Understanding and predicting the diffusivity of organic chemicals for diffusive gradients in thin-films using a QSPR model

Systematic evaluation of sampling rate influences and variability in POCIS using meta-analysis and quantitative structure property relationship (QSPR)

Despite the significant benefits of aquatic passive sampling (low detection limits and time-weighted average concentrations), the use of passive samplers is impeded by uncertainties, particularly concerning the accuracy of sampling rates. This study employed a systematic evaluation approach based on the combination of meta-analysis and quantitative structure-property relationships (QSPR) models to address these issues. A comprehensive meta-analysis based on extensive data from 298 studies on the Polar Organic Chemical Integrative Sampler (POCIS) identified essential configuration parameters, including the receiving phase (type, mass) and the diffusion-limiting membrane (type, thickness, pore size), as key factors influencing uptake kinetic parameters. The incomplete availability of these details across studies potentially impacts data reproducibility and comparability. The subsequent meta-regression and subgroup analysis were performed to reveal the most significant factors contributing to sampling rate variability and inter-study heterogeneity. The flow rate and octanol-water partitioning (Kow or pH-dependent Dow) were identified from all environmental factors and chemical properties. Furthermore, the impact of chemical properties on the sampling rates of POCIS was predicted by Quantitative Structure-Property Relationship (QSPR) models using 2D descriptors and random forest regression. The analysis highlighted that the electrotopological state and molecular mass are the most important chemical properties influencing the sampling rate. This study systematically unraveled the most important impact factors on reliable estimates of passive sampling rates, and these causes of uncertainty should be further considered in aquatic monitoring and assessment with passive samplers.

Development and validation of diffusive gradients in thin-films for in situ monitoring of ionic liquids in waters

Due to their wide applications, occurrence and "PFAS-like" environmental behaviors, ionic liquids (ILs) represent a new challenge for the environmental monitoring community, who require robust analytical methods that can determine accurately and efficiently their environmentally relevant concentrations. A new passive sampling method based on the diffusive gradients in thin films (DGT) technique was developed for the measurement of imidazole-based ILs in waters using a mixed-mode cation exchange (MCX) resin as the adsorbent. The selected binding gel had a high binding capacity (>170 μg per disc) for ILs. Diffusion coefficients measured using a diffusion cell correlated well with alkyl chain lengths (r2 = 0.95) and retention times (r2 = 0.88), providing a simple and rapid prediction approach for other ILs. The assembled MCX-DGT sampler exhibited a linear accumulation for at least 120 h. MCX-DGT also showed good performance under typical freshwater conditions (pH 5-8, ionic strength 0.001-0.01 M, and humic acid 0-5 mg L-1), while still being problematic for aquatic conditions with higher ionic strength (>0.1 M) or DOM (>10 mg L-1). Laboratory deployment (for up to 3 days) in spiked natural freshwater (SNW) resulted in linear mass uptakes for the short-chain ILs (C2-C8), and their DGT-measured concentrations agreed well with solution concentrations. However, MCX-DGT significantly overestimated the concentrations of the long-chain ILs (C10-C12) when deployed in SNW for one day or more, which is attributed to the strong competitive adsorption of the long-chain ILs by natural organic matter. In situ field evaluation along with grab sampling found no target ILs in a wastewater treatment plant and its receiving river, implying that these new chemicals might not be widely used in South China now. This is the first report on the DGT technique for ILs and might provide an effective tool for monitoring short chain length ILs in the aquatic environment in the near future.

Molecular Interactions Governing the Rat Aryl Hydrocarbon Receptor Activities of Polycyclic Aromatic Compounds and Predictive Model Development

Polycyclic aromatic compounds (PACs) exhibit rat aryl hydrocarbon receptor (rAhR) activities, leading to diverse biological or toxic effects. In this study, the key amino residues and molecular interactions that govern the rAhR activity of PACs were investigated using in silico strategies. The homology model of rAhR was first docked with 90 PACs to yield complexes, and the results of the molecular dynamics simulations of 16 typical complexes showed that the binding energies of the complexes range from -7.37 to -26.39 kcal/mol. The major contribution to the molecular interaction comes from van der Waals forces, and Pro295 and Arg316 become the key residues involved in most complexes. Two QSAR models were further developed to predict the rAhR activity of PACs (in terms of log IEQ for PACs without halogen substitutions and log%-TCDD-max for halogenated PACs). Both models have good predictive ability, robustness, and extrapolation ability. Molecular polarizability, electronegativity, size, and nucleophilicity are identified as the important factors affecting the rAhR activity of PACs. The developed models could be employed to predict the rAhR activity of other reactive PACs. This work provides insight into the mechanisms and interactions of the rAhR activity of PACs and assists in the assessment of their fate and risk in organisms.

