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

Application of Bioinformatics and Machine Learning Tools in Food Safety

PURPOSE OF REVIEW

Food safety is a fundamental challenge in public health and sustainable development, facing threats from microbial, chemical, and physical contamination. Innovative technologies improve our capacity to detect contamination early and prevent disease outbreaks, while also optimizing food production and distribution processes.

RECENT FINDINGS

This article discusses the role of new bioinformatics and machine learning technologies in promoting food safety and contamination control, along with various related articles in this field. By analyzing genetic and proteomic data, bioinformatics helps to quickly and accurately identify pathogens and sources of contamination. Machine learning, as a powerful tool for massive data processing, also can discover hidden patterns in the food production and distribution chain, which helps to improve risk prediction and control processes. By reviewing previous research and providing new solutions, this article emphasizes the role of these technologies in identifying, preventing, and improving decisions related to food safety. This study comprehensively shows how the integration of bioinformatics and machine learning can help improve food quality and safety and prevent foodborne disease outbreaks.

Electrochemical reduction for chlorinated hydrocarbons contaminated groundwater remediation: Mechanisms, challenges, and perspectives

Electrochemical reduction technology is a promising method for addressing the persistent contamination of groundwater by chlorinated hydrocarbons. Current research shows that electrochemical reductive dechlorination primarily relies on direct electron transfer (DET) and active hydrogen (H⁎) mediated indirect electron transfer processes, thereby achieving efficient dechlorination and detoxification. This paper explores the influence of the molecular charge structure of chlorinated hydrocarbons, including chlorolefin, chloroalkanes, chlorinated aromatic hydrocarbons, and chloro-carboxylic acid, on reductive dechlorination from the perspective of molecular electrostatic potential and local electron affinity. It reveals the affinity characteristics of chlorinated hydrocarbon pollutants, the active dechlorination sites, and the roles of substituent groups. It also comprehensively discusses the current progress on electrochemical reductive dechlorination using metal, carbon-based, and 3D electrode catalysts, with an emphasis on the design and optimization of electrode materials and the impact of catalyst microstructure regulation on dechlorination performance. It delves into the current application status of coupling electrochemical reduction technology with biodegradation and electrochemical circulating well technology for the remediation of groundwater contaminated by chlorinated hydrocarbons. The paper discusses practical application challenges such as electron transfer, electrode corrosion, water chemistry environment, and aquifer heterogeneity. Finally, considerations are presented from the perspectives of environmental impact and sustainable application, along with a summary and analysis of potential future research directions and technological prospects.

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

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

Artificial intelligence: A key fulcrum for addressing complex environmental health issues

Environmental health (EH) is a complex and interdisciplinary field dedicated to the examination of environmental behaviours, toxicological effects, health risks, and strategies for mitigating harmful environmental factors. Traditional EH research investigates correlations between risk factors and health outcomes through control variables, but this route is difficult to address complex EH issue. Artificial intelligence (AI) technology not only has accelerated the innovation of the scientific research paradigm but also has become an important tool for solving complex EH problems. However, the in-depth and comprehensive implementation of AI in the field of EH still faces many barriers, such as model generalizability, data privacy protection, algorithm transparency, and regulatory and ethical issues. This review focuses on the compound exposures of EH and explores the potential, challenges, and development directions of AI in four key phases of EH research: (1) data collection, fusion, and management, (2) hazard identification and screening, (3) risk modeling and assessment and (4) EH management. It is not difficult to see that in the future, artificial intelligence technology will inevitably carry out multidimensional simulation of complex exposure factors through multi-mode data fusion, so as to achieve accurate identification of environmental health risks, and eventually become an efficient tool for global environmental health management. This review will help researchers re-examine this strategy and provide a reference for AI to solve complex exposure problems.

Extracellular electron transfer-dependent bioremediation of uranium-contaminated groundwater: Advancements and challenges

Efficient and sustainable remediation of uranium-contaminated groundwater is critical for groundwater safety and the sustainable development of nuclear energy, particularly in the context of global carbon neutrality goals. This review explores the potential of microbial reduction processes that utilize extracellular electron transfer (EET) to convert soluble uranium (U(VI)) into its insoluble form (U(IV)), presenting a promising approach to groundwater remediation. The review first outlines the key processes and factors influencing the effectiveness of dissimilatory metal-reducing bacteria (DMRB), such as Geobacter and Shewanella, during uranium bioremediation and recovery. The cutting-edge progress on the molecular mechanism of EET-driven U(VI) reduction mediated by c-type cytochromes, conductive pili, and electron mediators, is critically reviewed. Additionally, advanced strategies such as optimizing electron transfer, leveraging synthetic biology approach, and integration with machine learning are discussed to enhance the efficiency of EET-driven processes. The review also considers the integration of EET processes into practical engineering applications, highlighting the need for optimization and innovation in bioremediation technologies. By providing a comprehensive overview of current progress and challenges, this review aims to inspire novel research and practical advancements in the field of uranium-contaminated groundwater remediation.

