Machine-Learning-Based Prediction of Plant Cuticle-Air Partition Coefficients for Organic Pollutants: Revealing Mechanisms from a Molecular Structure Perspective.

Accurately predicting plant cuticle-air partition coefficients (Kca) is essential for assessing the ecological risk of organic pollutants and elucidating their partitioning mechanisms. The current work collected 255 measured Kca values from 25 plant species and 106 compounds (dataset (I)) and averaged them to establish a dataset (dataset (II)) containing Kca values for 106 compounds. Machine-learning algorithms (multiple linear regression (MLR), multi-layer perceptron (MLP), k-nearest neighbors (KNN), and gradient-boosting decision tree (GBDT)) were applied to develop eight QSPR models for predicting Kca. The results showed that the developed models had a high goodness of fit, as well as good robustness and predictive performance. The GBDT-2 model (Radj2 = 0.925, QLOO2 = 0.756, QBOOT2 = 0.864, Rext2 = 0.837, Qext2 = 0.811, and CCC = 0.891) is recommended as the best model for predicting Kca due to its superior performance. Moreover, interpreting the GBDT-1 and GBDT-2 models based on the Shapley additive explanations (SHAP) method elucidated how molecular properties, such as molecular size, polarizability, and molecular complexity, affected the capacity of plant cuticles to adsorb organic pollutants in the air. The satisfactory performance of the developed models suggests that they have the potential for extensive applications in guiding the environmental fate of organic pollutants and promoting the progress of eco-friendly and sustainable chemical engineering.

Optimal allocation of agricultural water and land resources integrated with virtual water trade: A perspective on spatial virtual water coordination.

Agricultural production consumes the majority of global freshwater resources. The worsening water scarcity has imposed significant stress on agricultural production when regions seek food self-sufficiency. To seek optimal allocation of spatial agricultural water and land resources in each water function zone of the objective region, a multi-objective optimization model was developed to tackle the trade-offs between the water-saving objective and the economic benefit objective considering virtual water trade (VWT). The cultivated area of each crop in each water function zone was taken into account as the decision variable, while a set of strong constraints were used to restrict land resources and water availability. Then, a decomposition-simplex method aggregation algorithm (DSMA) was proposed to solve this nonlinear, bounding-constrained, and multi-objective optimization model. Based on the quantitative analysis of the spatial blue and green virtual water in each agricultural product, the proposed methodology was applied to a real-world, provincial-scale region in China (i.e., Jiangsu Province). The optimized results provided 18 Pareto solutions to reallocate the land resources in the 21 IV-level water function zones of Jiangsu Province, considering four major rainy-season crops and two dry-season crops. Compared to the actual scenario, the superior scheme increased by 7.95% (5.6 × 109 RMB) for economic trade and decreased by 1.77% (2.0 × 109 m3) for agricultural water consumption. It was mainly because the potential of spatial blue and green virtual water in Jiangsu was fully exploited by improving spatial land resource allocation. The food security of Jiangsu could be guaranteed by achieving self-sufficiency in the superior scheme, and the total VWT in the optimal scheme was 2.2 times more than the actual scenario. The results provided a systematic decision-support methodology from the perspective of spatial virtual water coordination, yet, the methodology is widely applicable.

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 (KPE) 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 (KPE-W), LDPE-air (KPE-A) and LDPE-seawater (KPE-SW) partition coefficients. These developed models have satisfying predictability (R2adj: 0.805-0.966, 0.963-0.991 and 0.817-0.941; RMSEtra: 0.233-0.565, 0.200-0.406 and 0.260-0.459) and robustness (Q2ext: 0.840-0.943, 0.968-0.984 and 0.797-0.842; RMSEext: 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 KPE 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.

Development of pp-LFER and QSPR models for predicting the diffusion coefficients of hydrophobic organic compounds in LDPE.