Application of passive sampling device for exploring the occurrence, distribution, and risk of pharmaceuticals and pesticides in surface water

Pharmaceuticals and pesticides are compounds of high concern in surface waters around the world. However, few studies have used passive sampling methods to screen and detect these compounds in natural waters. In this study, a self-developed passive sampler was employed to measure pharmaceuticals and pesticides in the rivers of Nanjing, China. A total of 41 pharmaceuticals and 11 pesticides were detected, among which antibiotic and insecticide were the predominant classes, respectively. Valproic acid, caffeine and triclosan from the pharmaceuticals, and isoprocarb and imidacloprid from the pesticides were found frequently with high concentrations. At most sampling sites, the concentration ratios of caffeine versus carbamazepine exceeded 10, and even above 50, indicating relatively poor efficiency of wastewater treatment, or possibly the direct discharge of raw sewage, or other unknown source of pollution. It was found that the concentrations and ecological risks in the northern area of Yangtze River were higher than those in the southern area of Yangtze River, implying that economic development and population density were not the main contributors to the discovered pollution. The total concentration of pharmaceuticals and pesticides in Qinhuai River increased gradually with the direction of water flow, demonstrating the success of water diversion project in flushing and scouring pollutants.

Assessing the bioavailability of antibiotics in soil with the diffusive gradients in thin films (DGT)

The diffusive gradients in thin films (DGT) technique is an excellent method for investigating the dynamic processes of antibiotics in soils. However, whether it is applicable in antibiotic bioavailability assessment is yet to be disclosed. This study employed DGT to determine the antibiotic bioavailability in soil, and compared the results with plant uptake, soil solutions, and solvent extraction methods. DGT exhibited predictive capability for plant taking in antibiotics proved by the significant linear relationship between the DGT based concentration (C_DGT) and antibiotic concentration in roots and shoots. Although the performance of soil solution was acceptable based on linear relationship analysis, its stability was weaker than DGT. The results based on plant uptake and DGT indicated the bioavailable antibiotic contents in different soils were inconsistent because of the distinct mobility and resupply of sulphonamides and trimethoprim in different soils, as represented by K_d and R_ds, which were affected by soil properties. Plant species played an important role in antibiotic uptake and translocation. Antibiotic uptake by plants depends on antibiotic, plant and soil. These results confirmed the capability of DGT in determining antibiotic bioavailability for the first time. This work provided a simple and powerful tool for environmental risk evaluation of antibiotics in soils.

Versatile in silico modelling of microplastics adsorption capacity in aqueous environment based on molecular descriptor and machine learning

To comprehensively evaluate the hazards of microplastics and their coexisting organic pollutants, the sorption capacity of microplastics is a major issue that is quantified through the microplastic-aqueous sorption coefficient (K_d). Almost all quantitative structure-property relationship (QSPR) models that describe K_d apply only to narrow, relatively homogeneous groups of reactants. Herein, non-hybrid QSPR-based models were developed to predict PE-water (K_PE-w), PE-seawater (K_PE-sw), PVC-water (K_PVC-w) and PP-seawater (K_PP-sw) sorption coefficients at different temperatures, with eight machine learning algorithms. Moreover, novel hybrid intelligent models for predicting K_d more accurately were innovatively developed by applying GA, PSO and AdaBoost algorithms to optimize MLP and ELM models. The results indicated that all three optimization algorithms could improve the robustness and predictability of the standalone MLP and ELM models. In all models trained with K_PE-w, K_PE-sw, K_PVC-w and K_PP-sw data sets, GBDT-1 and XGBoost-1 models, MLP-GA-2 and MLP-PSO-2 models, MLR-3 and MLR-4 models performed better in terms of goodness of fit (R_adj²: 0.907-0.999), robustness (Q_BOOT²: 0.900-0.937) and predictability (R_ext²: 0.889-0.970), respectively. Analyzing the descriptors revealed that temperature, lipophilicity, ionization potential and molecular size were correlated closely with the adsorption capacity of microplastics to organic pollutants. The proposed QSPR models may assist in initial environmental exposure assessments without imposing heavy costs in the early experimental phase.

