Deep Learning Neural Network Approach for Predicting the Sorption of Ionizable and Polar Organic Pollutants to a Wide Range of Carbonaceous Materials

Predicting Cd(II) adsorption capacity of biochar materials using typical machine learning models for effective remediation of aquatic environments

The screening and design of "green" biochar materials with high adsorption capacity play a pivotal role in promoting the sustainable treatment of Cd(II)-containing wastewater. In this study, six typical machine learning (ML) models, namely Linear Regression, Random Forest, Gradient Boosting Decision Tree, CatBoost, K-Nearest Neighbors, and Backpropagation Neural Network, were employed to accurately predict the adsorption capacity of Cd(II) onto biochars. A large dataset with 1051 data points was generated using 21 input variables obtained from batch adsorption experiments, including preparation conditions for biochar (2 features), physical properties of biochar (4 features), chemical composition of biochar (9 features), and adsorption experiment conditions (6 features). The rigorous evaluation and comparison of the ML models revealed that the CatBoost model exhibited the highest test R2 value (0.971) and the lowest RMSE (20.54 mg/g), significantly outperforming all other models. The feature importance analysis using Shapley Additive Explanations (SHAP) indicated that biochar chemical compositions had the greatest impact on model predictions of adsorption capacity (42.2 %), followed by adsorption conditions (37.57 %), biochar physical characteristics (12.38 %), and preparation conditions (7.85 %). The optimal experimental conditions optimized by partial dependence plots (PDP) are as follows: as high Cd(II) concentration as possible, C(%) of 33 %, N(%) of 0.3 %, adsorption time of 600 min, pyrolysis time of 50 min, biochar dosage of less than 2 g/L, O(%) of 42 %, biochar pH value of 11.2, and DBE of 1.15. This study unveils novel insights into the adsorption of Cd(II) and provides a comprehensive reference for the sustainable engineering of biochars in Cd(II) wastewater treatment.

Competitive sorption experiments reveal new regression models to predict PhACs sorption on carbonaceous materials

Sorption of hydrophobic organic contaminants onto thermally altered carbonaceous materials (TACM) constitutes a widely used technology for remediation of polluted waters. This process is typically described by sorption isotherms, with one of the most used models, the Polanyi-Dubinin-Manes (PDM) equation, including water solubility (Sw) as a normalizing factor. In case of pharmaceutical active compounds (PhACs), Sw depends on the pH of the environment due to the ionic/ionizable behavior of these chemicals, a fact frequently ignored in sorption studies of PhACs. In this work, we set the theoretical framework to include the variation of Sw with pH in the definition of the PDM model, and we applied this approach to describe the effect of ambient pH in the competitive sorption of three commonly detected PhACs (carbamazepine, ibuprofen, and sulfamethoxazole) onto three carbonaceous sorbents (biochar, powder activated carbon, and colloidal activated carbon). Changes in the ambient pH and hence in the hydrophobicity of the compounds could explain the strong variations observed in single-solute sorption and also in competitive sorption. Furthermore, Sw was used as a parameter for the linear regression model of sorption coefficients of our experiments, suggesting the incorporation of this variable as an improvement to existing approaches for prediction of PhACs sorption onto TACM.

Predicting the binding configuration and release potential of heavy metals on iron (oxyhydr)oxides: A machine learning study on EXAFS

Heavy metals raise a global concern and can be easily retained by ubiquitous iron (oxyhydr)oxides in natural and engineered systems. The complex interaction between iron (oxyhydr)oxides and heavy metals results in various mineral-metal binding configurations, such as outer-sphere complexes and edge-sharing inner-sphere complexes, which determine the accumulation and release of heavy metals in the environment. However, traditional experimental approaches are time-consuming and inadequate to elucidate the complex binding relationships and configurations between iron (oxyhydr)oxides and heavy metals. Herein, a workflow that integrates the binding configuration data of 11 heavy metals on 7 iron (oxyhydr)oxides and then trains machine learning models to predict unknown binding configurations was proposed. The well-trained multi-grained cascade forest models exhibited high accuracy (> 90%) and predictive performance (R2 ∼ 0.75). The underlying effects of mineral properties, metal ion species, and environmental conditions on mineral-metal binding configurations were fully interpreted with data mining. Moreover, the metal release rate was further successfully predicted based on mineral-metal binding configurations. This work provides a method to accurately and quickly predict the binding configuration of heavy metals on iron (oxyhydr)oxides, which would provide guidance for estimating the potential release behavior of heavy metals and remediating heavy metal pollution in natural and engineered environments.

Bridging Nanomanufacturing and Artificial Intelligence-A Comprehensive Review

Nanomanufacturing and digital manufacturing (DM) are defining the forefront of the fourth industrial revolution-Industry 4.0-as enabling technologies for the processing of materials spanning several length scales. This review delineates the evolution of nanomaterials and nanomanufacturing in the digital age for applications in medicine, robotics, sensory technology, semiconductors, and consumer electronics. The incorporation of artificial intelligence (AI) tools to explore nanomaterial synthesis, optimize nanomanufacturing processes, and aid high-fidelity nanoscale characterization is discussed. This paper elaborates on different machine-learning and deep-learning algorithms for analyzing nanoscale images, designing nanomaterials, and nano quality assurance. The challenges associated with the application of machine- and deep-learning models to achieve robust and accurate predictions are outlined. The prospects of incorporating sophisticated AI algorithms such as reinforced learning, explainable artificial intelligence (XAI), big data analytics for material synthesis, manufacturing process innovation, and nanosystem integration are discussed.

The application of machine learning methods for prediction of heavy metal by activated carbons, biochars, and carbon nanotubes

Carbonaceous materials are commonly used as adsorbents for heavy metals. The determination of the adsorption capacity needs time and energy, and the key factors affecting the adsorption capacity have not been determined. Therefore, a new and efficient method is needed to predict the adsorption capacity and explore the decisive factors in the adsorption process. In this study, three tree-based machine learning models (i.e., random forest, gradient boosting decision tree, and extreme gradient boosting) were developed to predict the adsorption capacity of eight heavy metals (i.e., As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) on activated carbons, biochars, and carbon nanotubes using 3674 data points extracted from 151 journal articles. After a comprehensive comparison, the gradient boosting decision tree had the best performance for a combined model based on all data (R2 = 0.9707, RMSE = 0.1420). Moreover, independent models were developed for three datasets classified by the adsorbent and eight datasets classified by the heavy metals. In addition, a graphical user interface was built to predict the adsorption capacity of heavy metals. This study provides a novel strategy and convenient tool for the removal of heavy metals and can help to improve the removal efficiency of heavy metals to build a healthier world.

Bioretention Design Modifications Increase the Simulated Capture of Hydrophobic and Hydrophilic Trace Organic Compounds

Stormwater rapidly moves trace organic contaminants (TrOCs) from the built environment to the aquatic environment. Bioretention cells reduce loadings of some TrOCs, but they struggle with hydrophilic compounds. Herein, we assessed the potential to enhance TrOC removal via changes in bioretention system design by simulating the fate of seven high-priority stormwater TrOCs (e.g., PFOA, 6PPD-quinone, PAHs) with log KOC values between -1.5 and 6.74 in a bioretention cell. We evaluated eight design and management interventions for three illustrative use cases representing a highway, a residential area, and an airport. We suggest two metrics of performance: mass advected to the sewer network, which poses an acute risk to aquatic ecosystems, and total mass advected from the system, which poses a longer-term risk for persistent compounds. The optimized designs for each use case reduced effluent loadings of all but the most polar compound (PFOA) to <5% of influent mass. Our results suggest that having the largest possible system area allowed bioretention systems to provide benefits during larger events, which improved performance for all compounds. To improve performance for the most hydrophilic TrOCs, an amendment like biochar was necessary; field-scale research is needed to confirm this result. Our results showed that changing the design of bioretention systems can allow them to effectively capture TrOCs with a wide range of physicochemical properties, protecting human health and aquatic species from chemical impacts.

