A review of quantitative structure-property relationships for the fate of ionizable organic chemicals in water matrices and identification of knowledge gaps

Meta-Analysis and Machine Learning Models for Anaerobic Biodegradation Rates of Organic Contaminants in Sediments and Sludge

Anaerobic biodegradation rates (half-lives) of organic chemicals are pivotal for environmental risk assessment and remediation. Traditional experimental evaluation, constrained by prolonged, oxygen-free conditions, struggles to keep pace with emerging contaminants. Data-driven machine learning (ML) models serve as promising complements. However, reported quantitative structure-biodegradation relationships or ML models on anaerobic biodegradation are mostly based on small data sets (<100 records) and neglect experimental conditions, usually achieving compromised predictions. This work aimed to develop ML models for predicting the biodegradation half-lives of organic pollutants in anaerobic environments (i.e., sediment/soil and sludge). Focusing on important features of both chemicals and experimental conditions, we first curated two data sets, one for sediment/soil (SED) and the other for sludge (SLD), covering 978 records for 206 chemicals from the literature, and then conducted a meta-analysis. Next, we built a binary classification (half-life of 30 days as the cutoff) model with an accuracy of 81% and a regression model with R2 of 0.56 for SED based on LightGBM (80% and 0.31 for SLD based on Extra tree, respectively). The model interpretations underscored the significance of experimental conditions (e.g., temperature and inoculum dosage), as evidenced by their high feature importance, and the models were found to correctly capture the effects of chemical substructures, for example, branched structures and aromatic rings prolonged half-lives while methyl group and ortho-substitution on rings shortened half-lives. The applicability domains of the models were also defined, resulting in reasonable prediction for the half-lives of 41% (SED) or 67% (SLD) of over 4000 persistent, bioaccumulative, and toxic chemicals. Overall, this study pioneers ML models for predicting the anaerobic degradation half-lives, offering valuable support for future evaluation and implementation of chemical anaerobic biodegradation.

300-fold higher neuro- and immunotoxicity from low-redox transformation of carbamazepine

Current challenges in (eco)toxicology are in understanding the transformation of (reactive) substances, and how transformation affects toxic modes of action. Empirical assessment of transformation products of, practically an infinite number of substances, via experimentation, is impossible. Predicting transformation products for (benchmarking) compounds from conditions, facilitates risk analyses. This study applied calculus to predict transformation products of an important environmental and medicinal/toxicological marker, carbamazepine. As radicals are ubiquitous in humans and the environment, we looked into radical-mediated transformations of carbamazepine as a benchmark. We calculated proportions of their speciation states as function of redox conditions, which we took as pH and O2 concentration, describing transformation via covalent and ionic interactions. Formation of ring-contracted products with neuro-immunological activity is thermodynamically favored under anaerobic conditions and at low pH. Experimentally observed product distributions and toxicities reflect that pattern. Our predictive method may support toxicity predictions for other substances and conditions 'similar' to the current case study via interpolation. This paves the way for a more coherent, effective and easier risk assessment of transformation products.

Modelling and prediction of the effect of operational parameters on the fate of contaminants of emerging concern in WWTPs

Wastewater treatment plants (WWTPs) provide a barrier against the discharge of contaminants of emerging concern (CECs) into the environment. The removal of CECs is highly WWTP-specific and the underlying mechanisms are still poorly understood, hampering the optimization of biological treatment steps for their removal. To fill this knowledge gap, we assessed the influence of four operational parameters of activated sludge biological treatment, namely total suspended solids, temperature, pH and redox conditions, on the sorption and biodegradation of four CECs under controlled laboratory conditions. Design of Experiments was used to better address the factors influencing CECs removal and interactions among operational parameters. The derived statistical models showed results in concordance with previous studies and indicated how sorption and biodegradation of the investigated CECs depend on most tested parameters and few of their interactions. The predictions of the developed models have been compared with literature values, indicating how the tested parameters are responsible for most of the variability of sorption, while they could not reliably generalize biodegradation rates. The developed models were also implemented as an extension of a mechanistic biological treatment model, successfully describing the dynamic behaviour of a large-scale WWTP, which was observed during a three-day continuous monitoring campaign. Compared to a traditional modelling approach, the one including the developed models showed on average almost a three-fold uncertainty reduction, favouring its use to aid WWTP managers and regulators for improved assessment of CEC fate and removal. Finally, the models highlighted that, while higher temperatures and solids concentrations generically favoured CECs removal, removal efficiency vary significantly due to operational parameters and no globally optimum conditions for CECs removal exist. The use of these models opens the door to the combined dynamic management of both traditional contaminants and CECs in WWTPs.