Predictive Models of Gas/Particulate Partition Coefficients (KP) for Polycyclic Aromatic Hydrocarbons and Their Oxygen/Nitrogen Derivatives

Polycyclic aromatic hydrocarbons (PAHs) and their oxygen/nitrogen derivatives released into the atmosphere can alternate between a gas phase and a particulate phase, further affecting their environmental behavior and fate. The gas/particulate partition coefficient (KP) is generally used to characterize such partitioning equilibrium. In this study, the correlation between log KP of fifty PAH derivatives and their n-octanol/air partition coefficient (log KOA) was first analyzed, yielding a strong linear correlation (R2 = 0.801). Then, Gaussian 09 software was used to calculate quantum chemical descriptors of all chemicals at M062X/6-311+G (d,p) level. Both stepwise multiple linear regression (MLR) and support vector machine (SVM) methods were used to develop the quantitative structure-property relationship (QSPR) prediction models of log KP. They yield better statistical performance (R2 > 0.847, RMSE < 0.584) than the log KOA model. Simulation external validation and cross validation were further used to characterize the fitting performance, predictive ability, and robustness of the models. The mechanism analysis shows intermolecular dispersion interaction and hydrogen bonding as the main factors to dominate the distribution of PAH derivatives between the gas phase and particulate phase. The developed models can be used to predict log KP values of other PAH derivatives in the application domain, providing basic data for their ecological risk assessment.

Desorption kinetics of antipsychotic drugs from sandy sediments by diffusive gradients in thin-films technique

Dynamic processes of organic contaminants in sediments can have important toxicological implications in aquatic systems. The current study used diffusive gradients in thin-films (DGT) devices in sandy sediments spiked with nine antipsychotics and in field sandy sediments. Samplers were deployed for 1 to 30 days to determine the flux of these compounds to DGT devices and the exchange rates between the porewater and sediment solid phase. The results showed a continuous removal of antipsychotics to a binding gel and induced a mobile flux from the DGT device to the adjacent sediment solution. A dynamic model, DGT-induced fluxes in soils and sediments, was used to derive rate constants of resupply of antipsychotics from solid phase to aqueous phase (response time, T_c) and distribution coefficients for labile antipsychotics. The largest labile pool was found for lamotrigine and carbamazepine in spiked sediments. Carbamazepine, clozapine, citalopram, and lamotrigine were resupplied rapidly by sediments with T_c (25-30 min). T_c values of bupropion and amitriptyline were the longest (≈5 h), which exhibited slow desorption rates in sediments. In field sediments, high resupply was found for carbamazepine and lamotrigine, which did not show higher labile pool. The T_c values were obviously higher in the filed sediments (52-171 h). Although the adsorption process is dominant for most studied antipsychotics in both spiked sediments and field sediments, the kinetic resupply of antipsychotic compounds may not be accurately estimated by laboratory-controlled incubation experiments. More studies are needed to explore the mechanisms of desorption kinetics by using in situ DGT technique in the field.

Determination of diffusion coefficients in agarose and polyacrylamide gels for 112 organic chemicals for passive sampling by organic Diffusive Gradients in Thin films (o-DGT)

The diffusive gradient in thin film technique was recently adapted to organic compounds. The diffusional coefficient (D) is a key parameter needed to calculate the time-weighted average concentration. In this study, two methods are used for D measurement in two gels (agarose and polyacrylamide): the diffusion cell method (D_cell) and the slice stacking method (D_stack). Thus, D were discussed and compared for 112 organic compounds, including pesticides, hormones, and pharmaceuticals. D_stack tends to be higher than D_cell. It could be explained by the presence of a non-negligible diffusive boundary layer thickness in diffusion cell. Consequently, the use of sampling rates (R_S) should be more adequate to determine water concentration, for a given bulk flow velocity. D_stack also corresponds to the diffusion in gel only, allowing the determination of the maximal R_S, and would be considered as a reference value that can be adjusted to in situ conditions, by applying the appropriate DBL thickness. The range and variability of D values found in the literature and obtained in this work were discussed. Relationships between D and compound physicochemical properties (molecular mass, log Dow, polar surface area, van der Waals volume) were investigated. We did not find clear and robust correlation between D and any single physicochemical property, for the set of compounds tested.

