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

A machine learning framework to predict PPCP removal through various wastewater and water reuse treatment trains

The persistence of pharmaceuticals and personal care products (PPCPs) through wastewater treatment and resulting contamination of aquatic environments and drinking water is a pervasive concern, necessitating means of identifying effective treatment strategies for PPCP removal. In this study, we employed machine learning (ML) models to classify 149 PPCPs based on their chemical properties and predict their removal via wastewater and water reuse treatment trains. We evaluated two distinct clustering approaches: C1 (clustering based on the most efficient individual treatment process) and C2 (clustering based on the removal pattern of PPCPs across treatments). For this, we grouped PPCPs based on their relative abundances by comparing peak areas measured via non-target profiling using ultra-performance liquid chromatography-tandem mass spectrometry through two field-scale treatment trains. The resulting clusters were then classified using Abraham descriptors and log K ow as input to the three ML models: support vector machines (SVM), logistic regression, and random forest (RF). SVM achieved the highest accuracy, 79.1%, in predicting PPCP removal. Notably, a 58-75% overlap was observed between the ML clusters of PPCPs and the Abraham descriptor and log K ow clusters of PPCPs, indicating the potential of using Abraham descriptors and log K ow to predict the fate of PPCPs through various treatment trains. Given the myriad of PPCPs of concern, this approach can supplement information gathered from experimental testing to help optimize the design of wastewater and water reuse treatment trains for PPCP removal.

Dual cross-linked magnetic gelatin/carboxymethyl cellulose cryogels for enhanced Congo red adsorption: Experimental studies and machine learning modelling

To achieve highly efficient and environmentally degradable adsorbents for Congo red (CR) removal, we synthesized a dual-network nanocomposite cryogel composed of gelatin/carboxymethyl cellulose, loaded with Fe3O4 nanoparticles. Gelatin and sodium carboxymethylcellulose were cross-linked using transglutaminase and calcium chloride, respectively. The cross-linking process enhanced the thermal stability of the composite cryogels. The CR adsorption process exhibited a better fit to the pseudo-second-order model and Langmuir model, with maximum adsorption capacity of 698.19 mg/g at pH of 7, temperature of 318 K, and initial CR concentration of 500 mg/L. Thermodynamic results indicated that the CR adsorption process was both spontaneous and endothermic. The performance of machine learning model showed that the Extreme Gradient Boosting model had the highest test determination coefficient (R2 = 0.9862) and the lowest root mean square error (RMSE = 10.3901 mg/g) among the 6 models. Feature importance analysis using SHapley Additive exPlanations (SHAP) revealed that the initial concentration had the greatest influence on the model's prediction of adsorption capacity. Density functional theory calculations indicated that there were active sites on the CR molecule that can undergo electrostatic interactions with the adsorbent. Thus, the synthesized cryogels demonstrate promising potential as adsorbents for dye removal from wastewater.

A novel interpretable machine learning and metaheuristic-based protocol to predict and optimize ciprofloxacin antibiotic adsorption with nano-adsorbent

The existence of antibiotics in water sources poses substantial hazards to both the environment and public health. To effectively monitor and combat this problem, accurate predictive models are essential. This research focused on employing machine learning (ML) techniques to construct some models for analyzing the adsorption capacity of ciprofloxacin (CIP) antibiotic from contaminated water. The robustness of ten machine learning algorithms was evaluated using performance metrics such as the Coefficient of determination (R2), Mean Square Error (MSE), Median Absolute Error (MedAE), Mean Absolute Error (MAE), Correlation coefficient (R), Nash-Sutcliffe Efficiency (NSE), Kling-Gupta Efficiency (KGE), and Root Mean Square Error (RMSE). The hyperparameters of the ML models were fine-tuned using the Bayesian optimization algorithm. The optimized models were comprehensively evaluated using feature importance analysis to quantify the relative significance of operational variables accurately. After a thorough assessment and comparison of various machine learning models, it was evident that the HistGradientBoosting (HGB) model outperformed others in terms of CIP adsorption performance. This was supported by their low MAE value of 0.1865 and high R2 value of 0.9999. The modeling projected the highest antibiotic adsorption (99.28%) under optimized conditions, including 10 mg/L of CIP, 357 mg/L of CuWO4@TiO2 adsorbent, a contact time of 60 min at room temperature, and near neutral pH (7.5). The combination of advanced ML algorithms and nano adsorbents has great potential for addressing the problem of antibiotic pollution in water sources.

