Prediction of Soil Heavy Metal Immobilization by Biochar Using Machine Learning

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.

New Insights on the Formation of Nucleation Mode Particles in a Coastal City Based on a Machine Learning Approach

Atmospheric particles have profound implications for the global climate and human health. Among them, ultrafine particles dominate in terms of the number concentration and exhibit enhanced toxic effects as a result of their large total surface area. Therefore, understanding the driving factors behind ultrafine particle behavior is crucial. Machine learning (ML) provides a promising approach for handling complex relationships. In this study, three ML models were constructed on the basis of field observations to simulate the particle number concentration of nucleation mode (PNCN). All three models exhibited robust PNCN reproduction (R2 > 0.80), with the random forest (RF) model excelling on the test data (R2 = 0.89). Multiple methods of feature importance analysis revealed that ultraviolet (UV), H2SO4, low-volatility oxygenated organic molecules (LOOMs), temperature, and O3 were the primary factors influencing PNCN. Bivariate partial dependency plots (PDPs) indicated that during nighttime and overcast conditions, the presence of H2SO4 and LOOMs may play a crucial role in influencing PNCN. Additionally, integrating additional detailed information related to emissions or meteorology would further enhance the model performance. This pilot study shows that ML can be a novel approach for simulating atmospheric pollutants and contributes to a better understanding of the formation and growth mechanisms of nucleation mode particles.

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.

Intelligent leaching rare earth elements from waste fluorescent lamps

Rare earth elements (REEs), one of the global key strategic resources, are widely applied in electronic information and national defense, etc. The sharply increasing demand for REEs leads to their overexploitation and environmental pollution. Recycling REEs from their second resources such as waste fluorescent lamps (WFLs) is a win-win strategy for REEs resource utilization and environmental production. Pyrometallurgy pretreatment combined with acid leaching is proven as an efficient approach to recycling REEs from WFLs. Unfortunately, due to the uncontrollable components of wastes, many trials were required to obtain the optimal parameters, leading to a high cost of recovery and new environmental risks. This study applied machine learning (ML) to build models for assisting the leaching of six REEs (Tb, Y, Eu, La, and Gd) from WFLs, only needing the measurement of particle size and composition of the waste feed. The feature importance analysis of 40 input features demonstrated that the particle size, Mg, Al, Fe, Sr, Ca, Ba, and Sb content in the waste feed, the pyrometallurgical and leaching parameters have important effects on REEs leaching. Furthermore, their influence rules on different REEs leaching were revealed. Finally, some verification experiments were also conducted to demonstrate the reliability and practicality of the model. This study can quickly get the optimal parameters and leaching efficiency for REEs without extensive optimization experiments, which significantly reduces the recovery cost and environmental risks. Our work carves a path for the intelligent recycling of strategic REEs from waste.

End-to-end machine-learning for high-gravity ammonia stripping: Bridging the gap between scientific research and user-friendly applications

The removal and recovery of ammonia from wastewater are critical processes for achieving global environmental sustainability and promoting circular economic development. High-gravity technology is an advanced solution to achieve ammonia stripping from wastewater. This study used machine-learning (ML) techniques to provide more comprehensive insights on various influencing factors, including the operating parameters, wastewater characteristics, and design parameters of rotating packed beds. Bayesian auto-optimization combined with a boosting algorithm effectively overcame the challenges of modeling complex datasets with small sample sizes, multidimensional data, missing values, and skewed distributions. Accurate ML based predictive models for the ammonia removal efficiency (η) and mass transfer coefficient (KLa) were developed, the performance on the training set was R2 = 0.98 and R2 = 0.89, and on the testing set was R2 = 0.98 and R2 = 0.82. The developed model revealed that the stripping stage and gas-liquid ratio were the most influential features for predicting η, whereas the liquid flow and high-gravity factor were the most important features for predicting KLa. The well-trained model was then deployed in an online software application that could provide both predictive and auto-update functions for operators and managers, ensuring that practitioners could use the model. The end-to-end machine-learning approach used in this study-that is, covering data collection, model development, and application-could improve the availability of research results, providing valuable references for the further advancement of technology in the field of environmental.

Machine learning based prediction and experimental validation of arsenite and arsenate sorption on biochars

Arsenic (As) contamination in water is a significant environmental concern with profound implications for human health. Accurate prediction of the adsorption capacity of arsenite [As(III)] and arsenate [As(V)] on biochar is vital for the reclamation and recycling of polluted water resources. However, comprehending the intricate mechanisms that govern arsenic accumulation on biochar remains a formidable challenge. Data from the literature on As adsorption to biochar was compiled and fed into machine learning (ML) based modelling algorithms, including AdaBoost, LGBoost, and XGBoost, in order to build models to predict the adsorption efficiency of As(III) and As(V) to biochar, based on the compositional and structural properties. The XGBoost model showed superior accuracy and performance for prediction of As adsorption efficiency (for As(III): coefficient of determination (R2) = 0.93 and root mean square error (RMSE) = 1.29; for As(V), R2 = 0.99, RMSE = 0.62). The initial concentrations of As(III) and As(V) as well as the dosage of the adsorbent were the most significant factors influencing adsorption, explaining 48 % and 66 % of the variability for As(III) and As(V), respectively. The structural properties and composition of the biochar explained 12 % and 40 %, respectively, of the variability of As(III) adsorption, and 13 % and 21 % of that of As(V). The XGBoost models were validated using experimental data. R2 values were 0.9 and 0.84, and RMSE values 6.5 and 8.90 for As(III) and As(V), respectively. The ML approach can be a valuable tool for improving the treatment of inorganic As in aqueous environments as it can help estimate the optimal adsorption conditions of As in biochar-amended water, and serve as an early warning for As-contaminated water.

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

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

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.

