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

Machine learning-aided model for predicting oily sludge pyrolysis under various feedstock and operating conditions

Oily sludge pyrolysis technology has the advantages of potential recovery of valuable resources and safe disposal of non-recoverable residues. However, experimentally determining the optimal pyrolysis operating conditions is time-consuming and expensive. In this study, a machine learning (ML) approach was developed to predict and optimize the oily sludge pyrolysis process. Among the six machine learning models, eXtreme Gradient Boosting (XGB) was found to have the best prediction results. A multi-task XGB model was then developed with oily sludge ultimate and proximate composition and pyrolysis operating conditions as the modeling inputs. The modeling results indicated that the sludge ash and hydrogen contents as well as the pyrolysis temperature are the most critical factors affecting pyrolysis process and its performance. The contribution of sludge ultimate composition to the pyrolysis performance is 42.5 %, followed by sludge proximate properties (35.8 %) and pyrolysis operating conditions (21.7 %). The multi-task XGB ML model achieved an average R2 of 0.90 through model verification. The ML-aided modeling approach provides new insights for understanding and optimizing the oily sludge pyrolysis.

Exploring the potential of green-synthesized hydroxyapatite for cadmium remediation in paddy soils

The Cadmium contamination in paddy soils significantly threatens soil health and food safety. This study addresses the urgent need for cost-effective and sustainable remediation methods by evaluating the efficacy of green-synthesized hydroxyapatite (GHAP) in immobilizing Cd in paddy soils. The investigation focused on the effects of applying GHAP, commercial hydroxyapatite (CHAP), and pure hydroxyapatite (CK) on soil physicochemical properties, as well as Cd mobility and transfer within the soil-rice system. Our results demonstrate that GHAP significantly enhanced Cd adsorption and reduced bioavailable Cd content by up to 48.47 %, with the most effective results observed at a 1 % amendment rate. Additionally, GHAP application elevated soil pH and available phosphorus, contributing to better soil structure, fertility, and rice growth. Correlation and principal component analyses revealed that the GHAP dosage was a key factor in modifying soil properties and influencing Cd speciation and mobility. These findings suggest that a 1 % GHAP dosage achieves an optimal balance between improving soil quality and effectively passivating Cd, making GHAP a promising amendment for remediating Cd-contaminated soils.

Integrating machine learning and reliability analysis: A novel approach to predicting heavy metal removal efficiency using biochar

Soil contamination with heavy metals (HMs) presents critical environmental and public health risks due to their long-term persistence and tendency to bioaccumulate. Biochar has gained recognition as an effective amendment for HM immobilization, owing to its cost-effectiveness, environmental sustainability, and multifunctional properties. Nevertheless, consistent removal efficiency remains challenging to achieve due to the inherent variability of biochar and its interactions with complex environmental factors. This research introduces an advanced machine learning (ML) framework, utilizing deep forest (DF) algorithms, to predict and optimize the efficiency HM removal through biochar applications. The framework addresses key challenges by employing data imputation to manage missing information, data augmentation to overcome limitations of small datasets, and reliability analysis to assess predictive uncertainties, thereby improving the model's reliability and generalization capability. The findings reveal that the DF model surpasses conventional ML approaches, achieving a testing dataset coefficient of determination (R²) of 0.88. Additionally, probabilistic reliability analysis offers valuable insights into the likelihood of reaching various levels of remediation efficiency (RE). For lower RE thresholds, such as 20-30 %, the model predicts a high probability (over 80 %) of substantial HM removal, confirming biochar's effectiveness in mitigating contamination. However, as the target RE thresholds rise to moderate levels (50-70 %), the probability drops significantly (to below 5 %), highlighting the increasing difficulty of achieving higher remediation efficiencies. Furthermore, this study has developed an accessible and intuitive web-based application, enabling engineers to input relevant parameters and receive immediate predictive outputs, thus facilitating the practical application of advanced ML models in real-world scenarios.

