Modeling phytoremediation of heavy metal contaminated soils through machine learning

Prediction of zinc, cadmium, and arsenic in european soils using multi-end machine learning models

Heavy metal contamination in soil is a major environmental and public health concern, especially in regions with substantial industrial and agricultural activities. Conventional predictive models often focus on single contaminants, limiting their utility for comprehensive environmental monitoring. This study addressed these limitations by developing an advanced multi-end ensemble convolutional neural network model capable of simultaneously predicting the concentrations of cadmium, arsenic, and zinc in European soils. A comprehensive dataset with 18 diverse factors was prepared, including soil properties, climatic factors, and anthropogenic activities. Moreover, the model compared four ensemble learning techniques in contamination prediction, including simple averaging, snapshot ensembles, integrated stacking, and separate stacking. Among these, the separate stacking model with random forest regressor meta-model achieved the highest accuracy, with a mean spared error of 0.0378, a mean absolute error of 0.0785, and a coefficient of determination of 0.79 in the testing phases. Sensitivity analysis highlighted farming area, road length, nitrogen content, and mean annual temperature as key factors influencing metal concentrations. To enhance accessibility, a GUI-based web application was developed, allowing users to enter relevant factors and receive real-time predictions of contamination levels. This application empowers stakeholders, such as environmental regulators and policymakers, to make informed, data-driven decisions for targeted remediation. These findings underscore the critical role of integrated machine learning approaches in environmental science, offering a powerful tool for identifying contamination hotspots, supporting soil health management, and promoting sustainable land use.

Characterizing soil COPs eco-risk in China

Although soil combustible organic pollutants (COPs) pose a serious threat to human well-being, their spatial distribution patterns, responses to environmental constraints, and areas of risk throughout China are still unclear. This knowledge gap hinders the control of soil COPs, causing us to overlook their impact on climate change and the environment. In this study, a total of 420 soil samples, distributed in typical regions of China, were tested for COPs content, including black carbon (BC) and polycyclic aromatic hydrocarbons (PAHs). Interest points (POI) such as parking lots, gas stations, and car services have become the main factors that influence soil COPs enrichment, and can be considered new indicators in other organic pollution studies. By comparing various machine learning simulations and predictions, this study accurately predicted the content of soil COPs in China and pointed out that, as the "third pole of the world", the Qinghai Tibet Plateau will face an unprecedented crisis. We established a method for assessing the comprehensive risk of soil COPs and identified at-risk areas, which accounted for 38.9 % of China's total soil area. Our research findings emphasize the main driving factors for soil COPs and identify areas in China that require prioritized soil COPs control.

Recovery of valuable metals from spent hydrodesulfurization (HDS) catalysts: A comprehensive research review and specific industrial cases

Spent hydrodesulfurization (HDS) catalysts, produced in the petroleum refining process, are usually classified in hazardous solid waste. Recovery of valuable metals from spent HDS catalyst not only reduce substantially environmental risk but is an important way to alleviate global resource shortages for high-valuable metals. This study reviews numerous references regarding to recovery valuable metals from spent HDS catalyst in last decades, and divided current methods into three processes: pretreatment, oxidation-leaching, and separation-purification processes. Roasting and solvent washing usually emerge as primary methods in the pretreatment process, and effectively eliminate the surface oily substances and sulfur. Sodium salt roasting-leaching are considered as higher efficient among all leaching methods. The application of organic acid in the leaching can separate valuable metals selectively and simplify subsequent purification steps. In separation-purification processes, solvent extraction is still a standout method to isolate challenging metals such as Mo, W and V. However, the burgeoning field of ion imprinting technology exhibits the promising potential. Additionally, Random Forest and XGBoost model are used to analyze reported methods to recovery Mo and Ni and predict the key factor to regulate recovery efficiency. The results show that Mo recovery process is depended on the spent HDS characteristics and solid-liquid ratio in leaching process, while Ni recovery processes is depended on the roasting time and roasting temperature. Finally, serval specific industrial cases on recycling valuable metals from spent HDS were given, and found that sodium salt roasting-water leaching process was still frequent used in practical application due to its characteristics of high efficiency and low cost.

Optimisation led energy-efficient arsenite and arsenate adsorption on various materials with machine learning

The contamination of water by arsenic (As) poses a substantial environmental challenge with far-reaching influence on human health. Accurately predicting adsorption capacities of arsenite (As(III)) and arsenate (As(V)) on different materials is crucial for the remediation and reuse of contaminated water. Nonetheless, predicting the optimal As adsorption on various materials while considering process energy consumption continues to pose a persistent challenge. Literature data regarding the As adsorption on diverse materials were collected and employed to train machine learning models (ML), such as CatBoost, XGBoost, and LGBoost. These models were utilized to predict both As(III) and As(V) adsorption on a variety of materials using their reaction parameters, structural properties, and composition. The CatBoost model exhibited superior accuracy, achieving a coefficient of determination (R²) of 0.99 and a root mean square error (RMSE) of 1.24 for As(III), and an R² of 0.99 and RMSE of 5.50 for As(V). The initial As(III) and As(V) concentrations were proved to be the primary factors influencing adsorption, accounting for 27.9 % and 26.6 % of the variance for As(III) and As(V) individually. The genetic optimization led optimisation process, considering the low energy consumption, determined maximum adsorption capacities of 291.66 mg/g for As(III) and 271.56 mg/g for As(V), using C-Layered Double Hydroxide with reduced graphene oxide and chitosan combined with rice straw biochar, respectively. To further facilitate the process design for different real-life applications, the trained ML models are embedded into a web-app that the user can use to estimate the As(III) and As(V) adsorption under different design conditions. The utilization of ML for the energy-efficient As(III) and As(V) adsorption is deemed essential for advancing the treatment of inorganic As in aquatic settings. This approach facilitates the identification of optimal adsorption conditions for As in various material-amended waters, while also enabling the timely detection of As-contaminated water.