Diffusion coefficient (D) is important to evaluate the performance of passive samplers and to monitor the concentration of chemicals effectively. Herein, we developed a polyparameter linear free energy relationship (pp-LFER) model and a quantitative structure-property relationship (QSPR) model for the prediction of diffusion coefficients of hydrophobic organic contaminants (HOCs) in low density polyethylene (LDPE). A dataset of 120 various chemicals was used to develop both models. The pp-LFER model was developed with two descriptors (V and E) and the statistical parameters of the model showed satisfactory results. As a further exploration of the diffusion behavior of the compounds, a QSPR model with five descriptors (ETA_Alpha, ASP-6, IC1, TDB6r and ATSC2v) was constructed with adjusted determination coefficient (R2) of 0.949 and cross-validation coefficient (QLoo2) of 0.941. The regression results indicated that both models had satisfactory goodness-of-fit and robustness. This study proves that pp-LFER and QSPR approaches are available for the prediction of log D values for the hydrophobic organic compounds within the applicability domain.

Prediction of polydimethylsiloxane-water partition coefficients based on the pp-LFER and QSAR models.

Obtaining accurate measurements of the partition coefficients between sorbent materials and water is of major importance for the analysis of the adsorption behavior and dissolved concentrations of organic compounds in the environment. In the passive-sampling approach, polydimethylsiloxane (PDMS) has a wide range of applications. Therefore, we established a poly-parameter linear-free energy relationship (pp-LFER) and a quantitative structure-activity relationship (QSAR) model to predict the log KPDMS-w values for a large dataset of 290 organic chemicals from 11 diverse classes. For the pp-LFER model, E (excess molar refractivity), A (molecular H-bond donor ability), V (McGowan volume), and B (the H-bond acceptor properties) were introduced as the main correlated variables. However, the obtained model is much limited in terms of acquiring available descriptors. For this reason, we developed a QSAR model, and CrippenLogP (Crippen octanol-water partition coefficient), RNCG (Relative negative charge-most negative charge/total negative charge), ATSC4e (Centered Broto-Moreau autocorrelation-lag4/weighted by Sanderson electronegativities) and GATS6p (Geary autocorrelation-lag6/weighted by polarizabilities) were selected as the significant parameters. The predictive power and functional reliability of the presented models were confirmed with validation methods as described in previous studies. The adjusted determination coefficients (R2adj) of 0.851 and 0.922 and leave-one-out cross-validated (Q2LOO) of 0.841 and 0.907 revealed that the models have good predictive power and generalizability. Thus, the proposed models are simple yet accurate tools for predicting the log KPDMS-w values and providing new insights to further understand the adsorption mechanism of organic compounds.

Development and evaluation of MTLSER and QSAR models for predicting polyethylene-water partition coefficients.

Current study was aimed to make further improvements in measuring low density polyethylene (LDPE) -water partition coefficient (KPE-w) for organic chemicals. Modified theoretical linear solvation energy relationship (MTLSER) model and quantitative structure activity relationship (QSAR) model were developed for predicting KPE-w values from chemical descriptors. With the MTLSER model, α (average molecular polarizability), µ (dipole moment) and q- (net charge of the most negative atoms) as significant variables were screened. With the QSAR model, main control factors of KPE-w values, such as CrippenLogP (Crippen octanol-water partition coefficient), CIC0 (neighborhood symmetry of 0-order) and GATS2p (Geary autocorrelation-lag2/weighted by polarizabilities) were studied. As per our best knowledge, this is the first attempt to predict polymer-water partition coefficient using the MTLSER model. Statistical parameters, correlation coefficient (R2) and cross-validation coefficients (Q2) were ranging from 0.811 to 0.951 and 0.761 to 0.949, respectively, which indicated that the models appropriately fit the results, and also showed robustness and predictive capacity. Mechanism interpretation suggested that the main factors governing the partition process between LDPE and water were the molecular polarizability and hydrophobicity. The results of this study provide an excellent tool for predicting log KPE-w values of most common hydrophobic organic compounds, within the applicability domains to reduce experimental cost and time for innovation.