Nanomaterial Texture-Based Machine Learning of Ciprofloxacin Adsorption on Nanoporous Carbon

Drug substances in water bodies and groundwater have become a significant threat to the surrounding environment. This study focuses on the ability of the nanoporous carbon materials to remove ciprofloxacin from aqueous solutions under specific experimental conditions and on the development of the mathematical model that would allow describing the molecular interactions of the adsorption process and calculating the adsorption capacity of the material. Thus, based on the adsorption measurements of the 87 carbon materials, it was found that, depending on the porosity and pore size distribution, adsorption capacity values varied between 55 and 495 mg g-1. For a more detailed analysis of the effects of different carbon textures and pores characteristics, a Quantitative nano-Structure-Property Relationship (QnSPR) was developed to describe and predict the ability of a nanoporous carbon material to remove ciprofloxacin from aqueous solutions. The adsorption capacity of potential nanoporous carbon-based adsorbents for the removal of ciprofloxacin was shown to be sufficiently accurately described by a three-parameter multi-linear QnSPR equation (R2 = 0.70). This description was achieved only with parameters describing the texture of the carbon material such as specific surface area (Sdft) and pore size fractions of 1.1-1.2 nm (VN21.1-1.2) and 3.3-3.4 nm (VN23.3-3.4) for pores.

Predicting and refining acid modifications of biochar based on machine learning and bibliometric analysis: Specific surface area, average pore size, and total pore volume

Acid-modified biochar is a modified biochar material with convenient preparation, high specific surface area, and rich pore structure. It has great potential for application in the heavy metal remediation, soil amendments, and carrying catalysts. Specific surface area (SSA), average pore size (APS), and total pore volume (TPV) are the key properties that determine its adsorption capacity, reactivity, and water holding capacity, and an intensive study of these properties is essential to optimize the performance of biochar. But the complex interactions among the preparation conditions obstruct finding the optimal modification strategy. This study collected dataset through bibliometric analysis and used four typical machine learning models to predict the SSA, APS, and TPV of acid-modified biochar. The results showed that the extreme gradient boosting (XGB) was optimal for the test results (SSA R2 = 0.92, APS R2 = 0.87, TPV R2 = 0.96). The model interpretation revealed that the modification conditions were the major factors affecting SSA and TPV, and the pyrolysis conditions were the major factors affecting APS. Based on the XGB model, the modification conditions of biochar were optimized, which revealed the ideal preparation conditions for producing the optimal biochar (SSA = 727.02 m2/g, APS = 5.34 nm, TPV = 0.68 cm3/g). Moreover, the biochar produced under specific conditions verified the generalization ability of the XGB model (R2 = 0.99, RMSE = 12.355). This study provides guidance for optimizing the preparation strategy of acid-modified biochar and promotes its potentiality for industrial application.

Estimation of gross calorific value of coal based on the cubist regression model

The gross calorific value (GCV) of coal is an important parameter for evaluating coal quality, and regression analysis methods can be used to predict GCV. In this study, we proposed a GCV prediction model based on cubist regression. To develop a good regression model, feature selection of input variables was performed using a correlation analysis and a recursive feature elimination algorithm. Thus, in this study, we determined three sets of variables as the optimal combination for regression models: proximate analysis variables (Set 1: moisture, standard ash, and volatile matter), element analysis variables (Set 2: carbon, sulfur, and oxygen), and comprehensive index variables (Set 3: carbon, volatile matter, standard ash, sulfur, moisture, and hydrogen). Results for comparison with multiple linear regression, random forest regression, and numerous previous prediction models, such as gradient boosting regression tree, support vector regression (SVR), backpropagation neural networks, and particle swarm optimization-artificial neural network (PSO-ANN), indicate that these seven regression models have the best fitting effect on the comprehensive index variables among the three sets of input variables. The cubist model showed higher prediction accuracy and lower error than most other models (R2, mean absolute error, root mean square error, and average absolute relative deviation percentage values are 0.990, 0.476, 0.668, and 0.086% for the proximate analysis variables; 0.992, 0.381, 0.596, and 0.140% for element analysis variables; and 0.999, 0.161, 0.219, and 0.087% for comprehensive index variables, respectively). The cubist model combines the advantages of decision tree and linear regression, which not only enables it to perform well in terms of accuracy but also makes the model highly interpretable because it is based on multiple sublinear equations. In addition, the cubist model shows obvious advantages in terms of running speed, especially compared with SVR and PSO-ANN, which require complex parameter optimization. In summary, the cubist model considers the prediction accuracy, model interpretability, and computational efficiency as well as provides a new and effective method for GCV prediction.