Entropy-informed multi-stage sampling design for soil pollution mapping in heavy metal contaminated areas

The accuracy of soil heavy metal pollution mapping is heavily reliant on the sampling strategies utilized in both the preliminary and detailed survey stages of site investigations. This study introduces an entropy-informed multi-stage sampling design (EIMSD) method that leverages preliminary survey data as background information and utilizes relative entropy to progressively select sampling points in detailed surveys. Results indicate that the EIMSD method outperforms the grid sampling design (GSD) and conventional sampling design (CSD) methods across both hypothetical and real-world study areas. This superiority is evidenced by a notable rise in R2 values, ranging from 6.4% to 60.4% and a decrease in RMSE values from 16.7% to 54.0% relative to GSD, and a similar trend with an increase in R2 values from 6.7% to 44.1% and a reduction in RMSE values from 16.5% to 39.7% when compared to CSD. This study also investigates the optimal configurations for EIMSD, focusing on the number of detailed sampling points per stage (Nadd) and the ratio of preliminary-to-detailed survey sample sizes (Np/Nd) when the sum of Np and Nd is held constant. Our findings highlight that adding one detailed sampling point per stage (Nadd = 1) is the most effective. For areas with strong spatial variability, a larger Np/Nd value of approximately 3/2 is recommended, whereas a ratio close to 1 is apt for areas with moderate variability. Conversely, for areas with weak variability, a smaller Np/Nd value of about 2/3 is advised. EIMSD provides a more detailed and accurate map of soil heavy metal contamination, facilitating more targeted and effective remediation strategies.

Machine learning models with innovative outlier detection techniques for predicting heavy metal contamination in soils

Machine learning (ML) models for accurately predicting heavy metals with inconsistent outputs have improved owing to dataset outliers, which influence model reliability and accuracy. A comprehensive technique that combines machine learning and advanced statistical methods was applied to assess data outlier's effects on ML models. Ten ML models with three outlier detection methods predicted Cr, Ni, Cd, and Pb in Narayanganj soils. XGBoost with density-based spatial clustering of applications with noise (DBSCAN) improved model efficacy (R2). The R2 of Cr, Ni, Cd, and Pb was considerably enhanced by 11.11 %, 6.33 %, 14.47 %, and 5.68 %, respectively, indicating that outliers affected the model's HM prediction. Soil factors affected Cr (80 %), Ni (72.61 %), Cd (53.35 %), and Pb (63.47 %) concentrations based on feature importance. Contamination factor prediction showed considerable contamination for Cr, Ni, and Cd. LISA revealed Cd (55.4 %), Cr (49.3 %), and Pb (47.3 %) as the significant pollutant (p < 0.05). Moran's I index values for Cr, Ni, Cd, and Pb were 0.65, 0.58, 0.60, and 0.66, respectively, indicating strong positive spatial autocorrelation and clusters with similar contamination. Finally, this work successfully assessed the influence of data outliers on the ML model for soil HM contamination prediction, identifying crucial regions that require rapid conservation measures.

Selenium increases antimony uptake in As-hyperaccumulators Pteris vittata and Pteris cretica by promoting antimonate reduction: GSH-GSSG cycle and arsenate reductases HAC1/ACR2

Selenium-enhanced arsenic uptake by As-hyperaccumulators Pteris vittata and Pteris cretica is known, but how it impacts antimony (Sb) uptake and associated mechanisms are unclear. Here, we investigated the effects of 2.5 μM selenate (Se2.5) on Sb uptake by two plants after growing for 10 days under hydroponics containing 10 or 50 μM antimonate (SbV) (Sb10 or Sb50). Both plants were efficient in taking up SbV, which was reduced to SbIII (17-40 %) and mainly accumulated in the roots (86-97 %). The addition of Se increased the Sb contents by 78-97 and 29-33 % to 242-1358 and 132-697 mg kg-1 in P. vittata and P. cretica roots. Compared with the Sb10 and Sb50 treatments, addition of Se increased the SbV reduction, with more increase in P. vittata than P. cretica roots (181-273 % vs. 17-29 %). Enhanced GSH-GSSG cycle mediated by glutathione peroxidase (GPX) and glutathione reductase (GR) may play an important role in SbV reduction in the roots. Compared with the Sb treatments, addition of Se increased the GPX and GR activity by 71-97 and 2-50 % in P. vittata roots, and 59-153 and 22-63 % in P. cretica roots. Besides, Se upregulated the expression of arsenate reductases PvHAC1 and PvACR2 in P. vittata roots by 1.7-3.4 folds but not in P. cretica. Se-enhanced SbV reduction in P. vittata explains why it was more effective in Sb accumulation than P. cretica. Taken together, Se is effective in increasing the Sb uptake in both plants probably by promoting SbV reduction via GSH-GSSG cycle and/or PvHAC1/PvACR2, suggesting that Se may be used to enhance phytostabilization of Sb-contaminated soils.

Utilizing machine learning to evaluate heavy metal pollution in the world's largest mangrove forest

The world's largest mangrove forest (Sundarbans) is facing an imminent threat from heavy metal pollution, posing grave ecological and human health risks. Developing an accurate predictive model for heavy metal content in this area has been challenging. In this study, we used machine learning techniques to model sediment pollution by heavy metals in this vital ecosystem. We collected 199 standardized sediment samples to predict the accumulation of eleven heavy metals using ten different machine learning algorithms. Among them, the extremely randomized tree model exhibited the best performance in predicting Fe (0.87), Cr (0.89), Zn (0.85), Ni (0.83), Cu (0.87), Co (0.62), As (0.68), and V (0.90), achieving notable R2 values. On the other hand, the random forest outperformed for predicting Cd (0.72) and Mn (0.91), whereas the decision tree model showed the best performance for Pb (0.73). The feature attribute analysis identified FeV, CrV, CuZn, CoMn, PbCd, and AsCd relationships resembled with correlation coefficients among them. Based on the established models, the prediction of the contamination factor of metals in sediments showed very high Cd contamination (CF ≥ 6). The Moran's I index for Cd, Cr, Pb, and As were 0.71, 0.81, 0.71, and 0.67, respectively, indicating strong positive spatial autocorrelation and suggesting clustering of similar contamination levels. Conclusively, this research provides a comprehensive framework for predicting heavy metal sediment pollution in the Sundarbans, identifying key areas needing urgent conservation. Our findings support the adoption of integrated management strategies and targeted remedial actions to mitigate the harmful effects of heavy metal contamination in this vital ecosystem.