Application of machine learning for environmentally friendly advancement: exploring biomass-derived materials in wastewater treatment and agricultural sector - a review

There are several uses for biomass-derived materials (BDMs) in the irrigation and farming industries. To solve problems with material, process, and supply chain design, BDM systems have started to use machine learning (ML), a new technique approach. This study examined articles published since 2015 to understand better the current status, future possibilities, and capabilities of ML in supporting environmentally friendly development and BDM applications. Previous ML applications were classified into three categories according to their objectives: material and process design, performance prediction and sustainability evaluation. ML helps optimize BDMs systems, predict material properties and performance, reverse engineering, and solve data difficulties in sustainability evaluations. Ensemble models and cutting-edge Neural Networks operate satisfactorily on these datasets and are easily generalized. Ensemble and neural network models have poor interpretability, and there have not been any studies in sustainability assessment that consider geo-temporal dynamics; thus, building ML methods for BDM systems is currently not practical. Future ML research for BDM systems should follow a workflow. Investigating the potential uses of ML in BDM system optimization, evaluation and sustainable development requires further investigation.

Riparian Soil Heavy Metal Contamination and Pollution Assessment and Management Planning Integrating Multiple Indices, Statistical and Geospatial Approaches

This research assesses heavy metal contamination within the riparian zone of the Danro River, a tributary of the Ganges River basin in India, particularly impacted by sand mining activities. The study conducted analyses on major and trace elements in soil samples, focusing on those identified as ecologically hazardous by the Water Framework Directive of India. Utilizing a combination of indices (Enrichment Factor, Pollution Load Index, and Index of geo-accumulation) and statistical techniques such as Principal Component Analysis (PCA), the investigation aimed to evaluate contamination severity, ecological risks, and pollution sources. Results revealed arsenic concentrations ranging from 0.00-0.54 mg/kg to 117-136 mg/kg, and ecological risks for cadmium exceeding 30. PCA identified three dominant factors explaining over 95% of variance. This study also employed the Analytic Hierarchy Process (AHP) method to assess land use suitability. Results unveiled that chromium and nickel predominantly stemmed from natural origins, while arsenic, cadmium, lead, and zinc exhibited a mixed origin. While most sites displayed low to moderate contamination, south-western portion of the basin demonstrated significantly elevated copper concentrations. Cadmium emerged as a particular concern, posing downstream ecological risks alongside chromium, nickel, and zinc, surpassing established thresholds. Further examination using PCA analysis pinpointed three primary pollution sources: traffic emissions, industrial activities, and natural processes. The research concludes by proposing a novel approach for remediation, including the Miyawaki technique alongside traditional methods like electrokinetic remediation and soil leaching. Policy suggestions advocate for collaborative efforts between economic entities and governments to promote sustainable practices that minimize heavy metal pollution.

Machine learning-assisted prediction of engineered carbon systems' capacity to treat textile dyeing wastewater via adsorption technology

Dyes are widely used in industries like printing, cosmetics, paper, leather processing, textiles, and manufacturing to add color to products. However, improper disposal of dyes into wastewater has raised major concerns due to their harmful effects on plants, animals, and humans. Using engineered carbon systems (ECSs) to treat dye-contaminated wastewater has shown promise for sustainable waste management. Dye adsorption on ECSs is a complex, non-linear process, making it essential to understand ECSs' dye removal capabilities through a modeling framework that includes experimental and environmental factors. To support this, a database of ECSs used in dye removal from textile wastewater was compiled. Twelve machine learning models, including XGBoost, Light Gradient Boost, Random Forest, Gradient Boost, CatBoost, AdaBoost, Decision Tree, Artificial Neural Network, K-Nearest Neighbor, Support Vector Machine, Huber, and Ridge Regressor, were applied to analyze ECSs' dye removal potential. Out of all the models, XGBoost exhibited the highest coefficient of determination (R2) of 0.986 during the training and 0.978 during testing, alongside the lowest prediction error (MSE) of 0.01 and 0.136 in the training phase and testing phase. The quantity of ECS, concentration of dye (Co), and pH of wastewater highly influenced the adsorption process. The optimization results indicated the highest affinity of direct, reactive, and dispersed dyes towards ECSs in the acidic solution. In contrast, the maximum adsorption of Basic and VAT dye on ECSs was found in the alkaline solution. The partial dependence analysis provided valuable insights into the interaction between ECS dose and water matrix parameters that can lead to efficient extraction of dyes from aqueous matrices.

