Machine learning for the prediction of heavy metal removal by chitosan-based flocculants

Machine learning predicts heavy metal adsorption on iron (oxyhydr)oxides: A combined insight into the adsorption efficiency and binding configuration

The adsorption of heavy metal on iron (oxyhydr)oxides is one of the most vital geochemical/chemical processes controlling the environmental fate of these contaminants in natural and engineered systems. Traditional experimental methods to investigate this process are often time-consuming and labor-intensive due to the complexity of influencing factors. Herein, a comprehensive database containing the adsorption data of 11 heavy metals on 7 iron (oxyhydr)oxides was constructed, and the machine learning models was successfully developed to predict the adsorption efficiency. The random forest (RF) models achieved high prediction performance (R2 > 0.9, RMSE < 0.1, and MAE < 0.07) and interpretability. Key factors influencing heavy metal adsorption efficiency were identified as mineral surface area, solution pH, metal concentration, and mineral concentration. Additionally, by integrating our previous binding configuration models, we elucidated the simultaneous effects of input features on adsorption efficiency and binding configuration through partial dependence analysis. Higher pH simultaneously enhanced adsorption efficiency and affinity for cations, whereas lower pH benefited that for oxyanions. While higher mineral surface area improved the metal adsorption efficiency, the adsorption affinity could be weakened. This work presents a data-driven approach for investigating metal adsorption behavior and elucidating the influencing mechanisms from macroscopic to microcosmic scale, thereby offering comprehensive guidance for predicting and managing the environmental behavior of heavy metals.

Refining hydrogel-based sorbent design for efficient toxic metal removal using machine learning-Bayesian optimization

Hydrogel-based sorbents show promise in the removal of toxic metals from water. However, optimizing their performance through conventional trial-and-error methods is both costly and challenging due to the inherent high-dimensional parameter space associated with complex condition combinations. In this study, machine learning (ML) was employed to uncover the relationship between the fabrication condition of hydrogel sorbent and their efficiency in removing toxic metals. The developed XGBoost models demonstrated exceptional accuracy in predicting hydrogel adsorption coefficients (Kd) based on synthesis materials and fabrication conditions. Key factors such as reaction temperature (50-70 °C), time (5-72 h), initiator ((NH4)2S2O8: 2.3-10.3 mol%), and crosslinker (Methylene-Bis-Acrylamide: 1.5-4.3 mol%) significantly influenced Kd. Subsequently, ten hydrogels were fabricated utilizing these optimized feature combinations based on Bayesian optimization, exhibiting superior toxic metal adsorption capabilities that surpassed existing limits (logKd (Cu): increased from 2.70 to 3.06; logKd (Pb): increased from 2.76 to 3.37). Within these determined combinations, the error range (0.025-0.172) between model predictions and experimental validations for logKd (Pb) indicated negligible disparity. Our research outcomes not only offer valuable insights but also provide practical guidance, highlighting the potential for custom-tailored hydrogel designs to combat specific contaminants, courtesy of ML-based Bayesian optimization.

Application and innovation of artificial intelligence models in wastewater treatment

At present, as the problem of water shortage and pollution is growing serious, it is particularly important to understand the recycling and treatment of wastewater. Artificial intelligence (AI) technology is characterized by reliable mapping of nonlinear behaviors between input and output of experimental data, and thus single/integrated AI model algorithms for predicting different pollutants or water quality parameters have become a popular method for simulating the process of wastewater treatment. Many AI models have successfully predicted the removal effects of pollutants in different wastewater treatment processes. Therefore, this paper reviews the applications of artificial intelligence technologies such as artificial neural networks (ANN), adaptive network-based fuzzy inference system (ANFIS) and support vector machine (SVM). Meanwhile, this review mainly introduces the effectiveness and limitations of artificial intelligence technology in predicting different pollutants (dyes, heavy metal ions, antibiotics, etc.) and different water quality parameters such as biochemical oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP) in wastewater treatment process, involving single AI model and integrated AI model. Finally, the problems that need further research together with challenges ahead in the application of artificial intelligence models in the field of environment are discussed and presented.