Optimized immobilization of lead and cadmium in soil using dithiocarboxy functionalized silica: A long-term effectiveness study

The efficient immobilization of heavy metals often requires a high dosage of remediation material, resulting in increased remediation costs and potential ecological risks. In this study, we developed a novel silica-based material, RNS-TS, characterized by a high density of dithiocarboxy groups, aimed at remediating Pb and Cd contaminated soils and evaluating the long-term efficacy via aging experiments. The synthesized RNS-TS achieved a functional group density of 2.59 mmol/g. At a concentration of 1 %, it effectively reduced the content of bioaccessibility Pb, Cd, and Cu in slightly contaminated soil by 86 %, 82 %, and 100 %; in moderately contaminated soil by 94 %, 75 %, and 100 %; and in heavily contaminated soil by 68 %, 60 %, and 100 %. Furthermore, the remediation process was relatively fast, with equilibrium achieved within one day after adding the RNS-TS. Aging experiments revealed that the remediated products exhibited excellent stability under simulated climate conditions such as extreme temperatures, freeze-thaw cycles and dry-wet cycles etc. Field experiments demonstrated that a 0.2 wt% application of RNS-TS reduced the content of bioaccessible Cd from 0.6 mg/kg to 0.3 mg/kg (approximately 50.8 % reduction), while Cd content in wheat grains decreased from 0.18 mg/kg to 0.08 mg/kg (approximately 54.4 % reduction). This successful application ensured safe wheat production. This material shows great promise as a risk element stabilization agent for heavy metal-contaminated soils.

Heavy metal adsorption efficiency prediction using biochar properties: a comparative analysis for ensemble machine learning models

The contamination of water and soils with heavy metals poses a significant environmental threat, making the development of effective removal strategies a global priority. Hence, the determination of heavy metals can play an essential role in environmental monitoring and assessment. In the current research, ensemble machine learning (ML) models (i.e., Random Forest Regressor (RFR), Adaptive Boosting (Adaboost), Gradient Boosting (GB), HistGradientBoosting, Extreme Gradient Boosting (XGBoost), and Light Gradient-Boosting Machine (LightGBM)) were applied in attempt to predict the adsorption efficiency of several heavy metals (i.e., Pb, Cd, Ni, Cu, and Zn) according to different factors including temperature, pH, and biochar characteristics. Data were collected from open-source literature review including 353 samples. At the first stage, data processing was performed including outliers' removal and scaling for better data modeling applicability; whereas, in the second stage the predictive models were conducted. The results showed that XGBoost model attained the superior accuracy in comparison with other models by achieving the highest determination coefficient (R2 = 0.92). The research was extended to investigate the feature importance analysis which indicated that the initial concentration ratio of metals to biochar and pH were the most influential factors toward the adsorption efficiency followed by Pyrolysis temperature, while other features like physical properties as surface area and pore structure had a minimal effect on efficiency. These findings highlighted the importance of using ensemble ML models in guiding heavy metals removal solutions as it provides an efficient prediction and ease the selection of the environmental application.

Applications of machine learning in potentially toxic elemental contamination in soils: A review

Soil contamination by potentially toxic elements (PTEs) poses substantial risks to the environment and human health. Traditional investigational methods are often inadequate for large-scale assessments because they are time-consuming, costly, and have a limited accuracy. Machine learning (ML) techniques have emerged as promising tools in environmental studies because of their superiority in processing high-dimensional and unstructured data. However, critical evaluations of contemporary ML applications and methods in PTEs content, distribution, and identification remain scarce. To address this research gap, this study reviews applications of ML to soil PTEs contamination including content prediction, spatial distribution, source identification, and other related tasks. Hyperspectral data combined with ML methods can predict the content of PTEs in large-scale areas at a low cost. In addition, ML algorithms that integrate environmental covariates offer superior performance in spatial predictions compared with traditional geostatistical methods. Moreover, ML techniques incorporated with receptor models provide important advances in the quantitative identification and apportioning of PTE sources, thereby supporting effective environmental management and risk assessment. Based on the frequency of the variables used, we propose that soil pH, soil organic matter (SOM), industrial activities, soil texture, and other relevant factors are key environmental variables that enhance the accuracy of predictions regarding the spatial distribution and source identification of PTEs. From these findings, ML techniques, through their powerful data processing capabilities, provide new perspectives and tools for the efficient assessment and management of soil PTEs contamination.

Visible light-driven copper vanadate/biochar nanocomposite for heterogeneous photocatalysis degradation of tetracycline: Performance, mechanism, and application of machine learning