Using machine learning to explore oxyanion adsorption ability of goethite with different specific surface area

In this study, we developed prediction models for the adsorption of divalent and trivalent oxyanions on goethite based on machine learning algorithms. After verifying the reliability of the models, the importance of goethite specific surface area (SSA) and the average oxyanion adsorption capacities of goethite with different SSAs were calculated by shapley additive explanations (SHAP) importance analysis and partial dependence (PD) analysis. Despite there were differences in the feature importance of divalent and trivalent oxyanions, the contribution of goethite's SSA to the adsorption amount ranked the fourth based on SHAP importance, indicating SSA played the important role in oxyanion adsorption. Meanwhile, the PD values of SSA and the optimized complexation constants from surface complexation modeling (SCM) both indicated a non-monotonic relationship between the goethite with different SSA and its oxyanions binding capacity. When the total site concentration and crystal face composition were used as the machine learning model input features, the SHAP importance values of crystal faces and the PD decomposition results indicated that the (001) face showed the crucial influence on oxyanions adsorption amount. These findings demonstrated the important role of crystal face composition in goethite's adsorption ability, and provided a theoretical explanation for the variations of oxyanions adsorption amount on different SSA goethite.

Multitask Deep Learning Enabling a Synergy for Cadmium and Methane Mitigation with Biochar Amendments in Paddy Soils

Biochar has demonstrated significant promise in addressing heavy metal contamination and methane (CH4) emissions in paddy soils; however, achieving a synergy between these two goals is challenging due to various variables, including the characteristics of biochar and soil properties that influence biochar's performance. Here, we successfully developed an interpretable multitask deep learning (MTDL) model by employing a tensor tracking paradigm to facilitate parameter sharing between two separate data sets, enabling a synergy between Cd and CH4 mitigation with biochar amendments. The characteristics of biochar contribute similar weightings of 67.9% and 62.5% to Cd and CH4 mitigation, respectively, but their relative importance in determining biochar's performance varies significantly. Notably, this MTDL model excels in custom-tailoring biochar to synergistically mitigate Cd and CH4 in paddy soils across a wide geographic range, surpassing traditional machine learning models. Our findings deepen our understanding of the interactive effects of Cd and CH4 mitigation with biochar amendments in paddy soils, and they also potentially extend the application of artificial intelligence in sustainable environmental remediation, especially when dealing with multiple objectives.

Machine learning prediction and interpretation of the impact of microplastics on soil properties

The annual microplastic (MP) release into soils is 4-23 times higher than that into oceans, significantly impacting soil quality. However, the mechanisms underlying how MPs impact soil properties remain largely unknown. Soil-MP interactions are complex because of soil heterogeneity and varying MP properties. This lack of understanding was exacerbated by the diverse experimental conditions and soil types used in this study. Predicting changes in soil properties in the presence of MPs is challenging, laborious, and time-consuming. To address these issues, machine learning was applied to fit datasets from peer-reviewed publications to predict and interpret how MPs influence soil properties, including pH, dissolved organic carbon (DOC), total P, NO3--N, NH4+-N, and acid phosphatase enzyme activity (acid P). Among the developed models, the gradient boost regression (GBR) model showed the highest R2 (0.86-0.99) compared to the decision tree and random forest models. The GBR model interpretation showed that MP properties contributed more than 50% to altering the acid P and NO3--N concentrations in soils, whereas they had a negligible impact on total P and 10-20% impact on soil pH, DOC, and NH4+-N. Specifically, the size of MPs was the dominant factor influencing acid P (89.3%), pH (71.6%), and DOC (44.5%) in soils. NO3--N was mainly affected by the MP type (52.0%). The NH4+-N was mainly affected by the MP dose (46.8%). The quantitative insights into the impact of MPs on soil properties of this study could aid in understanding the roles of MPs in soil systems.

Adsorption of tetracycline by polycationic straw: Density functional theory calculation for mechanism and machine learning prediction for tetracyclines' remediation

The abuse of antibiotics causes serious environmental pollution, whose removal has become a hot topic. The adsorption of tetracycline (TC) on a prepared polycationic straw (MMS) was investigated. The kinetic, thermodynamic and adsorption isotherm models showed that adsorption of TC by MMS was a spontaneous, monolayer reaction with coexistence of physical and chemical process. Density functional theory indicated that the adsorption of TC resulted from electrostatic interaction and hydrogen bonds, which proved the mechanism of TC by macromolecular biomass for the first time. The expected and empirical values of TC adsorption showed a high fit degree, through predication of machine learning, indicating the feasibility and avoiding lots of experiments. Further, the adsorption ability of MMS to other TCs was predicted, founding that the highest removal efficiency was doxycycline, which provides a novel strategy for removal of other pollution and reduce of economic and time cost in practical application.

Modeling sorption of environmental organic chemicals from water to soils

Reliable estimation of chemical sorption from water to solid phases is an essential prerequisite for reasonable assessments of chemical hazards and risks. However, current fate and exposure models mostly rely on algorithms that lack the capability to quantify chemical sorption resulting from interactions with multiple soil constituents, including amorphous organic matter, carbonaceous organic matter, and mineral matter. Here, we introduce a novel, generic approach that explicitly combines the gravimetric composition of various solid constituents and poly-parameter linear free energy relationships to calculate the solid-water sorption coefficient (Kd) for non-ionizable or predominantly neutral organic chemicals with diverse properties in a neutral environment. Our approach demonstrates an overall statistical uncertainty of approximately 0.9 log units associated with predictions for different types of soil. By applying this approach to estimate the sorption of 70 diverse chemicals from water to two types of soils, we uncover that different chemicals predominantly exhibit sorption onto different soil constituents. Moreover, we provide mechanistic insights into the limitation of relying solely on organic carbon normalized sorption coefficient (KOC) in chemical hazard assessment, as the measured KOC can vary significantly across different soil types, and therefore, a universal cut-off threshold may not be appropriate. This research highlights the importance of considering chemical properties and multiple solid constituents in sorption modeling and offers a valuable theoretical approach for improved chemical hazard and exposure assessments.

Exploring Optimization of Zeolites as Adsorbents for Rare Earth Elements in Continuous Flow by Machine Learning Techniques

Unsupervised machine learning (ML) techniques are applied to the characterization of the adsorption of rare earth elements (REEs) by zeolites in continuous flow. The successful application of principal component analysis (PCA) and K-Means algorithms from ML allowed for a wide range assessment of the adsorption results. This global approach permits the evaluation of the different stages of the sorption cycles and their optimization and improvement. The results from ML are also used for the definition of a regression model to estimate other REEs' recoveries based on the known values of the tested REEs. Overall, it was possible to remove more than 70% of all REEs from aqueous solutions during the adsorption assays and to recover over 80% of the REEs entrapped on the zeolites using an optimized desorption cycle.

Prediction of arsenic adsorption onto metal organic frameworks and adsorption mechanisms interpretation by machine learning

Metal-organic frameworks (MOFs) are promising adsorbents for the removal of arsenic (As) from wastewater. The As removal efficiency is influenced by several factors, such as the textural properties of MOFs, adsorption conditions, and As species. Examining all of the relevant factors through traditional experiments is challenging. To predict the As adsorption capacities of MOFs toward organic, inorganic, and total As and reveal the adsorption mechanisms, four machine learning-based models were developed, with the adsorption conditions, MOF properties, and characteristics of different As species as inputs. The results demonstrated that the extreme gradient boosting (XGBoost) model exhibited the best predictive performance (test R2 = 0.93-0.96). The validation experiments demonstrated the high accuracy of the inorganic As-based XGBoost model. The feature importance analysis showed that the concentration of As, the surface area of MOFs, and the pH of the solution were the three key factors governing inorganic-As adsorption, while those governing organic-As adsorption were the concentration of As, the pHpzc value of MOFs, and the oxidation state of the metal clusters. The formation of coordination complexes between As and MOFs is possibly the major adsorption mechanism for both inorganic and organic As. However, electrostatic interaction may have a greater effect on organic-As adsorption than on inorganic-As adsorption. Overall, this study provides a new strategy for evaluating As adsorption on MOFs and discovering the underlying decisive factors and adsorption mechanisms, thereby facilitating the investigation of As wastewater treatment.

