Predicting plant cuticle-water partition coefficients for organic pollutants using pp-LFER model

Qi, Xiaojuan; Li, Xuehua; Yao, Hongye; Huang, Yang; Cai, Xiyun; Chen, Jingwen; Zhu, Hao

doi:10.1016/j.scitotenv.2020.138455

Cited by 14 publications

(3 citation statements)

References 53 publications

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“…Developing models with the ability to capture the molecular interactions between different compounds and plant cuticles is a complex task. When few independent variables are included, it is difficult, or sometimes even impossible, for a model to adequately reflect the mechanisms of interest precisely due to the limited number of independent variables available, which are representative of the influencing factors [ 2 , 15 ]. Some studies have solved this problem by developing poly-parameter linear free-energy relationship (pp-LFER) models [ 6 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

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

Tao,

Zhu

2024

Molecules

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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.

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Section: Introductionmentioning

confidence: 99%

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

Tao,

Zhu

2024

Molecules

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“…To predict the adsorption characteristics of organic compounds, quantitative structure–property relationship-based linear relationships ,,− have been extensively studied. ML has also garnered considerable attention for developing prediction models for organic contaminants on various carbonaceous materials. − Nevertheless, the primary obstacle in establishing these models is the requirement of a series of experimental data.…”

Section: Introductionmentioning

confidence: 99%

Elucidating Adsorption Mechanisms and Characteristics of Emerging Aromatic Organic Contaminants to Graphene Material by Quantum Chemical Calculation Integrated with Interpretable Machine Learning

Herath,

Zhang,

Dwinandha

et al. 2024

ACS EST Water

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As a complementary or alternative approach to experiments, theoretical computation of adsorption between carbon materials and emerging aromatic organic contaminants (AOCs) is increasingly important in elucidating adsorption mechanisms and characteristics, as well as their predictions. In this study, the adsorption energies between graphene and 112 AOCs were first analyzed by density functional theory (DFT-D). By the use of quantum molecular descriptors, different machine learning (ML) algorithms were developed. EXtreme gradient boosting exhibited the best performance among the four ML algorithms investigated, showing the lowest root-mean-square percentage error of 4.5% for the test data set. Accordingly, the interpretable ML technique (i.e., SHAP) assessed the importance and dependence of descriptors in the adsorption mechanisms of AOCs to graphene. The global interpretation confirmed that the molecular-volume-induced van der Waals interactions including π−π stacking are dominant, whereas the other interactions (e.g., induced hydrogen and electrostatic interactions) are comparably less significant in the adsorption of most AOCs on graphene. In contrast, using local interpretation, hydrogen bonds and induced dipole interactions with surrounding water were identified as important explanatory variables in the adsorption of AOCs containing carbonyl and sulfur functional groups. Therefore, the developed DFT-D-based ML models could be a reference model for theoretical and experimental studies.

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“…At present, the polyparameter linear free energy relationship (pp-LFER) model has been widely applied to predict the equilibrium partition coefficients of organic chemicals to various environmental and biological phases. − The model takes into account the various intermolecular interactions of organic chemicals between two phases and predicts partition coefficients of various organics using a single equation log nobreak0em.25em⁡ K = c + e E + s S + a A + b B + v V where K is the partition coefficient of an organic chemical in a given two-phase system (e.g., K DOC for DOC–water). The capital letters E , S , A , B , and V are solute parameters or called Abraham parameters.…”

Section: Introductionmentioning

confidence: 99%

Improved Predictions of Dissolved Organic Carbon–Water Partitioning and Unveiling the Chemodiversity of Dissolved Organic Matter

Shi

Ling

Jiang

et al. 2023

ACS Earth Space Chem.

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Dissolved organic matter (DOM) is a complex mixture of organic compounds with chemical heterogeneity in natural soils, sediments, and waters. Predicting the dissolved organic carbon (DOC)–water partition coefficients (K DOC) of organic pollutants to DOMs from various sources is important for assessing their fate and bioavailability. However, the accuracy of the recently developed polyparameter linear free energy relationship (pp-LFER) K DOC models is generally restricted by the small data set, inclusion of experimental data from unreliable measurement methods, or undefined selection standards. The aim of this study was to establish pp-LFER models for predicting the K DOC of nonionic organic chemicals to a variety of sources of DOMs and to get an understanding of DOM chemodiversity. A reliable and expanded experimental K DOC data set was compiled. Improved pp-LFER models were developed and assessed for All DOM (DOM from all sources), Aldrich humic acid (HA), Roth HA, soil porewater DOM, sediment porewater DOM, natural aquatic DOM, natural terrestrial DOM, natural DOM, and commercial DOM. The models developed in this study were reasonably robust and accurate with a root mean square error (RMSE) of 0.538 for the All DOM model and RMSE from 0.196 to 0.860 for the specific DOMs. Also, the models generally performed better than previously published ones. Moreover, the system parameters of the models well described the chemical variabilities between soil porewater DOM and sediment porewater DOM, between natural aquatic DOM and natural terrestrial DOM, and between natural DOM and commercial DOM regarding polarizability, dipolarity, H-bond-accepting and -donating ability, and cavity formation energy. This study provides effective tools to assess the tendencies of organic chemicals to DOMs and improves our understanding of the chemical heterogeneity of DOMs from different sources.

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Predicting plant cuticle-water partition coefficients for organic pollutants using pp-LFER model

Cited by 14 publications

References 53 publications

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

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

Elucidating Adsorption Mechanisms and Characteristics of Emerging Aromatic Organic Contaminants to Graphene Material by Quantum Chemical Calculation Integrated with Interpretable Machine Learning

Improved Predictions of Dissolved Organic Carbon–Water Partitioning and Unveiling the Chemodiversity of Dissolved Organic Matter

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