Enhanced phytoremediation of vanadium using coffee grounds and fast-growing plants: Integrating machine learning for predictive modeling

Vanadium (V) contamination posed a significant environmental challenge, while phytoremediation offered a sustainable solution. Phytoremediation performance was often limited by the slow growth cycles of traditional plants. A novel approach to enhancing V phytoremediation by integrating coffee grounds with fast-growing plants such as barley grass, wheat grass, and ryegrass was investigated in this study. The innovative use of coffee grounds leveraged not only their nutrient-rich composition but also their ability to reduce oxidative stress in plants, thereby significantly boosting phytoremediation efficiency. Notably, ryegrass achieved 48.7% V5+ removal within 6 d with initial 20 mg/L V5+ (0.338 mg/L·d·g ryegrass). When combined with coffee grounds, V5+ removal by using wheat grass increased substantially, rising from 30.51% to 62.91%. Gradient Boosting and XGBoost models, trained with optimized parameters including a learning rate of 0.1, max depth of 3, and n_estimators of 300, were employed to predict and optimize V5+ concentrations in the phytoremediation process. These models were evaluated using mean squared error (MSE) and coefficient of determination (R2) metrics, achieving high predictive accuracy (R2 = 0.95, MSE = 1.20). Feature importance analysis further identified the initial V5+ concentration and experimental duration as the most significant factors influencing the model's predictions. The addition of coffee grounds not only mitigated the stress of heavy metals on ryegrass, leading to significant reductions in CAT (87.2%), POD (98.8%), and SOD (39.2%) activities in ryegrass roots, but also significantly altered the microbial community abundance in the plant roots. This research provided an innovative enhancement to traditional phytoremediation methods, and established a new paradigm for using machine learning to predict and optimize V5+ remediation outcomes. The effectiveness of this technology in multi-metal polluted environments warrants further investigation in the future.

Nano-enabled strategies to promote safe crop production in heavy metal(loid)-contaminated soil

Nanobiotechnology is a potentially safe and sustainable strategy for both agricultural production and soil remediation, yet the potential of nanomaterials (NMs) application to remediate heavy metal(loid)-contaminated soils is still unclear. A meta-analysis with approximately 6000 observations was conducted to quantify the effects of NMs on safe crop production in soils contaminated with heavy metal(loid) (HM), and a machine learning approach was used to identify the major contributing features. Applying NMs can elevate the crop shoot (18.2 %, 15.4-21.2 %) and grain biomass (30.7 %, 26.9-34.9 %), and decrease the shoot and grain HM concentration by 31.8 % (28.9-34.5 %) and 46.8 % (43.7-49.8 %), respectively. Iron-NMs showed a greater potential to inhibit crop HM uptake compared to other types of NMs. Our result further demonstrates that NMs application substantially reduces the potential health risk of HM in crop grains by human health risk assessment. The NMs-induced reduction in HM accumulation was associated with decreasing HM bioavailability, as well as increased soil pH and organic matter. A random forest model demonstrates that soil pH and total HM concentration are the two significant features affecting shoot HM accumulation. This analysis of the literature highlights the significant potential of NMs application in promoting safe agricultural production in HM-contaminated agricultural lands.

Unveiling biochar potential to promote safe crop production in toxic metal(loid) contaminated soil: A meta-analysis

Biochar application emerges as a promising and sustainable solution for the remediation of soils contaminated with potentially toxic metal (loid)s (PTMs), yet its potential to reduce PTM accumulation in crops remains to be fully elucidated. In our study, a hierarchical meta-analysis based on 276 research articles was conducted to quantify the effects of biochar application on crop growth and PTM accumulation. Meanwhile, a machine learning approach was developed to identify the major contributing features. Our findings revealed that biochar application significantly enhanced crop growth, and reduced PTM concentrations in crop tissues, showing a decrease trend of grains (36.1%, 33.6-38.6%) > shoots (31.1%, 29.3-32.8%) > roots (27.5%, 25.7-29.2%). Furthermore, biochar modifications were found to amplify its remediation potential in PTM-contaminated soils. Biochar application was observed to provide favorable conditions for reducing PTM uptake by crops, primarily through decreasing available PTM concentrations and improving overall soil quality. Employing machine learning techniques, we identified biochar properties, such as surface area and C content as a key factor in decreasing PTM bioavailability in soil-crop systems. Furthermore, our study indicated that biochar application could reduce probabilistic health risks associated with of the presence of PTMs in crop grains, thereby contributing to human health protection. These findings highlighted the essential role of biochar in remediating PTM-contaminated lands and offered guidelines for enhancing safe crop production.

Development and application of machine learning models for prediction of soil available cadmium based on soil properties and climate features

Identifying the key influencing factors in soil available cadmium (Cd) is crucial for preventing the Cd accumulation in the food chain. However, current experimental methods and traditional prediction models for assessing available Cd are time-consuming and ineffective. In this study, machine learning (ML) models were developed to investigate the intricate interactions among soil properties, climate features, and available Cd, aiming to identify the key influencing factors. The optimal model was obtained through a combination of stratified sampling, Bayesian optimization, and 10-fold cross-validation. It was further explained through the utilization of permutation feature importance, 2D partial dependence plot, and 3D interaction plot. The findings revealed that pH, surface pressure, sensible heat net flux and organic matter content significantly influenced the Cd accumulation in the soil. By utilizing historical soil surveys and climate change data from China, this study predicted the spatial distribution trend of available Cd in the Chinese region, highlighting the primary areas with heightened Cd activity. These areas were primarily located in the eastern, southern, central, and northeastern China. This study introduces a novel methodology for comprehending the process of available Cd accumulation in soil. Furthermore, it provides recommendations and directions for the remediation and control of soil Cd pollution.