Water pollution caused by antibiotics is considered a major and growing issue. To address this challenge, high-performance copper vanadate-based biochar (CuVO/BC) nanocomposite photocatalysts were prepared to develop an efficient visible light-driven photocatalytic system for the remediation of tetracycline (TC) contaminated water. The effects of photocatalyst mass, solution pH, pollutant concentration, and common anions on the TC degradation were investigated in detail. Analytical techniques indicated that the CuVO exhibited a nanobelt-like structure with a uniform distribution on the wrinkled biochar surface. The XRD spectrum confirmed that the as-prepared nanomaterial was composed of Cu3V2O7(OH)2·2H2O. Meanwhile, XPS analysis revealed that copper was present in two forms: monovalent and divalent, while vanadium remained pentavalent. The CuVO/BC exhibited excellent stability and high visible light photocatalytic activity towards TC degradation over a wide pH range. The presence of SO42-, H2PO4-, CO32-, and citric acid inhibited the degradation process due to the consuming of photogenerated h+ and •OH, while Cl- enhanced the efficiency of photocatalytic reactions due to generating chlorine oxidizing species. The CuVO/BC showed lower electron-hole recombination rate, more effective separation of photogenerated carriers, lower charge transfer resistance, and higher visible light absorption capacity comparing to pure CuVO by the addition of BC, thus improving the overall photocatalytic performance. In terms of oxidation mechanism, the EPR test and quenching experiment revealed that the contribution of the active species to the degradation of TC followed the order h+ > 1O2 > •OH > •O2-. Through the application of machine learning models to analyse the influencing factors of photocatalytic processes, it was discovered that the GBDT model exhibited optimal reliability for the photocatalytic system, and the simulation results were in agreement with the experimental findings.

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.

From Bulk to Nano: Formation, Features, and Functions of Nano-Black Carbon in Biogeochemical Processes

Globally increasing wildfires and widespread applications of biochar have led to a growing amount of black carbon (BC) entering terrestrial ecosystems. The significance of BC in carbon sequestration, environmental remediation, and the agricultural industry has long been recognized. However, the formation, features, and environmental functions of nanosized BC, which is one of the most active fractions in the BC continuum during global climate change, are poorly understood. This review highlights the formation, surface reactivity (sorption, redox, and heteroaggregation), biotic, and abiotic transformations of nano-BC, and its major differences compared to other fractions of BC and engineered carbon nanomaterials. Potential applications of nano-BC including suspending agent, soil amendment, and nanofertilizer are elucidated based on its unique properties and functions. Future studies are suggested to develop more reliable detection techniques to provide multidimensional information on nano-BC in environmental samples, explore the critical role of nano-BC in promoting soil and planetary health from a one health perspective, and extend the multifield applications of nano-BC with a lower environmental footprint but higher efficiency.

Optimal biochar selection for cadmium pollution remediation in Chinese agricultural soils via optimized machine learning

Biochar is effective in mitigating heavy metal pollution, and cadmium (Cd) is the primary pollutant in agricultural fields. However, traditional trial-and-error methods for determining the optimal biochar remediation efficiency are time-consuming and inefficient because of the varied soil, biochar, and Cd pollution conditions. This study employed the machine learning method to predict the Cd immobilization efficiency of biochar in soil. The predictive accuracy of the random forest (RF) model was superior to that of the other common linear and nonlinear models. Furthermore, to improve the reliability and accuracy of the RF model, it was optimized by employing a root-mean-squared-error-based trial-and-error approach. With the aid of the optimized model, the empirical categories for soil Cd immobilization efficiency were biochar properties (60.96 %) > experimental conditions (19.6 %) ≈ soil properties (19.44 %). Finally, this study identified the optimal biochar properties for enhancing agricultural soil Cd remediation in different regions of China, which was beneficial for decision-making regarding nationwide agricultural soil remediation using biochar. The immobilization effect of alkaline biochar was pronounced in acidic soils with relatively high organic matter. This study provides insights into the immobilization mechanism and an approach for biochar selection for Cd immobilization in agricultural soil.

Engineered lignocellulosic based biochar to remove endocrine-disrupting chemicals: Assessment of binding mechanism

The safety and health of aquatic organisms and humans are threatened by the increasing presence of pollutants in the environment. Endocrine disrupting chemicals are common pollutants which affect the function of endocrine and causes adverse effects on human health. These chemicals can disrupt metabolic processes by interacting with hormone receptors upon consumptions by humans or aquatic species. Several studies have reported the presence of endocrine disrupting chemicals in waterbodies, food, air and soil. These chemicals are associated with increasing occurrence of obesity, metabolic disorders, reproductive abnormalities, autism, cancer, epigenetic variation and cardiovascular risk. Conventional treatment processes are expensive, not environment friendly and unable to achieve complete removal of these harmful chemicals. In recent years, biochar from different sources has gained a considerable interest due to their adsorption efficiency with porous structure and large surface areas. biochar derived from lignocellulosic biomass are widely used as sustainable catalysts in soil remediation, carbon sequestration, removal of organic and inorganic pollutants and wastewater treatment. This review conceptualizes the production techniques of biochar from lignocellulosic biomass and explores the functionalization and interaction of biochar with endocrine-disrupting chemicals. This review also identifies the further needs of research. Overall, the environmental and health risks of endocrine-disrupting chemicals can be dealt with by biochar produced from lignocellulosic biomass as a sustainable and prominent approach.