Ocean Stratification Impacts on Dissolved Polycyclic Aromatic Hydrocarbons (PAHs): From Global Observation to Deep Learning

Ocean stratification plays a crucial role in many biogeochemical processes of dissolved matter, but our understanding of its impact on widespread organic pollutants, such as polycyclic aromatic hydrocarbons (PAHs), remains limited. By analyzing dissolved PAHs collected from global oceans and marginal seas, we found different patterns in vertical distributions of PAHs in relation to ocean primary productivity and stratification index. Notably, a significant positive logarithmic relationship (R2 = 0.50, p < 0.05) was observed between the stratification index and the PAH stock. To further investigate the impact of ocean stratification on PAHs, we developed a deep learning neural network model. This model incorporated input variables determining the state of the seawater or the stock of PAHs. The modeled PAH stocks displayed substantial agreement with the observed values (R2 ≥ 0.92), suggesting that intensified stratification could prompt the accumulation of PAHs in the water column. Given the amplified effect of global warming, it is imperative to give more attention to increased ocean stratification and its impact on the environmental fate of organic pollutants.

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.

Predicting adsorption of organic compounds onto graphene and black phosphorus by molecular dynamics and machine learning

With an increase in production and application of various engineering nanomaterials (ENMs), they will inevitably be released into the environment. Adsorption of various organic chemicals onto ENMs will impact on their environmental behavior and toxicology. It is unrealistic to experimentally determine adsorption equilibrium constants (K) for the vast number of organics and ENMs due to high cost in expenditure and time. Herein, appropriate molecular dynamics (MD) methods were evaluated and selected by comparing experimental K values of seven organics adsorbed onto graphene with the MD-calculated ones. Machine learning (ML) models on K of organics adsorption onto graphene and black phosphorus nanomaterials were constructed based on a benchmark data set from the MD simulations. Lasso models based on Mordred descriptors outperformed ML models built by support vector machine, random forest, k-nearest neighbor, and gradient boosting decision tree, in terms of cross-validation coefficients (Q2 > 0.90). The Lasso models also outperformed conventional poly-parameter linear free energy relationship models for predicting logK. Compared with previous models, the Lasso models considered more compounds with different functional groups and thus have broader applicability domains. This study provides a promising way to fill the data gap in logK for chemicals adsorbed onto the ENMs.

Predicting aqueous sorption of organic pollutants on microplastics with machine learning

Microplastics (MPs) are ubiquitously distributed in freshwater systems and they can determine the environmental fate of organic pollutants (OPs) via sorption interaction. However, the diverse physicochemical properties of MPs and the wide range of OP species make a deeper understanding of sorption mechanisms challenging. Traditional isotherm-based sorption models are limited in their universality since they normally only consider the nature and characteristics of either sorbents or sorbates individually. Therefore, only specific equilibrium concentrations or specific sorption isotherms can be used to predict sorption. To systematically evaluate and predict OP sorption under the influence of both MPs and OPs properties, we collected 475 sorption data from peer-reviewed publications and developed a poly-parameter-linear-free-energy-relationship-embedded machine learning method to analyze the collected sorption datasets. Models of different algorithms were compared, and the genetic algorithm and support vector machine hybrid model displayed the best prediction performance (R2 of 0.93 and root-mean-square-error of 0.07). Finally, comparison results of three feature importance analysis tools (forward step wise method, Shapley method, and global sensitivity analysis) showed that chemical properties of MPs, excess molar refraction, and hydrogen-bonding interaction of OPs contribute the most to sorption, reflecting the dominant sorption mechanisms of hydrophobic partitioning, hydrogen bond formation, and π-π interaction, respectively. This study presents a novel sorbate-sorbent-based ML model with a wide applicability to expand our capacity in understanding the complicated process and mechanism of OP sorption on MPs.

Two-Dimensional Energy Histograms as Features for Machine Learning to Predict Adsorption in Diverse Nanoporous Materials

A major obstacle for machine learning (ML) in chemical science is the lack of physically informed feature representations that provide both accurate prediction and easy interpretability of the ML model. In this work, we describe adsorption systems using novel two-dimensional energy histogram (2D-EH) features, which are obtained from the probe-adsorbent energies and energy gradients at grid points located throughout the adsorbent. The 2D-EH features encode both energetic and structural information of the material and lead to highly accurate ML models (coefficient of determination R2 ∼ 0.94-0.99) for predicting single-component adsorption capacity in metal-organic frameworks (MOFs). We consider the adsorption of spherical molecules (Kr and Xe), linear alkanes with a wide range of aspect ratios (ethane, propane, n-butane, and n-hexane), and a branched alkane (2,2-dimethylbutane) over a wide range of temperatures and pressures. The interpretable 2D-EH features enable the ML model to learn the basic physics of adsorption in pores from the training data. We show that these MOF-data-trained ML models are transferrable to different families of amorphous nanoporous materials. We also identify several adsorption systems where capillary condensation occurs, and ML predictions are more challenging. Nevertheless, our 2D-EH features still outperform structural features including those derived from persistent homology. The novel 2D-EH features may help accelerate the discovery and design of advanced nanoporous materials using ML for gas storage and separation in the future.

Unveiling the migration of Cr and Cd to biochar from pyrolysis of manure and sludge using machine learning

Heavy metal (HM) in biochar derived from pyrolysis of sludge or manure is the main issue for its large-scale application in soils for carbon sequestration. However, there is a paucity of efficient approaches to predict and comprehend the HM migration during pyrolysis for preparing low HM-contained biochar. Herein, the data on the feedstock information (FI), additive, total concentration of feedstock (FTC) of HM Cr and Cd, and pyrolysis condition, were extracted from the literature, to predict total concentration (TC) and retention rate (RR) of Cr and Cd in sludge/manure biochar using ML for mapping their migration during pyrolysis. Two datasets for Cr and Cd were compiled with 388 and 292 data points from 48 and 37 peer-review papers. The results indicated that the TC and RR of Cr and Cd could be predicted by the Random Forest model with test R² of 0.74-0.98. Their TC and RR in biochar were dominated by the FTC and FI, respectively; while pyrolysis temperature was the most important to Cd RR. Moreover, potassium-based inorganic additives decreased the TC and RR of Cr while increased those of Cd. The predictive models and insights provided by this work could aid the understanding of HM migration during manure and sludge pyrolysis and guide the preparation of low HM-contained biochar.

A machine learning based approach for estimating site-specific partition coefficient Kd of organic compounds: Application to nonionic pesticides

The partitioning coefficient K_d for a specific compound and location is not only a key input parameter of fate and transport models, but also critical in estimating the safety environmental concentration threshold. In order to reduce the uncertainty caused by non-linear interactions among environmental factors, machine learning based models for predicting K_d were developed in this work based on literature datasets of nonionic pesticides including molecular descriptors, soil properties, and experimental settings. The equilibrium concentration (C_e) values were specifically included for the reason that a varied range of K_d corresponding to a given C_e occurred in a real environment. By transforming 466 isotherms reported in the literature, 2618 paired equilibrium concentrations of liquid-solid (C_e-Q_e) data points were obtained. Results of SHapley Additive exPlanations revealed that soil organic carbon, C_e, and cavity formation were the most important. The distance-based applicability domain analysis was conducted for the 27 most frequently used pesticides with 15952 pieces of soil information from the HWSD-China dataset by setting three C_e scenarios (i.e., 10, 100, and 1000 μg L^-1). It was revealed the groups of compounds showing log K_d < 0.06 and log K_d > 1.19 were composed mostly of those with log K_ow of -0.800 and 5.50, respectively. When log K_d varied between 0.100 and 1.00, it was impacted by interactions among soil types, molecular descriptors, and C_e comprehensively, which accounted for 55% of the total 2618 calculations. It could be concluded that site-specific models developed in this work are necessary and practicable for the environmental risk assessment and management of nonionic organic compounds.

Is sorption technology fit for the removal of persistent and mobile organic contaminants from water?