Calibration and application of an automated seepage meter for monitoring water flow across the sediment-water interface.

The advective flow of sediment pore water is an important parameter for understanding natural geochemical processes within lake, river, wetland, and marine sediments and also for properly designing permeable remedial sediment caps placed over contaminated sediments. Automated heat pulse seepage meters can be used to measure the vertical component of sediment pore water flow (i.e., vertical Darcy velocity); however, little information on meter calibration as a function of ambient water temperature exists in the literature. As a result, a method with associated equations for calibrating a heat pulse seepage meter as a function of ambient water temperature is fully described in this paper. Results of meter calibration over the temperature range 7.5 to 21.2 °C indicate that errors in accuracy are significant if proper temperature-dependence calibration is not performed. The proposed calibration method allows for temperature corrections to be made automatically in the field at any ambient water temperature. The significance of these corrections is discussed.

Effect of saponin on the phytoextraction of Pb, Cd and Zn from soil using Italian ryegrass.

Chemically enhanced phytoextraction has been proposed as an effective approach to remove metals from contaminated soil through the Italian ryegrass (Lolium multiflorum Lam.). The bioconcentration factor (BCF) and translocation factor (TF) are important determinants for phytoextraction of metals. In microcosm experiments, effects of saponin on the uptake of Pb, Zn and Cd by Italian ryegrass were studied. Results of BCF indicated that Italian ryegrass was the most efficient in Zn uptake, followed by Cd and Pb (Zn > Cd > Pb). TF results were identical to the BCF results. In addition, the effect of metal stress on antioxidative enzyme activity was studied. Results revealed that under the metal stress, saponin played an important role in the antioxidative activities of Italian ryegrass.

Integrating molecular descriptors for enhanced prediction: Shedding light on the potential of pH to model hydrated electron reaction rates for organic compounds.

Hydrated electron reaction rate constant (ke-aq) is an important parameter to determine reductive degradation efficiency and to mitigate the ecological risk of organic compounds (OCs). However, OC species morphology and the concentration of hydrated electrons (e-aq) in water vary with pH, complicating OC fate assessment. This study introduced the environmental variable of pH, to develop models for ke-aq for 701 data points using 3 descriptor types: (i) molecular descriptors (MD), (ii) quantum chemical descriptors (QCD), and (iii) the combination of both (MD + QCD). Models were screened using 2 descriptor screening methods (MLR and RF) and 14 machine learning (ML) algorithms. The introduction of QCDs that characterized the electronic structure of OCs greatly improved the performance of models while ensuring the need for fewer descriptors. The optimal model MLR-XGBoost(MD + QCD), which included pH, achieved the most satisfactory prediction: R2tra = 0.988, Q2boot = 0.861, R2test = 0.875 and Q2test = 0.873. The mechanistic interpretation using the SHAP method further revealed that QCDs, polarizability, volume, and pH had a great influence on the reductive degradation of OCs by e-aq. Overall, the electrochemical parameters (QCDs, pH) related to the solvent and solute are of significance and should be considered in any future ML modeling that assesses the fate of OCs in aquatic environment.

Remediation of neonicotinoid-contaminated soils using peanut shell biochar and composted chicken manure: Transformation mechanisms of geochemical fractions.