Machine learning models for predicting thermal desorption remediation of soils contaminated with polycyclic aromatic hydrocarbons

Among various remediation methods for organic-contaminated soil, thermal desorption stands out due to its broad treatment range and high efficiency. Nonetheless, analyzing the contribution of factors in complex soil remediation systems and deducing the results under multiple conditions are challenging, given the complexities arising from diverse soil properties, heating conditions, and contaminant types. Machine learning (ML) methods serve as a powerful analytical tool that can extract meaningful insights from datasets and reveal hidden relationships. Due to insufficient research on soil thermal desorption for remediation of organic sites using ML methods, this study took organic pollutants represented by polycyclic aromatic hydrocarbons (PAHs) as the research object and sorted out a comprehensive data set containing >700 data points on the thermal desorption of soil contaminated with PAHs from published literature. Several ML models, including artificial neural network (ANN), random forest (RF), and support vector regression (SVR), were applied. Model optimization and regression fitting centered on soil remediation efficiency, with feature importance analysis conducted on soil and contaminant properties and heating conditions. This approach enabled the quantitative evaluation and prediction of thermal desorption remediation effects on soil contaminated with PAHs. Results indicated that ML models, particularly the RF model (R2 = 0.90), exhibited high accuracy in predicting remediation efficiency. The hierarchical significance of the features within the RF model is elucidated as follows: heating conditions account for 52 %, contaminant properties for 28 %, and soil properties for 20 % of the model's predictive power. A comprehensive analysis suggests that practical applications should emphasize heating conditions for efficient soil remediation. This research provides a crucial reference for optimizing and implementing thermal desorption in the quest for more efficient and reliable soil remediation strategies.

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.

Quantifying the mitigating effect of organic matter on heavy metal availability in soils with different manure applications: A geochemical modelling study

Manure is one of the main sources of heavy metal (HM) pollution on farmlands. It has become the focus of global ecological research because of its potential threat to human health and the sustainability of food systems. Soil pH and organic matter are improved by manure and play pivotal roles in determining soil HM behavior. Geochemical modeling has been widely used to assess and predict the behavior of soil HMs; however, there remains a research gap in manure applications. In this study, a geochemical model (LeachXS) coupled with a pH-dependent leaching test with continuously simulations over a broad pH range was used to determine the effects and pollution risks of pig or cattle manure separate application on soil HMs distribution. Both pig and cattle manure applications led to soil pH reduction in alkaline soils and increased organic matter content. Pig manure application resulted in a potential 90.5-156.0 % increase in soil HM content. Cattle manure did not cause significant HM contamination. The leaching trend of soil HMs across treatments exhibited a V-shaped change, with the lowest concentration at pH = 7, gradually increasing toward strong acids and bases. The dissolved organic matter-bound HM content directly increased the HM availability, especially for Cu (up to 8.4 %) after pig manure application. However, more HMs (Cr, Cu, Zn, Ni) were in the particulate organic matter-bound state than in other solid phases (e.g., Fe-Al(hydr) oxides, clay minerals), which inhibited the HMs leaching by more than 19.3 % after cattle manure application. Despite these variations, high HM concentrations introduced by pig manure raised the soil contamination risk, potentially exceeding 40 times at pH ±1. When manure is returned to the field, reducing its HM content and mitigating possible pollution is necessary to realize the healthy and sustainable development of circular agriculture.

Machine learning-based prediction of heavy metal immobilization rate in the solidification/stabilization of municipal solid waste incineration fly ash (MSWIFA) by geopolymers

Geopolymer is an environmentally friendly solidification/stabilization (S/S) binder, exhibiting significant potential for immobilizing heavy metals in municipal solid waste incineration fly ash (MSWIFA). However, due to the diversity in geopolymer raw materials and heavy metal properties, predicting the heavy metal immobilization rate proves to be challenging. In order to enhance the application of geopolymers in immobilizing heavy metals in MSWIFA, a universal method is required to predict the heavy metal immobilization rate. Therefore, this study employs machine learning to predict the heavy metal immobilization rate in S/S of MSWIFA by geopolymers. A gradient boosting regression (GB) model with superior performance (R2 = 0.9214) was obtained, and a graphical user interface (GUI) software was developed to facilitate the convenient accessibility of researchers. The feature categories influencing heavy metal immobilization rate are ranked in order of importance as heavy metal properties > geopolymer raw material properties > curing conditions > alkali activator properties. This study facilitates the rapid prediction, improvement, and optimization of heavy metal immobilization in S/S of MSWIFA by geopolymers, and also provides a theoretical basis for the resource utilization of industrial solid waste, contributing to the environmental protection.