Machine learning-driven prediction of phosphorus adsorption capacity of biochar: Insights for adsorbent design and process optimization

Phosphorus (P) pollution in aquatic environments poses significant environmental challenges, necessitating the development of effective remediation strategies, and biochar has emerged as a promising adsorbent for P removal at the cost of extensive research resources worldwide. In this study, a machine learning approach was proposed to simulate and predict the performance of biochar in removing P from water. A dataset consisting of 190 types of biochar was compiled from literature, encompassing various variables including biochar characteristics, water quality parameters, and operating conditions. Subsequently, the random forest and CatBoost algorithms were fine-tuned to establish a predictive model for P adsorption capacity. The results demonstrated that the optimized CatBoost model exhibited high prediction accuracy with an R2 value of 0.9573, and biochar dosage, initial P concentration in water, and C content in biochar were identified as the predominant factors. Furthermore, partial dependence analysis was employed to examine the impact of individual variables and interactions between two features, providing valuable insights for adsorbent design and operating condition optimization. This work presented a comprehensive framework for applying a machine learning approach to address environmental issues and provided a valuable tool for advancing the design and implementation of biochar-based water treatment systems.

Hybrid machine learning approach integrating GMDH and SVR for heavy metal concentration prediction in dust samples

In agricultural regions prone to dust storms, heavy metal contamination of soil and crops from airborne particulates poses significant risks to food safety and public health. This study has assessed the potential of machine learning models for predicting concentrations of toxic heavy metals like arsenic, chromium, and lead in dust from the agricultural Sistan region of southeastern Iran. This region experiences frequent dust storms mobilizing particulates from local dried lakes onto agricultural lands. The metals including nickel, copper, magnesium, cobalt, zinc, chromium, arsenic, and lead were measured in summer dust samples during 2012-2018 across 15 stations. Two hybrid models were developed combining group method of data handling (GMDH) and support vector regression (SVR) machine learning with harmony search optimization (H) so as to predict toxic metals arsenic, chromium, and lead using nickel, copper, magnesium, cobalt, and zinc inputs. Standard error maps were uncertainty higher in southern and western areas, and they are most impacted by dust storms. Results demonstrated that the hybrid GMDH + H and SVR + H models improved the accuracy of individual GMDH and SVR models in predicting heavy metals. The GMDH + H model performed the best for the lead with an agreement index (d-index) of 0.98, root mean square error (RMSE) of 2.87 ppm, normalized RMSE (NRMSE) of 0.12, and coefficient of determination (RR) of 0.96. The SVR + H model showed the highest accuracy for arsenic and chromium, obtaining d-index 0.96, RMSE 0.47 ppm, NRMSE 0.09, and RR 0.92 for arsenic, and d-index 0.96, RMSE 11.24 ppm, NRMSE 0.16, and RR 0.93 for chromium. Taylor's diagram and heatmap analysis confirmed the superiority of the hybrid techniques. This work demonstrates the utility of state-of-the-art computing for addressing complex environmental health challenges.

Predicting bioavailability of potentially toxic elements (PTEs) in sediment using various machine learning (ML) models: A case study in Mahabad Dam and River-Iran

Considering the significant impact of potentially toxic elements (PTEs) on the ecosystem and human health, this paper, investigated the contamination level of four PTEs (Zn, Cu, Mo and Pb) and their mobility in sediments of Mahabad dam and river. Choosing the most effective machine learning algorithms is very important in accurately predicting bioavailability of PTEs. Therefore, four machine learning (ML) models including decision tree regression (DTR), random forest regression (RFR), multi-layer perceptron regression (MLPR) and support vector regression (SVR), were used and compared for estimating the selected PTEs bioavailability. For these models, 9 variables (total concentration, pH, EC, OM and five chemical forms F1 to F5 obtained by sequential extraction) in 100 sediment samples were considered. The results showed that contamination level decreases from Zn and Cu to Pb and Mo, but the order of the mobility coefficient of the elements in the sediment follows the trend of zinc > copper > molybdenum > lead, and variation coefficient indicated more variability of spatial distribution for Zn and Cu. Among the four tested models, DTR and RFR performed the best for predicting PTEs bioavailability variations (with roc_auc>0.9, R2 > 0.8 and MSE>0.5), followed by MLPR and SVR. Furthermore, the relevance of the factors controlling the metals availability, evaluated using the RFR-based feature importance method and Pearson correlation, revealed that the most important physicochemical property for Zn, Cu and Mo bioavailability was pH, whereas for Pb, EC was the determinant factor. In the case of chemical speciation, F5 had an inverse correlation with the target, while F1 and F2 had a direct correlation. These fractions contributed significantly to the prediction results. This study represents the potential successful application of ML to PTEs risk control in sediments and early warning for the surrounding water PTEs contamination.