Persistent, Mobile, and Toxic (PMT) and very persistent and very mobile (vPvM) substances are a growing threat to water security and safety. Many of these substances are distinctively different from other more traditional contaminants in terms of their charge, polarity, and aromaticity. This results in distinctively different sorption affinities towards traditional sorbents such as activated carbon. Additionally, an increasing awareness on the environmental impact and carbon footprint of sorption technologies puts some of the more energy-intensive practices in water treatment into question. Commonly used approaches may thus need to be readjusted to become fit for purpose to remove some of the more challenging PMT and vPvM substances, including for example short chained per- and polyfluoroalkyl substances (PFAS). We here critically review the interactions that drive sorption of organic compounds to activated carbon and related sorbent materials and identify opportunities and limitations of tailoring activated carbon for PMT and vPvM removal. Other less traditional sorbent materials, including ion exchange resins, modified cyclodextrins, zeolites and metal-organic frameworks are then discussed for potential alternative or complementary use in water treatment scenarios. Sorbent regeneration approaches are evaluated in terms of their potential, considering reusability, potential for on-site regeneration, and potential for local production. In this context, we also discuss the benefits of coupling sorption to destructive technologies or to other separation technologies. Finally, we sketch out possible future trends in the evolution of sorption technologies for PMT and vPvM removal from water.

Enhancing Biochar-Based Nonradical Persulfate Activation Using Data-Driven Techniques

Converting biomass into biochar (BC) as a functional biocatalyst to accelerate persulfate activation for water remediation has attracted much attention. However, due to the complex structure of BC and the difficulty in identifying the intrinsic active sites, it is essential to understand the link between various properties of BC and the corresponding mechanisms promoting nonradicals. Machine learning (ML) recently demonstrated significant potential for material design and property enhancement to help tackle this problem. Herein, ML techniques were applied to guide the rational design of BC for the targeted acceleration of nonradical pathways. The results showed a high specific surface area, and O% values can significantly enhance nonradical contribution. Furthermore, the two features can be regulated by simultaneously tuning the temperatures and biomass precursors for efficient directed nonradical degradation. Finally, two nonradical-enhanced BCs with different active sites were prepared based on the ML results. This work serves as a proof of concept for applying ML in the synthesis of tailored BC for persulfate activation, thereby revealing the remarkable capability of ML for accelerating bio-based catalyst development.

Yesterday, Today, and Tomorrow. Evolution of a Sleeping Beauty: The Freundlich Isotherm

The name of Herbert Freundlich is commonly associated with a power relationship for adsorbed amount of a substance (C_ads) against the concentration in solution (C_sln), such that C_ads = KC_slnⁿ; this isotherm (together with the Langmuir isotherm) is considered to be the model of choice for correlating the experimental adsorption data of micropollutants or contaminants of emerging concern (pesticides, pharmaceuticals, and personal care products), but it also concerns the adsorption of gases on solids. However, Freundlich's 1907 paper was a "sleeping beauty", which only started to attract significant citations from the early 2000s onward; moreover, these citations were too often wrong. In this paper, the main steps in the historical developments of Freundlich isotherm are identified, along with a discussion of several theoretical points: (1) derivation of the Freundlich isotherm from an exponential distribution of energies, leading to a more general equation, based on the Gauss hypergeometric function, of which the power Freundlich equation is an approximation; (2) application of this hypergeometric isotherm to the case of competitive adsorption, when the binding energies are perfectly correlated; and (3) new equations for estimating the Freundlich coefficient K_F from physicochemical properties such as the sticking surface or probability. From new data treatment of two examples from the literature, the influence of several parameters is highlighted, and the application of linear free-energy relationships (LFER) to the Freundlich parameters for different series of compounds is evoked, along with its limitations. We also suggest some ideas that may be worth exploring in the future, such as extending the range of applications of the Freundlich isotherm by means of its hypergeometric version, extending the competitive adsorption isotherm in the case of partial correlation, and exploring the interest of the sticking surfaces or probabilities instead of K_F for LFER analysis.

Machine learning assisted predicting and engineering specific surface area and total pore volume of biochar

Biochar produced from pyrolysis of biomass is a platform porous carbon material that have been widely used in many areas. Specific surface area (SSA) and total pore volume (TPV) are decisive to biochar application in hydrogen uptake, CO₂ adsorption, and organic pollutant removal, etc. Engineering biochar by traditional experimental methods is time-consuming and laborious. Machine learning (ML) was used to effectively aid the prediction and engineering of biochar properties. The prediction of biochar yield, SSA, and TPV was achieved via random forest (RF) and gradient boosting regression (GBR) with test R² of 0.89-0.94. ML model interpretation indicates pyrolysis temperature, biomass ash, and volatile matter were the most important features to the three targets. Pyrolysis parameters and biomass mixing ratios for biochar production were optimized via three-target GBR model, and the optimum schemes to obtain high SSA and TPV were experimentally verified, indicating the great potential of ML for biochar engineering.

Machine learning for hydrothermal treatment of biomass: A review

Hydrothermal treatment (HTT) (i.e., hydrothermal carbonization, liquefaction, and gasification) is a promising technology for biomass valorization. However, diverse variables, including biomass compositions and hydrothermal processes parameters, have impeded in-depth mechanistic understanding on the reaction and engineering in HTT. Recently, machine learning (ML) has been widely employed to predict and optimize the production of biofuels, chemicals, and materials from HTT by feeding experimental data. This review comprehensively analyzed the application of ML for HTT of biomass and systematically illustrated basic ML procedure and descriptors for inputs and outputs of ML models (e.g., biomass compositions, operation conditions, yield and physicochemical properties of derived products) that could be applied in HTT. Moreover, this review summarized ML-aided HTT prediction of yield, compositions, and physicochemical properties of HTT hydrochar or biochar, bio-oil, syngas, and aqueous phase. Ultimately, future prospects were proposed to enhance predictive performance, mechanistic interpretation, process optimization, data sharing, and model application during ML-aided HTT.

Modeling phytoremediation of heavy metal contaminated soils through machine learning

As an important subtopic within phytoremediation, hyperaccumulators have garnered significant attention due to their ability of super-enriching heavy metals. Identifying the factors that affecting phytoextraction efficiency has important application value in guiding the efficient remediation of heavy metal contaminated soil. However, it is challenging to identify the critical factors that affect the phytoextraction of heavy metals in soil-hyperaccumulator ecosystems because the current projections on phytoremediation extrapolations are rudimentary at best using simple linear models. Here, machine learning (ML) approaches were used to predict the important factors that affecting phytoextraction efficiency of hyperaccumulators. ML analysis was based on 173 data points with consideration of soil properties, experimental conditions, plant families, low-molecular-weight organic acids from plants, plant genes, and heavy metal properties. Heavy metal properties, especially the metal ion radius, were the most important factors that affect heavy metal accumulation in shoots, and the plant family was the most important factor that affect the bioconcentration factor, metal extraction ratio, and remediation time. Furthermore, the Crassulaceae family had the highest potential as hyperaccumulators for phytoremediation, which was related to the expression of genes encoding heavy metal transporting ATPase (HMA), Metallothioneins (MTL), and natural resistance associated macrophage protein (NRAMP), and also the secretion of malate and threonine. New insights into the effects of plant characteristics, experimental conditions, soil characteristics, and heavy metal properties on phytoextraction efficiency from ML model interpretation could guide the efficient phytoremediation by identifying the best hyperaccumulators and resolving its efficient remediation mechanisms.

A spectral learning path for simultaneous multi-parameter detection of water quality

Water quality parameters (WQP) are the most intuitive indicators of the environmental quality of water body. Due to the complexity and variability of the chemical environment of water body, simple and rapid detection of multiple parameters of water quality becomes a difficult task. In this paper, spectral images (named SPIs) and deep learning (DL) techniques were combined to construct an intelligent method for WQP detection. A novel spectroscopic instrument was used to obtain SPIs, which were converted into feature images of water chemistry and then combined with deep convolutional neural networks (CNNs) to train models and predict WQP. The results showed that the method of combining SPIs and DL has high accuracy and stability, and good prediction results with average relative error of each parameter (anions and cations, TOC, TP, TN, NO₃^--N, NH₃-N) at 1.3%, coefficient of determination (R²) of 0.996, root mean square error (RMSE) of 0.1, residual prediction deviation (RPD) of 16.2, and mean absolute error (MAE) of 0.067. The method can achieve rapid and accurate detection of high-dimensional water quality multi-parameters, and has the advantages of simple pre-processing and low cost. It can be applied not only to the intelligent detection of environmental waters, but also has the potential to be applied in chemical, biological and medical fields.