Tracking the impact of heavy metals on human health and ecological environments in complex coastal aquifers using improved machine learning optimization

The rising heavy metal (HM) pollution in coastal aquifers in rapidly urbanizing areas such as Dammam leads to significant risks to public health and environmental sustainability, challenging compliance with Environmental Protection Agency (EPA) guidelines, World Health Organization (WHO) standards, and Sustainable Development Goals (SDGs) related to clean water and life on land. This study developed the predictive-based monitoring of HM concentrations, including cadmium (Cd), chromium (Cr), and mercury (Hg) in the coastal aquifers of Dammam, influenced by industrial, agricultural, and urban activities. For this purpose, dynamic system identification and machine learning (ML) models integrated with three ensemble techniques, namely, simple averaging (SAE), weighted averaging (WAE), and neuro-ensemble (N-ESB), were employed to enhance the accuracy, reliability, and efficiency of environmental monitoring systems. The experimental data were calibrated and validated in addition to k-fold cross-validation to ensure the predictive skills of the models. The methodology integrates extensive data collection across varied land uses in Dammam and accurate model calibration and validation phases to develop highly accurate predictive models. The findings proved that the N-ESB and Hammerstein-Wiener (HW) models surpassed other models in predicting the concentrations of all HM. For Cd, the N-ESB model achieved a root mean square error (RMSE = 0.0010 mg/kg). Similarly, Cr demonstrated superior performance (RMSE = 0.0179 mg/kg). Further numerical results indicated that the HW algorithm proved the most effective for Hg, with RMSE = 0.0000 mg/kg. The quantitative comparison suggested that the N-ESB model's consistently high performance and low error rates make it an optimal choice for real-time, precise monitoring and management of HM pollution in coastal aquifers. The outcomes of this research highlighted the importance of integrating advanced predictive modeling techniques in environmental science, providing significant and practical implications for policymaking and ecological management.

Phytobial remediation advances and application of omics and artificial intelligence: a review

Industrialization and urbanization increased the use of chemicals in agriculture, vehicular emissions, etc., and spoiled all environmental sectors. It causes various problems among living beings at multiple levels and concentrations. Phytoremediation and microbial association are emerging as a potential method for removing heavy metals and other contaminants from soil. The treatment uses plant physiology and metabolism to remove or clean up various soil contaminants efficiently. In recent years, omics and artificial intelligence have been seen as powerful techniques for phytobial remediation. Recently, AI and modeling are used to analyze large data generated by omics technologies. Machine learning algorithms can be used to develop predictive models that can help guide the selection of the most appropriate plant and plant growth-promoting rhizobacteria combination that is most effective at remediation. In this review, emphasis is given to the phytoremediation techniques being explored worldwide in soil contamination.

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.

Development of simplified probabilistic models for predicting phytoextraction timeframes of soil contaminants: demonstration at the DDX-contaminated Kolleberga tree nursery in Sweden

Phytoextraction, utilizing plants to remove soil contaminants, is a promising approach for environmental remediation but its application is often limited due to the long time requirements. This study aims to develop simplified and user-friendly probabilistic models to estimate the time required for phytoextraction of contaminants while considering uncertainties. More specifically we: i) developed probabilistic models for time estimation, ii) applied these models using site-specific data from a field experiment testing pumpkin (Cucurbita pepo ssp. pepo cv. Howden) for phytoextraction of DDT and its metabolites (ΣDDX), iii) compared timeframes derived from site-specific data with literature-derived estimates, and iv) investigated model sensitivity and uncertainties through various modelling scenarios. The models indicate that phytoextraction with pumpkin to reduce the initial total concentration of ΣDDX in the soil (10 mg/kg dw) to acceptable levels (1 mg/kg dw) at the test site is infeasible within a reasonable timeframe, with time estimates ranging from 48-123 years based on literature data or 3 570-9 120 years with site-specific data using the linear or first-order exponential model, respectively. Our results suggest that phytoextraction may only be feasible at lower initial ΣDDX concentrations (< 5 mg/kg dw) for soil polishing and that alternative phytomanagement strategies should be considered for this test site to manage the bioavailable fraction of DDX in the soil. The simplified modes presented can be useful tools in the communication with site owners and stakeholders about time approximations for planning phytoextraction interventions, thereby improving the decision basis for phytomanagement of contaminated sites.

A unified framework of response surface methodology and coalescing of Firefly with random forest algorithm for enhancing nano-phytoremediation efficiency of chromium via in vitro regenerated aquatic macrophyte coontail (Ceratophyllum demersum L.)

Nano-phytoremediation is a novel green technique to remove toxic pollutants from the environment. In vitro regenerated Ceratophyllum demersum (L.) plants were exposed to different concentrations of chromium (Cr) and exposure times in the presence of titania nanoparticles (TiO2NPs). Response surface methodology was used for multiple statistical analyses like regression analysis and optimizing plots. The supplementation of NPs significantly impacted Cr in water and Cr removal (%), whereas NP × exposure time (T) statistically regulated all output parameters. The Firefly metaheuristic algorithm and the random forest (Firefly-RF) machine learning algorithms were coalesced to optimize hyperparameters, aiming to achieve the highest level of accuracy in predicted models. The R2 scores were recorded as 0.956 for Cr in water, 0.987 for Cr in the plant, 0.992 for bioconcentration factor (BCF), and 0.957 for Cr removal through the Firefly-RF model. The findings illustrated superior prediction performance from the random forest models when compared to the response surface methodology. The conclusion is drawn that metal-based nanoparticles (NPs) can effectively be utilized for nano-phytoremediation of heavy metals. This study has uncovered a promising outlook for the utilization of nanoparticles in nano-phytoremediation. This study is expected to pave the way for future research on the topic, facilitating further exploration of various nanoparticles and a thorough evaluation of their potential in aquatic ecosystems.