Predicting thermal desorption efficiency of PAHs in contaminated sites based on an optimized machine learning approach

Thermal desorption (TD) remediation of polycyclic aromatic hydrocarbon (PAH)-contaminated sites is known for its high energy consumption and cost implications. The key to solving this issue lies in analyzing the PAHs desorption process, defining remediation endpoints, and developing prediction models to prevent excessive remediation. Establishing an accurate prediction model for remediation efficiency, which involves a systematic consideration of soil properties, TD parameters, and PAH characteristics, poses a significant challenge. This study employed a machine learning approach for predicting the remediation efficiency based on batch experiment results. The results revealed that the extreme gradient boosting (XGB) model yielded the most accurate predictions (R2 = 0.9832). The importance of features in the prediction process was quantified. A model optimization scheme was proposed, which involved integrating features based on their relevance, importance, and partial dependence. This integration not only reduced the number of input features but also enhanced prediction accuracy (R2 = 0.9867) without eliminating any features. The optimized XGB model was validated using soils from sites, demonstrating a prediction error of less than 30%. The optimized XGB model aids in identifying the most optimal conditions for thermal desorption to maximize the remediation efficiency of PAH-contaminated sites under relative cost and energy-saving conditions.

Revolutionizing Solid-State NASICON Sodium Batteries: Enhanced Ionic Conductivity Estimation through Multivariate Experimental Parameters Leveraging Machine Learning

Na superionic conductor (NASICON) materials hold promise as solid-state electrolytes due to their wide electrochemical stability and chemical durability. However, their limited ionic conductivity hinders their integration into sodium-ion batteries. The conventional approach to electrolyte design struggles with comprehending the intricate interactions of factors impacting conductivity, encompassing synthesis parameters, structural characteristics, and electronic descriptors. Herein, we explored the potential of machine learning in predicting ionic conductivity in NASICON. We compile a database of 211 datasets, covering 160 NASICON materials, and employ facile descriptors, including synthesis parameters, test conditions, molecular and structural attributes, and electronic properties. Random forest (RF) and neural network (NN) models were developed and optimized, with NN performing notably better, particularly with limited data (R2=0.820). Our analysis spotlighted the pivotal role of Na stoichiometric count in ionic conductivity. Furthermore, the NN algorithm highlighted the comparable significance of synthesis parameters to structural factors in determining conductivity. In contrast, the impact of electronegativity on doped elements appears less significant, underscoring the importance of dopant size and quantity. This work underscores the potential of machine learning in advancing NASICON electrolyte design for sodium-ion batteries, offering insights into conductivity drivers and a more efficient path to optimizing materials.

Is Cr(III) re-oxidation occurring in Cr-contaminated soils after remediation: Meta-analysis and machine learning prediction

Whether Cr(III) in Cr(III)-containing sites formed after Cr(VI) reduction and stabilization remediation are re-oxidized and pose toxicity risks again has been a growing concern. In this study, 1030 data were collected to perform a meta-analysis to clarify the effects of various factors (oxidant type, soil and Cr(III) solid compound properties, aging conditions, and testing methods) on Cr(III) oxidation. We observed that the soil properties of clay, pH ≥ 8, the lower CEC capacity, easily reducible Mn content, and Cr(III) content, and the higher Eh value and Fe content can promote the re-oxidation of Cr(III). Publication bias and sensitivity analyses confirmed the stability and reliability of the meta-analysis. Subsequently, we used five machine learning algorithms to construct and optimize the models. The prediction results of the RF model (RMSE <1.36, R2 >0.71) with good algorithm performance showed that after ten years of remediation, the extractable Cr(VI) concentration in the soil was 0.0087 mg/L, indicating a negligible secondary pollution risk of Cr(III) re-oxidation. This study provides theoretical support for subsequent risk management and control after Cr(VI) soil remediation and provides a solution for the quantitative prediction of Cr(III) re-oxidation.

