Assessing sorption of fluoroquinolone antibiotics in soils from a Kd compilation based on pure organic and mineral components.

The presence of fluoroquinolone (FQ) antibiotics in soils may cause a threat to human health due to overexposure and the generation of antibiotic resistance genes. Understanding their sorption behavior in soils is important to predict subsequent FQ (bio) availability. Here, FQ sorption in pure soil organic (i.e., humic substances) and mineral (i.e., metal oxides; phyllosilicates) components is evaluated through a solid-liquid distribution coefficient (Kd (FQ)) dataset consisting of 243 entries originated from 80 different studies, to elucidate their respective contribution to the overall Kd (FQ) in bulk soils. First, different factors affecting FQ sorption and desorption in each of these soil phases are critically discussed. The strong role of pH in Kd (FQ), due to the simultaneous effect on both FQ speciation and surface charge changes, encouraged the derivation of normalized sorption coefficients for the cationic, zwitterionic and anionic FQ species in humic substances and in different phyllosilicates. Kd (FQ) in metal oxides revealed a key role of metal nature and material specific surface area due to complexation sorption mechanisms at neutral pH. Cumulative distribution functions (CDF) were applied to each dataset to establish a sorption affinity range for each phase and to derive best estimate Kd (FQ) values for those materials where normalized sorption coefficients to FQ species were unavailable. The data analysis conducted in the different soil phases set the basis for a Kd (FQ) prediction model, which combined the respective sorption affinity of each phase for FQ and phase abundance in soil to estimate Kd (FQ) in bulk soils. The model was subsequently validated with sorption data in well characterized soils compiled from the literature.

PFΔScreen - an open-source tool for automated PFAS feature prioritization in non-target HRMS data.

Per- and polyfluoroalkyl substances (PFAS) are a huge group of anthropogenic chemicals with unique properties that are used in countless products and applications. Due to the high stability of their C-F bonds, PFAS or their transformation products (TPs) are persistent in the environment, leading to ubiquitous detection in various samples worldwide. Since PFAS are industrial chemicals, the availability of authentic PFAS reference standards is limited, making non-target screening (NTS) approaches based on high-resolution mass spectrometry (HRMS) necessary for a more comprehensive characterization. NTS usually is a time-consuming process, since only a small fraction of the detected chemicals can be identified. Therefore, efficient prioritization of relevant HRMS signals is one of the most crucial steps. We developed PFΔScreen, a Python-based open-source tool with a simple graphical user interface (GUI) to perform efficient feature prioritization using several PFAS-specific techniques such as the highly promising MD/C-m/C approach, Kendrick mass defect analysis, diagnostic fragments (MS2), fragment mass differences (MS2), and suspect screening. Feature detection from vendor-independent MS raw data (mzML, data-dependent acquisition) is performed via pyOpenMS (or custom feature lists) with subsequent calculations for prioritization and identification of PFAS in both HPLC- and GC-HRMS data. The PFΔScreen workflow is presented on four PFAS-contaminated agricultural soil samples from south-western Germany. Over 15 classes of PFAS (more than 80 single compounds with several isomers) could be identified, including four novel classes, potentially TPs of the precursors fluorotelomer mercapto alkyl phosphates (FTMAPs). PFΔScreen can be used within the Python environment and is easily automatically installable and executable on Windows. Its source code is freely available on GitHub ( https://github.com/JonZwe/PFAScreen ).

Using a batch test to derive sorption data of fluoroquinolone antibiotics in humic acids.

Fluoroquinolone antibiotics (FQs) are of concern due to their disrupting effects on environmental bacterial communities. Evaluating FQ sorption by soil components is important to understand their interaction with soils and to address their environmental (bio)availability. However, data in soil organic components, especially humic acids, are scarce. Batch experiments following OECD guidelines are suitable for testing the sorption of pollutants in solid matrices. Here, we applied this methodology, with specific changes in the experimental setup, to derive sorption data and to identify the factors affecting sorption of four common FQs in seven humic acids with contrasting properties. The effect of shaking time, pH, calcium concentration in solution and dissolved organic carbon (DOC) content on the quantification of the solid-liquid distribution coefficient (Kd) of norfloxacin in three reference humic acids was evaluated. Sorption reversibility and sorption analogy of four FQs were additionally assessed in these three reference materials, whereas the effect of initial norfloxacin concentration was evaluated in the overall set of seven humic acids. Sorption was fast, strong, non-linear, irreversible and affected by changes in the pH and calcium levels in solution. The bell-shaped sorption trend at varying pH values confirmed the key role of FQ speciation in sorption and the high Kd values indicated a positive effect of soil organic matter components on FQ sorption in bulk soils at environmentally relevant pH values.•Relevant factors affecting sorption of pollutants in environmental matrices must be considered to derive Kd values with low variability and high representativeness.•In this article we modify the experimental conditions of standard batch tests to identify the factors that affect the sorption of FQs in humic acids.•The methodological approach followed can be extrapolated to the evaluation of other combinations of pollutant and environmental matrix.

Deriving parametric and probabilistic Kd values for fluoroquinolones in soils.