Machine learning-based prediction of adsorption capacity of metal-doped and undoped activated carbon: Assessing the role of metal doping

This research developed five ensemble-based machine learning (ML) models to predict the adsorption capacity of both pristine and metal-doped activated carbon (AC) and identified key influencing features. Results indicated that Extreme Gradient Boosting (XGB) model provided the most accurate predictions for both types of AC, with metal-doped AC exhibiting 1.7 times higher adsorption capacity than pristine AC showing 254.66 and 148.28 mg/g, respectively. Feature analysis using SHAP values revealed that adsorbent characteristics accounted for 53.5 % of the adsorption capacity in pristine AC, while experimental conditions were crucial for metal-doped AC (61.3%), with surface area and initial concentration being the most significant features, showing mean SHAP values of 0.317 and 0.117, respectively. Statistical comparisons of adsorbent characteristics between pristine and metal-doped AC showed that metal doping significantly altered surface area (p-value = 0.0014), pore volume (p-value = 0.0029), and elemental composition (C% (p-value = 3.9513∗10^-7) and O% (p-value = 0.0007)) of AC. Despite the reduction in surface area and consistent pore volume after metal doping, the enhanced adsorption capacity of metal-doped AC was attributed to increased oxygen content from 10.89% to 17.28 % as mean values. This suggests that oxygen-containing functional groups play a critical role in the improved adsorption capacity of metal-doped AC. This research lays the groundwork for optimizing AC adsorbents by identifying key factors in metal-doped AC and suggest further studies on the interaction between specific metal dopants and resulting functional groups to improve adsorption capacity and reduce repeated labor work.

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.

Status and future trends in wastewater management strategies using artificial intelligence and machine learning techniques

The two main things needed to fulfill the world's impending need for water in the face of the widespread water crisis are collecting water and recycling. To do this, the present study has placed a greater focus on water management strategies used in a variety of contexts areas. To distribute water effectively, save it, and satisfy water quality requirements for a variety of uses, it is imperative to apply intelligent water management mechanisms while keeping in mind the population density index. The present review unveiled the latest trends in water and wastewater recycling, utilizing several Artificial Intelligence (AI) and machine learning (ML) techniques for distribution, rainfall collection, and control of irrigation models. The data collected for these purposes are unique and comes in different forms. An efficient water management system could be developed with the use of AI, Deep Learning (DL), and the Internet of Things (IoT) structure. This study has investigated several water management methodologies using AI, DL and IoT with case studies and sample statistical assessment, to provide an efficient framework for water management.

Coupling machine learning and theoretical models to compare key properties of biochar in adsorption kinetics rate and maximum adsorption capacity for emerging contaminants

Insights into key properties of biochar with a fast adsorption rate and high adsorption capacity are urgent to design biochar as an adsorbent in pollution emergency treatment. Machine learning (ML) incorporating classical theoretical adsorption models was applied to build prediction models for adsorption kinetics rate (i.e., K) and maximum adsorption capacity (i.e., Qm) of emerging contaminants (ECs) on biochar. Results demonstrated that the prediction performance of adaptive boosting algorithm significantly improved after data preprocessing (i.e., log-transformation) in the small unbalanced datasets with R2 of 0.865 and 0.874 for K and Qm, respectively. The surface chemistry, primarily led by ash content of biochar significantly influenced the K, while surface porous structure of biochar showed a dominant role in predicting Qm. An interactive platform was deployed for relevant scientists to predict K and Qm of new biochar for ECs. The research provided practical references for future engineered biochar design for ECs removal.