The role of the sewer system in estimating urban emissions of chemicals of emerging concern

The use of chemicals by society has resulted in calls for more effective control of their emissions. Many of these chemicals are poorly characterized because of lacking data on their use, environmental fate and toxicity, as well as lacking detection techniques. These compounds are sometimes referred to as contaminants of emerging concern (CECs). Urban areas are an important source of CECs, where these are typically first collected in sewer systems and then discharged into the environment after being treated in a wastewater treatment plant. A combination of emission estimation techniques and environmental fate models can support the early identification and management of CEC-related environmental problems. However, scientific insight in the processes driving the fate of CECs in sewer systems is limited and scattered. Biotransformation, sorption and ion-trapping can decrease CEC loads, whereas enzymatic deconjugation of conjugated metabolites can increase CEC loads as metabolites are back-transformed into their parent respective compounds. These fate processes need to be considered when estimating CEC emissions. This literature review collates the fragmented knowledge and data on in-sewer fate of CECs to develop practical guidelines for water managers on how to deal with in-sewer fate of CECs and highlights future research needs. It was assessed to what extent empirical data is in-line with text-book knowledge and integrated sewer modelling approaches. Experimental half-lives (n = 277) of 96 organic CECs were collected from literature. The findings of this literature review can be used to support environmental modelling efforts and to optimize monitoring campaigns, including field studies in the context of wastewater-based epidemiology.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11157-022-09638-9.

Direct Spectroscopic Evidence for Charge-Assisted Hydrogen-Bond Formation between Ionizable Organic Chemicals and Carbonaceous Materials

The direct evidence for the formation of charge-assisted hydrogen bond (CAHB) between the charged groups of ionizable organic chemicals (IOCs) and carbonaceous materials with similar proton affinity remains elusive. We therefore selected three pharmaceutical contaminants (PCs) as representative IOCs to provide the direct evidence of CAHB formation between IOCs and functionalized carbon nanotubes (CNTs) and its intensity/contribution to PC sorption on CNTs by NMR, FTIR, and DFT analyses. Sorption of PCs on functionalized CNTs resulted in the FTIR characteristic peak that appeared at a higher frequency (3787 cm^-1) and the ¹H NMR characteristic peak that emerged at an extremely low-field region (<18.0 ppm), which can be used as the direct spectroscopic evidence for CAHB formation. Both homonuclear CAHB (HM-CAHB, e.g., [O-H···O]^-) and heteronuclear CAHB (HT-CAHB, e.g., [N⁺-H···O^-]/[O-H···N]⁺) exhibited a much higher sorption energy (|E_ads| ≥ 56.24 kJ/mol) than ordinary hydrogen bond (OHB, |E_ads| ≤ 6.136 kJ/mol), leading to a greater sorption contribution (HM-/HT-CAHB ≥ 42.3%, OHB ≤ 36.5%) and irreversibility (hysteresis index: HM-/HT-CAHB ≥ 1.69, OHB ≤ 0.43) of PCs on CNTs. This work presents the direct evidence for CAHB formation between IOCs and CNTs, which is significant for understanding and predicting the environmental fate and risk of IOCs, thus providing new insights for controlling their pollution using specifically designed carbonaceous materials.