Machine learning applications for biochar studies: A mini-review

Biochar is a promising carbon sink whose application can assist in reducing carbon emissions. Development of this technology currently relies on experimental trials, which are time-consuming and labor-intensive. Machine learning (ML) technology presents a potential solution for streamlining this process. This review summarizes the current research on ML's applications in biochar production, characterization, and applications. It briefly explains commonly used machine learning algorithms and discusses prospects and challenges. A hybrid model that combines ML with mechanism-based analysis could be a future trend, addressing the ML's black-box nature. While biochar studies have adopted ML technology, current works mostly use lab-scale data for model training. Further work is needed to develop ML models based on pilot or industrial-scale data to realize the use of ML techniques for the field application of biochar.

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.

Integrating artificial intelligence, machine learning, and deep learning approaches into remediation of contaminated sites: A review

The growing number of contaminated sites across the world pose a considerable threat to the environment and human health. Remediating such sites is a cumbersome process with the complexity originating from the need for extensive sampling and testing during site characterization. Selection and design of remediation technology is further complicated by the uncertainties surrounding contaminant attributes, concentration, as well as soil and groundwater properties, which influence the remediation efficiency. Additionally, challenges emerge in identifying contamination sources and monitoring the affected area. Often, these problems are overly simplified, and the data gathered is underutilized rendering the remediation process inefficient. The potential of artificial intelligence (AI), machine-learning (ML), and deep-learning (DL) to address these issues is noteworthy, as their emergence revolutionized the process of data management/analysis. Researchers across the world are increasingly leveraging AI/ML/DL to address remediation challenges. Current study aims to perform a comprehensive literature review on the integration of AI/ML/DL tools into contaminated site remediation. A brief introduction to various emerging and existing AI/ML/DL technologies is presented, followed by a comprehensive literature review. In essence, ML/DL based predictive models can facilitate a thorough understanding of contamination patterns, reducing the need for extensive soil and groundwater sampling. Additionally, AI/ML/DL algorithms can play a pivotal role in identifying optimal remediation strategies by analyzing historical data, simulating scenarios through surrogate models, parameter-optimization using nature inspired algorithms, and enhancing decision-making with AI-based tools. Overall, with supportive measures like open-data policies and data integration, AI/ML/DL possess the potential to revolutionize the practice of contaminated site remediation.

Genetic programming-based predictive model for the Cr removal effect of in-situ electrokinetic remediation in contaminated soil

Soil electrokinetic remediation is an emerging and efficient in-situ remediation technology for reducing environmental risks. Promoting the dissolution and migration of Cr in soil under the electric field is crucial to decrease soil toxicity and ecological influences. However, it is difficult to establish strong relationships between soil treatment and impact factors and to quantify their contributions. Machine learning can help establish pollutant migration models, but it is challenging to derive predictive formulas to improve remediation efficiency, describe the predictive model construction process, and reflect the importance of the predictors for better regulation. Therefore, this paper established a predictive model for the electrokinetic remediation of Cr-contaminated soil using genetic programming (GP) after determining the characteristic parameters which influenced the remediation effect, described the model's adaptive optimization process through the algorithm's function, and identified the sensitivity factors affecting the Cr removal effect. Results showed that the Cr(VI) and total Cr concentrations predicted by GP were in satisfactory agreement with the experimental values, 92% of the training data and 90% of the validation data achieved errors within 1%, and could fully reflect the target ions' content variation in different soil layers. By substituting the above prediction formulas into Sobol sensitivity analysis, it was determined that conductivity, pH, current, and moisture content dramatically affected the Cr content variation in distinguished regions. For overall contaminated area, the system current and soil pH were the most sensitive factors for Cr(VI) and total Cr contents. Remediation efforts throughout the contaminated area should focus on the role of current versus soil pH. GP and sensitivity analysis can provide decision support and operational guidance for in-situ soil electrokinetic treatment by establishing a remediation effect prediction model, expediting the development and innovation of electrokinetic technology.

Characteristics prediction of hydrothermal biochar using data enhanced interpretable machine learning

Hydrothermal biochar is a promising sustainable soil remediation agent for plant growth. Demands for biochar properties differ due to the diversity of soil environment. In order to achieve accurate biochar properties prediction and overcome the interpretability bottleneck of machine learning models, this study established a series of data-enhanced machine learning models and conducted relevant sensitivity analysis. Compared with traditional support vector machine, artificial neural network, and random forest models, the accuracy after data enhancement increased in average from 5.8% to 15.8%, where the optimal random forest model showed the average of accuracy was 94.89%. According to sensitivity analysis results, the essential factors influencing the predicting results of the models were reaction temperature, reaction pressure, and specific element of biomass feedstock. As a result, data-enhanced interpretable machine learning proved promising for the characteristics prediction of hydrothermal biochar.