Machine learning-based exploration of biochar for environmental management and remediation

Biochar has a wide range of applications, including environmental management, such as preventing soil and water pollution, removing heavy metals from water sources, and reducing air pollution. However, there are several challenges associated with the usage of biochar for these purposes, resulting in an abundance of experimental data in the literature. Accordingly, the purpose of this study is to examine the use of machine learning in biochar processes with an eye toward the potential of biochar in environmental remediation. First, recent developments in biochar utilization for the environment are summarized. Then, a bibliometric analysis is carried out to illustrate the major trends (demonstrating that the top three keywords are heavy metal, wastewater, and adsorption) and construct a comprehensive perspective for future studies. This is followed by a detailed review of machine learning applications, which reveals that adsorption efficiency and capacity are the primary utilization targets in biochar utilization. Finally, a comprehensive perspective is provided for the future. It is then concluded that machine learning can help to detect hidden patterns and make accurate predictions for determining the combination of variables that results in the desired properties which can be later used for decision-making, resource allocation, and environmental management.

Machine-learning-driven discovery of metal-organic framework adsorbents for hexavalent chromium removal from aqueous environments

HYPOTHESIS

Metal-organic frameworks (MOFs) have been widely studied for Cr(VI) adsorption in water. Theoretically, numerous MOFs can be synthesised by assembling diverse metals and ligands. However, the traditional manual experimentation for screening high-performance MOFs is resource-intensive and inefficient.

EXPERIMENTS

A screening strategy for MOFs based on machine learning was proposed for the adsorption and removal of Cr(VI) from water. By collecting the characteristics of MOFs and the experimental parameters of Cr(VI) adsorption from the literature, a dataset was constructed to predict the adsorption performance. Among the six regression models, the model trained by the extreme gradient boosted tree algorithm had the best performance and was used to simulate the adsorption and screen potential high-performance adsorbents.

FINDINGS

Structure-property analysis indicated that prepared MOF adsorbents with properties of 0.37 < largest cavity diameter < 0.71 nm, 0.18 < pore volume < 0.57 cm3/g, 412 < specific surface area < 1588 m2/g, 0.43 < void fraction < 0.62 will achieve enhanced adsorption of Cr(VI) in water. High-performance adsorbents were successfully screened using a combination of machine-learning prediction and analysis. Experiments were conducted to verify the exceptional adsorption capacity of UiO-66 and MOF-801. This method effectively identified adsorbents and accelerated the development of new MOF adsorbents for contaminant removal, providing a novel approach for the discovery of superior adsorbents.

Exploring optical descriptors for rapid estimation of coastal sediment organic carbon and nearby land-use classifications via machine learning models

This study utilizes ultraviolet and fluorescence spectroscopic indices of dissolved organic matter (DOM) from sediments, combined with machine learning (ML) models, to develop an optimized predictive model for estimating sediment total organic carbon (TOC) and identifying adjacent land-use types in coastal sediments from the Yellow and Bohai Seas. Our results indicate that ML models surpass traditional regression techniques in estimating TOC and classifying land-use types. Penalized Least Squares Regression (PLR) and Cubist models show exceptional TOC estimation capabilities, with PLR exhibiting the lowest training error and Cubist achieving a correlation coefficient 0.79. In land-use classification, Support Vector Machines achieved 85.6 % accuracy in training and 92.2 % in testing. Maximum fluorescence intensity and ultraviolet absorbance at 254 nm were crucial factors influencing TOC variations in coastal sediments. This study underscores the efficacy of ML models utilizing DOM optical indices for near real-time estimation of marine sediment TOC and land-use classification.

Active Learning-Based Guided Synthesis of Engineered Biochar for CO2 Capture

Biomass waste-derived engineered biochar for CO2 capture presents a viable route for climate change mitigation and sustainable waste management. However, optimally synthesizing them for enhanced performance is time- and labor-intensive. To address these issues, we devise an active learning strategy to guide and expedite their synthesis with improved CO2 adsorption capacities. Our framework learns from experimental data and recommends optimal synthesis parameters, aiming to maximize the narrow micropore volume of engineered biochar, which exhibits a linear correlation with its CO2 adsorption capacity. We experimentally validate the active learning predictions, and these data are iteratively leveraged for subsequent model training and revalidation, thereby establishing a closed loop. Over three active learning cycles, we synthesized 16 property-specific engineered biochar samples such that the CO2 uptake nearly doubled by the final round. We demonstrate a data-driven workflow to accelerate the development of high-performance engineered biochar with enhanced CO2 uptake and broader applications as a functional material.