Quantitative structure-property relationships for the calculation of the soil adsorption coefficient using machine learning algorithms with calculated chemical properties from open-source software

The soil adsorption coefficient (K_oc) is an environmental fate parameter that is essential for environmental risk assessment. However, obtaining K_oc requires a significant amount of time and enormous expenditure. Thus, it is necessary to efficiently estimate K_oc in the early stages of a chemical's development. In this study, a quantitative structure-property relationship (QSPR) model was developed using calculated physicochemical properties and molecular descriptors with the OPEn structure-activity/property Relationship App (OPERA) and Mordred software using the largest available K_oc dataset. Specifically, we compared the accuracies of the model using the light gradient boosted machine (LightGBM), a gradient boosting decision tree (GBDT) algorithm, with those of previous models. The experimental results suggested the potential to develop a QSPR model that will produce highly accurate K_oc values using molecular descriptors and physicochemical properties. Unlike previous studies, the use of a combination of LightGBM, OPERA and Mordred enables the prediction of K_oc for many chemicals with high accuracy. In this study, OPERA was used to calculate the physicochemical properties, and Mordred was used to calculate molecular descriptors. The wide range of chemicals covered by OPERA and Mordred enables the analysis of a diverse range of chemical compounds. We also report a method to tune the LightBGM program. The use of fast-processing software, such as LightGBM, enables parameter tuning of a method required to obtain best performance. Our research represents one of the few studies in the field of environmental chemistry to use LightGBM. Using physicochemical properties as well as molecular descriptors, we could develop highly accurate K_oc prediction models when compared to prior studies. In addition, our QSPR models may be useful for preliminary environmental risk assessment without incurring significant costs during the early chemical developmental stage.

Bioconcentration of Organotin Cations during Molting Inhibits Heterocypris incongruens Growth

The densely populated North Sea region encompasses catchments of rivers such as Scheldt and Meuse. Herein, agricultural, industrial, and household chemicals are emitted, transported by water, and deposited in sediments, posing ecological risks. Though sediment monitoring is often costly and time-intensive, modeling its toxicity to biota has received little attention. Due to high complexity of interacting variables that induce overall toxicity, monitoring data only sporadically validates current models. Via a range of concepts, we related bio-physicochemical constituents of sediment in Flanders to results from toxicity bioassays performed on the ostracod Heterocypris incongruens. Depending on the water body, we explain up to 90% of the variance in H. incongruens growth. Though variable across Flanders' main water bodies, organotin cations and ammonia dominate the observed toxicity according to toxic unit (TU) assessments. Approximately 10% relates to testing conditions/setups, species variabilities, incoherently documented pollutant concentrations, and/or bio-physicochemical sediment properties. We elucidated the influence of organotin cations and ammonia relative to other metal(oxides) and biocides. Surprisingly, the tributylin cation appeared ∼1000 times more toxic to H. incongruens as compared to "single-substance" bioassays for similar species. We inferred indirect mixture effects between organotin, ammonia, and phosphate. Via chemical speciation calculations, we observed strong physicochemical and biological interactions between phosphate and organotin cations. These interactions enhance bioconcentration and explain the elevated toxicity of organotin cations. Our study aids water managers and policy makers to interpret monitoring data on a mechanistic basis. As sampled sediments differ, future modeling requires more emphasis on characterizing and parametrizing the interactions between bioassay constituents. We envision that this will aid in bridging the gap between testing in the laboratory and field observations.

QSAR models for the acute toxicity of 1,2,4-triazole fungicides to zebrafish (Danio rerio) embryos