Machine Learning Models for Inverse Design of the Electrochemical Oxidation Process for Water Purification

In this study, a machine learning (ML) framework is developed toward target-oriented inverse design of the electrochemical oxidation (EO) process for water purification. The XGBoost model exhibited the best performances for prediction of reaction rate (k) based on training the data set relevant to pollutant characteristics and reaction conditions, indicated by Rext2 of 0.84 and RMSEext of 0.79. Based on 315 data points collected from the literature, the current density, pollutant concentration, and gap energy (Egap) were identified to be the most impactful parameters available for the inverse design of the EO process. In particular, adding reaction conditions as model input features allowed provision of more available information and an increase in the sample size of the data set to improve the model accuracy. The feature importance analysis was performed for revealing the data pattern and feature interpretation by using Shapley additive explanations (SHAP). The ML-based inverse design for the EO process was generalized to a random case for tailoring the optimum conditions with phenol and 2,4-dichlorophenol (2,4-DCP) serving as model pollutants. The resulting predicted k values were close to the experimental k values by experimental verification, accounting for the relative error lower than 5%. This study provides a paradigm shift from conventional trial-and-error mode to data-driven mode for advancing research and development of the EO process by a time-saving, labor-effective, and environmentally friendly target-oriented strategy, which makes electrochemical water purification more efficient, more economic, and more sustainable in the context of global carbon peaking and carbon neutrality.

Prediction of Photochemical Properties of Dissolved Organic Matter Using Machine Learning

Apparent quantum yields (Φ) of photochemically produced reactive intermediates (PPRIs) formed by dissolved organic matter (DOM) are vital to element cycles and contaminant fates in surface water. Simultaneous determination of ΦPPRI values from numerous water samples through existing experimental methods is time consuming and ineffective. Herein, machine learning models were developed with a systematic data set including 1329 data points to predict the values of three ΦPPRIs (Φ3DOM*, Φ1O2, and Φ·OH) based on DOM spectral parameters, experimental conditions, and calculation parameters. The best predictive performances for Φ3DOM*, Φ1O2, and Φ·OH were achieved using the CatBoost model, which outperformed the traditional linear regression models. The significances of the wavelength range and spectral parameters on the three ΦPPRI predictions were revealed, suggesting that DOM with lower molecular weight, lower aromatic content, and a more autochthonous portion possessed higher ΦPPRIs. Chain models were constructed by adding the predicted Φ3DOM* as a new feature into the Φ1O2 and Φ·OH models, which consequently improved the predictive performance of Φ1O2 but worsened the Φ·OH prediction likely due to the complex formation pathways of ·OH. Overall, this study offered robust ΦPPRI prediction across interlaboratory differences and provided new insights into the relationship between PPRIs formation and DOM properties.

Accurate Prediction of Soil Heavy Metal Pollution Using an Improved Machine Learning Method: A Case Study in the Pearl River Delta, China

In traditional soil heavy metal (HM) pollution assessment, spatial interpolation analysis is often carried out on the limited sampling points in the study area to get the overall status of heavy metal pollution. Unfortunately, in many machine learning spatial information enhancement algorithms, the additional spatial information introduced fails to reflect the hierarchical heterogeneity of the study area. Therefore, we designed hierarchical regionalization labels based on three interpolation techniques (inverse distance weight, ordinary kriging, and trend surface interpolation) as new spatial covariates for a machine learning (ML) model. It was demonstrated that regional spatial information improved the prediction performance of the model (R2 > 0.7). On the basis of the prediction results, the status of HM pollution in the Pearl River Delta (PRD) region was evaluated: cadmium (Cd) and copper (Cu) were the most serious pollutants in the PRD (the point overstandard rates are 18.77% and 12.95%, respectively). The analysis of index importance and bivariate local indicators of spatial association (LISA) shows that the key factors affecting the spatial distribution of heavy metals are geographical and climatic conditions [namely, altitude, humidity index, and normalized vegetation difference index (NDVI)] and some industrial activities (such as metal processing, printing and dyeing, and electronics industry). This study develops a novel approach to improve existing spatial interpolation techniques, which will enable more precise and scientific soil environmental management.

Exploring the Complexities of Dissolved Organic Matter Photochemistry from the Molecular Level by Using Machine Learning Approaches

Dissolved organic matter (DOM) sustains a substantial part of the organic matter transported seaward, where photochemical reactions significantly affect its transformation and fate. The irradiation experiments can provide valuable information on the photochemical reactivity (photolabile, photoresistant, and photoproduct) of molecules. However, the inconsistency of the fate of irradiated molecules among different experiments curtailed our understanding of the roles the photochemical reactions have played, which cannot be properly addressed by traditional approaches. Here, we conducted irradiation experiments for samples from two large estuaries in China. Molecules that occurred in irradiation experiments were characterized by the Fourier transform ion cyclotron resonance mass spectrometry and assigned probabilistic labels to define their photochemical reactivity. These molecules with probabilistic labels were used to construct a learning database for establishing a suitable machine learning (ML) model. We further applied our well-trained ML model to "un-matched" (i.e., not detected in our irradiation experiments) molecules from five estuaries worldwide, to predict their photochemical reactivity. Results showed that numerous molecules with strong photolability can be captured solely by the ML model. Moreover, comparing DOM photochemical reactivity in five estuaries revealed that the riverine DOM chemistry largely determines their subsequent photochemical transformation. We offer an expandable and renewable approach based on ML to compatibly integrate existing irradiation experiments and shed insight into DOM transformation and degradation processes.

Biochar may alter plant communities when remediating the cadmium-contaminated soil in the saline-alkaline wetland

It is thought remediating cadmium pollution with biochar can affect plant traits. However, the potential impact of this practice on plant communities is poorly understood. Here, we established natural-germinated plant communities using soil seed bank from a saline-alkaline wetland and applied a biochar treatment in Cd-polluted wetland soil. The outcomes illustrated that Juglans regia biochar (JBC), Spartina alterniflora biochar (SBC), and Flaveria bidentis biochar (FBC) promoted exchangeable Cd transform into FeMn oxide bound Cd. Additionally, most biochar addition reduced species abundance, root-shoot ratio, biomass, diversity, and community stability, yet enhanced community height. Among all treatments, the 5 % SBC demonstrated the most significant reduction in species abundance, biomass, species richness and functional richness. Specifically, it resulted in a reduction of 92.80 % in species abundance, 73.80 % in biomass, 66.67 % in species richness, and 95.14 % in functional richness compared to the CK. We also observed changes in root morphological traits and community structure after biochar addition. Soil pH, salinity, and nutrients played a dominant role in shaping plant community. These findings have implications for biodiversity conservation, and the use of biochar for the remediation of heavy metals like cadmium should be approached with caution due to its potential negative impacts on plant communities.