Predicting the speciation of ionizable antibiotic ciprofloxacin by biochars with varying carbonization degrees†

Sorption mechanisms of ionizable organic pollutants by biochars and approaches for the prediction of sorption are still unclear. In this study, batch experiments were conducted to explore the sorption mechanisms of woodchip-derived biochars prepared at 200–700 °C (referred as WC200–WC700) for cationic, zwitterionic and anionic species of ciprofloxacin (referred as CIP⁺, CIP^± and CIP⁻, respectively). The results revealed that the sorption affinity of WC200 for different CIP species was in the order of CIP^± > CIP⁺ > CIP⁻, while that of WC300–WC700 remained the order of CIP⁺ > CIP^± > CIP⁻. WC200 exhibited a strong sorption ability, which could be attributed to hydrogen bonding and electrostatic attraction with CIP⁺, electrostatic attraction with CIP^±, and charge-assisted hydrogen bonding with CIP⁻. Pore filling and π–π interactions contributed to the sorption of WC300–WC700 for CIP⁺, CIP^± and CIP⁻. Rising temperature facilitated CIP sorption to WC400 as verified by site energy distribution analysis. Proposed models including the proportion of the three CIP species and sorbent aromaticity index (H/C) can quantitatively predict CIP sorption to biochars with varying carbonization degrees. These findings are vital to elucidating the sorption behaviors of ionizable antibiotics to biochars and exploring potential sorbents for environmental remediation.

This study revealed the evolution of sorption mechanisms with pyrolysis temperature of biochar and CIP speciation, and provided a novel approach for the sorption prediction of ionizable antibiotics.

Machine learning-assisted evaluation of potential biochars for pharmaceutical removal from water

A popular approach to select optimal adsorbents is to perform parallel experiments on adsorbents based on an initially decided goal such as specified product purity, efficiency, or binding capacity. To screen optimal adsorbents, we focused on the max adsorption capacity of the candidates at equilibrium in this work because the adsorption capacity of each adsorbent is strongly dependent on certain conditions. A data-driven machine learning tool for predicting the max adsorption capacity (Q_m) of 19 pharmaceutical compounds on 88 biochars was developed. The range of values of Q_m (mean 48.29 mg/g) was remarkably large, with a high number of outliers and large variability. Modified biochars enhanced the Q_m and surface area values compared with the original biochar, with a statistically significant difference (Chi-square value = 7.21-18.25, P < 0.005). K- nearest neighbors (KNN) was found to be the most optimal algorithm with a root mean square error (RMSE) of 23.48 followed by random forest and Cubist with RMSE of 26.91 and 29.56, respectively, whereas linear regression and regularization were the worst algorithms. KNN model achieved R² of 0.92 and RMSE of 16.62 for the testing data. A web app was developed to facilitate the use of the KNN model, providing a reliable solution for saving time and money in unnecessary lab-scale adsorption experiments while selecting appropriate biochars for pharmaceutical adsorption.

Prediction models on biomass and yield of rice affected by metal (oxide) nanoparticles using nano-specific descriptors

The use of in silico tools to investigate the interactions between metal (oxide) nanoparticles (NPs) and plant biological responses is preferred because it allows us to understand molecular mechanisms and improve prediction efficiency by saving time, labor, and cost. In this study, four models (C5.0 decision tree, discriminant function analysis, random forest, and stepwise multiple linear regression analysis) were applied to predict the effect of NPs on rice biomass and yield. Nano-specific descriptors (size-dependent molecular descriptors and image-based descriptors) were introduced to estimate the behavior of NPs in plants to appropriately represent the wide space of NPs. The results showed that size-dependent molecular descriptors (e.g., E-state and connectivity indices) and image-based descriptors (e.g., extension, area, and minimum ferret diameter) were associated with the behavior of NPs in rice. The performance of the constructed models was within acceptable ranges (correlation coefficient ranged from 0.752 to 0.847 for biomass and from 0.803 to 0.905 for yield, while the accuracy ranged from 64% to 77% for biomass and 81% to 89% for yield). The developed model can be used to quickly and efficiently evaluate the impact of NPs under a wide range of experimental conditions and sufficient training data.

Effect of pH-Dependent Homo/Heteronuclear CAHB on Adsorption and Desorption Behaviors of Ionizable Organic Compounds on Carbonaceous Materials

Herein, the adsorption/desorption behaviors of benzoic acid (BA) and phthalic acid (PA) on three functionalized carbon nanotubes (CNTs) at various pH were investigated, and the charge-assisted H-bond (CAHB) was verified by DFT and FTIR analyses to play a key role. The results indicated that the adsorption order of BA and PA on CNTs was different from K_ow of that at pH 2.0, 4.0, and 7.0 caused by the CAHB interaction. The strength of homonuclear CAHB (≥78.96 kJ·mol^-1) formed by BA/PA on oxidized CNTs is stronger than that of heteronuclear CAHB formed between BA/PA and amino-functionalized CNTs (≤51.66 kJ·mol^-1). Compared with the heteronuclear CAHB (Hysteresis index, HI ≥ 1.47), the stronger homonuclear CAHB leads to clearly desorption hysteresis (HI ≥ 3.51). Additionally, the contribution of homonuclear CAHB (≥52.70%) was also greater than that of heteronuclear CAHB (≤45.79%) at pH 7.0. These conclusions were further confirmed by FTIR and DFT calculation, and the crucial evidence of CAHB formation in FTIR was found. The highlight of this work is the identification of the importance and difference of pH-dependent homonuclear/heteronuclear CAHB on the adsorption and desorption behaviors of ionizable organic compounds on carbonaceous materials, which can provide a deeper understanding for the removal of ionizable organic compounds by designed carbonaceous materials.

Capturing In Situ Glyphosate (De)sorption Kinetics in Floodplain Aquifer Sediment Columns: Geophysical Measurements and Reactive Transport Modeling

Glyphosate, an ionizable organic herbicide, is frequently detected in soils and groundwater globally despite its strong retention via sorption. Understanding its apparent mobility hinges on our ability to quantify its system-specific sorption behavior, hindered by its affinity to adsorb onto sediments, yielding very low aqueous concentrations. Here, we present findings from a saturated flow-through column experiment in which we monitored glyphosate sorption onto a natural calcareous aquifer sediment, using the noninvasive geophysical method spectral induced polarization (SIP). Our kinetic sorption reactive transport model predicted the strong nonlinear reversible retention of glyphosate and reproduced the spatial profile of retained glyphosate in the sediment, with a measured maximum of 0.06 mg g^-1. The strong contribution of sorption to pore fluid conduction masked the expected variations in imaginary conductivity, σ″. However, time constants derived from a Cole-Cole model matched the timing and spatial distribution of model-predicted sorbed concentration changes, increasing from 0.8 × 10^-3 to 1.7 × 10^-3 s with an increase in sorbed glyphosate of 0.1 mg g^-1. Thus, glyphosate sorption modified the surface charging properties of the sediment proportional to the solid-bound concentrations. Our findings link SIP signal variations to sorption dynamics and provide a framework for improved monitoring of charged organic contaminants in natural sediments.

Predicting pesticide dissipation half-life intervals in plants with machine learning models

Pesticide dissipation half-life in plants is an important factor to assessing environmental fate of pesticides and establishing pre-harvest intervals critical to good agriculture practices. However, empirically measured pesticide dissipation half-lives are highly variable and the accurate prediction with models is challenging. This study utilized a dataset of pesticide dissipation half-lives containing 1363 datapoints, 311 pesticides, 10 plant types, and 4 plant component classes. Novel dissipation half-life intervals were proposed and predicted to account for high variations in empirical data. Four machine learning models (i.e., gradient boosting regression tree [GBRT], random forest [RF], supporting vector classifier [SVC], and logistic regression [LR]) were developed to predict dissipation half-life intervals using extended connectivity fingerprints (ECFP), temperature, plant type, and plant component class as model inputs. GBRT-ECFP had the best model performance with F1-micro_binary score of 0.698 ± 0.010 for the binary classification compared with other machine learning models (e.g., LR-ECFP, F1-micro_binary= 0.662 ± 0.009). Feature importance analysis of molecular structures in the binary classification identified aromatic rings, carbonyl group, organophosphate, =C-H, and N-containing heterocyclic groups as important substructures related to pesticide dissipation half-lives. This study suggests the utility of machine learning models in assessing the environmental fate of pesticides in agricultural crops.