In recent decades, the 1,2,4-triazole fungicides are widely used for crop diseases control, and their toxicity to wild lives and pollution to ecosystem have attracted more and more attention. However, how to quickly and efficiently evaluate the toxicity of these compounds to environmental organisms is still a challenge. In silico method, such like Quantitative Structure-Activity Relationship (QSAR), provides a good alternative to evaluate the environmental toxicity of a large number of chemicals. At the present study, the acute toxicity of 23 1,2,4-triazole fungicides to zebrafish (Danio rerio) embryos was firstly tested, and the LC₅₀ (median lethal concentration) values were used as the bio-activity endpoint to conduct QSAR modelling for these triazoles. After the comparative study of several QSAR models, the 2D-QSAR model was finally constructed using the stepwise multiple linear regression algorithm combining with two physicochemical parameters (logD and μ), an electronic parameter (Q_N1) and a topological parameter (X^vPC₄). The optimal model could be mathematically described as following: pLC₅₀ = -7.24-0.30X^vPC₄ + 0.76logD - 26.15Q_N1 - 0.08μ. The internal validation by leave-one-out (LOO) cross-validation showed that the R²_adj (adjusted noncross-validation squared correlation coefficient), Q² (cross-validation correlation coefficient) and RMSD (root-mean-square error) was 0.88, 0.84 and 0.17, respectively. The external validation indicated the model had a robust predictability with the q² (predictive squared correlation coefficient) of 0.90 when eliminated tricyclazole. The present study provided a potential tool for predicting the acute toxicity of new 1,2,4-triazole fungicides which contained an independent triazole ring group in their molecules to zebrafish embryos, and also provided a reference for the development of more environmentally-friendly 1,2,4-triazole pesticides in the future.

Quantitative structure-activity relationship models for predicting singlet oxygen reaction rate constants of dissociating organic compounds

As singlet oxygen (¹O₂) is ubiquitous in the environment, ¹O₂-involved oxidation may play an important role in the transformation and fate of organic pollutants. Accordingly, the reaction rate constants (k_1O2) of organic compounds with ¹O₂ are important to determine the environmental fate and persistence assessment of organic pollutants. However, currently there are limited k_1O2 data available, especially for organic chemicals with different charged (deprotonated/protonated) forms. Herein three quantitative structure-activity relationship (QSAR) models (one comprehensive model and two models for neutral and deprotonated molecules) were created for predicting aqueous k_1O2 values for diversely dissociating molecules. The models include larger datasets (180 chemicals) and have wider applicability domain than previous ones. Molecular structural characteristics (only half-wave potential is present in both models) determining the ¹O₂ reaction rate of neutral and deprotonated molecules vary greatly. The comparison results of predicting k_1O2 values of organic compounds at certain pH conditions show that the combination of the QSAR models for neutral and deprotonated molecules has advantages over the comprehensive QSAR model. This work is the first study to predict k_1O2 for a wide variety of neutral and deprotonated molecules and provides an important tool for assessing the fate of organic pollutants in aquatic environments.

Prediction of Soil Adsorption Coefficient in Pesticides Using Physicochemical Properties and Molecular Descriptors by Machine Learning Models

The soil adsorption coefficient (K_OC ) plays an important role in environmental risk assessment of pesticide registration. Based on this risk assessment, applied and registered pesticides can be allowed in the European Union. Almost 1 yr is required to study and obtain the K_OC value of a pesticide. Furthermore, acquiring the K_OC requires a large cost. It is necessary to efficiently estimate the K_OC value in the early stages of pesticide development. In the present study, the experimental values of physicochemical properties and molecular descriptors of chemical structures were collected to develop a quantitative structure-property relationship (QSPR) model, and the prediction performance of the model was evaluated. More specifically, we compared the accuracies of models based on a gradient boosting decision tree, multiple linear regression, and support vector machine. The experimental results suggest that it is possible to develop a QSPR model with high accuracy using both the molecular descriptors calculated from the structural formula and experimental values of physicochemical properties from open literature and databases. Comparing to the previously established models, we achieved high prediction accuracy, fitness, and robustness by only using freeware. Therefore, our developed QSPR models can be useful preliminary risk assessment in the early developmental stages of pesticides. Environ Toxicol Chem 2020;39:1451-1459. © 2020 SETAC.