Assembly of core-shell Fe3O4 @CD-MOFs derived hollow magnetic microcubes for efficient extraction of hazardous substances: Plausible mechanisms for selective adsorption

Hazardous heavy metals and organic substances removal is of great significance for ensuring the safety of aquatic-ecosystem, yet the highly effective and selective extraction always remains challenging. To address this problem, magnetic hollow microcubes were fabricated through thermal carbonization of Fe3O4-COOH@ γ-CD-MOFs, and core-shell structured precursors were in-situ greenly constructed on a large scale via microwave-assisted self-assembly strategy. As noted, the development of secondary crystallization was utilized to achieve uniform dispersion of cores within MOFs frameworks and thus improved magnetic and adsorption ability of composites. Acquired magnetic Fe3O4 @HC not only can harvest excellent extraction of heavy metals (Cd, Pb, and Cu of 129.87, 151.05, and 106.98 mg·g-1) but also exhibit highly selective adsorption ability for cationic organics (separation efficiency higher than 95.0 %). Impressively, Fe3O4 @HC achieved outstanding adsorption (60-80 %) of Cd in realistic mussel cooking broth with no obvious loss in amino acid. Characterizations better offer mechanistic insight into the enhanced selectivity of positively charged pollutants can be attributed to synergistic effect of ions exchange and electrostatic interaction of abundant oxygen-containing functional groups. Our study provides a feasible route by rationally developing core-shell structured composites to promote the practical applications of sustainable water treatment and value-added utilization of processing by-products.

Recent progress and perspectives of typical renewable bio-based flocculants: characteristics and application in wastewater treatment

The research on bio-based flocculants for waste resource utilization and environmental protection has garnered significant attention. Bio-based flocculants encompass plant-based, animal-based, and microbial variants that are prepared and modified through biological, chemical, and physical methods. These flocculants possess abundant functional groups, unique structures, and distinctive characteristics. This review comprehensively discussed the removal rates of conventional pollutants and emerging pollutants by bio-based flocculants, the interaction between these flocculants and pollutants, their impact on flocculation performance in wastewater treatment, as well as their application cost. Furthermore, it described the common challenges faced by bio-based flocculants in practical applications along with various improvement strategies to address them. With their safety profile, environmental friendliness, efficiency, renewability, and wide availability from diverse sources, bio-based flocculants hold great potential for widespread use in wastewater treatment.

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.

Magnesium source with function of slowly releasing Mg and pH control for impurity-resistance synthesis ultra-large struvite from wastewater