Transformer-based deep learning models for adsorption capacity prediction of heavy metal ions toward biochar-based adsorbents

Biochar adsorbents synthesized from food and agricultural wastes are commonly applied to eliminate heavy metal (HM) ions from wastewater. However, biochar's diverse characteristics and varied experimental conditions make the accurate estimation of their adsorption capacity (qe) challenging. Herein, various machine-learning (ML) and three deep learning (DL) models were built using 1518 data points to predict the qe of HM on various biochars. The recursive feature elimination technique with 28 inputs suggested that 14 inputs were significant for model building. FT-transformer with the highest test R2 (0.98) and lowest root mean square error (RMSE) (0.296) and mean absolute error (MAE) (0.145) outperformed various ML and DL models. The SHAP feature importance analysis of the FT-transformer model predicted that the adsorption conditions (72.12%) were more important than the pyrolysis conditions (25.73%), elemental composition (1.39%), and biochar's physical properties (0.73%). The two-feature SHAP analysis proposed the optimized process conditions including adsorbent loading of 0.25 g, initial concentration of 12 mg/L, and solution pH of 9 using phosphoric-acid pre-treated biochar synthesized from banana-peel with a higher O/C ratio. The t-SNE technique was applied to transform the 14-input matrix of the FT-Transformer into two-dimensional data. Finally, we outlined the study's environmental implications.

Improving algal bloom detection using spectroscopic analysis and machine learning: A case study in a large artificial reservoir, South Korea

The prediction of algal blooms using traditional water quality indicators is expensive, labor-intensive, and time-consuming, making it challenging to meet the critical requirement of timely monitoring for prompt management. Using optical measures for forecasting algal blooms is a feasible and useful method to overcome these problems. This study explores the potential application of optical measures to enhance algal bloom prediction in terms of prediction accuracy and workload reduction, aided by machine learning (ML) models. Compared to absorption-derived parameters, commonly used fluorescence indices such as the fluorescence index (FI), humification index (HIX), biological index (BIX), and protein-like component improved the prediction accuracy. However, the prediction accuracy was decreased when all optical indices were considered for computation due to increased noise and uncertainty in the models. With the exception of chemical oxygen demand (COD), this study successfully replaced biochemical oxygen demand (BOD), dissolved organic carbon (DOC), and nutrients with selected fluorescence indices, demonstrating relatively analogous performance in either training or testing data, with consistent and good coefficient of determination (R2) values of approximately 0.85 and 0.74, respectively. Among all models considered, ensemble learning models consistently outperformed conventional regression models and artificial neural networks (ANNs). However, there was a trade-off between accuracy and computation efficiency among the ensemble learning models (i.e., Stacking and XGBoost) for algal bloom prediction. Our study offers a glimpse of the potential application of spectroscopic measures to improve accuracy and efficiency in algal bloom prediction, but further work should be carried out in other water bodies to further validate our proposed hypothesis.

Biochar production and its environmental applications: Recent developments and machine learning insights

Biochar production through thermochemical processing is a sustainable biomass conversion and waste management approach. However, commercializing biochar faces challenges requiring further research and development to maximize its potential for addressing environmental concerns and promoting sustainable resource management. This comprehensive review presents the state-of-the-art in biochar production, emphasizing quantitative yield and qualitative properties with varying feedstocks. It discusses the technology readiness level and commercialization status of different production strategies, highlighting their environmental and economic impacts. The review focuses on integrating machine learning algorithms for process control and optimization in biochar production, improving efficiency. Additionally, it explores biochar's environmental applications, including soil amendment, carbon sequestration, and wastewater treatment, showcasing recent advancements and case studies. Advances in biochar technologies and their environmental benefits in various sectors are discussed herein.

Machine learning implemented exploration of the adsorption mechanism of carbon dioxide onto porous carbons

Adsorption of CO₂ on porous carbons has been identified as one of the promising methods for carbon capture, which is essential for meeting the sustainable developmental goal (SDG) with respect to climate action, i.e., SDG 13. This research implemented six supervised machine learning (ML) models (gradient boosting decision tree (GBDT), extreme gradient boosting (XGB), light boost gradient machine (LBGM), random forest (RF), categorical boosting (Catboost), and adaptive boosting (Adaboost)) to understand and predict the CO₂ adsorption mechanism and adsorption uptake, respectively. The results recommended that the GBDT outperformed the remaining five ML models for CO₂ adsorption. However, XGB, LBGM, RF, and Catboost also represented the prediction in the acceptable range. The GBDT model indicated the accurate prediction of CO₂ uptake onto the porous carbons considering adsorbent properties and adsorption conditions as model input parameters. Next, two-factor partial dependence plots revealed a lucid explanation of how the combinations of two input features affect the model prediction. Furthermore, SHapley Additive exPlainations (SHAP), a novel explication approach based on ML models, were employed to understand and elucidate the CO₂ adsorption and model prediction. The SHAP explanations, implemented on the GBDT model, revealed the rigorous relationships among the input features and output variables based on the GBDT prediction. Additionally, SHAP provided clear-cut feature importance analysis and individual feature impact on the prediction. SHAP also explained two instances of CO₂ adsorption. Along with the data-driven insightful explanation of CO₂ adsorption onto porous carbons, this study also provides a promising method to predict the clear-cut performance of porous carbons for CO₂ adsorption without performing any experiments and open new avenues for researchers to implement this study in the field of adsorption because a lot of data is being generated.