Reducing the accumulation of cadmium and phenanthrene in rice by optimizing planting spacing: Role of low-abundance but core rhizobacterial communities

Optimizing planting spacing is a common agricultural practice for enhancing rice growth. However, its effect on the accumulation of cadmium (Cd) and phenanthrene (Phen) in soil-rice systems and the response mechanisms of rhizobacteria to co-contaminants remain unclear. This study found that reducing rice planting spacing to 5 cm and 10 cm significantly decreased the bioavailability of Cd (by 7.9 %-29.5 %) and Phen (by 12.9 %-47.6 %) in the rhizosphere soil by converting them into insoluble forms. The increased accumulation of Cd and Phen in roots and iron plaques (IPs) ultimately led to decreased Cd (by 32.2 %-39.9 %) and Phen (by 4.2 %-17.3 %) levels in brown rice, and also significantly affected the composition of rhizobacteria. Specifically, reducing rice planting spacing increased the abundance of low-abundance but core rhizobacteria in the rhizosphere soil and IPs, including Bacillus, Clostridium, Sphingomonas, Paenibacillus, and Leifsonia. These low-abundance but core rhizobacteria exhibited enhanced metabolic capacities for Cd and Phen, accompanied by increased abundances of Cd-resistance genes (e.g., czcC and czcB) and Phen-degradation genes (e.g., pahE4 and pahE1) within the rhizosphere soil and IPs. Reduced planting spacing had no noticeable impact on rice biomass. These findings provide new insights into the role of low-abundance but core rhizobacterial communities in Cd and Phen uptake by rice, highlighting the potential of reduced planting spacing as an eco-friendly strategy for ensuring the safety of rice production on contaminated paddy soils.

Prediction of adsorption of metal cations by clay minerals using machine learning

Adsorption of heavy metals by clay minerals occurs widely at the solid-liquid interface in natural environments, and in this paper, the phenomenon of adsorption of Cd2+, Cu2+, Pb2+, Zn2+, Ni2+ and Co2+ by montmorillonite, kaolinite and illite was simulated using machine learning. We firstly used six machine learning models including Random Forest(R), Extremely Forest(E), Gradient Boosting Decision Tree(G), Extreme Gradient Boosting(X), Light Gradient Boosting(LGB) and Category Boosting(CAT) to feature engineer the metal cations and the parameters of the minerals, and based on the feature engineering results, we determined the first order hydrolysis constant(log K), solubility product constant(SPC), and higher hydrolysis constant (HHC) as the descriptors of the metal cations, and site density(SD) and cation exchange capacity(CEC) as the descriptors of the clay minerals. After comparing the predictive effects of different data cleaning methods (pH50 method, Box method and pH50-Box method) and six model combinations, it was finally concluded that the best simulation results could be achieved by using the pH 50-Box method for data cleaning and Extreme Gradient Boosting for modelling (RMSE = 4.158 %, R2 = 0.977). Finally, model interpretation was carried out using Shapley explanation plot (SHAP) and partial dependence plot(PDP) to analyse the potential connection between each input variable and the output results. This study combines machine learning with geochemical analysis of the mechanism of heavy metal adsorption by clay minerals, which provides a different research perspective from the traditional surface complexation model.

Meta-analysis of impacts of microplastics on plant heavy metal(loid) accumulation

The co-occurrence of microplastics (MPs) and heavy metal(loid)s (HMs) has attracted growing scientific interest because of their wide distribution and environmental toxicity. Nevertheless, the interactions between MPs and HMs in soil-plant systems remain unclear. We conducted a meta-analysis with 3226 observations from 87 independent studies to quantify the impact of MPs addition on the plant biomass and HMS accumulation. Co-occurrence of MPs and HMs (except for As) induced synergistic toxicity to plant growth. MPs promoted their uptake in the shoot by 11.0% for Cd, 30.0% for Pb, and 47.1% for Cu, respectively. In contrast, MPs caused a significant decrease (22.6%, 17.9-26.9%) in the shoot As accumulation. The type and dose of MPs were correlated with the accumulation of HMs. MPs increased available concentrations of Cd, Pb, and Cu, but decreased available As concentration in soils. Meanwhile, MPs addition significantly lowered soil pH. These findings may provide explanations for MPs-mediated effects on influencing the accumulation of HMs in plants. Using a machine learning approach, we revealed that soil pH and total HMs concentration are the major contributors affecting their accumulation in shoot. Overall, our study indicated that MPs may increase the environmental risks of HMs in agroecosystems, especially metal cations.

Speciation, leachability, and phytoaccessibility of heavy metals during thermochemical liquefaction of contaminated peanut straw

In this study, the speciation, leachability, phytoaccessibility, and environmental risks of heavy metals (Cd, Zn, and Cu) during liquefaction of contaminated peanut straw in ethanol at different temperatures (220, 260, 300, 340, and 380 °C) were comprehensively investigated. The results showed that elevated temperatures facilitated heavy metal accumulation in the biochar. The acid-soluble/exchangeable and reducible fraction percentages of heavy metals were substantially reduced in the biochar after liquefaction as the temperature increased, and the oxidizable fraction became the dominant heavy metal fraction, accounting for 44.14-78.67%. Furthermore, although an excessively high liquefaction temperature (380 °C) increased the residual fraction percentages of Zn and Cu, it was detrimental to Cd immobilization. The acid-soluble/exchangeable Cd in the contaminated peanut straw readily migrates to the bio-oil during liquefaction, with the highest concentration of 1.60 mg/kg at 260 °C liquefaction temperature, whereas Zn and Cu are predominantly bound to the unexchangeable fraction in the bio-oil. Liquefaction inhibited heavy metal leachability and phytoaccessibility in biochar, the lowest extraction rates of Cd, Zn, and Cu were 0.71%, 1.66% and 0.95% by diethylenetriamine pentaacetic acid, respectively. However, the leaching and extraction concentrations increased when the temperature was raised to 380 °C. Additionally, heavy metal risk was reduced from medium and high risk to no and low risk. In summary, liquefaction reduces heavy metal toxicity and the risks associated with contaminated peanut straw, and a temperature range of 300-340 °C for ethanol liquefaction can be considered optimal for stabilizing heavy metals.