Soil remediation techniques are promising approaches to relieve the adverse environmental impacts in soils caused by neonicotinoids application. This study systematically investigated the remediation mechanisms for peanut shell biochar (PSB) and composted chicken manure (CCM) on neonicotinoid-contaminated soils from the perspective of transformation of geochemical fractions by combining a 3-step sequential extraction procedure and non-steady state model. The neonicotinoid geochemical fractions were divided into labile, moderate-adsorbed, stable-adsorbed, bound, and degradable fractions. The PSB and CCM addition stimulated the neonicotinoid transformation in soils from labile fraction to moderate-adsorbed and stable-adsorbed fractions. Compared with unamended soils, the labile fractions decreased from 47.6% ± 11.8% of the initial concentrations to 12.1 ± 9.3% in PSB-amended soils, and 7.1 ± 4.9% in PSB and CCM-amended soils, while the proportions of moderate-adsorbed and stable-adsorbed fractions correspondingly increased by 1.8-2.4 times and 2.3-4.8 times, respectively. A small proportion (<4.8%) in bound fractions suggested there were rather limited bound-residues after 48 days incubation. The PSB stimulated the -NO2-containing neonicotinoid-degraders, which promoted the degradable fractions of corresponding neonicotinoids by 8.2 ± 6.3%. Degradable fraction of neonicotinoids was the dominant fate in soils, which accounted for 58.3 ± 16.7%. The findings made beneficial theoretical supplements and provided valuable empirical evidence for the remediation of neonicotinoid-contaminated soils.

Spatiotemporal dynamics and modeling of thiacloprid in paddy multimedia systems with the effect of wetting-drying cycles.

The widespread presence of thiacloprid (THI), a neonicotinoid, raises concerns for human health and the aquatic environment due to its persistence, toxicity, and bioaccumulation. The fate of THI in paddy multimedia systems is mainly governed by irrigation practices, but the potential impacts remain poorly documented. This study investigated the effects of water management practices on THI spatiotemporal dynamics in paddy multimedia systems by combining soil column experiments and a non-steady-state multimedia model. The results indicated the wetting-drying cycle (WDC) irrigation reduced THI occurrences in environmental phases (i.e., soil, interstitial water, and overlying water) and accelerated the THI loss through the THI aerobic degradation process. THI occurrences in the soil and water phases decreased from 18.8% for conventional flooding (CF) treatment to 9.2% for severe wetting-drying cycle (SW) treatment after 29 days, while the half-lives shortened from 11.1 days to 7.3 days, respectively. Meanwhile, the WDC decreased THI outflow from leakage water, which reduced the THI risk of leaching. There was no significant difference in THI plant uptake and volatilization between CF and WDC treatments. The mean proportions of THI fate in paddy multimedia systems followed the order: THI degradation (57.7%), outflow from leakage water (25.5%), occurrence in soil (12.4%), plant uptake (3.4%), occurrence in interstitial water (0.7%), occurrence in overlying water (0.3%), volatilization (<0.1%) after 29 days. The sensitivity analysis identified the soil organic carbon partition coefficient (KOC) as the most sensitive parameter affecting THI's fate. In addition, the topsoil layers of 0-4 cm were the main sink of THI, holding 67% of THI occurrence in the soil phase. The THI occurrence in interstitial water was distributed evenly throughout the soil profile. These findings made beneficial theoretical supplements and provided valuable empirical evidence for water management practices to reduce the THI ecological risk.

Multiple machine learning algorithms assisted QSPR models for aqueous solubility: Comprehensive assessment with CRITIC-TOPSIS.

As an essential environmental property, the aqueous solubility quantifies the hydrophobicity of a compound. It could be further utilized to evaluate the ecological risk and toxicity of organic pollutants. Concerned about the proliferation of organic contaminants in water and the associated technical burden, researchers have developed QSPR models to predict aqueous solubility. However, there are no standard procedures or best practices on how to comprehensively evaluate models. Hence, the CRITIC-TOPSIS comprehensive assessment method was first-ever proposed according to a variety of statistical parameters in the environmental model research field. 39 models based on 13 ML algorithms (belonged to 4 tribes) and 3 descriptor screening methods, were developed to calculate aqueous solubility values (log Kws) for organic chemicals reliably and verify the effectiveness of the comprehensive assessment method. The evaluations were carried out for exhibiting better predictive accuracy and external competitiveness of the MLR-1, XGB-1, DNN-1, and kNN-1 models in contrast to other prediction models in each tribe. Further, XGB model based on SRM (XGB-1, C = 0.599) was selected as an optimal pathway for prediction of aqueous solubility. We hope that the proposed comprehensive evaluation approach could act as a promising tool for selecting the optimum environmental property prediction methods.