Struvite (MgNH4PO4·6H2O, Magnesium ammonium phosphate, MAP), recovered from wastewater, has potential application as a slow-release fertilizer. However, crystal size distribution (CSD) of recovered MAP typically lied in the range of 50-300 μm, due to fast nucleation rate and notably narrow metastable zone width (MSZW) of MAP, with purity levels 40-90 %. In order to control the rate of nucleation, a novel magnesium source with the form of MgHPO4·3H2O wrapped with Mg(OH)2 was prepared, referred to as P-3. This compound gradually released Mg2+ and PO43-, regulating solution concentration kept in MSZW to promote crystal growth. The inherent Mg(OH)2 within P-3 also acted as a pH regulator in wastewater, eliminating the necessity for additional acid or alkali adjustments during crystallization process. The MAP precipitated by P-3 exhibited an impressive CSD of 5000-7000 μm, with a maximum size reaching 10,000 μm. This represented the largest CSD reported in literature for recovered MAP from wastewater. The significance of the ultra-large MAP precipitated by P-3 lied in its enhanced resistance to impurity adsorption, resulting in MAP with a remarkable purity 97 %, under conditions of low heavy metal ion concentration approximately 5 mg/L. Furthermore, the removal efficiency of ammonia nitrogen (NH4+) can reach 92 %. In comparison, two other magnesium sources, soluble salts (MgCl2 and Na2HPO4, P-1) and a combination of insoluble salts (Mg(OH)2 and MgHPO4, P-2) were evaluated alongside P-3. The CSD of MAP precipitated from P-1, P-2 was both <100 μm, with purity levels of 90 and 92 % and NH4+ removal efficiency of 92 and 90 %, respectively. Importantly, the strategy of obtaining ultra-large size MAP from wastewater in this study provided novel insights into the crystallization of other insoluble salts with large sizes.

Application of machine learning in prediction of Pb2+ adsorption of biochar prepared by tube furnace and fluidized bed

Data mining by machine learning (ML) has recently come into application in heavy metals purification from wastewater, especially in exploring lead removal by biochar that prepared using tube furnace (TF-C) and fluidized bed (FB-C) pyrolysis methods. In this study, six ML models including Random Forest Regression (RFR), Gradient Boosting Regression (GBR), Support Vector Regression (SVR), Kernel Ridge Regression (KRR), Extreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LGBM) were employed to predict lead adsorption based on a dataset of 1012 adsorption experiments, comprising 422 TF-C groups from our experiments and 590 FB-C groups from literatures. The XGB model showed superior accuracy and predictive performance for adsorption, achieving R2 values for TF-C (0.992) and FB-C (0.981), respectively. Contrasting inferior results were observed in other models, including RF (0.962 and 0.961), GBR (0.987 and 0.975), SVR (0.839 and 0.763), KRR (0.817 and 0.881), and LGBM (0.975 and 0.868). Additionally, a hybrid dataset combining both biochars in Pb adsorption also indicated high accuracy (0.972) as obtained from XGB model. The investigation revealed that the influence of char characteristics and adsorption conditions on Pb adsorption differs between the two biochar. Specific char characteristics, particularly nitrogen content, significantly influence lead adsorption in both biochar. Interestingly, the influence of pyrolysis temperature (PT) on lead adsorption is found to be greater for TF-C than for FB-C. Consequently, careful consideration of PT is crucial when preparing TF-C biochar. These findings offer practical guidance for optimizing biochar preparation conditions during heavy metal removal from wastewater.

Heavy metals prediction in coastal marine sediments using hybridized machine learning models with metaheuristic optimization algorithm

This study proposes different standalone models viz: Elman neural network (ENN), Boosted Tree algorithm (BTA), and f relevance vector machine (RVM) for modeling arsenic (As (mg/kg)) and zinc (Zn (mg/kg)) in marine sediments owing to anthropogenic activities. A heuristic algorithm based on the potential of RVM and a flower pollination algorithm (RVM-FPA) was developed to improve the prediction performance. Several evaluation indicators and graphical methods coupled with visualized cumulative probability function (CDF) were used to evaluate the accuracy of the models. Akaike (AIC) and Schwarz (SCI) information criteria based on Dickey-Fuller (ADF) and Philip Perron (PP) tests were introduced to check the reliability and stationarity of the data. The prediction performance in the verification phase indicated that RVM-M2 (PBAIS = -o.0465, MAE = 0.0335) and ENN-M2 (PBAIS = 0.0043, MAE = 0.0322) emerged as the best model for As (mg/kg) and Zn (mg/kg), respectively. In contrast with the standalone approaches, the simulated hybrid RVM-FPA proved merit and the most reliable, with a 5 % and 18 % predictive increase for As (mg/kg) and Zn (mg/kg), respectively. The study's findings validated the potential for estimating complex HMs through intelligent data-driven models and heuristic optimization. The study also generated valuable insights that can inform the decision-makers and stockholders for environmental management strategies.