The application of machine learning methods for prediction of metal immobilization remediation by biochar amendment in soil

Biochar has been used widely in heavy metal contaminated sites as a soil remediation agent. However, due to the diversity of soils, biochars, and heavy metal contamination status, the remediation efficiency is difficult to measure, owing to a variety of parameters such as soil, biochar properties, and remediation procedure. Thus, an appropriate method to predict the remediation results and to select the appropriate biochar for the remediation is required. We initially created a database on soil remediation by biochars, which has 930 datasets with 74 biochars and 43 soils in it, based on collecting and organizing data from published literatures. Then, using data from the database, we modeled the remediation of five heavy metals and metalloids (lead, cadmium, arsenic, copper, and zinc) by biochars using machine learning (ML) methods such as artificial neural network (ANN) and random forest (RF) to predict remediation efficiency based on biochar characteristics, soil physiochemical properties, incubation conditions (e.g., water holding capacity and remediation time), and the initial state of heavy metal. The ANN and RF models outperform the lineal model in terms of accuracy and predictive performance (R² > 0.84). Meanwhile, model tolerance of the missing data and reliability of the interpolation were studied by the predicted outputs of the models. The results showed that both ANN and RF have excellent performances, with the RF model having a higher tolerance for missing data. Finally, through the interpretability of ML models, the contribution of factors used in the model were analyzed and the findings revealed that the most influential elements of remediation were the type of heavy metals, the pH value of biochar, and the dosage and remediation time. The relative importance of variables could provide the right direction for better remediation of heavy metals in soil.

Mechanism evolution and prediction of carbamazepine sorption by mangrove plant residue-derived biochars

A mechanism for carbamazepine (CBZ) sorption by mangrove plant residue-derived biochars pyrolyzed at 200-700 °C (referred as MPR200-MPR700) was elucidated in this study. The experimental results demonstrated that the dominant sorption mechanism of biochars for CBZ was evolved from partition to adsorption with increasing pyrolysis temperature. The CBZ concentration-dependent contributions of partition and adsorption were controlled by the relative noncarbonized and carbonized fractions of biochars. The partition medium changed from a polymeric aliphatic fraction (mangrove plant residue [MPR]200-MPR400) to a more condensed aromatic phase (MPR500-MPR600), which made the partition less favorable. Meanwhile, the adsorption was selectively regulated by polarity (MPR200-MPR300) and porosity (MPR400-MPR700) for different biochars. A pragmatic model including the sorbent aromaticity index (H/C) was put forward to predict CBZ sorption to MPR200-MPR700 and other carbonaceous materials reported in the literature. The findings can be helpful in understanding CBZ-biochar interactions and developing effective sorbents (such as biochars) for pollutant sequestration.

Source, fate, transport and modelling of selected emerging contaminants in the aquatic environment: Current status and future perspectives

The occurrence of emerging contaminants (ECs), such as pharmaceuticals and personal care products (PPCPs), perfluoroalkyl and polyfluoroalkyl substances (PFASs) and endocrine-disrupting chemicals (EDCs) in aquatic environments represent a major threat to water resources due to their potential risks to the ecosystem and humans even at trace levels. Mathematical modelling can be a useful tool as a comprehensive approach to study their fate and transport in natural waters. However, modelling studies of the occurrence, fate and transport of ECs in aquatic environments have generally received far less attention than the more widespread field and laboratory studies. In this study, we reviewed the current status of modelling ECs based on selected representative ECs, including their sources, fate and various mechanisms as well as their interactions with the surrounding environments in aquatic ecosystems, and explore future development and perspectives in this area. Most importantly, the principles, mathematical derivations, ongoing development and applications of various ECs models in different geographical regions are critically reviewed and discussed. The recommendations for improving data quality, monitoring planning, model development and applications were also suggested. The outcomes of this review can lay down a future framework in developing a comprehensive ECs modelling approach to help researchers and policymakers effectively manage water resources impacted by rising levels of ECs.

A review of the application of machine learning in water quality evaluation

With the rapid increase in the volume of data on the aquatic environment, machine learning has become an important tool for data analysis, classification, and prediction. Unlike traditional models used in water-related research, data-driven models based on machine learning can efficiently solve more complex nonlinear problems. In water environment research, models and conclusions derived from machine learning have been applied to the construction, monitoring, simulation, evaluation, and optimization of various water treatment and management systems. Additionally, machine learning can provide solutions for water pollution control, water quality improvement, and watershed ecosystem security management. In this review, we describe the cases in which machine learning algorithms have been applied to evaluate the water quality in different water environments, such as surface water, groundwater, drinking water, sewage, and seawater. Furthermore, we propose possible future applications of machine learning approaches to water environments.

Application of machine learning in predicting the adsorption capacity of organic compounds onto biochar and resin

Detailed prediction of the adsorption amounts of organic pollutants in water is essential to the clean development and management of water resources. In this study, Kriging and polyparameter linear free energy relationship model are coupled to predict adsorption capacity of organic pollutants by biochar and resin. It's based on 1750 adsorption experimental data sets which contains 73 organic compounds on 50 biochars and 30 polymer resins. The Kriging-LFER model shows better accuracy and predictive performance for adsorption (R² are 0.940 and 0.976) than the published NN-LFER model (R² are 0.870 and 0.880). Local sensitivity analysis method is adopted to evaluate the influence of each variable on the adsorption coefficient of resin and find out that top sensitive parameters are V and log C_e, to guide parameter optimization. Data's uncertainty analysis is presented by Monte Carlo method. It predicts that the adsorption coefficient will range from 0.062 to 0.189 under the 95% confidence interval. The Kriging-LFER model provides great significance for understanding the importance of various parameters, reducing the number of experiments, adjusting the direction of experimental improvement, and evaluating the fate of organic pollutants in the environment.

Linking the effect of temperature on adsorption from aqueous solution with solute dissociation

Imperative decarbonization of water purification processes entails alternative regeneration methods for activated carbon. Regeneration based on changing dissociation equilibria, i.e. a major influencing factor on adsorption, usually requires the addition of acids/bases, but may also be triggered by temperature swing. Although adsorption and dissociation are both temperature-dependent phenomena, their conjunction has received little attention regarding trace organic compounds (TrOCs) and large temperature intervals, in particular above ΔT ≥ 50 ^∘C. Therefore, we studied the adsorption equilibria of 16 TrOCs onto one granular activated carbon at temperatures ranging from 20 to 95 ^∘C. The majority of compounds (12/16) exhibited an exothermic apparent adsorption enthalpy, while 3 out of 16 exhibited an endothermic apparent enthalpy. The range spanned from - 46 to + 50 kJ mol^-1 (median at - 17 kJ mol^-1). The possible origins of endothermic adsorption were discussed. A rationale of shifting pK_a and thus changing dissociation of TrOCs was introduced and traded off against existing rationales, i.e. changing solute solubility, changing adsorption heat capacity, and saturation effects of the adsorbates. This knowledge may allow designing temperature swing adsorption processes that unlock the dissociation switch. The augmented process efficiency can thus provide the foundation for low-carbon emission, circular water purification processes.