Development and field evaluation of the organic-diffusive gradients in thin-films (o-DGT) passive water sampler for microcystins

The presence of microcystins (MCs) in waterbodies requires a simple and reliable monitoring technique to characterize better their spatiotemporal distribution and ecological risks. An organic-diffusive gradients in thin films (o-DGT) passive sampler based on polyacrylamide diffusive gel and hydrophilic-lipophilic balance (HLB) binding gel was developed for MCs in water. The mass accumulation of three MCs (MC-LR, -RR, and -YR) was linear over 10 days (R² ≥ 0.98). Sampling rates (2.68-3.22 mL d^-1) and diffusion coefficients (0.90-1.08 × 10^-6 cm² s^-1) of three MCs were obtained at 20 °C. Two different passive samplers, o-DGT and the Solid Phase Adsorption Toxin Tracking device (SPATT), were co-deployed to estimate MC levels at three lakes in California, USA. Measured total MC concentrations were up to 10.9 μg L^-1, with MC-LR the primary variant at a measured maximum concentration of 2.74 μg L^-1. Time-weighted average MC concentrations by o-DGT were lower than grab water samples, probably because grab sampling measures both dissolved and particulate phases (i.e., MCs in cyanobacteria). Passive water samplers by design can only measure dissolved-phase MCs, which are considerably less during the cyanobacteria-laden periods observed. Both o-DGT and grab samples gave comparable results for three MC variants at low levels of MCs, e.g., <0.1 μg L^-1. o-DGT showed a higher correlation with grab sampling than SPATT did. This study demonstrates that o-DGT can be effectively used for monitoring and evaluation of dissolved MCs in waters.

Improved structural annotation of triterpene metabolites of traditional Chinese medicine in vivo based on quantitative structure-retention relationships combined with characteristic ions: Alismatis Rhizoma as an example

As a fast, sensitive and selective method, liquid chromatography-tandem high-resolution mass spectrometry (LC-HRMS) has been used for studying the in vivo metabolism of traditional Chinese medicine (TCM). However, the rapid discovery and characterization of metabolites, especially isomers, remain challenging due to their complexity and low concentration in vivo. This study proposed a strategy to improve the structural annotation of prototypes and metabolites through characteristic ions and a quantitative structure-retention relationship (QSRR) model, and Alismatis Rhizoma (AR) triterpenes were used as an example. This strategy consists of four steps. First, based on an in-house database reported previously, prototypes and metabolites in biosamples were preliminarily identified. Second, the candidate structures of prototype compounds and metabolites were determined by characteristic ions, databases or potential metabolic pathways. Then, a QSRR model was established to predict the retention times of the proposed structure. Finally, the structures of unknown prototypes and metabolites were determined by matching experimental retention times with the predicted values. The QSRR model built by the genetic algorithm-multiple linear regression (GA-MLR) has excellent regression correlation (R² = 0.9966). Based on this strategy, a total of 118 compounds were identified, including 47 prototypes and 71 metabolites, among which 61 unknown compounds were reasonably characterized. The typical compound identified by this strategy was successfully validated using a triterpene standard. This strategy can improve the annotation confidence of in vivo metabolites of TCM and facilitate further pharmacological research.

Selective diffusive gradients in thin-films with molecularly imprinted polymer for measuring fluoroquinolone antibiotics in waters

Fluoroquinolones (FQs) have raised significant concerns due to their ubiquitous occurrence and promoting of antimicrobial resistance (AMR). In this study, a molecularly imprinted polymer-diffusive gradient in thin-films (MIP-DGT) sampler is developed for selective measurement of FQs in waters by using a commercial available MIP material as the binding agent. The MIP-DGT shows selective adsorption of the FQs and linearly accumulates the FQs over the deployment time. MIP-DGT measurement is independent of pH (6-8) and ionic strength (IS) (0.01-0.5 M) but is affected by DOM at higher concentrations (~10 mg•L^-1), which is due to the altered diffusion coefficients and reduced adsorption on the MIP binding gel. Significant interaction effects of DOM with pH or IS indicate that this is the predominant influence on the MIP-DGT performance, which results in a lower measurement by the MIP-DGT but this is curtailed to some extend with increasing IS or pH. The MIP-DGT measurements, however, correlate well with those by grab sampling in a wastewater treatment plant, suggesting it is reliable for measuring FQs in waters. For the first time, we demonstrate that key water chemistry parameters do have interaction effects on the DGT measurements, which should be considered for the data interpretation. The MIP-DGT is a promising tool to understand the interaction effects of the environmental parameters on the fate, behaviours and bioavailability/toxicity of organic contaminants and improve environmental risk assessments in the environment and modelling.