Aqueous Pb(II) Removal Using ZIF-60: Adsorption Studies, Response Surface Methodology and Machine Learning Predictions

Zeolitic imidazolate frameworks (ZIFs) are increasingly gaining attention in many application fields due to their outstanding porosity and thermal stability, among other exceptional characteristics. However, in the domain of water purification via adsorption, scientists have mainly focused on ZIF-8 and, to a lesser extent, ZIF-67. The performance of other ZIFs as water decontaminants is yet to be explored. Hence, this study applied ZIF-60 for the removal of lead from aqueous solutions; this is the first time ZIF-60 has been used in any water treatment adsorption study. The synthesized ZIF-60 was subjected to characterization using FTIR, XRD and TGA. A multivariate approach was used to investigate the effect of adsorption parameters on lead removal and the findings revealed that ZIF-60 dose and lead concentration are the most significant factors affecting the response (i.e., lead removal efficiency). Further, response surface methodology-based regression models were generated. To further explore the adsorption performance of ZIF-60 in removing lead from contaminated water samples, adsorption kinetics, isotherm and thermodynamic investigations were conducted. The findings revealed that the obtained data were well-fitted by the Avrami and pseudo-first-order kinetic models, suggesting that the process is complex. The maximum adsorption capacity (q_max) was predicted to be 1905 mg/g. Thermodynamic studies revealed an endothermic and spontaneous adsorption process. Finally, the experimental data were aggregated and used for machine learning predictions using several algorithms. The model generated by the random forest algorithm proved to be the most effective on the basis of its significant correlation coefficient and minimal root mean square error (RMSE).

Preparation of Biochar with Developed Mesoporous Structure from Poplar Leaf Activated by KHCO3 and Its Efficient Adsorption of Oxytetracycline Hydrochloride

The effective removal of oxytetracycline hydrochloride (OTC) from the water environment is of great importance. Adsorption as a simple, stable, and cost-effective technology is regarded as an important method for removing OTC. Herein, a low-cost biochar with a developed mesoporous structure was synthesized via pyrolysis of poplar leaf with potassium bicarbonate (KHCO₃) as the activator. KHCO₃ can endow biochar with abundant mesopores, but excessive KHCO₃ cannot continuously promote the formation of mesoporous structures. In comparison with all of the prepared biochars, PKC-4 (biochar with a poplar leaf to KHCO₃ mass ratio of 5:4) shows the highest adsorption performance for OTC as it has the largest surface area and richest mesoporous structure. The pseudo-second-order kinetic model and the Freundlich equilibrium model are more consistent with the experimental data, which implies that the adsorption process is multi-mechanism and multi-layered. In addition, the maximum adsorption capacities of biochar are slightly affected by pH changes, different metal ions, and different water matrices. Moreover, the biochar can be regenerated by pyrolysis, and its adsorption capacity only decreases by approximately 6% after four cycles. The adsorption of biochar for OTC is mainly controlled by pore filling, though electrostatic interactions, hydrogen bonding, and π-π interaction are also involved. This study realizes biomass waste recycling and highlights the potential of poplar leaf-based biochar for the adsorption of antibiotics.