Sorption and Mobility of Charged Organic Compounds: How to Confront and Overcome Limitations in Their Assessment

Permanently charged and ionizable organic compounds (IOC) are a large and diverse group of compounds belonging to many contaminant classes, including pharmaceuticals, pesticides, industrial chemicals, and natural toxins. Sorption and mobility of IOCs are distinctively different from those of neutral compounds. Due to electrostatic interactions with natural sorbents, existing concepts for describing neutral organic contaminant sorption, and by extension mobility, are inadequate for IOC. Predictive models developed for neutral compounds are based on octanol-water partitioning of compounds (K_ow) and organic-carbon content of soil/sediment, which is used to normalize sorption measurements (K_OC). We revisit those concepts and their translation to IOC (D_ow and D_OC) and discuss compound and soil properties determining sorption of IOC under water saturated conditions. Highlighting possible complementary and/or alternative approaches to better assess IOC mobility, we discuss implications on their regulation and risk assessment. The development of better models for IOC mobility needs consistent and reliable sorption measurements at well-defined chemical conditions in natural porewater, better IOC-, as well as sorbent characterization. Such models should be complemented by monitoring data from the natural environment. The state of knowledge presented here may guide urgently needed future investigations in this field for researchers, engineers, and regulators.

Identifying Balls Feature in a Large-Scale Laser Point Cloud of a Coal Mining Environment by a Multiscale Dynamic Graph Convolution Neural Network

In the process of coal mining, a certain amount of gas will be produced. Environmental perception is very important to realize intelligent and unmanned coal mine production and operation and to reduce the accident rate of gas explosion and other disasters. The identification of geometric features of the coal mine working face is the main part of the environmental perception of the working face. In this study, we identify geometric features in a large-scale coal mine working face point cloud (we take the ball as an example) so as to provide a method for the environmental perception of the coal mine working face. On the basis of previous research, we upgrade the dynamic graph convolution neural network (DGCNN) for directly processing point clouds from two aspects: extracting local features and global features of point clouds. First, a multiscale dynamic graph convolution neural network (MS-DGCNN) is proposed, and the combination of max-pooling and average-pooling is used as the symmetry function. Second, we use MS-DGCNN to learn the features of a variety of geometric point clouds in the point cloud data set we make and then look for the ball in the large-scale point cloud of the coal mining working face. Finally, we compare the performance of MS-DGCNN with that of other deep neural networks directly processing point clouds. This study enables MS-DGCNN to obtain more powerful feature expression ability and enhance the generalization of the model. In addition, this study provides a solid foundation for the geometric feature identification of MS-DGCNN in the environmental perception of the coal mine working face and creates a precedent for the application of MS-DGCNN in the field of energy. At the same time, this study makes a beneficial exploration for the development of a transparent coal mine working face.

Insights into the adsorption of pharmaceuticals and personal care products (PPCPs) on biochar and activated carbon with the aid of machine learning

The science-informed design of 'green' carbonaceous materials (e.g., biochar and activated carbon) with high removal capacity of recalcitrant organic contaminants (e.g., pharmaceuticals and personal care products (PPCPs)) is indispensable for promoting sustainable wastewater treatment. In this study, machine learning (ML) incorporating PPCPs and biochar properties as well as adsorption conditions were applied to build adsorption prediction models and explore the contributions of various biochar's inherent properties to their PPCPs adsorption capacity. The results demonstrated that the models developed by detailed biochar properties (e.g., surface functionality and hierarchical porous structure) from advanced microscopic and spectroscopic techniques were more accurate (i.e., the root-mean-square error decreased by 18-24%) than those by general information such as bulk elemental composition and total pore volume. The relative importance of surface carbon functionalities ranked in the order of C-O bond > CO bond > non-polar carbon for predicting the adsorption capacity. According to the partial dependence analysis, the average pore diameters of adsorbents that were larger than the maximum diameter of PPCPs molecules by 1.5-fold to 2.5-fold favored the PPCPs adsorption. This study reveals new insights into the adsorption of PPCPs and provides a comprehensive reference for the sustainable engineering of biochar adsorbents for PPCPs wastewater treatment.

Combining Experimental Sorption Parameters with QSAR to Predict Neonicotinoid and Transformation Product Sorption to Carbon Nanotubes and Granular Activated Carbon

We recently discovered that transformation of the neonicotinoid insecticidal pharmacophore alters sorption propensity to activated carbon, with products adsorbing less than parent compounds. To assess the environmental fate of novel transformation products that lack commercially available standards, researchers must rely on predictive approaches. In this study, we combined computationally derived quantitative structure-activity relationship (QSAR) parameters for neonicotinoids and neonicotinoid transformation products with experimentally determined Freundlich partition constants (log K F for sorption to carbon nanotubes [CNTs] and granular activated carbon [GAC]) to model neonicotinoid and transformation product sorption. QSAR models based on neonicotinoid sorption to functionalized/nonfunctionalized CNTs (used to generalize/simplify neonicotinoid-GAC interactions) were iteratively generated to obtain a multiple linear regression that could accurately predict neonicotinoid sorption to CNTs using internal and external validation (within 0.5 log units of the experimentally determined value). The log K F,CNT values were subsequently related to log K F,GAC where neonicotinoid sorption to GAC was predicted within 0.3 log-units of experimentally determined values. We applied our neonicotinoid-specific model to predict log K F,GAC for a suite of novel neonicotinoid transformation products (i.e., formed via hydrolysis, biotransformation, and chlorination) that do not have commercially available standards. We present this modeling approach as an innovative yet relatively simple technique to predict fate of highly specialized/unique polar emerging contaminants and/or transformation products that cannot be accurately predicted via traditional methods (e.g., pp-LFER), and highlights molecular properties that drive interactions of emerging contaminants.

Potential application of machine learning for exploring adsorption mechanisms of pharmaceuticals onto biochars

The increasing accumulation of pharmaceuticals in aquatic ecosystems could impair freshwater quality and threaten human health. Despite the adsorption of pharmaceuticals on biochars is one of the most cost-effective and eco-friendly removal methods, the wide variation of experimental designs and research aims among previous studies pose significant challenge in selecting biochar for optimal removal. In this work, literature data of 1033 sets with 21 variables collected from 267 papers over ten years (2010-2020) covering 19 pharmaceuticals onto 88 biochars were assessed by different machine learning (ML) algorithms i.e., Linear regression model (LM), Feed-forward neural networks (NNET), Deep neutral networks (DNN), Cubist, K-nearest neighbor (KNN), and Random forest (RF), to predict equilibrium adsorption capacity (Q_e) and explore adsorption mechanisms. LM showed the best performance on ranking importance of input variables. Except for initial concentration of pharmaceuticals, Q_e was strongly governed by biochars' properties including specific surface area (BET), pore volume (PV), and pore structure (PS) rather than pharmaceuticals' properties and experimental conditions. The most accurate model for estimating Q_e was achieved by Cubist, followed by KNN, RF, KNN, NNET and LM. The generalization ability was observed by the tuned Cubist with 26 rules for the prediction of the unseen data. This study not only provides an insightful evidence for data-based adsorption mechanisms of pharmaceuticals on biochars, but also offers a potential method to accurately predict the biochar adsorption performance without conducting any experiments, which will be of high interests in practice in terms of water/wastewater treatment using biochars.

Direct Prediction of Bioaccumulation of Organic Contaminants in Plant Roots from Soils with Machine Learning Models Based on Molecular Structures

Root concentration factor (RCF) is an important characterization parameter to describe accumulation of organic contaminants in plants from soils in life cycle impact assessment (LCIA) and phytoremediation potential assessment. However, building robust predictive models remains challenging due to the complex interactions among chemical-soil-plant root systems. Here we developed end-to-end machine learning models to devolve the complex molecular structure relationship with RCF by training on a unified RCF data set with 341 data points covering 72 chemicals. We demonstrate the efficacy of the proposed gradient boosting regression tree (GBRT) model based on the extended connectivity fingerprints (ECFP) by predicting RCF values and achieved prediction performance with R-squared of 0.77 and mean absolute error (MAE) of 0.22 using 5-fold cross validation. In addition, our results reveal nonlinear relationships among properties of chemical, soil, and plant. Further in-depth analyses identify the key chemical topological substructures (e.g., -O, -Cl, aromatic rings and large conjugated π systems) related to RCF. Stemming from its simplicity and universality, the GBRT-ECFP model provides a valuable tool for LCIA and other environmental assessments to better characterize chemical risks to human health and ecosystems.