Effect of EDDS on the rhizosphere ecology and microbial regulation of the Cd-Cr contaminated soil remediation using king grass combined with Piriformospora indica

The negative impacts of soil heavy metals composite pollution on agricultural production and human health are becoming increasingly prevalent. The applications of green chelating agents and microorganisms have emerged as promising alternate methods for enhancing phytoremediation. The regulatory effects of root secretion composition, microbial carbon source utilization, key gene expression, and soil microbial community structure were comprehensively analyzed through a combination of HPLC, Biolog EcoPlates, qPCR, and high-throughput screening techniques. The application of EDDS resulted in a favorable rhizosphere ecological environment for the king grass Piriformospora indica, characterized by a decrease in soil pH by 0.41 units, stimulation of succinic acid and fumaric acid secretion, and an increase in carbon source metabolic activity of amino acids and carbohydrates. Consequently, this improvement enhanced the bioavailability of Cd/Cr and increased the biomass of king grass by 25.7%. The expression of dissimilatory iron-reducing bacteria was significantly upregulated by 99.2%, while there was no significant difference in Clostridium abundance. Furthermore, the richness of the soil rhizosphere fungal community (Ascomycota: 45.8%, Rozellomycota: 16.7%) significantly increased to regulate the proportion of tolerant microbial dominant groups, promoting the improvement of Cd/Cr removal efficiency (Cd: 23.4%, Cr: 18.7%). These findings provide a theoretical basis for the sustainable development of chelating agent-assisted plants-microorganisms combined remediation of heavy metals in soil.

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

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

Biodegradable chelating agents for enhancing phytoremediation: Mechanisms, market feasibility, and future studies

Heavy metals in soil significantly threaten human health, and their remediation is essential. Among the various techniques used, phytoremediation is one of the safest, most innovative, and effective. In recent years, the use of biodegradable chelators to assist plants in improving their remediation efficiency has gained popularity. These biodegradable chelators aid in the transformation of metal ions or metalloids, thereby facilitating their mobilization and uptake by plants. Developed countries are increasingly adopting biodegradable chelators for phytoremediation, with a growing emphasis on green manufacturing and technological innovation in the chelating agent market. Therefore, it is crucial to gain a comprehensive understanding of the mechanisms and market prospects of biodegradable chelators for phytoremediation. This review focuses on elucidating the uptake, translocation, and detoxification mechanisms of chelators in plants. In this study, we focused on the effects of biodegradable chelators on the growth and environmental development of plants treated with phytoremediation agents. Finally, the potential risks associated with biodegradable chelator-assisted phytoremediation are presented in terms of their availability and application prospects in the market. This study provides a valuable reference for future research in this field.

Research progress of the detection and analysis methods of heavy metals in plants

Heavy metal (HM)-induced stress can lead to the enrichment of HMs in plants thereby threatening people's lives and health via the food chain. For this reason, there is an urgent need for some reliable and practical techniques to detect and analyze the absorption, distribution, accumulation, chemical form, and transport of HMs in plants for reducing or regulating HM content. Not only does it help to explore the mechanism of plant HM response, but it also holds significant importance for cultivating plants with low levels of HMs. Even though this field has garnered significant attention recently, only minority researchers have systematically summarized the different methods of analysis. This paper outlines the detection and analysis techniques applied in recent years for determining HM concentration in plants, such as inductively coupled plasma mass spectrometry (ICP-MS), atomic absorption spectrometry (AAS), atomic fluorescence spectrometry (AFS), X-ray absorption spectroscopy (XAS), X-ray fluorescence spectrometry (XRF), laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS), non-invasive micro-test technology (NMT) and omics and molecular biology approaches. They can detect the chemical forms, spatial distribution, uptake and transport of HMs in plants. For this paper, the principles behind these techniques are clarified, their advantages and disadvantages are highlighted, their applications are explored, and guidance for selecting the appropriate methods to study HMs in plants is provided for later research. It is also expected to promote the innovation and development of HM-detection technologies and offer ideas for future research concerning HM accumulation in plants.

Mitigation of the mobilization and accumulation of toxic metal(loid)s in ryegrass using sodium sulfide

Remediation of soils contaminated with toxic metal(loid)s (TMs) and mitigation of the associated ecological and human health risks are of great concern. Sodium sulfide (Na2S) can be used as an amendment for the immobilization of TMs in contaminated soils; however, the effects of Na2S on the leachability, bioavailability, and uptake of TMs in highly-contaminated soils under field conditions have not been investigated yet. This is the first field-scale research study investigating the effect of Na2S application on soils with Hg, Pb and Cu contents 70-to-7000-fold higher than background values and also polluted with As, Cd, Ni, and Zn. An ex situ remediation project including soil replacement, immobilization with Na2S, and safe landfilling was conducted at Daiziying and Anle (China) with soils contaminated with As, Cd, Cu, Hg, Ni, Pb and Zn. Notably, Na2S application significantly lowered the sulfuric-nitric acid leachable TMs below the limits defined by Chinese regulations. There was also a significant reduction in the DTPA-extractable TMs in the two studied sites up to 85.9 % for Hg, 71.4 % for Cu, 71.9 % for Pb, 48.1 % for Cd, 37.1 % for Zn, 34.3 % for Ni, and 15.7 % for As compared to the untreated controls. Moreover, Na2S treatment decreased the shoot TM contents in the last harvest to levels lower than the TM regulation limits concerning fodder crops, and decreased the TM root-to-shoot translocation, compared to the untreated control sites. We conclude that Na2S has great potential to remediate soils heavily tainted with TMs and mitigate the associated ecological and human health risks.

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.

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.

PANI/MCM-41 adsorption for removal of Cr(VI) ions and its application in enhancing electrokinetic remediation of Cr(VI)-contaminated soil

In this study, a polyaniline/mesoporous silica (PANI/MCM-41) composite material that can be used as a filler for permeable reactive barrier (PRB) was prepared by in situ polymerization. Firstly, the adsorption capacity of PANI/MCM-41 on Cr (VI) in solution was investigated. The results show that the prepared PANI/MCM-41 exhibits a significant Cr (VI) adsorption capacity (~ 340 mg/g), and the adsorption process is more accurately described by the Langmuir isotherm and pseudo-second-order kinetic model. The thermodynamic functions evidenced that the Cr(VI) adsorption was an endothermic spontaneous process. In addition, adsorption-desorption cycle experiments proved the excellent reusability of the material. Subsequently, the material was utilized as a filler in the PRB for the remediation of Cr(VI)-contaminated soil using electrokinetic-permeable reactive barrier (EK-PRB) technology. The results show that compared with traditional electrokinetic remediation, the use of PANI/MCM-41 as an active filler can enlarge the current during remediation and enhance the conductivity of soil, which increases the removal rates of total Cr and Cr(VI) in soil (17.4% and 10.2%).