Membrane-based nanoconfined heterogeneous catalysis for water purification: A critical review✰

Progress in heterogeneous advanced oxidation processes (AOPs) is hampered by several issues including mass transfer limitation, limited diffusion of short-lived reactive oxygen species (ROS), aggregation of nanocatalysts, and loss of nanocatalysts to treated water. These issues have been addressed in recent studies by executing the heterogeneous AOPs in confinement, especially in the nanopores of catalytic membranes. Under nanoconfinement (preferably at the length of less than 25 nm), the oxidant-nanocatalyst interaction, ROS-micropollutant interaction and diffusion of ROS have been observed to significantly improve, which results in enhanced ROS yield and mass transfer, improved reaction kinetics and reduced matrix effect as compared to conventional heterogenous AOP configuration. Given the significance of nanoconfinement effect, this study presents a critical review of the current status of membrane-based nanoconfined heterogeneous catalysis system for the first time. A succinct overview of the nanoconfinement concept in the context of membrane-based nanofluidic platforms is provided to elucidate the theoretical and experimental findings related to reaction kinetics, reaction mechanisms and molecule transport in membrane-based nanoconfined AOPs vs. conventional AOPs. In addition, strategies to construct membrane-based nanoconfined catalytic systems are explained along with conflicting arguments/opinions, which provides critical information on the viability of these strategies and future research directions. To show the desirability and applicability of membrane-based nanoconfined catalysis systems, performance governing factors including operating conditions and water matrix effect are particularly focused. Finally, this review presents a systematic account of the opportunities and technological constraints in the development of membrane-based nanoconfined catalytic platform to realize effective micropollutant elimination in water treatment.

Exploring the nonlinear partitioning mechanism of volatile organic contaminants between soil and soil vapor using machine learning

Traditional phase equilibrium models usually depend on simplified assumptions and empirical parameters, which are difficult to obtain during regular site investigations. As a result, they often under- or over-estimate soil vapor concentrations for assessing the risks of volatile organic compound (VOC)-contaminated sites. In this study, we develop several machine learning models to predict soil vapor concentrations using 2225 soil-soil vapor data pairs collected from seven contaminated sites in northern China. Compared to the classic dual equilibrium desorption model, the random forest (RF) model can provide more accurate predictions of soil vapor concentrations by at least 1-2 orders of magnitude. Among the employed covariates, soil concentration and organic carbon-water partition coefficient are two of the most significant explanatory covariates affecting soil vapor concentrations. Further examination of the developed RF model reveals the phase equilibrium behavior of VOCs in soil is that: the soil vapor concentration increases with soil concentration at different rates in the first two intervals but remains almost unchanged in the last interval; the solid-vapor partitioning interface may still exist at up to 15% mass water content in our simulations. These findings can help site investigators perform more accurate risk assessments at VOC-contaminated sites.

Predictive modeling of pharmaceutical product removal by a managed aquifer recharge system: Comparison and optimization of models using ensemble learners

Pharmaceutical products (PPs) are emerging water pollutants with adverse environmental and health-related impacts, owing to their toxic, persistent, and undetectable microscopic nature. Globally, increasing scientific knowledge and advanced technologies have allowed researchers to study PP-associated problems and their removal for water reuse. Experimental modeling methods require laborious, lengthy, expensive, and environmentally hazardous lab-work to optimize the process. On the other hand, predictive machine learning (ML) models can trace the complex input-output relationship of a process using available datasets. In this study, ensemble ML techniques, including decision tree (DT), random forest (RF), and Xtreme gradient boost (XGB), were used to explore PP (diclofenac, iopromide, propranolol, and trimethoprim) removal by a managed aquifer recharge (MAR) system. The model input parameters included characteristics of reclaimed water and soil used in the columns, pH, dissolved organic carbon, operating time, nitrogen dioxide, sulfate, nitrate, electrical conductivity, manganese, and iron. The selected PP removal was the model output. Datasets were collected through a one-year experimental study of continuous MAR system operation to predict the removal of PPs. DT, RF, and XGB models were then developed for one of the selected compounds and tested for the others to check the reliability of the ML model results. The developed models were assessed using statistical performance matrices. The experimental results showed >80% removal of propranolol and trimethoprim; however, removal of diclofenac and iopromide was only ≈50% by the MAR system. The proposed DT and RF models presented higher coefficients of determination (R² ≥ 0.92) for diclofenac, propranolol, and trimethoprim than for iopromide (R² ≤ 0.63). In contrast, the XGB model showed better results for diclofenac, iopromide, propranolol, and trimethoprim, with R² values of 0.92, 0.72, 0.96, and 0.97, respectively. Therefore, XGB could be the best predictive model to provide insight into the adaptation of ML models to predict PP removal by the MAR system, thereby minimizing experimental work.

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.