Machine learning framework for modeling flocculation kinetics using non-intrusive dynamic image analysis

The implementation of a machine learning (ML) model to improve both the effectiveness and sustainability of the water treatment system is a significant challenge in the water sector, with the optimization of flocculation processes being a major setback. The objective of this study was to develop a ML model for predicting flocs evolution of the flocculation process in water treatment. Furthermore, we have devised a framework for its potential adoption in large-scale water treatment. Therefore, the paper can be split into two parts. In the first one, flocculation evolution has been studied from an experimental setup, using a non-intrusive image acquisition method. Subsequently, the ML framework has been implemented. Batch assay data of two velocity gradients (Gf 20 and 60 s-1) and flocculation time of three hours were partitioned into five groups for flocs length range 0.27-3.5 mm and upscaled using linear method. Multilayer Perceptron (MLP) and Long-Short Term Memory (LSTM) models, and traditional time series model, Auto Regressive Integrated Moving Average (ARIMA) were explored to predict floc length evolution data. The experiments illustrate the kinetics of flocculation, where the initial stage is characterized by a rapid floc growth followed by a plateau during which floc length fluctuates within a narrow range. Results demonstrate that ML is sensitive to flocculation; however, the model should be selected with care. ARIMA model is not suitable for predicting number of flocs with negative test accuracy (R2). In contrast, MLP recorded R2 of 0.86-1.0 for training and 0.92-1.0 for testing, across Gf 20 s-1 and Gf 60 s-1. LSTM model has the best prediction R2 of 0.92-1.00 for Gf 20 s-1 and accurately predicts the number of flocs across all groups and Gfs. Our study has proven that the developed framework could be replicated for water treatment modeling and promotes the application of smart technology in water treatment.

Influence of the Extraction Solution on the Removal of Heavy Metals from Polluted Soils

Soil pollution with heavy metals is a problem for the whole geosystem. The aim of the research is to identify new solutions for extracting heavy metals from polluted soils so that they can be further exploited. To this end, investigations of the physicochemical characteristics of the soil sample under study were carried out. Following the analyses, the soil was characterised as lute-coarse sand (UG) with a strongly acidic pH (4.67), a hygroscopicity coefficient (CH = 4.8% g/g), and a good supply of nutrients: nitrogen (Nt): 0.107%; mobile phosphorus (PAL): 6 mg kg-1 and mobile potassium (KAL): 26 mg kg-1, but is low in humus (2.12%). The metal content of the soil was determined by atomic absorption spectrometry (AAS), and the analyses showed high concentrations of metals (Pb: 27,660 mg kg-1; Cu: 5590 mg kg-1; Zn: 2199 mg kg-1; Cd: 11.68 mg kg-1; Cr: 146 mg kg-1). The removal of metals (Pb, Cu, Zn, Cd, and Cr) from polluted soil by different extraction agents (water, humus, malic acid, chitosan, and gluconic acid) was investigated. Metal extraction experiments were carried out in a continuous orbital rotation-oscillation stirrer at a solid/liquid/ (S/L ratio; g:mL) of 1:4, at two concentrations of extraction solution (1% and 3%), and at different stirring times (2, 4, 6, and 8 h). The yield of the extraction process is very low for all proposed extraction solutions. The maximum values of extraction efficiency are: 0.5% (Pb); 3.28% (Zn); and 5.72% (Cu). Higher values were obtained in the case of Cr (11.97%) in the variant of using humus 3% as an extraction solution at a stirring time of 6 h. In the investigated experimental conditions, the best removal efficiencies were obtained in the case of cadmium (26.71%) when using a 3% malic acid solution. In conclusion, it is considered that, from case to case, the type of extraction solution as well as the nature of the metal influence the mechanism of the depollution process, i.e., the capacity of the fine soil granules to free themselves from the pollutant metal that has adhered to them, and further research is considered necessary in the future.