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.

A review on control and abatement of soil pollution by heavy metals: Emphasis on artificial intelligence in recovery of contaminated soil

"Save Soil Save Earth" is not just a catchphrase; it is a necessity to protect soil ecosystem from the unwanted and unregulated level of xenobiotic contamination. Numerous challenges such as type, lifespan, nature of pollutants and high cost of treatment has been associated with the treatment or remediation of contaminated soil, whether it be either on-site or off-site. Due to the food chain, the health of non-target soil species as well as human health were impacted by soil contaminants, both organic and inorganic. In this review, the use of microbial omics approaches and artificial intelligence or machine learning has been comprehensively explored with recent advancements in order to identify the sources, characterize, quantify, and mitigate soil pollutants from the environment for increased sustainability. This will generate novel insights into methods for soil remediation that will reduce the time and expense of soil treatment.

Elucidating nitrogen removal performance and response mechanisms of anammox under heavy metal stress using big data analysis and machine learning

In this study, machine learning algorithms and big data analysis were used to decipher the nitrogen removal rate (NRR) and response mechanisms of anammox process under heavy metal stresses. Spearman algorithm and Statistical analysis revealed that Cr⁶⁺ had the strongest inhibitory effect on NRR compared to other heavy metals. The established machine learning model (extreme gradient boost) accurately predicted NRR with an accuracy greater than 99%, and the prediction error for new data points was mostly less than 20%. Additionally, the findings of feature analysis demonstrated that Cu²⁺ and Fe³⁺ had the strongest effect on the anammox process, respectively. According to the new insights from this study, Cr⁶⁺ and Cu²⁺ should be removed preferentially in anammox processes under heavy metal stress. This study revealed the feasible application of machine learning and big data analysis for NRR prediction of anammox process under heavy metal stress.

Prediction heavy metals accumulation risk in rice using machine learning and mapping pollution risk

Rapid and accurate prediction of metal bioaccumulation in crops are important for assessing metal environmental risks. We aimed to incorporate machine learning modeling methods to predict heavy metal contents in rice crops and identify influencing factors. We conducted a field study in Jiangsu province, China, collecting 2123 pairs of soil-rice samples in a uniform measurement and using 10 machine learning algorithms to predict the uptake of Cd, Hg, As, and Pb in rice grain. The Extremely Randomized Tree model exhibited the best performance for rice-Cd and rice-Hg (Cd: R² = 0.824; Hg: R² = 0.626), while the Random Forest model performed best for As and Pb (As: R² = 0.389; Pb: R² = 0.325). The feature importance analysis showed that soil-Cd and pH had the highest impact on rice-Cd risk, which is in line with previous studies; while temperature and soil organic carbon were more important to rice-Hg than soil-Hg. Then, based on another set of 1867 uniformly distributed paddy soil samples in Jiangsu province, the Cd and Hg risks of soil and rice were visualized using the established models. Mapping result revealed an inconsistent pattern of hotspot distribution between soil-Hg and rice-Hg, i.e., a higher rice-Hg risk in the northern area, while higher soil-Hg in south. Our findings highlight the importance of temperature on Hg bioaccumulation risk to crops, which has often been overlooked in previous risk assessment processes.

Investigating the use of synthetic humic-like acid as a soil amendment for metal-contaminated soil

Humic acid can effectively bind several metals and is regarded as a promising soil amendment. In this study, a novel synthetic humic-like acid (SHLA) was applied as a soil amendment to immobilize metals (Cu, Zn, Ni, As) in a contaminated agricultural soil (pH 6.17 ± 0.11; total organic carbon 5.91 ± 0.40%; Cu 302.86 ± 3.97 mg/kg; Zn 700.45 ± 14.30 mg/kg; Ni 140.16 ± 1.59 mg/kg). With increasing additions of SHLA from 0 to 10% (w/w), the soil pH constantly decreased from 6.17 ± 0.11 to 4.91 ± 0.10 (p < 0.001), while both total organic carbon (from 6.10 ± 0.12% to 10.55 ± 0.18%) and water-soluble carbon content (from 171.01 ± 10.15 mg/kg to 319.18 ± 20.74 mg/kg) of soil significantly increased (p < 0.001). Based on the results of 0.01 M CaCl2-extractable concentration of different metals, SHLA could lower the bioavailability of Cu (from 1.26 ± 0.04 mg/kg to 0.55 ± 0.05 mg/kg), Zn (from 6.74 ± 0.12 mg/kg to 3.26 ± 0.23 mg/kg), and Ni (from 5.16 ± 0.07 mg/kg to 0.12 ± 0.02 mg/kg), but increase the bioavailability of As (from 0.31 ± 0.02 to 1.83 ± 0.09 mg/kg). The immobilization mechanisms of metals in soils amended with SHLA involved surface complexation, electrostatic attraction, and cation-π interaction. Overall, SHLA shows great potential as a soil amendment for cationic heavy metal immobilization.