A comprehensive evaluation of liposome/water partition coefficient prediction models based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method: Challenges from different descriptor dimension reduction methods and machine learning algorithms.

The liposome/water partition coefficient (Klip/w) is a key parameter to evaluate the bioaccumulation potential of pollutants. Considering that it is difficult to determine the Klip/w values of all pollutants through experiments, researchers gradually developed models to predict it. However, there is currently no research on how to comprehensively evaluate prediction models and recommend a compelling optimal modeling method. To remedy the defect of single parameters in a traditional model comparison, the TOPSIS evaluation method, based on entropy weight, was first proposed. We use this method to comprehensively evaluate models from multiple angles in this study. Thirty QSPR models, including 3 descriptor dimension reduction methods and 10 algorithms (belonging to 4 tribes), were used to predict Klip/w and verify the effectiveness of the comprehensive assessment method. The results showed that RF (descriptor dimension reduction method), symbolism (tribes) and RF (algorithm) exhibited significant advantages in establishing the Klip/w value prediction model. At present, the application of TOPSIS in environmental model evaluations is almost absent. We hope that the proposed TOPSIS evaluation method can be applied to more chemical datasets and provide a more systematic and comprehensive basis for the application of the QSPR model in environmental studies and other fields.

A new perspective on predicting the reaction rate constants of hydrated electrons for organic contaminants: Exploring molecular structure characterization methods and ambient conditions.

Hydrated electrons (eaq-) exhibit rapid degradation of diverse persistent organic contaminants (OCs) and hold great promise as a formidable reducing agent in water treatment. However, the diverse structures of compounds exert different influences on the second-order rate constant of hydrated electron reactions (keaq-), while the same OCs demonstrate notable discrepancies in keaq- values across different pH levels. This study aims to develop machine learning (ML) models that can effectively simulate the intricate reaction kinetics between eaq- and OCs. Furthermore, the introduction of the pH variable enables a comprehensive investigation into the impact of ambient conditions on this process, thereby improving the practicality of the model. A dataset encompassing 701 keaq- values derived from 351 peer-reviewed publications was compiled. To comprehensively investigate compound properties, this study introduced molecular descriptor (MD), molecular fingerprint (MF), and the integration of both (MD + MF) as model variables. Furthermore, 60 sets of predictive models were established utilizing two variable screening methodologies (MLR and RF) and ten prominent algorithms. Through statistical parameter analysis, it was determined that descriptors combined with MD and MF, the RF screening method, and the symbolism algorithm exhibited the best predictive efficacy. Importantly, the combination of descriptor models exhibited significantly superior performance compared to individual MF and MD models. Notably, the optimal model, denoted as RF - (MF + MD) - LGB, exhibited highly satisfactory predictive results (R2tra = 0.967, Q2tra = 0.840, R2ext = 0.761). The mechanistic explanation study based on Shapley Additive Explanations (SHAP) values further elucidated the crucial influences of polarity, pH, molecular weight, electronegativity, carbon-carbon double bonds, and molecular topology on the degradation of OCs by eaq-. The proposed modeling approach, particularly the integration of MF and MD, alongside the introduction of pH, may furnish innovative ideas for advanced reduction or oxidation processes (ARPs/AOPs) and machine learning applications in other domains.

Prediction of organic contaminant rejection by nanofiltration and reverse osmosis membranes using interpretable machine learning models.