Prediction of heavy metals adsorption by hydrochars and identification of critical factors using machine learning algorithms

Hydrochar has become a popular product for immobilizing heavy metals in water bodies. However, the relationships between the preparation conditions, hydrochar properties, adsorption conditions, heavy metal types, and the maximum adsorption capacity (Q_m) of hydrochar are not adequately explored. Four artificial intelligence models were used in this study to predict the Q_m of hydrochar and identify the key influencing factors. The gradient boosting decision tree (GBDT) showed excellent predictive capability for this study (R²=0.93, RMSE=25.65). Hydrochar properties (37%) controlled heavy metal adsorption. Meanwhile, the optimal hydrochar properties were revealed, including the C, H, N, and O contents of 57.28-78.31%, 3.56-5.61%, 2.01-6.42%, and 20.78-25.37%. Higher hydrothermal temperatures (>220 °C) and longer hydrothermal time (>10 h) lead to the optimal type and density of surface functional groups for heavy metal adsorption, which increased the Q_m values. This study has great potential for instructing industrial applications of hydrochar in treating heavy metal pollution.

Quantification of the antagonistic and synergistic effects of Pb2+, Cu2+, and Zn2+ bioaccumulation by living Bacillus subtilis biomass using XGBoost and SHAP

Bioaccumulation and adsorption are efficient methods for removing heavy metal ions (HMIs) from aqueous environments. However, methods to quantifiably characterize the removal selectivity for co-existing HMIs are limited. In this study, we applied Shapley additive explanations (SHAP) following extreme gradient boosting (XGBoost) modeling, to generate SHAP values. We used these values to create an affinity interference index (AII) that quantitatively represented the interference between metal ions in a multi-metal bioaccumulation system. The selectivity for simultaneous bioaccumulation of Pb²⁺, Cu²⁺, and Zn²⁺ by living Bacillus subtilis biomass was then characterized as a proof of concept. The AII indicated that the bioaccumulation of Zn²⁺ was more strongly inhibited by Pb²⁺/Cu²⁺ (AII = 1) than that of Pb²⁺/Cu²⁺ by Zn²⁺. Moreover, the presence of Zn²⁺ promoted the bioaccumulation of Pb²⁺ (AII = 0.39), which was confirmed in further experiments where the bioaccumulation of Pb²⁺ (300 μM) was increased by 38% with Zn²⁺ (300 μM). This study demonstrated that the combination of XGBoost and SHAP is effective in the quantifiable characterization of the antagonistic and synergistic effects in a multi-metal simultaneous bioaccumulation system. This method could also be generalized to similar tasks for analyzing the selectivity effects in a multi-component system.

Electrocoagulation removal of Pb, Cd, and Cu ions from wastewater using a new configuration of electrodes

A new configuration of aluminum electrodes has been performed in an electrocoagulation reactor (ECR) to remove toxic metals from synthetic wastewater. The ECR contains four concentric-cubic electrodes that were connected to the DC power supply with a bipolar mode. The ability of this reactor to eliminate 200 ppm Pb, 200 ppm Cd and 200 ppm Cu from wastewater was investigated under the effect of pH (4-10), applied current (0.2-2.6 A), and the reaction time of (4-60 min). Two grams of NaCl were added to each experiment to enhance the electrical conductivity and minimize the passivation of cathode surfaces. The experiments, analysis, and optimization were conducted using response surface methodology type Box-Behnken design (RSM-BBD) and the Minitab-statistical software program. The highest elimination of heavy metals was: Pb-99.73%, Cd-98.54%, and Cu-98.92% at pH 10, 1.4 A of the applied current, and 60 min of the reaction time. The total real consumption of anodes under these conditions was 0.55 g, and the energy consumption was 12.71 kWh/m3. All reactions of metal removal that occurred in the present EC reactor obey the kinetic of a first-order reaction. Thermodynamics parameters of present electrocoagulation removal of heavy metals indicate an endothermic, spontaneous nature, and random irregularity at the liquid-solid interaction. The highest values of removal efficiencies and the considerably lowest values of energy and electrode consumption proved that the electrocoagulation technology applies in wastewater treatment containing toxic metals.•The anode electrodes were perforated to decrease the amount of electrode consumption, while the cathode electrodes were not perforated.•The new EC reactor eliminated Pb-99.73%, Cd-98.54%, and Cu-98.92% of 200 mg/l of each metal at pH 10, applied current of 1.4 A, and reaction time of 60 min. Moreover, the consumption of energy and electrodes was significantly low.•The performance indicator (R²) of the studied responses was higher than 0.95.