Prediction of cadmium contents in rice grains from Quaternary sediment-distributed farmland using field investigations and machine learning

The Quaternary sediment-distributed regions of South China are suitable for rice cultivation, which is crucial for ensuring food security. Spatial correlations between soil cadmium (Cd) and rice Cd contents are generally poor, making the evaluation of rice quality and associated health risks challenging. In this study, we developed machine learning algorithms for predicting rice Cd contents using 654 data pairs of soil-rice samples collected in Guangxi province, China. After a comprehensive comparison, our results showed that the random forest (RF) had the better performance than artificial neural network (ANN) based on all the data (RMSEtesting 0.066 vs. 0.099 and R2testing 0.860 vs. 0.688). The feature importance analysis showed that soil CaO, Cd, elevation, and rainfall were the four most important features affecting the rice Cd contents in the study area. Using the established RF-predicated model, the rice Cd contents were predicted at the provincial level with an additional dataset of 1176 paddy soil samples. The prediction result revealed about 23 % of farmland cultivated rice with Cd content over 0.2 mg kg-1 in the study area. Therefore, it is recommended to implement strict measures by local agricultural departments to reduce rice Cd contents and ensure food safety in these areas. Our study provides valuable insights into the prediction of rice Cd contents, thus contributing to ensuring food safety and preventing Cd exposure-associated health risks.

Effects of biochar and zero valent iron on the bioavailability and potential toxicity of heavy metals in contaminated soil at the field scale

Heavy metals (HMs) such as copper, nickel and chromium are toxic, so soil contaminated with these metals is of great concern. In situ HM immobilization by adding amendments can decrease the risk of contaminants being released. A five-month field-scale study was performed to assess how different doses of biochar and zero valent iron (ZVI) affect HM bioavailability, mobility, and toxicity in contaminated soil. The bioavailabilities of HMs were determined and ecotoxicological assays were performed. Adding 5 % biochar, 10 % ZVI, 2 % biochar + 1 % ZVI, and 5 % biochar + 10 % ZVI to soil decreased Cu, Ni and Cr bioavailability. Metals were most effectively immobilized by adding 5 % biochar + 10 % ZVI, and the extractable Cu, Ni, and Cr contents were 60.9 %, 66.1 % and 38.9 % lower, respectively, for soil with 5 % biochar + 10 % ZVI added than unamended soil. The extractable Cu, Ni, and Cr contents were 64.2 %, 59.7 % and 16.7 % lower, respectively, for soil with 2 % biochar + 1 % ZVI added than unamended soil. Experiments using wheat, pak choi and beet seedlings were performed to assess the remediated soil toxicity. Growth was markedly inhibited in seedlings grown in extracts of soil with 5 % biochar, 10 % ZVI, or 5 % biochar + 10 % ZVI added. More growth occurred in wheat and beet seedlings after 2 % biochar + 1 % ZVI treatment than the control, possibly because 2 % biochar + 1 % ZVI simultaneously decreased the extractable HM content and increased the soluble nutrient (carbon and Fe) content of the soil. A comprehensive risk assessment indicated that adding 2 % biochar + 1 % ZVI gave optimal remediation at the field scale. Using ecotoxicological methods and determining the bioavailabilities of HMs can allow remediation methods to be identified to efficiently and cost-effectively decrease the risks posed by multiple metals in soil at contaminated sites.

Unmasking air quality: A novel image-based approach to align public perception with pollution levels

In the quest to reconcile public perception of air pollution with scientific measurements, our study introduced a pioneering method involving a gradient boost-regression tree model integrating PM2.5 concentration, visibility, and image-based data. Traditional stationary monitoring often falls short of accurately capturing public air quality perceptions, prompting the need for alternative strategies. Leveraging an extensive dataset of over 20,000 public visibility perception evaluations and over 8,000 stationary images, our models effectively quantify diverse air quality perceptions. The predictive prowess of our models was validated by strong performance metrics for perceived visibility (R = 0.98, RMSE = 0.19), all-day PM2.5 concentrations (R: 0.77-0.78, RMSE: 8.31-9.40), and Central Weather Bureau visibility records (R = 0.82, RMSE = 9.00). Interestingly, image contrast and light intensity hold greater importance than scenery clarity in the visibility perception model. However, clarity is prioritized in PM2.5 and Central Weather Bureau models. Our research also unveiled spatial limitations in stationary monitoring and outlined the variations in predictive image features between near and far stations. Crucially, all models benefit from the characterization of atmospheric light sources through defogging techniques. The image-based insights highlight the disparity between public perception of air pollution and current policy implementation. In other words, policymakers should shift from solely emphasizing the reduction of PM2.5 levels to also incorporating the public's perception of visibility into their strategies. Our findings have broad implications for air quality evaluation, image mining in specific areas, and formulating air quality management strategies that account for public perception.

Contribution of molecular structures and quantum chemistry technique to root concentration factor: An innovative application of interpretable machine learning

Root concentration factor (RCF) is a significant parameter to characterize uptake and accumulation of hazardous organic contaminants (HOCs) by plant roots. However, complex interactions among chemicals, plant roots and soil make it challenging to identify underlying mechanisms of uptake and accumulation of HOCs. Here, nine machine learning techniques were applied to investigate major factors controlling RCF based on variable combinations of molecular descriptors (MD), MACCS fingerprints, quantum chemistry descriptors (QCD) and three physicochemical properties related to chemical-soil-plant system. Compared to models with variables including MACCS fingerprints or solitary physicochemical properties, the XGBoost-6 model developed by the variable combination of MD, QCD and three physicochemical properties achieved the most remarkable performance, with R2 of 0.977. Model interpretation achieved by permutation variable importance and partial dependence plots revealed the vital importance of HOCs lipophilicity, lipid content of plant roots, soil organic matter content, the overall deformability and the molecular dispersive ability of HOCs for regulating RCF. The integration of MD and QCD with physicochemical properties could improve our knowledge of underlying mechanisms regarding HOCs accumulation in plant roots from innovative structural perspectives. Multiple variables combination-oriented performance improvement of model can be extended to other parameters prediction in environmental risk assessment field.