Delineating and identifying risk zones of soil heavy metal pollution in an industrialized region using machine learning

The ability to control the risk of soil heavy metal pollution is limited by the inability to accurately depict their spatial distributions and to reasonably delineate the risk zones. To overcome this limitation and develop machine learning methods, a hybrid data-driven method supported by random forest (RF) and fuzzy c-means with the aid of inverse distance weighted interpolation was proposed to delineate and further identify risk zones of soil heavy metal pollution on the basis of 577 soil samples and 12 environmental covariates. The results indicated that, compared to multiple linear regression, RF had a better prediction performance for As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn, with the corresponding R² values of 0.86, 0.85, 0.78, 0.85, 0.84, 0.78, 0.79 and 0.76, respectively. The relative concentrations (predicted concentrations divided by risk screening values) of Cd (17.69), Cr (1.38), Hg (0.31), Pb (6.52), and Zn (8.24) were relatively high in the north central part of the study area. There were large differences in the key influencing factors and their contributions among the eight heavy metals. Overall, industrial enterprises (21.60% for As), soil pH (31.60% for Cd), and population (15.50% for Cr) were the key influencing factors for the heavy metals in soil. Four risk zones, including one high risk zone, one medium risk zone, and two low risk zones were delineated and identified based on the characteristics of the eight heavy metals and their influencing factors, and accordingly discriminated risk control strategies were developed. In the high risk zone, it will be necessary to strictly control the discharge of heavy metals from the various industrial enterprises and mines by the adoption of cleaner production practices, centralizedly treat the domestic wastes from residents, substantially reduce the irrigation of polluted river water, and positively remediate the Cd, Cr, and Ni-polluted soil.

Contamination vertical distribution and key factors identification of metal(loid)s in site soil from an abandoned Pb/Zn smelter using machine learning

Soil heterogeneity makes the vertical distribution of metal(loid)s in site soil vary considerably and poses a challenge for identifying the key factors of metal(loid)s migration in site soil profiles. In this study, a machine learning (ML) model was developed to study a typical abandoned Pb/Zn smelter using 267 site soils from 46 drilling points. Results showed that a well-trained ML model could be used to identify the key factors in determining the contamination vertical distribution and predict the metal(loid)s contents in subsurface soil. As, Cd, Pb, and Zn were the primary pollutants and their vertical migration depth arrived to 4-6 m. Based on the predictive performance of different ML algorithms, the extreme gradient boosting (XGB) was selected as the best model to produce accurate predictions for the most metal(loid)s content. Contents of As, Cd, Pb, and Zn in the heavily contaminated zones declined with an increase of soil depth. The metal(loid) contents in surface soil of 0-2 m could be readily used to predict the content of Cd, Cr, Hg, and Zn in subsurface soil from 2 m to 10 m. Based on the metal-specific XGB models, sulfur content, functional area, and soil texture were identified as key factors affecting the vertical distribution of As, Cd, Pb, and Zn in site soil. Results suggested the ML method is helpful to manage the potential environmental risks of metal(loid)s in Pb/Zn smelting site.

Modeling, optimization and understanding of adsorption process for pollutant removal via machine learning: Recent progress and future perspectives

It is crucial to reduce the concentration of pollutants in water environment to below safe levels. Some cost-effective pollutant removal technologies have been developed, among which adsorption technology is considered as a promising solution. However, the batch experiments and adsorption isotherms widely employed at present are inefficient and time-consuming to some extent, which limits the development of adsorption technology. As a new research paradigm, machine learning (ML) is expected to innovate traditional adsorption models. This reviews summarized the general workflow of ML and commonly employed ML algorithms for pollutant adsorption. Then, the latest progress of ML for pollutant adsorption was reviewed from the perspective of all-round regulation of adsorption process, including adsorption efficiency, operating conditions and adsorption mechanism. General guidelines of ML for pollutant adsorption were presented. Finally, the existing problems and future perspectives of ML for pollutant adsorption were put forward. We highly expect that this review will promote the application of ML in pollutant adsorption and improve the interpretability of ML.