The evaluation of the sorption affinity of fluoroquinolone antibiotics (FQs) in soils, by means of the derivation of solid-liquid distribution coefficients (Kd), is a valuable information for assessing their environmental mobility. Aiming to develop Kd (FQ) prediction tools in soils, in the first stage of this study we constructed a Kd (FQ) sorption dataset using current literature data. Furthermore, additional sorption and desorption data for norfloxacin were obtained in seven different soils of contrasting properties. Sorption isotherms of norfloxacin were linear under the experimental conditions tested and desorption percentages increased for scenarios in which low sorption was noted. Sorption tests in the same soils were then extended to ciprofloxacin, enrofloxacin and ofloxacin and pooled in the dataset, revealing comparable Kd (FQ) values among the FQ tested after analyzing the overall dataset consisting in 312 entries of Kd (FQ). A partial least square (PLS) regression model was then developed to predict values of Kd (FQ) based on specific relevant soil properties (i.e., pH, cation exchange capacity and organic carbon and texture information), and, for the first time, FQ properties (fraction of cationic FQ species) affecting sorption. Additionally, probabilistic, Kd (FQ) best estimates in soils were derived through cumulative distribution functions (CDFs) for the overall and for partial datasets created by grouping Kd (FQ) values according to key soil properties affecting FQ sorption (i.e., pH, organic carbon content and texture information). This latter approach permitted to derive more representative Kd (FQ) best estimates for the soils to be assessed, and with a lower related variability than that derived from the overall dataset. Best estimates Kd (FQ) values were > 1000 L kg-1 for most acidic to neutral soils, suggesting strong sorption, although lower sorption and thus higher environmental mobility may be expected in scenarios with soils with alkaline pH, low OC and high sand contents. SYNOPSIS: This study aims to derive parametric and probabilistic Kd values for fluoroquinolone antibiotics in soils on the basis of a few relevant soil physicochemical properties.

Examining sorption of perfluoroalkyl substances (PFAS) in biochars and other carbon-rich materials.

The use of carbon-rich sorbents to remove and/or immobilize perfluoroalkyl substances (PFAS) in contaminated environmental scenarios is attracting increasing interest. The identification of key sorbent properties responsible for PFAS sorption and the development of models that can predict the distribution coefficients (Kd) for PFAS sorption in these materials are crucial in the screening of candidate materials for environmental remediation. In this study, sorption kinetics, sorption isotherms, and the effects of pH, calcium concentration and dissolved organic carbon (DOC) content on PFAS sorption were evaluated in four representative carbon-rich materials: two biochars with contrasting properties, a compost, and charcoal fines rejected by the metallurgical industry. Subsequently, the sorption of seven PFAS with numbers of fluorinated carbons ranging from 4 to 11 was evaluated in a total of ten carbon-rich materials, including activated carbons, so as to build up a Kd prediction model. The sorption of PFAS increased with greater fluorinated chain length, suggesting that hydrophobic interactions play a major role in sorption and electrostatic interactions a minor one. These results were confirmed by a principal component analysis, which revealed that the CORG/O molar ratio and the specific surface area of the material were the two main sorbent properties affecting PFAS sorption. Furthermore, the DOC content in solution had a negative effect on PFAS sorption. Using this information, a simple Kd prediction model applicable to a wide range of materials and PFAS was developed, using only a few easily-derived physicochemical properties of sorbent (CORG/O molar ratio and SSA) and PFAS (number of CF2), and was externally validated with data gathered from the literature.

Modelling the sorption behaviour of perfluoroalkyl carboxylates and perfluoroalkane sulfonates in soils.

A simple parametric model was developed to predict the sorption of perfluoroalkyl substances (PFASs) in soils. Initially, sorption and desorption solid-liquid distribution coefficients (Kd and Kd,des respectively) of eight PFASs (five perfluoroalkyl carboxylates, PFCAs, and three perfluoroalkane sulfonates, PFSAs) in seven soils with organic carbon (OC) content ranging from 1.6 to 41% were quantified using batch experiments. The information obtained helped to fill the gaps in a literature-based database of Kd values of PFASs, which was lacking data on soils with high OC content. The overall dataset finally comprised 435 entries. Normalized sorption coefficients for the soil OC and mineral fraction contents (KOC and KMIN respectively) were deduced for each PFAS by correlating the corresponding Kd values obtained under a wide range of experimental conditions with the fraction of organic carbon (fOC) of the soils. Furthermore, the sorption mechanisms in each phase were shown to depend mainly on PFAS chain length. The dependence of KOC and KMIN values on PFAS chain length defined the basic equations to construct the model for predicting PFAS sorption, applicable to both PFCAs and PFSAs with chain lengths ranging from 3 to 11 fluorinated carbons. The validation of the proposed model confirmed its ability to predict the Kd of PFASs based only on the soil OC and silt+clay contents and PFAS chain length. Therefore, it can be used in the first stages of a risk assessment process aiming at estimating the potential mobility of PFASs in soils after a contamination event. SYNOPSIS: This study develops a new parametric model to predict the sorption of perfluoroalkyl substances (PFASs) in soils.