Recent advancements and challenges in emerging applications of biochar-based catalysts

The sustainable utilization of biochar produced from biomass waste could substantially promote the development of carbon neutrality and a circular economy. Due to their cost-effectiveness, multiple functionalities, tailorable porous structure, and thermal stability, biochar-based catalysts play a vital role in sustainable biorefineries and environmental protection, contributing to a positive, planet-level impact. This review provides an overview of emerging synthesis routes for multifunctional biochar-based catalysts. It discusses recent advances in biorefinery and pollutant degradation in air, soil, and water, providing deeper and more comprehensive information of the catalysts, such as physicochemical properties and surface chemistry. The catalytic performance and deactivation mechanisms under different catalytic systems were critically reviewed, providing new insights into developing efficient and practical biochar-based catalysts for large-scale use in various applications. Machine learning (ML)-based predictions and inverse design have addressed the innovation of biochar-based catalysts with high-performance applications, as ML efficiently predicts the properties and performance of biochar, interprets the underlying mechanisms and complicated relationships, and guides biochar synthesis. Finally, environmental benefit and economic feasibility assessments are proposed for science-based guidelines for industries and policymakers. With concerted effort, upgrading biomass waste into high-performance catalysts for biorefinery and environmental protection could reduce environmental pollution, increase energy safety, and achieve sustainable biomass management, all of which are beneficial for attaining several of the United Nations Sustainable Development Goals (UN SDGs) and Environmental, Social and Governance (ESG).

Elaborating the mechanism of lead adsorption by biochar: Considering the impacts of water-washing and freeze-drying in preparing biochar

This paper examined the impacts of different pretreatments on the characteristics of biochar and its adsorption behavior for Pb2+. Biochar with combined pretreatment of water-washing and freeze-drying (W-FD-PB) performed a maximum adsorption capacity for Pb2+ of 406.99 mg/g, higher than that of 266.02 mg/g on water-washing pretreated biochar (W-PB) and 188.21 mg/g on directly pyrolyzed biochar (PB). This is because the water-washing process partially removed the K and Na, resulting in the relatively enriched Ca and Mg on W-FD-PB. And the freeze-drying pretreatment broke the fiber structure of pomelo peel, favoring the development of a fluffy surface and large specific surface area during pyrolysis. Quantitative mechanism analysis implied that cation ion exchange and precipitation were the driving forces in Pb2+ adsorption on biochar, and both mechanisms were enhanced during Pb2+ adsorption on W-FD-PB. Furthermore, adding W-FD-PB to Pb-contaminated soil increased the soil pH and significantly reduced the availability of Pb.

Assessing surface water pollution in Hanoi, Vietnam, using remote sensing and machine learning algorithms

Rapid urbanization led to significant land-use changes and posed threats to surface water bodies worldwide, especially in the Global South. Hanoi, the capital city of Vietnam, has been facing chronic surface water pollution for more than a decade. Developing a methodology to better track and analyze pollutants using available technologies to manage the problem has been imperative. Advancement of machine learning and earth observation systems offers opportunities for tracking water quality indicators, especially the increasing pollutants in the surface water bodies. This study introduces machine learning with the cubist model (ML-CB), which combines optical and RADAR data, and a machine learning algorithm to estimate surface water pollutants including total suspended sediments (TSS), chemical oxygen demand (COD), and biological oxygen demand (BOD). The model was trained using optical (Sentinel-2A and Sentinel-1A) and RADAR satellite images. Results were compared with field survey data using regression models. Results show that the predictive estimates of pollutants based on ML-CB provide significant results. The study offers an alternative water quality monitoring method for managers and urban planners, which could be instrumental in protecting and sustaining the use of surface water resources in Hanoi and other cities of the Global South.

Artificial Intelligence Modeling-Based Optimization of an Industrial-Scale Steam Turbine for Moving toward Net-Zero in the Energy Sector

Augmentation of energy efficiency in the power generation systems can aid in decarbonizing the energy sector, which is also recognized by the International Energy Agency (IEA) as a solution to attain net-zero from the energy sector. With this reference, this article presents a framework incorporating artificial intelligence (AI) for improving the isentropic efficiency of a high-pressure (HP) steam turbine installed at a supercritical power plant. The data of the operating parameters taken from a supercritical 660 MW coal-fired power plant is well-distributed in the input and output spaces of the operating parameters. Based on hyperparameter tuning, two advanced AI modeling algorithms, i.e., artificial neural network (ANN) and support vector machine (SVM), are trained and, subsequently, validated. ANN, as turned out to be a better-performing model, is utilized to conduct the Monte Carlo technique-based sensitivity analysis toward the high-pressure (HP) turbine efficiency. Subsequently, the ANN model is deployed for evaluating the impact of individual or combination of operating parameters on the HP turbine efficiency under three real-power generation capacities of the power plant. The parametric study and nonlinear programming-based optimization techniques are applied to optimize the HP turbine efficiency. It is estimated that the HP turbine efficiency can be improved by 1.43, 5.09, and 3.40% as compared to that of the average values of input parameters for half-load, mid-load, and full-load power generation modes, respectively. The annual reduction in CO₂ measuring 58.3, 123.5, and 70.8 kilo ton/year (kt/y) corresponds to half-load, mid-load, and full load, respectively, and noticeable mitigation of SO₂, CH₄, N₂O, and Hg emissions is estimated for the three power generation modes of the power plant. The AI-based modeling and optimization analysis is conducted to enhance the operation excellence of the industrial-scale steam turbine that promotes higher-energy efficiency and contributes to the net-zero target from the energy sector.

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.