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.

Shaping the concentration of petroleum hydrocarbon pollution in soil: A machine learning and resistivity-based prediction method

A new method relying on machine learning and resistivity to predict concentrations of petroleum hydrocarbon pollution in soil was proposed as a means of investigation and monitoring. Currently, determining pollutant concentrations in soil is primarily achieved through costly sampling and testing of numerous borehole samples, which carries the risk of further contamination by penetrating the aquifer. Additionally, conventional petroleum hydrocarbon geophysical surveys struggle to establish a correlation between survey results and pollutant concentration. To overcome these limitations, three machine learning models (KNN, RF, and XGBOOST) were combined with the geoelectrical method to predict petroleum hydrocarbon concentrations in the source area. The results demonstrate that the resistivity-based prediction method utilizing machine learning is effective, as validated by R-squared values of 0.91 and 0.94 for the test and validation sets, respectively, and a root mean squared error of 0.19. Furthermore, this study confirmed the feasibility of the approach using actual site data, along with a discussion of its advantages and limitations, establishing it as an inexpensive option to investigate and monitor changes in petroleum hydrocarbon concentration in soil.

Phytoremediation of chloride from marine dredged sediments: A new model based on a natural vegetation recolonization

Phytoremediation is a type of bioremediation process that involves the use of plants to remove or degrade contaminants from soil, water, or air. In most of the observed phytoremediation models, plants are introduced and planted on a polluted site to take up, absorb, or transform contaminants. This study aims to explore a new mixed phytoremediation approach that involves natural recolonization of a contaminated substrate, by identifying the species growing naturally, their bioaccumulation capacity, and by modeling annual mowing cycles of their aerial parts. This approach aims to evaluate the phytoremediation potential of such a model. Both natural and human interventions are involved in this approach, which is referred to as a mixed phytoremediation process. The study focuses on chloride phytoremediation from a chloride-rich and regulated substrate that is marine dredged sediments abandoned for 12 years and recolonized for 4 years. The sediments are colonized by a Suaeda vera dominated vegetation and possess heterogeneity in lixiviate chloride and conductivity. The study found that despite Suaeda vera is the well adapted species for this environment, it is not an effective species for phytoremediation as it has low bioaccumulation and translocation rates (9.3 and 2.6 respectively), and disturbs chloride leaching below in the substrate. Other identified species, such as Salicornia sp., Suaeda maritima, and Halimione portulacoides, have better phytoaccumulation (respectively 39.8, 40.1, 34.8) and translocation rates (respectively 7.0, 4.5, 5.6) and can successfully remediate the sediment in 2-9 years. The following species have been found to bioaccumulate chloride in aboveground biomass at the following rates: Salicornia sp. (181 g/kg DW), Suaeda maritima (160 g/kg DW), Sarcocornia perennis (150 g/kg DW), Halimione portulacoides (111 g/kg DW) and Suaeda vera (40 g/kg DW).

Effects of Cadmium Stress on Carbon Sequestration and Oxygen Release Characteristics in A Landscaping Hyperaccumulator-Lonicera japonica Thunb

The carbon sequestration and oxygen release of landscape plants are dominant ecological service functions, which can play an important role in reducing greenhouse gases, improving the urban heat island effect and achieving carbon peaking and carbon neutrality. In the present study, we are choosing Lonicera japonica Thunb. as a model plant to show the effects of Cd stress on growth, photosynthesis, carbon sequestration and oxygen release characteristics. Under 5 mg kg^-1 of Cd treatment, the dry weight of roots and shoots biomass and the net photosynthetic rate (P_N) in L. japonica had a significant increase, and with the increase in Cd treatment concentration, the dry weight of roots and shoots biomass and P_N in the plant began to decrease. When the Cd treatment concentration was up to 125 mg kg^-1, the dry weight of root and shoots biomass and P_N in the plant decreased by 5.29%, 1.94% and 2.06%, and they had no significant decrease compared with the control, indicating that the plant still had a good ability for growth and photoenergy utilization even under high concentrations of Cd stress. The carbon sequestration and oxygen release functions in terms of diurnal assimilation amounts (P), carbon sequestration per unit leaf area (W_CO2), oxygen release per unit leaf area (W_O2), carbon sequestration per unit land area (P_CO2) and oxygen release per unit land area (P_O2) in L. japonica had a similar change trend with the photosynthesis responses under different concentrations of Cd treatments, which indicated that L. japonica as a landscaping Cd-hyperaccumulator, has a good ability for carbon sequestration and oxygen release even under high concentrations of Cd stress. The present study will provide a useful guideline for effectively developing the ecological service functions of landscaping hyperaccumulators under urban Cd-contaminated environment.

The influence of diverse fertilizer regimes on the phytoremediation potential of Pteris vittata in an abandoned nonferrous metallic mining site

Organic waste comprises a large amount of hydrocarbon containing organic substances, which is regarded as a potential resource rather than simply a waste. A field experiment was conducted in a poly-metallic mining area to investigate the potential of organic waste to facilitate the soil remediation process. Different organic wastes and a commonly used commercial fertilizer were added to heavy metal contaminated soil, which was under phytoremediation using the As hyperaccumulator Pteris vittata. The influence of diverse fertilizer regimes on the biomass of P. vittata and heavy metal removal by P. vittata, was investigated. The soil properties were analyzed after the application of phytoremediation with or without the addition of organic wastes. Results indicated that sewage sludge compost is an appropriate amendment to improve the phytoremediation efficiency. Compared to the control, the application of sewage sludge compost significantly reduced the extractability of As in soil by 26.8 %, and increased the removal of As and Pb by 26.9 % and 186.5 %, respectively. The highest removal of As and Pb reached 33 and 34 kg/ha, respectively. The sewage sludge compost-strengthened phytoremediation improved soil quality. And the diversity and richness of the bacterial community were improved, as represented by the increase in Shannon and Chao index. With improved efficiency and acceptable cost, the organic waste-strengthened phytoremediation can be used to control the risks posed by high concentrations of heavy metals in mining areas.