Adsorption of polar and ionic organic compounds on activated carbon: Surface chemistry matters

Persistent and mobile organic compounds (PMOCs) are often detected micropollutants in the water cycle, thereby challenging the conventional wastewater and drinking water treatment techniques. Carbon-based adsorbents are often less effective or even unable to remove this class of pollutants. Understanding of PMOC adsorption mechanisms is urgently needed for advanced treatment of PMOC-contaminated water. Here, we investigated the effect of surface modifications of activated carbon felts (ACFs) on the adsorption of six selected PMOCs carrying polar or ionic groups. Among three ACFs, defunctionalized ACF bearing net positive surface charge at neutral pH provides the most versatile sorption efficiency for all studied PMOC types representing neutral, anionic and cationic compounds. Ion exchange capacity giving quantitative information of sorbent surface charges at specified pH is recognized as a frequently underestimated key property for evaluating adsorbents aiming at PMOC adsorption. A most recently developed prediction tool for Freundlich parameters in PMOC adsorption was applied and the prediction results are compared to the experimental data. The comparison demonstrates the so far underestimated importance of the sorbent surface chemistry for PMOC adsorption affinity and capacity. PMOC adsorption mechanisms were additionally investigated by adsorption experiments at various temperatures, pH values and electrolyte concentrations. Exothermic sorption was observed for all sorbate-sorbent pairs. Adsorption is improved for ionic PMOCs on AC carrying sites of the same charge (positive or negative) at increased electrolyte concentration, while not affected for neutral PMOCs unless strong electron donor-acceptor yet weak non-Coulombic interactions exist. Our findings will allow for better design and targeted application of activated carbon-based sorbents in water treatment facilities.

Modern Carbon-Based Materials for Adsorptive Removal of Organic and Inorganic Pollutants from Water and Wastewater

Currently, a serious threat for living organisms and human life in particular, is water contamination with persistent organic and inorganic pollutants. To date, several techniques have been adopted to remove/treat organics and toxic contaminants. Adsorption is one of the most effective and economical methods for this purpose. Generally, porous materials are considered as appropriate adsorbents for water purification. Conventional adsorbents such as activated carbons have a limited possibility of surface modification (texture and functionality), and their adsorption capacity is difficult to control. Therefore, despite the significant progress achieved in the development of the systems for water remediation, there is still a need for novel adsorptive materials with tunable functional characteristics. This review addresses the new trends in the development of new adsorbent materials. Herein, modern carbon-based materials, such as graphene, oxidized carbon, carbon nanotubes, biomass-derived carbonaceous matrices-biochars as well as their composites with metal-organic frameworks (MOFs) and MOF-derived highly-ordered carbons are considered as advanced adsorbents for removal of hazardous organics from drinking water, process water, and leachate. The review is focused on the preparation and modification of these next-generation carbon-based adsorbents and analysis of their adsorption performance including possible adsorption mechanisms. Simultaneously, some weak points of modern carbon-based adsorbents are analyzed as well as the routes to conquer them. For instance, for removal of large quantities of pollutants, the combination of adsorption and other methods, like sedimentation may be recommended. A number of efficient strategies for further enhancing the adsorption performance of the carbon-based adsorbents, in particular, integrating approaches and further rational functionalization, including composing these adsorbents (of two or even three types) can be recommended. The cost reduction and efficient regeneration must also be in the focus of future research endeavors. The targeted optimization of the discussed carbon-based adsorbents associated with detailed studies of the adsorption process, especially, for multicomponent adsorbate solution, will pave a bright avenue for efficient water remediation.

Machine learning in natural and engineered water systems

Water resources of desired quality and quantity are the foundation for human survival and sustainable development. To better protect the water environment and conserve water resources, efficient water management, purification, and transportation are of critical importance. In recent years, machine learning (ML) has exhibited its practicability, reliability, and high efficiency in numerous applications; furthermore, it has solved conventional and emerging problems in both natural and engineered water systems. For example, ML can predict various water quality indicators in situ and real-time by considering the complex interactions among water-related variables. ML approaches can also solve emerging pollution problems with proven rules or universal mechanisms summarized from the related research. Moreover, by applying image recognition technology to analyze the relationships between image information and physicochemical properties of the research object, ML can effectively identify and characterize specific contaminants. In view of the bright prospects of ML, this review comprehensively summarizes the development of ML applications in natural and engineered water systems. First, the concept and modeling steps of ML are briefly introduced, including data preparation, algorithm selection and model evaluation. In addition, comprehensive applications of ML in recent studies, including predicting water quality, mapping groundwater contaminants, classifying water resources, tracing contaminant sources, and evaluating pollutant toxicity in natural water systems, as well as modeling treatment techniques, assisting characterization analysis, purifying and distributing drinking water, and collecting and treating sewage water in engineered water systems, are summarized. Finally, the advantages and disadvantages of commonly used algorithms are analyzed according to their structures and mechanisms, and recommendations on the selection of ML algorithms for different studies, as well as prospects on the application and development of ML in water science are proposed. This review provides references for solving a wider range of water-related problems and brings further insights into the intelligent development of water science.

MIL-101(Fe)-derived magnetic porous carbon as sorbent for stir bar sorptive-dispersive microextraction of sulfonamides

Using MIL-101(Fe) as the source of carbon and Fe, a magnetic porous carbon (MPC) material with Fe₃C nanoparticles encapsulated in porous carbon was prepared through one-pot pyrolysis under N₂ atmosphere. With MPC as adsorption material, a stir bar sorptive-dispersive microextraction (SBSDME) method was proposed to extract and preconcentrate sulfonamides (SAs) prior to HPLC-DAD determination. To investigate their extraction ability, different MPC materials were prepared under different carbonization temperatures (600, 700, 800, 900, and 1000 °C). The material prepared under 900 °C (MPC-900) exhibited the highest extraction ability for SAs. The as-prepared MPC materials were also characterized by Raman spectroscopy, scanning electron microscopy, X-ray photoelectron spectroscopy, zeta potential, and other techniques. The main parameters that affect extraction were systematically studied. Under optimal conditions, favorable linearity (R² ≥ 0.9938) and detection limits (0.02-0.04 ng mL^-1) of sulfonamides were obtained. The average recoveries for spiked milk and lake water samples ranged from 76.9 to 109% and from 75.4 to 118% with RSDs of 3.10-9.63% and 1.71-11.3%, respectively. Sulfameter and sulfisoxazole were detected in milk sample. Sulfisoxazole was detected in the lake water sample. The MPC-900 material demonstrated excellent reusability. It can be reused 24 times with peak areas having no obvious decline. The method can be applied to extract ultra-trace compounds in complex sample matrices. Schematic presentation of a stir bar sorptive-dispersive microextraction (SBSDME) by using magnetic porous carbon (MPC) composites as sorbent combined with high-performance liquid chromatography for sensitive analysis of sulfonamides in milk and lake water samples.

Novel and wet-resilient cellulose nanofiber cryogels with tunable porosity and improved mechanical strength for methyl orange dyes removal

Interconnected macro-porous cryogels with robust and pore-tunable structures have been fabricated using chemically crosslinked microfibrillated cellulose (MFC). Periodate oxidation was initially conducted to introduce aldehyde groups into the MFC surface, followed by the freeze-induced chemical crosslinking via the formation of hemiacetal bonds between aldehyde and hydroxyl at -12 °C. The cryogels with pore-tunable structures and sharply enhanced mechanical strengths were finally achieved by re-assembly of MFCs through soaking in NaIO₄ solution. Furthermore, the MFC cryogels were post-crosslinked by polyethyleneimine (PEI), bestowing the cryogels with the capability of adsorbing anionic dyes. The stress of the PEI-MFC cryogel at the 80% strain was determined to be 304.5 kPa, which is the maximum value for the nanocellulose-based cryogels reported so far. Finally, the adsorption performances of PEI-MFC cryogels for methyl orange (MO) were evaluated. Maximum adsorption capacity of 500 mg/g could be obtained by the Langmuir model, outperforming that of previous absorbent materials. Reuse experiments indicated that over 90% of adsorption capacity was retained after 6 cycles. Continuous clean-up experiments demonstrated excellent MO removal abilities of the PEI-MFC cryogel. This study shows that the novel, green strategy to fabricate the robust cryogel extends the practical applications of nanocellulose adsorbents for environmental remediation.