Digital exploration of selected heavy metals using Random Forest and a set of environmental covariates at the watershed scale

The current study was established for predicting some selected heavy metals (HMs) including Zn, Mn, Fe, Co, Cr, Ni, and Cu, by applying random forest (RF) and a set of environmental covariates at watershed scale. The objectives were to find out the most effective combination of variables and controlling factors on the variability of HMs in a semiarid watershed in central Iran. One hundred locations were selected in the given watershed in the hypercube manner and soil samples from a surface 0-20 cm depth and concentration of HMs and some soil properties were measured in the laboratory. Three scenarios of input variables were defined for HMs prediction. The results revealed that the first scenario (remote sensing + topographic attributes) explained about 27-34% of the variability in HMs. Inclusion of a thematic map to the scenario I, improved the prediction accuracy for all HMs. Scenario III (remote sensing data+ topographic attributes + soil properties) was the most efficient scenario for prediction of HMs with R² values ranging from 0.32 for Cu to 0.42 for Fe. Similarly, the lowest nRMSE was found for all HMs in scenario III, ranging from 0.271 for Fe to 0.351 for Cu. Among the soil properties, clay content and magnetic susceptibility were the most important variables, and also some remote sensing data (Carbonate index, Soil adjusted vegetation index, Band2, and Band7) and topographic attributes (mainly control soil redistribution along the landscape) were the most efficient variables for estimating HMs. We concluded that the RF model with a combination of remote sensing data, topographic attributes, and assisting of thematic maps such as land use in the studied watershed could reliably predict HMs content.

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.

Effect of titanium dioxide nanoparticles and co-composted biochar on growth and Cd uptake by wheat plants: A field study

Cadmium (Cd) is a common toxic trace element found in agricultural soils which is due to anthropogenic activities. Cadmium posed a significant risk to humans all around the world due to its cancer-causing ability. The current study demonstrated the effects of soil-applied biochar (BC) and foliar-applied titanium dioxide nanoparticles (TiO₂ NPs) (at a rate of 0.5% and 75 mg/L respectively) alone or in combination on growth and Cd accumulation in wheat plants under field experiment. Soil applied BC and foliar TiO₂ NPs, as well as BC coupled with TiO₂ NPs, reduced Cd contents in grains by 32%, 47%, and 79%, than control respectively. The usage of NPs and BC boosted the plant height as well as chlorophyll contents by lowering oxidative injury and changing antioxidant enzyme activities than control plants. The combined use of NPs and BC prevented excess Cd accumulation in grains over the critical level (0.2 mg/kg) for cereals. The health risk index (HRI) due to Cd was reduced by 79% by co-composted BC + TiO₂ NPs treatment than control. Although, HRI was lower than one for all treatments but this may exceed the limit if grains obtained from such field consumed over long periods. In conclusion, TiO₂ NPs and BC amendments can be implemented in fields across the globe where excess Cd is present soils. Additional studies on the use of such approaches in more precise experimental settings are needed in order to address this environmental problem at larger scale.

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

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

Biochar-Assisted Catalytic Pyrolysis of Oily Sludge to Attain Harmless Disposal and Residue Utilization for Soil Reclamation

Pyrolysis of oily sludge (OS) is a feasible technology to match the principle of reduction and recycling; however, it is difficult to confirm the feasible environmental destination and meet the corresponding requirements. Therefore, an integrated strategy of biochar-assisted catalytic pyrolysis (BCP) of OS and residue utilization for soil reclamation is investigated in this study. During the catalytic pyrolysis process, biochar as a catalyst intensifies the removal of recalcitrant petroleum hydrocarbons at the expense of liquid product yield. Concurrently, biochar as an adsorbent can inhibit the release of micromolecular gaseous pollutants (e.g. HCN, H₂S, and HCl) and stabilize heavy metals. Due to the assistance of biochar, pyrolysis reactions of OS are more likely to occur and require a lower temperature to achieve the same situation. During the soil reclamation process, the obtained residue as a soil amendment can not only provide a carbon source and mineral nutrients but can also improve the abundance and diversity of microbial communities. Thus, it facilitates the plant germination and the secondary removal of petroleum hydrocarbons. The integrated strategy of BCP of OS and residue utilization for soil reclamation is a promising management strategy, which is expected to realize the coordinated and benign disposal of more than one waste.

Heavy metals in riverine/estuarine sediments from an aquaculture wetland in metropolitan areas, China: Characterization, bioavailability and probabilistic ecological risk

Aquaculture wetlands, particularly those located within urban areas, are fragile ecosystems due to urban and aquaculture impacts. However, to date, there are no reports on the combined toxicity of heavy metal mixtures in aquatic biota in sediments from aquaculture wetlands in metropolitan areas. Thus, the characterization, bioavailability, and ecological probability risk of heavy metals were studied in the riverine/estuarine sediments of the Rongjiang River in an aquaculture wetland in Chaoshan metropolis, South China. In the study area, the average total concentrations (mg/kg) were 2.38 (Cd), 113.40 (Pb), 88.27 (Cr), 148.25 (Ni), 62.08 (Cu), 125.18 (Zn), 45,636.44 (Fe), and 797.18 (Mn), with the Cd pollution being regarded as extremely serious based on the enrichment factor (EF). There are two main sources of heavy metals in the study area; Ni, Pb, Zn, Fe and Mn are mainly from domestic waste, while Cr, Cd and Cu are possibly associated with industrial production activities. The bioavailability of most heavy metals accounted for more than 20% of the total concentration. The combined toxicity of heavy metal mixtures based on probabilistic risk assessment suggests that the surface sediments of the Rongjiang River and its estuary had a 15.71% probability of toxic effects on aquatic biota.

Effects of physical aging processes on the bioavailability of heavy metals in contaminated site soil amended with chicken manure and wheat straw biochars

The physicochemical properties of biochars undergo slow changes in soils due to the natural aging processes, which influences their interaction with heavy metals. The effects of aging on immobilization of co-existing heavy metals in contaminated soils amended with fecal and plant biochars possessing contrasting properties remain unclear. This study investigated the effects of wet-dry and freeze-thaw aging on the bioavailability (extractable by 0.01 M CaCl₂) and chemical fractionation of Cd and Pb in a contaminated site soil amended with 2.5% (w/w) chicken manure (CM) biochar and wheat straw (WS) biochar. Compared to that in the unamended soil, the contents of bioavailable Cd and Pb in CM biochar-amended soil decreased by 18.0% and 30.8%, respectively, after 60 wet-dry cycles, and by 16.9% and 52.5%, respectively, after 60 freeze-thaw cycles. CM biochar, which contained significant levels of phosphates and carbonates, effectively reduced the bioavailability of Cd and Pb and transformed them from the labile chemical fractions to the more stable ones in the soil during the accelerated aging processes, mainly through precipitation and complexation. In contrast, WS biochar failed to immobilize Cd in the co-contaminated soil in both aging regimes, and was only effective at immobilizing Pb under freeze-thaw aging. The changes in the immobilization of co-existing Cd and Pb in the contaminated soil resulted from aging-induced increase in oxygenated functional groups on biochar surface, destruction of the biochar's porous structure, and release of dissolved organic carbon from the aged biochar and soil. These findings could help guide the selection of suitable biochars for simultaneous immobilization of multiple heavy metals in co-contaminated soil under changing environmental conditions (e.g., rainfall, and freezing and thawing of soils).