QSAR modeling for the ozonation of diverse organic compounds in water

The ozonation-based advanced oxidation process is a promising treatment technology for wastewater with micropollutants. The second-order reaction rate constant (k_O3) of ozone (O₃) with organic compounds is an important index for estimating removal efficiency of organic pollutants in engineered treatment; however, the experimental k_O3 values are currently only available for hundreds of chemicals. In this study, two quantitative-structure activity relationship (QSAR) models were developed to predict k_O3 of various organic chemicals with multiple linear regression (MLR) and support vector machine (SVM) methods. The built QSAR models cover a large dataset (136 chemicals) and more structurally diverse chemicals as compared to the existing models. The MLR model possesses satisfactory goodness-of-fit (R²_tr = 0.734), robustness (Q²_LOO = 0.700, Q²_BOOT = 0.772) and predictive ability (R²_ext = 0.797, Q²_ext = 0.794), and the SVM model also has good fitness (R²_tr = 0.862) and predictability (R²_ext = 0.782, Q²_ext = 0.775). The applicability domain of the models has been extended and includes chemicals (especially some emerging pollutants) that are rarely covered in many previous models. The underlying molecular structural factors influencing ozonation are revealed. The energy of the highest occupied molecular orbital (E_HOMO) and the phenol/enol/carboxyl OH group (O-057) are the two most important molecular structural factors governing the reactivity of organic compounds with ozone. The developed models can serve as a prescreening tool for the removal prediction of organic pollutants by ozone.

Comparison of Small Molecule Biotransformation Half-Lives between Activated Sludge and Soil: Opportunities for Read-Across?

Compartment-specific degradation half-lives are essential pieces of information in the regulatory risk assessment of synthetic chemicals. However, their measurement according to regulatory testing guidelines is laborious and costly. Despite the obvious ecological and economic benefits of knowing environmental degradability as early as possible, its consideration in the early phases of rational chemical design is therefore challenging. Here, we explore the possibility to use half-lives determined in highly time- and work-efficient biotransformation experiments with activated sludge and mixtures of chemicals to predict soil half-lives from regulatory simulation studies. We experimentally determined half-lives for 52 structurally diverse agrochemical active ingredients in batch reactors with three concentrations of the same activated sludge. We then developed bi- and multivariate models for predicting half-lives in soil by regressing the experimentally determined half-lives in activated sludge against average soil half-lives of the same chemicals extracted from regulatory data. The models differed in how we accounted for sorption-related bioavailability differences in soil and activated sludge. The best-performing models exhibited good coefficients of determination (R² of around 0.8) and low average errors (<factor of 3 in half-life predictions) and were robust in cross-validation. From a practical perspective, these results suggest that it may indeed be possible to read across from half-lives determined in highly efficient biotransformation experiments in activated sludge to soil half-lives, which are obtained from much more work- and resource-intense regulatory studies, and that these predictions are clearly superior to predictions based on the output of BIOWIN, a publicly available quantitative structure-biodegradation relationship (QSBR) model. From a theoretical perspective, these results suggest that soil and activated sludge microbial communities, although certainly different in terms of taxonomic composition, may be functionally similar with respect to the enzymatic transformation of environmentally relevant concentrations of a diverse range of chemical compounds.

Disentanglement of the chemical, physical, and biological processes aids the development of quantitative structure-biodegradation relationships for aerobic wastewater treatment

Attenuation of organic compounds in sewage treatment plants (STPs) is affected by a complex interplay between chemical (e.g. ionization, hydrolysis), physical (e.g. sorption, volatilization), and biological (e.g. biodegradation, microbial acclimation) processes. These effects should be accounted for individually, in order to develop predictive cheminformatics tools for STPs. Using measured data from 70 STPs in the Netherlands for 69 chemicals (pharmaceuticals, herbicides, etc.), we highlighted the influences of 1) chemical ionization, 2) sorption to sludge, and 3) acclimation of the microbial consortia on the primary removal of chemicals. We used semi-empirical corrections for each of these influences to deduce biodegradation rate constants upon which quantitative structure-biodegradation relationships (QSBRs) were developed. As shown by a global QSBR, biodegradation in STPs generally relates to structural complexity, size, energetics, and charge distribution. Statistics of the global QSBR were reasonable, being R²_training=0.69 (training set of 51 compounds) and R²_validation=0.50 (validation set of 18 compounds). Class-specific QSBRs utilized electronic properties potentially relating to rate-limiting enzymatic steps. For class-specific QSBRs, values of R² of in between 0.7 and 0.8 were obtained. With caution, environmental risk assessment methodologies may apply these models to estimate biodegradation rates for 'data-poor' compounds. The approach also highlights 'meta data' on STP operational parameters needed to develop QSBRs of better predictability in the future.