Efficiency improvement in contaminant removal by nanofiltration (NF) and reverse osmosis (RO) membranes is a multidimensional process involving membrane material selection and experimental condition optimization. It is unrealistic to explore the contributions of diverse influencing factors to the removal rate by trial-and-error experimentation. However, the advanced machine learning (ML) method is a powerful tool to simulate this complex decision-making process. Here, 4 traditional learning algorithms (MLR, SVM, ANN, kNN) and 4 ensemble learning algorithms (RF, GBDT, XGBoost, LightGBM) were applied to predict the removal efficiency of contaminants. Results reported here demonstrate that ensemble models showed significantly better predictive performance than traditional models. More importantly, this study achieved a compelling tradeoff between accuracy and interpretability for ensemble models with an effective model interpretation approach, which revealed the mutual interaction mechanism between the membrane material, contaminants and experimental conditions in membrane separation. Additionally, feature selection was for the first time achieved based on the aforementioned model interpretation method to determine the most important variable influencing the contaminant removal rate. Ultimately, the four ensemble models retrained by the selected variables achieved distinguished prediction performance (R2adj = 92.4 %-99.5 %). MWCO (membrane molecular weight cut-off), McGowan volume of solute (V) and molecular weight (MW) of the compound were demonstrated to be the most important influencing factors in contaminant removal by the NF and RO processes. Overall, the proposed methods in this study can facilitate versatile complex decision-making processes in the environmental field, particularly in contaminant removal by advanced physicochemical separation processes.

Contribution of molecular structures and quantum chemistry technique to root concentration factor: An innovative application of interpretable machine learning.

Root concentration factor (RCF) is a significant parameter to characterize uptake and accumulation of hazardous organic contaminants (HOCs) by plant roots. However, complex interactions among chemicals, plant roots and soil make it challenging to identify underlying mechanisms of uptake and accumulation of HOCs. Here, nine machine learning techniques were applied to investigate major factors controlling RCF based on variable combinations of molecular descriptors (MD), MACCS fingerprints, quantum chemistry descriptors (QCD) and three physicochemical properties related to chemical-soil-plant system. Compared to models with variables including MACCS fingerprints or solitary physicochemical properties, the XGBoost-6 model developed by the variable combination of MD, QCD and three physicochemical properties achieved the most remarkable performance, with R2 of 0.977. Model interpretation achieved by permutation variable importance and partial dependence plots revealed the vital importance of HOCs lipophilicity, lipid content of plant roots, soil organic matter content, the overall deformability and the molecular dispersive ability of HOCs for regulating RCF. The integration of MD and QCD with physicochemical properties could improve our knowledge of underlying mechanisms regarding HOCs accumulation in plant roots from innovative structural perspectives. Multiple variables combination-oriented performance improvement of model can be extended to other parameters prediction in environmental risk assessment field.

Prediction models with multiple machine learning algorithms for POPs: The calculation of PDMS-air partition coefficient from molecular descriptor.

Polydimethylsiloxane-air partition coefficient (KPDMS-air) is a key parameter for passive sampling to measure POPs concentrations. In this study, 13 QSPR models were developed to predict KPDMS-air, with two descriptor selection methods (MLR and RF) and seven algorithms (MLR, LASSO, ANN, SVM, kNN, RF and GBDT). All models were based on a data set of 244 POPs from 13 different categories. The diverse model evaluation parameters calculated from training and test set were used for internal and external verification. Notably, the Radj2, QBOOT2 and Qext2 are 0.995, 0.980 and 0.951 respectively for GBDT model, showing remarkable superiority in fitting, robustness and predictability compared with other models. The discovery that molecular size, branches and types of the bonds were the main internal factors affecting the partition process was revealed by mechanism explanation. Different from the existing QSPR models based on single category compounds, the models developed herein considered multiple classes compounds, so that its application domain was more comprehensive. Therefore, the obtained models can fill the data gap of missing experimental KPDMS-air values for compounds in the application range, and help researchers better understand the distribution behavior of POPs from the perspective of molecular structure.

