this study aims to elucidate the underlying mechanisms of sorption by applying data analysis methods to a high-quality dataset. This dataset includes more than 60 isotherms for 22 PM candidates and well-characterized high-surface-area activated carbon (AC) materials. We demonstrate how tools such as distance correlation and clustering can be used effectively to identify the key parameters driving the sorption process. Using these approaches, we found that aromaticity, followed by hydrophobicity, are key sorbate descriptors for sorption, overshadowing steric and charge effects for a given sorbent. Aromatic PMs, although classified as mobile contaminants based on their sorption to soil, are well adsorbed by AC as engineered adsorbent via π-π interactions. Non-aromatic and especially anionic compounds show much greater variability in sorption. The influence of ionic strength and natural organic matter on adsorption was considered. Our approach will help in the analysis of solute-sorption systems and in the development of new adsorbents beyond the specific examples presented here. In order to make the approach accessible, the code is freely available and described on GitHub (https://github.com/Laura-Lotteraner/PM-Sorption), following the FAIR data principles.

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为了更好地理解活性炭对持久性和可移动污染物的吸附：在有限规模的实验数据集上应用数据分析技术

碳吸附剂对多种持久性、流动性和潜在毒性物质（PMs或pmt）的复杂吸附机制对理解和预测吸附行为提出了重大挑战。虽然吸附剂设计的定量预测工具的开发通常依赖于大量的训练数据，但明显缺乏pm的实验吸附数据以及详细的吸附剂表征。本研究的目的不是专注于预测工具的开发，而是通过将数据分析方法应用于高质量数据集来阐明吸收的潜在机制。该数据集包括22种PM候选材料和表征良好的高表面积活性炭（AC）材料的60多条等温线。我们演示了如何有效地使用距离相关和聚类等工具来识别驱动吸附过程的关键参数。使用这些方法，我们发现芳香性，其次是疏水性，是山梨酸盐吸附的关键描述符，掩盖了给定吸附剂的空间和电荷效应。芳香族pmms虽然根据其对土壤的吸附被归类为可移动污染物，但作为工程吸附剂，AC通过π-π相互作用对其进行了很好的吸附。非芳香族化合物，特别是阴离子化合物在吸附方面表现出更大的变异性。考虑了离子强度和天然有机质对吸附的影响。我们的方法将有助于溶质吸附系统的分析和新吸附剂的开发，超出了这里提出的具体例子。为了使这种方法易于访问，代码是免费提供的，并在GitHub （https://github.com/Laura-Lotteraner/PM-Sorption）上描述，遵循FAIR数据原则。

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来源期刊

Water Research 环境科学-工程：环境

CiteScore

20.80

自引率

9.40%

发文量

1307

审稿时长

38 days

期刊介绍： Water Research, along with its open access companion journal Water Research X, serves as a platform for publishing original research papers covering various aspects of the science and technology related to the anthropogenic water cycle, water quality, and its management worldwide. The audience targeted by the journal comprises biologists, chemical engineers, chemists, civil engineers, environmental engineers, limnologists, and microbiologists. The scope of the journal include: •Treatment processes for water and wastewaters (municipal, agricultural, industrial, and on-site treatment), including resource recovery and residuals management; •Urban hydrology including sewer systems, stormwater management, and green infrastructure; •Drinking water treatment and distribution; •Potable and non-potable water reuse; •Sanitation, public health, and risk assessment; •Anaerobic digestion, solid and hazardous waste management, including source characterization and the effects and control of leachates and gaseous emissions; •Contaminants (chemical, microbial, anthropogenic particles such as nanoparticles or microplastics) and related water quality sensing, monitoring, fate, and assessment; •Anthropogenic impacts on inland, tidal, coastal and urban waters, focusing on surface and ground waters, and point and non-point sources of pollution; •Environmental restoration, linked to surface water, groundwater and groundwater remediation; •Analysis of the interfaces between sediments and water, and between water and atmosphere, focusing specifically on anthropogenic impacts; •Mathematical modelling, systems analysis, machine learning, and beneficial use of big data related to the anthropogenic water cycle; •Socio-economic, policy, and regulations studies.