Simulation, prediction and optimization of typical heavy metals immobilization in swine manure composting by using machine learning models and genetic algorithm

Machine learning (ML) is a novel method of data analysis with potential to overcome limitations of traditional composting experiments. In this study, four ML models (multi-layer perceptron regression, support vector regression, decision tree regression, and gradient boosting regression) were integrated with genetic algorithm to predict and optimize heavy metal immobilization during composting. Gradient boosting regression performed best among the four models for predicting both heavy metal bioavailability variations and immobilization. Gradient boosting regression-based feature importance analysis revealed that the heavy metal initial bioavailability factor, total phosphorus, and composting duration were the determinant factors for heavy metal bioavailability variations (together contributing >75%). After genetic algorithm optimization, the maximum immobilization rates of Cu, Zn, Cd, As, and Cr were 79.53, 31.30, 14.91, 46.25, and 66.27%, respectively, superior to over 90% of the measured data. These findings demonstrate the potential application of ML to risk-control for heavy metals in livestock manure composting.

Machine learning-based prediction of toxic metals concentration in an acid mine drainage environment, northern Tunisia

In northern Tunisia, Sidi Driss sulfide ore valorization had produced a large waste amount. The long tailings exposure period and in situ minerals interactions produced an acid mine drainage (AMD) which contributed to a strong increase in the mobility and migration of huge heavy metal (HM) quantities to the surrounding soils. In this work, the soil mineral proportions, grain sizes, physicochemical properties, SO₄^2- and S contents, and Machine Learning (ML) algorithms such as the Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN) models were used to predict the soil HM quantities transferred from Sidi-Driss mine drainage to surrounding soils. The results showed that the HM concentrations had significantly increased with the increase of decomposition and oxidation of galena, marcasite, pyrite, and sphalerite-marcasite and Fe-oxide-hydroxides quantities and the sulfate dissolution (marked with SO₄^2- ions increase) that produced the decreased soil pH. Compared to SVM, and ANN models outputs, the RF model that revealed higher R²_val, RPD, RPIQ, and lower error indices had satisfactorily predicted the soil HM accumulation coming from the AMD environment.

A Multi-Dimensional Investigation on Water Quality of Urban Rivers with Emphasis on Implications for the Optimization of Monitoring Strategy

Water quality monitoring (WQM) of urban rivers has been a reliable method to supervise the urban water environment. Indiscriminate WQM strategies can hardly emphasize the concerning pollution and usually require high costs of money, time, and manpower. To tackle these issues, this work carried out a multi-dimensional study (large spatial scale, multiple monitoring parameters, and long time scale) on the water quality of two urban rivers in Jiujiang City, China, which can provide indicative information for the optimization of WQM. Of note, the spatial distribution of NH3-N concentration varied significantly both in terms of the two different rivers as well as the different sections (i.e., much higher in the northern section), with a maximal difference, on average greater, than five times. Statistical methods and machine learning algorithms were applied to optimize the monitoring objects, parameters, and frequency. The sharp decrease in water quality of adjacent sections was identified by Analytical Hierarchy Process of water quality assessment indexes. After correlation analysis, principal component analysis, and cluster analysis, the various WQM parameters could be divided into three principal components and four clusters. With the machine learning algorithm of Random Forest, the relation between concentration of pollutants and rainfall depth was fitted using quadratic functions (calculated Pearson correlation coefficients ≥ 0.89), which could help predict the pollution after precipitation and further determine the appropriate WQM frequency. Generally, this work provides a novel thought for efficient, smart, and low-cost water quality investigation and monitoring strategy determination, which contributes to the construction of smart water systems and sustainable water source management.