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.

Tree-based machine learning model for visualizing complex relationships between biochar properties and anaerobic digestion

The ideal conditions for anaerobic digestion experiments with biochar addition are challenging to thoroughly study due to different experimental purposes. Therefore, three tree-based machine learning models were developed to depict the intricate connection between biochar properties and anaerobic digestion. For the methane yield and maximum methane production rate, the gradient boosting decision tree produced R² values of 0.84 and 0.69, respectively. According to feature analysis, digestion time and particle size had a substantial impact on the methane yield and production rate, respectively. When particle sizes were in the range of 0.3-0.5 mm and the specific surface area was approximately 290 m²/g, corresponding to a range of O content (>31%) and biochar addition (>20 g/L), the maximum promotion of methane yield and maximum methane production rate were attained. Therefore, this study presents new insights into the effects of biochar on anaerobic digestion through tree-based machine learning.

Mechanistic insights into the removal of As(III) and As(V) by iron modified carbon based materials with the aid of machine learning

The machine learning (ML) technique was used to examine the effects of different microscopic material features on the ability of iron modified carbon-based materials (Fe-CBMs) to remove As(V) and As(III). The findings showed that specific CBMs and Fe-CBMs features (such as surface functionality) from sophisticated microscopic and spectroscopic techniques led to models that were more accurate than those constructed using more basic information, such as bulk elemental composition and surface area (the root-mean-square error fell by 44.7% for As(V) and 56.9% for As(III), respectively). The high non-polar carbon (NPC) content of CBMs and Fe-CBMs had a detrimental influence on As(V) and As(III) removal capability, whereas surface oxygen-containing functional groups (SOFGs) contents on CBMs and Fe-CBMs played an essential role in arsenic removal based on ML approaches. The relative importance of CO was greater by 77.8% and 40.6% than that of C-O on the elimination of As(V) and As(III), respectively. The accurate ML models are helpful for the future design of Fe-CBMs and the relative importance and partial dependence plot analysis can direct the use of Fe-CBMs for arsenic removal in a sensible manner under different application situations.

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.

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

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

Artificial intelligence-based approaches to evaluate and optimize phytoremediation potential of in vitro regenerated aquatic macrophyte Ceratophyllum demersum L

Water bodies or aquatic ecosystem are susceptible to heavy metal accumulation and can adversely affect the environment and human health especially in underdeveloped nations. Phytoremediation techniques of water bodies using aquatic plants or macrophytes are well established and are recognized as eco-friendly world over. Phytoremediation of heavy metals and other pollutants in aquatic environments can be achieved by using Ceratophyllum demersum L. - a well-known floating macrophyte. In vitro regenerated plants of C. demersum (7.5 g/L) were exposed to 24, 72, and 120 h to 0, 0.5, 1.0, 2.0, and 4.0 mg/L of cadmium (CdSO₄·8H₂O) in water. Results revealed significantly different relationship in terms of Cd in water, Cd uptake by plants, bioconcentration factor (BCF), and Cd removal (%) from water. The study showed that Cd uptake by plants and BCF values increased significantly with exposure time. The highest BCF value (3776.50) was recorded for plant samples exposed to 2 mg/L Cd for 72 h. Application of all Cd concentrations and various exposure duration yielded Cd removal (%) between the ranges of 93.8 and 98.7%. These results were predicted through artificial intelligence-based models, namely, random forest (RF), extreme gradient boosting (XGBoost), and multilayer perceptron (MLP). The tested models predicted the results accurately, and the attained results were further validated via three different performance metrics. The optimal regression coefficient (R²) for the models was recorded as 0.7970 (Cd water, mg/L), 0.9661 (Cd plants, mg/kg), 0.9797 bioconcentration factor (BCF), and 0.9996 (Cd removal, %), respectively. These achieved results suggest that in vitro regenerated C. demersum can be efficaciously used for phytoremediation of Cd-contaminated aquatic environments. Likewise, the proposed modeling of phytoremediation studies can further be employed more comprehensively in future studies aimed at data prediction and optimization.

Recent advances in biochar amendments for immobilization of heavy metals in an agricultural ecosystem: A systematic review

Over the last several decades, extensive and inefficient use of contemporary technologies has resulted in substantial environmental pollution, predominantly caused by potentially hazardous elements (PTEs), like heavy metals that severely harm living species. To combat the presence of heavy metals (HMs) in the agrarian system, biochar becomes an attractive approach for stabilizing and limiting availability of HMs in soils due to its high surface area, porosity, pH, aromatic structure as well as several functional groups, which mostly rely on the feedstock and pyrolysis temperature. Additionally, agricultural waste-derived biochar is an effective management option to ensure carbon neutrality and circular economy while also addressing social and environmental concerns. Given these diverse parameters, the present systematic evaluation seeks to (i) ascertain the effectiveness of heavy metal immobilization by agro waste-derived biochar; (ii) examine the presence of biochar on soil physico-chemical, and thermal properties, along with microbial diversity; (iii) explore the underlying mechanisms responsible for the reduction in heavy metal concentration; and (iv) possibility of biochar implications to advance circular economy approach. The collection of more than 200 papers catalogues the immobilization efficiency of biochar in agricultural soil and its impacts on soil from multi-angle perspectives. The data gathered suggests that pristine biochar effectively reduced cationic heavy metals (Pb, Cd, Cu, Ni) and Cr mobilization and uptake by plants, whereas modified biochar effectively reduced As in soil and plant systems. However, the exact mechanism underlying is a complex biochar-soil interaction. In addition to successfully immobilizing heavy metals in the soil, the application of biochar improved soil fertility and increased agricultural productivity. However, the lack of knowledge on unfavorable impacts on the agricultural systems, along with discrepancies between the use of biochar and experimental conditions, impeded a thorough understanding on a deeper level.