An ensemble optimizer with a stacking ensemble surrogate model for identification of groundwater contamination source

The application of the simulation-optimization method for groundwater contamination source identification (GCSI) encounters two main challenges: the substantial time cost of calling the simulation model, and the limitations on the accuracy of identification results due to the complexity, nonlinearity, and ill-posed nature of the inverse problem. To address these issues, we have innovatively developed an inversion framework based on ensemble learning strategies. This framework comprises a stacking ensemble model (SEM), which integrates three distinct machine learning models (Extremely Randomized Trees, Adaptive Boosting, and Bidirectional Gated Recurrent Unit), and an ensemble optimizer (E-GKSEEFO), which combines two newly proposed swarm intelligence optimizers (Genghis Khan Shark Optimizer and Electric Eel Foraging Optimizer). Specifically, the SEM serves as a surrogate model for the groundwater numerical simulation model. Compared to the original simulation model, it significantly reduces time cost while maintaining accuracy. The E-GKSEEFO, functioning as the search strategy for the optimization model, greatly enhances the accuracy of the optimization results. We have verified the performance of the SEM-E-GKSEEFO ensemble inversion framework through two hypothetical scenarios derived from an actual coal gangue pile. The results are as follows. (1) The SEM exhibits improved fitting performance compared to single machine learning models when dealing with high-dimensional nonlinear data from GCSI. (2) The E-GKSEEFO achieves significantly higher accuracy in the identification results of GCSI than individual optimizers. These findings affirm the effectiveness and superiority of the proposed SEM-E-GKSEEFO ensemble inversion framework.

Machine learning-assisted risk evaluation of heavy metals in the Hainan gold mining region, China

This study employed machine learning (ML) to thoroughly investigate the impact of informal mining activities on the distribution and pollution status of heavy metals in soils near private gold mines in Hainan Province, southern China, a region known for its ecological sensitivity and economic importance. By systematically collecting surface soil samples and samples at depths of 0.5-1 m from 175 drilling sites, a comprehensive quantitative analysis was conducted on major heavy metal elements, including lead (Pb), copper (Cu), cadmium (Cd), nickel (Ni), mercury (Hg), chromium (Cr), arsenic (As), and zinc (Zn). Combined with evaluation methods such as the Pollution Load Index (PLI), Normalized Pollution Index (NIPI), and Ecological Risk Index (ERI), the study revealed a high level of soil pollution at informal mining sites. The findings indicated that the average concentrations of Pb, Cd, Hg, As, and Zn in surface soils significantly exceeded the background values for soils in China, with a pronounced positive correlation observed between these heavy metal elements in both surface and deep soil profiles (r > 0.5). Furthermore, leveraging the heavy metal content in surface soils and the constructed environmental indicators, the predictive accuracy for metal content in deep soils was found to range from R2 = 0.27 to 0.68, suggesting that informal mining activities have led to substantial variations in metal content across different soil profiles. Through the application of a random forest model for predictive analysis of the PLI, NIPI, and ERI, high prediction accuracy was achieved (R2 = 0.78, 0.86, and 0.60, respectively). The study demonstrates that informal mining activities not only elevate the risk of soil pollution but also alter the distribution patterns of heavy metals. Also, this study provides a crucial foundation for the scientific assessment of soil quality and potential environmental hazards, while also affirming the efficacy of ML techniques in forecasting soil quality parameters.

Current Trends in Artificial Intelligence-Assisted Spine Surgery: A Systematic Review

This systematic review summarizes existing evidence and outlines the benefits of artificial intelligence-assisted spine surgery. The popularity of artificial intelligence has grown significantly, demonstrating its benefits in computer-assisted surgery and advancements in spinal treatment. This study adhered to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), a set of reporting guidelines specifically designed for systematic reviews and meta-analyses. The search strategy used Medical Subject Headings (MeSH) terms, including "MeSH (Artificial intelligence)," "Spine" AND "Spinal" filters, in the last 10 years, and English- from January 1, 2013, to October 31, 2023. In total, 442 articles fulfilled the first screening criteria. A detailed analysis of those articles identified 220 that matched the criteria, of which 11 were considered appropriate for this analysis after applying the complete inclusion and exclusion criteria. In total, 11 studies met the eligibility criteria. Analysis of these studies revealed the types of artificial intelligence-assisted spine surgery. No evidence suggests the superiority of assisted spine surgery with or without artificial intelligence in terms of outcomes. In terms of feasibility, accuracy, safety, and facilitating lower patient radiation exposure compared with standard fluoroscopic guidance, artificial intelligence-assisted spine surgery produced satisfactory and superior outcomes. The incorporation of artificial intelligence with augmented and virtual reality appears promising, with the potential to enhance surgeon proficiency and overall surgical safety.