Development and application of diffusive gradients in thin-films for in situ sampling of the bitterest chemical - denatonium benzoate in waters

Denatonium benzoate (DB), a commonly used bitter agent in numerous products, has recently been recognized as a waterborne contaminant due to concern about its potential persistence, mobility and toxicity (PMT). However, its occurrence, levels and fate in global aquatic environments are largely unknown. In this study, a new sampling method, based on diffusive gradients in thin films (DGT) with mixed-mode cation exchange (MCX) as the binding agent, was developed for measuring DB in waters. MCX shows a rapid adsorption and high capacity for DB. DB is linearly accumulated by MCX-DGT. pH (6-8), ionic strength (0.01-0.5 M), or DOM (0-10 M) do not show any significant effect on the MCX-DGT performance, confirming its reliability. The DGT measurements in a wastewater treatment plant (WWTP) are comparable to those by paralleled grab sampling. The field results suggest DB is persistent in WWTPs and could be a potential domestic wastewater indicator. Therefore, MCX-DGT is a promising technique for understanding the environmental occurrence, levels and fate of DB. This is a first report of using DGT for DB monitoring and of DB occurrence in Chinese environments. Further exploration of DGT as a reliable passive monitoring tool for a wide range of PMT substances in different applications is warranted.

In silico prediction of polyethylene-aqueous and air partition coefficients of organic contaminants using linear and nonlinear approaches

Low-density polyethylene (LDPE) passive sampling is very attractive for use in determining chemicals concentrations. Crucial to the measurement is the coefficient (K_PE) describing partitioning between LDPE and environmental matrices. 255, 117 and 190 compounds were collected for the development of datasets in three different matrices, i.e., water, air and seawater, respectively. Further, 3 pp-LFER models and 9 QSPR models based on classical multiple linear regression (MLR) coupled with prevalent nonlinear algorithms (artificial neural network, ANN and support vector machine, SVM) were performed to predict LDPE-water (K_PE-W), LDPE-air (K_PE-A) and LDPE-seawater (K_PE-SW) partition coefficients. These developed models have satisfying predictability (R²_adj: 0.805-0.966, 0.963-0.991 and 0.817-0.941; RMSE_tra: 0.233-0.565, 0.200-0.406 and 0.260-0.459) and robustness (Q²_ext: 0.840-0.943, 0.968-0.984 and 0.797-0.842; RMSE_ext: 0.308-0.514, 0.299-0.426 and 0.407-0.462) in three datasets (water, air and seawater), respectively. In particular, the reasonable mechanism interpretations revealed that the molecular size, hydrophobicity, polarizability, ionization potential, and molecular stability were the most relevant properties, for governing chemicals partitioning between LDPE and environmental matrices. The application domains (ADs) assessed here exhibited the satisfactory applicability. As such, the derived models can act as intelligent tools to predict unknown K_PE values and fill the experimental gaps, which was further beneficial for the construction of enormous and reliable database to facilitate a distinct understanding of the distribution for organic contaminants in total environment.

Limitations of Integrative Passive Samplers as a Tool for the Quantification of Pharmaceuticals in the Environment - A Critical Review with the Latest Innovations

This review starts with a presentation of the theory of kinetic uptake by passive sampling (PS), which is traditionally used to distinguish between integrative and equilibrium samplers. Demonstrated limitations of this model for the passive sampling of pharmaceuticals from water were presented. Most notably, the contribution of the protective membrane in the resistance to mass transfer of lipophilic analytes and the well documented effect of external parameters on sampling rates contributed to the greatest uncertainty in PS application. The diffusion gradient in thin layer (DGT) technique seems to reduce the effect of external parameters (e.g., flow rate) to some degree. The laboratory-determined integrative uptake periods over defined sampler deployments was compared, and the discrepancy found suggests that the most popular Polar Organic Chemical Integrative Sampler (POCIS) could in some cases utilized as an equilibrium sampler. This assertion is supported by own calculations for three pharmaceuticals with extremely different lipophilic characters. Finally, the reasons performance reference compounds (PRCs) are not recommended for the reduction in uncertainty of the TWAC found by adsorptive samplers were presented. It was concluded that techniques of passive sampling of pharmaceuticals need a new uptake model to fit the current situation.