Antagonistic impact on cadmium stress in alfalfa supplemented with nano-zinc oxide and biochar via upregulating metal detoxification

Eco-toxicological estimation of cadmium induced damages by morpho-physiological and cellular response could be an insightful strategy to alleviate negative impact of Cd in agricultural crops. The current study revealed novel patterns of Cd-bioaccumulation and cellular mechanism opted by alfalfa to acquire Cd tolerance under various soil applied zinc oxide nanoparticles (nZnO) doses (0, 30, 60, 90 mg kg^-1), combined with 2% biochar (BC). Herein, the potential impact of these soil amendments was justified by decreased Cd and increased Zn-bioaccumulation into roots by 38 % and 48 % and shoots by 51 % and 72 % respectively, with co-exposure of nZnO with BC. As, the transmission electron microscopy (TEM) and scanning electron microscopy and energy dispersive spectroscopy (SEM-EDS) ultrastructural observations confirmed that Cd-exposure induced stomatal closure, and caused damage to roots and leaves ultrastructure as compared to the control group. On the contrary, the damages to the above-mentioned traits were reversed by a higher nZnO dose, and the impact was further aggravated by adding BC along nZnO. Furthermore, higher nZnO and BC levels efficiently alleviated the Cd-mediated reductions in alfalfa biomass, antioxidant enzymatic response, and gaseous exchange traits than control. Overall, soil application of 90 mg kg^-1 nZnO with BC (2 %) was impactful in averting Cd stress damages and ensuring better plant performance. Thereby, applying soil nZnO and BC emerge as promising green remediation techniques to enhance crop tolerance in Cd-polluted soil.

Intelligent analysis of carbendazim in agricultural products based on a ZSHPC/MWCNT/SPE portable nanosensor combined with machine learning methods

A nano-ZnS-decorated hierarchically porous carbon (ZSHPC) was mixed with MWCNTs to obtain ZSHPC/MWCNT nanocomposites. Then, ZSHPC/MWCNTs were used to modify a screen-printed electrode, and a portable electrochemical detection system combined with machine learning methods was used to investigate carbendazim (CBZ) residues in rice and tea. The electrochemical performance of the constructed electrode showed that the electrode had good electrocatalytic ability, large effective surface area, strong stability and anti-interference ability. Support Vector Machine (SVM), Least Square Support Vector Machine (LS-SVM) and Back Propagation-Artificial Neural Network (BP-ANN) were used to establish the prediction model for CBZ residues in rice and tea, and the traditional linear regression was developed. The investigated results showed that the LS-SVM model had the best prediction performance and the lowest prediction error compared with the traditional linear regression, BP-ANN and SVM models. The R², RMSE, and MAE for the training set samples were 0.9969, 0.3605 and 0.2968, respectively. The R², RMSE, MAE and RPD for the prediction set samples were 0.9924, 0.6190, 0.5360 and 10.3097, respectively. The average recovery range of CBZ in tea and rice was 98.77-109.32% and that of RSD was 0.47-2.58%, indicating that the rapid analysis of CBZ pesticide residues in agricultural products based on a portable electrochemical detection system combined with machine learning was feasible.

A hybrid framework for delineating the migration route of soil heavy metal pollution by heavy metal similarity calculation and machine learning method

Soil heavy metal contamination was a global environmental issue that posed adverse impacts on ecological and human health risks. The controlling of soil heavy metal is mainly focused on the emission source and pipe-end treatment, less is known about the intermediate controlling process. The migration route of heavy metals exhibited the spatial evolution of pollutants from the sources to the pipe-end, which provided the more reasonable location for the target-oriented treatment of soil heavy metal. Here, we proposed a new view of heavy metal similarity, which quantitatively expressed how closely of the contaminations between the study area and the test areas. We found that the similarity of different heavy metals was unequally distributed across locations that were related with five main sources, namely agricultural activities, natural sources, traffic emissions, industrial activities, and other sources. Based on the similarity, a state-of-the-art machine learning method was applied to delineate the migration route of soil heavy metals. Thereinto, As was concentrated around livestock farms, and its migration route was close to the water system. Cd migration route was over-dispersed in the areas where located mine fields and chemical plants. Migration routes of Hg and Pb were along rivers, which were related to agricultural activities and natural sources. Overall, the perspective on similarity and migration routes provided theoretical basis and method to alleviate soil heavy metal pollution at regional scale and can be extended across largescale regions.

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.

Modeling phytoremediation of heavy metal contaminated soils through machine learning

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

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.

Machine learning in the evaluation and prediction models of biochar application: A review

This article reviews recent studies applying machine learning (ML) approaches to biochar applications. We first briefly introduce the general biochar production process. Various aspects are contained, including the biochar application in the elimination of heavy metals and/or organic compounds and the biochar application in environmental and economic scopes, for instance, food security, energy, and carbon emission. The utilization of ML methods, including ANN, RF, and NN, plays a vital role in evaluating and predicting the efficiency of biochar absorption. It has been proved that ML methods can validly predict the adsorption effectiveness of biochar for water heavy metals with higher accuracy. Moreover, the literature proposed a comprehensive data-driven model to forecast biochar yield and compositions under various biomass input feedstock and different pyrolysis criteria. They said a 12.7% improvement in prediction accuracy compared to the existing literature. However, it might need further optimization in this direction. In summary, this review concludes increasing studies that a well-trained ML method can sufficiently reduce the number of experiment trials and working times associated with higher prediction accuracy. Moreover, further studies on ML applications are needed to optimize the trade-off between biochar yield and its composition.