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 (Kd). Almost all quantitative structure-property relationship (QSPR) models that describe Kd apply only to narrow, relatively homogeneous groups of reactants. Herein, non-hybrid QSPR-based models were developed to predict PE-water (KPE-w), PE-seawater (KPE-sw), PVC-water (KPVC-w) and PP-seawater (KPP-sw) sorption coefficients at different temperatures, with eight machine learning algorithms. Moreover, novel hybrid intelligent models for predicting Kd 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 KPE-w, KPE-sw, KPVC-w and KPP-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 (Radj2: 0.907-0.999), robustness (QBOOT2: 0.900-0.937) and predictability (Rext2: 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.

Combined remediation effects of biochar and organic fertilizer on immobilization and dissipation of neonicotinoids in soils.

Neonicotinoid (NEO) pesticides have become a potential risk to ecological safety and human health after application. The combined use of biochar and organic fertilizer (OF) is a promising approach to reduce pesticide adverse effects and improve soil fertility in agricultural soils. However, the combined remediation effects of biochar and OF on immobilization and dissipation of NEOs in soils have not previously been systematically investigated. In this study, biochars derived from peanut shell prepared at low/high pyrolysis temperatures (PS400 and PS900) were combined with composted chicken manure (CCM) as an example for OF to remediate contaminated soils toward six typical NEOs, nitenpyram (NIT), thiamethoxam (THIA), clothianidin (CLO), imidacloprid (IMI), acetamiprid (ACE), thiacloprid (THI). Results shown that both biochars and CCM were effective in improving soil sorption capacity and immobilization efficiency. The Freundlich affinity parameters (Kf) of NEOs in soils increased 7.2-12.0 times after the combined remediation of biochar and CCM, and the Kf of six NEOs had negative correlation with their lipophilicity (p < 0.05), which followed by THI > ACE ≈ IMI > CLO > THIA > NIT. Meanwhile, NEOs-abiotic degradation was accelerated by biochar, CCM and their combined addition by adjusting soil pH and stimulating hydrolysis action. Biotic degradation was dominant in NEOs dissipation processes in amended soils, and the contribution ratios of biotic degradation (CRbio) were in the range of 25.4-99.0%. The combined use of biochar and CCM selectively stimulated the relative abundance of NEOs-degraders, which simplified abiotic degradation of -NO2-containing NEOs (viz., NIT, THIA, CLO, and IMI), but inhibited -C≡N-containing NEOs (viz., ACE and THI). The combined remediation provided a strategy for immobilizing NEOs and facilitating dissipation of -NO2-containing NEOs in soils. The results in this study provide valuable information for policymakers and decision-makers to choose appropriate soil remediation approaches with respect to the NEO types.

Versatile in silico modeling of XAD-air partition coefficients for POPs based on abraham descriptor and temperature.

The concentration of persistent organic pollutants (POPs) makes remarkable difference to environmental fate. In the field of passive sampling, the partition coefficients between polystyrene-divinylbenzene resin (XAD) and air (i.e., KXAD-A) are indispensable to obtain POPs concentration, and the KXAD-A is generally thought to be governed by temperature and molecular structure of POPs. However, experimental determination of KXAD-A is unrealistic for countless and novel chemicals. Herein, the Abraham solute descriptors of poly parameter linear free energy relationship (pp-LFER) and temperature were utilized to develop models, namely pp-LFER-T, for predicting KXAD-A values. Two linear (MLR and LASSO) and four nonlinear (ANN, SVM, kNN and RF) machine learning algorithms were employed to develop models based on a data set of 307 sample points. For the aforementioned six models, R2adj and Q2ext were both beyond 0.90, indicating distinguished goodness-of-fit and robust generalization ability. By comparing the established models, the best model was observed as the RF model with R2adj = 0.991, Q2ext = 0.935, RMSEtra = 0.271 and RMSEext = 0.868. The mechanism interpretation revealed that the temperature, size of molecules and dipole-type interactions were the predominant factors affecting KXAD-A values. Concurrently, the developed models with the broad applicability domain provide available tools to fill the experimental data gap for untested chemicals. In addition, the developed models were helpful to preliminarily evaluate the environmental ecological risk and understand the adsorption behavior of POPs between XAD membrane and air.