Evaluation of Existing Models to Estimate Sorption Coefficients for Ionisable Pharmaceuticals in Soils and Sludge

In order to assess the environmental risk of a pharmaceutical, information is needed on the sorption of the compound to solids. Here we use a high-quality database of measured sorption coefficients, all determined following internationally recognised protocols, to evaluate models that have been proposed for estimating sorption of pharmaceuticals from chemical structure, some of which are already being used for environmental risk assessment and prioritization purposes. Our analyses demonstrate that octanol-water partition coefficient (K_ow) alone is not an effective predictor of ionisable pharmaceutical sorption in soils. Polyparameter models based on pharmaceutical characteristics in combination with key soil properties, such as cation exchange capacity, increase model complexity but yield an improvement in the predictive capability of soil sorption models. Nevertheless, as the models included in this analysis were only able to predict a maximum of 71% and 67% of the sorption coefficients for the compounds to within one log unit of the corresponding measured value in soils and sludge, respectively, there is a need for new models to be developed to better predict the sorption of ionisable pharmaceuticals in soil and sludge systems. The variation in sorption coefficients, even for a single pharmaceutical across different solid types, makes this an inherently difficult task, and therefore requires a broad understanding of both chemical and sorbent properties driving the sorption process.

Bioavailability of Antibiotics at Soil-Water Interfaces: A Comparison of Measured Activities and Equilibrium Partitioning Estimates

There are growing concerns that antibiotic pollution impacts environmental microbiota and facilitates the propagation of antibiotic resistance. However, the prediction or analytical determination of bioavailable concentrations of antibiotics in soil is still subject to great uncertainty. Biological assays are increasingly recognized as valuable complementary tools that allow a more direct determination of the residual antibiotic activity. This study assessed the bioavailability of structurally diverse antibiotics at a soil-water interface applying activity-based analyses in conjunction with equilibrium partitioning (EqP) modeling. The activity against Gram-positive and Gram-negative bacteria of nine antibiotics from different classes was determined in the presence and absence of standard soil (LUFA St. 2.2). The addition of soil affected the activity of different antibiotics to highly varying degrees. Moreover, a highly significant correlation ( p < 0.0001) between the experimentally observed and the EqP-derived log EC₅₀ (half-maximal effective concentration) values was observed. The innovative experimental design of this study provided new insights on the bioavailability of antibiotics at soil-water interfaces. EqP appears to be applicable to a broad range of antibiotics for the purpose of screening-level risk assessment. However, EqP estimates cannot replace soil-specific ecotoxicity testing in higher-tier assessments, since their accuracy is still compromised by a number of factors.

In silico environmental chemical science: properties and processes from statistical and computational modelling

Quantitative structure-activity relationships (QSARs) have long been used in the environmental sciences. More recently, molecular modeling and chemoinformatic methods have become widespread. These methods have the potential to expand and accelerate advances in environmental chemistry because they complement observational and experimental data with "in silico" results and analysis. The opportunities and challenges that arise at the intersection between statistical and theoretical in silico methods are most apparent in the context of properties that determine the environmental fate and effects of chemical contaminants (degradation rate constants, partition coefficients, toxicities, etc.). The main example of this is the calibration of QSARs using descriptor variable data calculated from molecular modeling, which can make QSARs more useful for predicting property data that are unavailable, but also can make them more powerful tools for diagnosis of fate determining pathways and mechanisms. Emerging opportunities for "in silico environmental chemical science" are to move beyond the calculation of specific chemical properties using statistical models and toward more fully in silico models, prediction of transformation pathways and products, incorporation of environmental factors into model predictions, integration of databases and predictive models into more comprehensive and efficient tools for exposure assessment, and extending the applicability of all the above from chemicals to biologicals and materials.