Modified biochar/humic substance/fertiliser compound soil conditioner for highly efficient improvement of soil fertility and heavy metals remediation in acidic soils

Fertile and uncontaminated soil with appropriate pH is crucial in terms of the agricultural sustainable development. Herein, a compound soil conditioner containing chitosan modified straw biochar (CBC), kitchen waste compost product-derived humic substance (HS), NPK compound fertiliser (NPK-CF) was prepared to simultaneously adjust acidic soil pH, improve fertility, and immobilize heavy metal. The results exhibited that the best Pb and NH₄⁺ adsorption performance was obtained in CBC with chitosan:biochar of 1:5. Then, the acid soil pH was improved from 5.03 to 6.66 in the presence of CBC/HS (5:5) with 3% addition weight (the mass ratio of conditioner to soil). Meanwhile, compared with the control, the contents of organic matter, available nitrogen, and available phosphorus significantly increased by 52.4%, 92.6%, and 136.3%, respectively. Moreover, Pb was highly efficient immobilised by CBC, and the concentration of Pb in the soil was decreased by 55.2%. The optimal growth trend of ryegrass was obtained in the presence of 3% addition weight (the mass ratio of conditioner to soil) CBC/HS (CBC:HS = 5:5) combined with 60% of the recommended NPK-CF application weight, which was mainly contributed by the improvement of the soil microbial abundance and community structure diversity. The addition of CBC/HS could effectively reduce the addition of NPK-CF and contribute to simultaneous controlling nitrogen loss, releasing phosphorus, immobilising Pb, adjusting pH, improving soil quality and controlling nonpoint pollution.

New use for Lentinus edodes bran biochar for tetracycline removal

The abuse of antibiotics poses a threat to the ecological environment and biological health, and how to effectively reduce the residue of tetracycline (TC) in the environment has attracted much attention. In this study, three types of pristine biochar (BCs: PBC300, PBC500, and PBC700) were prepared using agricultural waste shiitake mushroom bran at different pyrolysis temperatures to remove TC from water. The structure and surface chemistry of the adsorbents were characterized using different analytical techniques such as scanning electron microscopy, Fourier transform infrared spectroscopy, X-ray diffraction, and thermogravimetric analysis. These changes in physicochemical properties improve the adsorption capacity of BC. The PBC300 and PBC500 conform to the Langmuir isothermal adsorption model, while the PBC700 is more compatible with the Freundlich model. According to the fitting results of the Langmuir isotherm model, the maximum saturated adsorption capacities of PBC300, PBC500 and PBC700 for TC were 7.568 mg/g, 14.994 mg/g and 17.684 mg/g, respectively. The correlation coefficients of the pseudo-second-order kinetic models were 0.9882, 0.9882 and 0.9996, respectively, which could well fit the adsorption process of TC by the three BCs, indicating that chemical adsorption was dominant. With the help of machine learning, the relationship between the physicochemical properties of BC and the adsorption capacity of TC was effectively explored. The random forest model was able to fit the adsorption process of BC on TC better. It is expected that this study will guide the rational application of BC in the treatment of TC wastewater.

A Comprehensive Evaluation System for the Stabilization Effect of Heavy Metal-Contaminated Soil Based on Analytic Hierarchy Process

Stabilization technology is widely used in the remediation of heavy metal-contaminated farmland soil. However, the evaluation method for the remediation effect is not satisfactory. To scientifically evaluate the remediation effect, this study constructed a comprehensive evaluation system by bibliometric analysis and an analytic hierarchy process (AHP). Ultimately, 16 indicators were selected from three aspects of the soil, crops, and amendment. The 16 indicators are divided into three groups, namely indicators I that can be evaluated according to the national standards of China, indicators II that can be evaluated according to the classification management of farmland and Indicators III that are the dynamic change indicators without an evaluation criterion. Comprehensive scores for 16 indicators were calculated using three response models, respectively. According to the difference between the scores before and after the remediation, the remediation effect is divided into five levels, which are excellent, good, qualified, poor, and very poor. This study provides a theoretical basis and insightful information for a farmland pollution remediation and a sustainable utilization.

Considerations for evaluating innovative stormwater treatment media for removal of dissolved contaminants of concern with focus on biochar

Stormwater from complex land uses is an important contributor of contaminants of concern (COCs) such as polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons (PAHs), Copper, and Zinc to receiving water bodies. A large portion of these COCs bind to particulate matter in stormwater, which can be removed through filtration by traditional media. However, the remaining dissolved COCs can be significant and require special attention such as engineered treatment measures and media. Biochar is a porous sorbent produced from a variety of organic materials. In the last decade biochar has been gaining attention as a stormwater treatment medium due to low cost compared to activated carbon. However, biochar is not a uniform product and selection of an appropriate biochar for the removal of specific contaminants can be a complex process. Biochars are synthesized from various feedstocks and using different manufacturing approaches, including pyrolysis temperature, impact the biochar properties thus affecting ability to remove stormwater contaminants. The local availability of specific biochar products is another important consideration. An evaluation of proposed stormwater control measure (SCM) media needs to consider the dynamic conditions associated with stormwater and its management, but the passive requirements of the SCM. The media should be able to mitigate flood risks, remove targeted COCs under high flow SCM conditions, and address practical considerations like cost, sourcing, and construction and maintenance. This paper outlines a process for selecting promising candidates for SCM media and evaluating their performance through laboratory tests and field deployment with special attention to unique stormwater considerations.

Toxicological effect assessment of aged biochar on Escherichia coli

Biochar (BC) is a biomass material that has a wide range of applications on the remediating heavy metals. In this experiment, we prepared BC (300 ºC, 500 ºC, and 700 ºC) and applied them to adsorb lead ions (Pb²⁺) to simulate BC treatment of Pb²⁺-contaminated soil. The retention capacity of BC for heavy metals was altered by means of bacterial culture, and the heavy metals released by BC can have toxicological effects on bacteria. This approach was used to assess the effects of long-term application of BC in heavily contaminated land with heavy metals on soil microorganisms. The results show that Escherichia coli survived in the medium containing lower doses of Pb²⁺-aged BC prepared at 300 ºC and 500 ºC (25 mg/L and 50 mg/L), depending on its ability of tolerating a certain amount of Pb²⁺. The addition of 100 mg/L Pb²⁺-aged BC prepared at 700 ºC not only significantly inhibited the growth of E. coli, but also promoted the release of citric acid from E. coli, which in turn triggered BC releasing more Pb²⁺. It is hoped that this will provide foundation to support the long-term application of BC in the remediation of heavy metal contaminated soils.