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.

Uptake, tolerance, and detoxification mechanisms of antimonite and antimonate in Boehmeria nivea L

Boehmeria nivea L. (ramie) is a promising phytoremediation plant for antimony (Sb)-contaminated soils. However, the uptake, tolerance, and detoxification mechanisms of ramie to Sb, which are the basis for finding efficient phytoremediation strategies, remain unclear. In the present study, ramie was exposed to 0, 1, 10, 50, 100, and 200 mg/L of antimonite (Sb(III)) or antimonate (Sb(V)) for 14 days in hydroponic culture. The Sb concentration, speciation, subcellular distribution, and antioxidant and ionomic responses in ramie were investigated. The results illustrated that ramie was more effective in the uptake of Sb(III) than Sb(V). Most of the Sb accumulated in ramie roots, with the highest level reaching 7883.58 mg/kg. Sb(V) was the predominant species in leaves, with 80.77-96.38% and 100% in the Sb(III) and Sb(V) treatments, respectively. Immobilization of Sb on the cell wall and leaf cytosol was the primary mechanism of accumulation. Superoxide dismutase (SOD), catalase (CAT), and peroxidase (POD) contributed significantly to root defense against Sb(III), while CAT and glutathione peroxidase (GPX) were the major antioxidants in leaves. CAT and POD played crucial roles in the defense against Sb(V). B, Ca, K, Mg, and Mn in Sb(V)-treated leaves and K and Cu in Sb(III)-treated leaves may be related to the biological processes of Sb toxicity mitigation. This study is the first to investigate the ionomic responses of plants toward Sb and could provide valuable information for the phytoremediation of Sb-polluted soils.

Metal tolerance mechanisms in plants and microbe-mediated bioremediation

The heavy metal contamination, which causes toxic effects on plants, has evolved into a significant constraint to plant quality and yield. This scenario has been exacerbated by booming population expansion and intrinsic food insecurity. Numerous studies have found that counteracting heavy metal tolerance and accumulation necessitates complex mechanisms at the biochemical, molecular, tissue, cellular and whole plant levels, which may demonstrate increased crop yields. Essential and non-essential elements have similar harmful impacts on plants including reduced biomass production, growth and photosynthesis inhibition, chlorosis, altered fluid balance and nutrient absorption, as well as senescence, all of which led to plant death. Notable biotechnological strategies for effective remediation require knowledge of metal stress and tolerance mechanisms in plants. Assimilation, cooperation and integration, of biotechnological improvements, are required for adequate environmental rehabilitation in the emerging area of bioremediation. This review emphasizes a deeper understanding of metal toxicity, stress, and potential tolerance mechanisms in plants exposed to metal stress. The microbe-mediated metal toxic effects and stress mitigation knowledge can be used to create a new strategic plan as feasible, sustainable, and environmentally friendly bioremediation techniques.

A review of important heavy metals toxicity with special emphasis on nephrotoxicity and its management in cattle

Toxicity with heavy metals has proven to be a significant hazard with several health problems linked to it. Heavy metals bioaccumulate in living organisms, pollute the food chain, and possibly threaten the health of animals. Many industries, fertilizers, traffic, automobile, paint, groundwater, and animal feed are sources of contamination of heavy metals. Few metals, such as aluminum (Al), may be eliminated by the elimination processes, but other metals like lead (Pb), arsenic (As), and cadmium (Ca) accumulate in the body and food chain, leading to chronic toxicity in animals. Even if these metals have no biological purpose, their toxic effects are still present in some form that is damaging to the animal body and its appropriate functioning. Cadmium (Cd) and Pb have negative impacts on a number of physiological and biochemical processes when exposed to sub-lethal doses. The nephrotoxic effects of Pb, As, and Cd are well known, and high amounts of naturally occurring environmental metals as well as occupational populations with high exposures have an adverse relationship between kidney damage and toxic metal exposure. Metal toxicity is determined by the absorbed dosage, the route of exposure, and the duration of exposure, whether acute or chronic. This can lead to numerous disorders and can also result in excessive damage due to oxidative stress generated by free radical production. Heavy metals concentration can be decreased through various procedures including bioremediation, pyrolysis, phytoremediation, rhizofiltration, biochar, and thermal process. This review discusses few heavy metals, their toxicity mechanisms, and their health impacts on cattle with special emphasis on the kidneys.

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.

Hormesis Responses of Growth and Photosynthetic Characteristics in Lonicera japonica Thunb. to Cadmium Stress: Whether Electric Field Can Improve or Not?

"Hormesis" is considered a dose-response phenomenon mainly observed at hyperaccumulator plants under heavy metals stress. In this study, the effects of electric fields on hormesis responses in Lonicera japonica Thunb. under cadmium (Cd) treatments were investigated by assessing the plant growth and photosynthetic characteristics. Under Cd treatments without electric fields, the parameters of plant growth and photosynthetic characteristics increased significantly when exposed to 5 mg L^-1 Cd, and decreased slightly when exposed to 25 mg L^-1 Cd, showing an inverted U-shaped trend, which confirmed that low concentration Cd has a hormesis effect on L. japonica. Under electric fields, different voltages significantly promoted the inverted U-shaped trend of the hormesis effect on the plant, especially by 2 V cm^-1 voltage. Under 2 V cm^-1 voltage, the dry weight of the root and leaf biomass exposed to 5 mg L^-1 Cd increased significantly by 38.38% and 42.14%, and the photosynthetic pigment contents and photosynthetic parameters were also increased significantly relative to the control, indicating that a suitable electric field provides better improvements for the hormesis responses of the plant under Cd treatments. The synergistic benefits of the 5 mg L^-1 Cd and 2 V cm^-1 electric field in terms of the enhanced hormesis responses of growth and photosynthetic characteristics could contribute to the promoted application of electro-phytotechnology.