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

Microplastics in water resources: Global pollution circle, possible technological solutions, legislations, and future horizon

Beneath the surface of our ecosystems, microplastics (MPs) silently loom as a significant threat. These minuscule pollutants, invisible to the naked eye, wreak havoc on living organisms and disrupt the delicate balance of our environment. As we delve into a trove of data and reports, a troubling narrative unfolds: MPs pose a grave risk to both health and food chains with their diverse compositions and chemical characteristics. Nevertheless, the peril extends further. MPs infiltrate the environment and intertwine with other pollutants. Worldwide, microplastic levels fluctuate dramatically, ranging from 0.001 to 140 particles.m-3 in water and 0.2 to 8766 particles.g-1 in sediment, painting a stark picture of pervasive pollution. Coastal and marine ecosystems bear the brunt, with each organism laden with thousands of microplastic particles. MPs possess a remarkable ability to absorb a plethora of contaminants, and their environmental behavior is influenced by factors such as molecular weight and pH. Reported adsorption capacities of MPs vary greatly, spanning from 0.001 to 12,700 μg·g-1. These distressing figures serve as a clarion call, demanding immediate action and heightened environmental consciousness. Legislation, innovation, and sustainable practices stand as indispensable defenses against this encroaching menace. Grasping the intricate interplay between microplastics and pollutants is paramount, guiding us toward effective mitigation strategies and preserving our health ecosystems.

A review of advanced bioremediation technologies for dioxin-contaminated soil treatment: Current and Future outlook

Polychlorinated dibenzo-p-dioxins (PCDD) and polychlorinated dibenzofurans (PCDF), namely known as dioxins, are persistent organic compounds with high toxicity. The presence of dioxins in soil is a major environmental issue worldwide, as it negatively impacts both ecosystems and human health. Thus, several advanced techniques have been applied to overcome this issue, offering promising treatment efficiency and cost-effectiveness. This review employs a meta-analysis strategy to provide an up-to-date assessment of the global situation of dioxin-contaminated soil. Dioxin concentrations are commonly higher in industrial and urban areas than in rural areas, primarily due to anthropogenic activities such as chemical manufacturing and waste incineration. Furthermore, several advanced bioremediation technologies for dioxin treatment, including biosurfactants, composting, and phytoremediation were highlighted and thoroughly discussed. Aerobic composting has proven to be robust in removing dioxins, achieving treatment efficiencies ranging from 65% to 85%. Whereas, phytoremediation, particularly when involving agricultural crops like zucchini, cucumber, and wheat, shows great promise in dioxin removal through various mechanisms, including root uptake and transpiration. Notably, biosurfactants such as rhamnolipids and sophorolipids have been effectively used to remediate dioxin-contaminated soil due to their significantly enhanced bioavailability of dioxins and their interaction with microbes. This review provides a comprehensive understanding of advanced biotechnologies for remediating dioxin-contaminated soil. It also addresses the technical and economic aspects of dioxin treatment and identifies future directions and research perspectives to fill knowledge gaps in this field.

Metal nanoparticles and pesticides under global climate change: Assessing the combined effects of multiple abiotic stressors on soil microbial ecosystems

The soil is a vital resource that hosts many microorganisms crucial in biogeochemical cycles and ecosystem health. However, human activities such as the use of metal nanoparticles (MNPs), pesticides and the impacts of global climate change (GCCh) can significantly affect soil microbial communities (SMC). For many years, pesticides and, more recently, nanoparticles have contributed to sustainable agriculture to ensure continuous food production to sustain the significant growth of the world population and, therefore, the demand for food. Pesticides have a recognized pest control capacity. On the other hand, nanoparticles have demonstrated a high ability to improve water and nutrient retention, promote plant growth, and control pests. However, it has been reported that their accumulation in agricultural soils can also adversely affect the environment and soil microbial health. In addition, climate change, with its variations in temperature and extreme water conditions, can lead to drought and increased soil salinity, modifying both soil conditions and the composition and function of microbial communities. Abiotic stressors can interact and synergistically or additively affect soil microorganisms, significantly impacting soil functioning and the capacity to provide ecosystem services. Therefore, this work reviewed the current scientific literature to understand how multiple stressors interact and affect the SMC. In addition, the importance of molecular tools such as metagenomics, metatranscriptomics, proteomics, or metabolomics in the study of the responses of SMC to exposure to multiple abiotic stressors was examined. Future research directions were also proposed, focusing on exploring the complex interactions between stressors and their long-term effects and developing strategies for sustainable soil management. These efforts will contribute to the preservation of soil health and the promotion of sustainable agricultural practices.

Perspectives on Advancing Multimodal Learning in Environmental Science and Engineering Studies

The environment faces increasing anthropogenic impacts, resulting in a rapid increase in environmental issues that undermine the natural capital essential for human wellbeing. These issues are complex and often influenced by various factors represented by data with different modalities. While machine learning (ML) provides data-driven tools for addressing the environmental issues, the current ML models in environmental science and engineering (ES&E) often neglect the utilization of multimodal data. With the advancement in deep learning, multimodal learning (MML) holds promise for comprehensive descriptions of the environmental issues by harnessing data from diverse modalities. This advancement has the potential to significantly elevate the accuracy and robustness of prediction models in ES&E studies, providing enhanced solutions for various environmental modeling tasks. This perspective summarizes MML methodologies and proposes potential applications of MML models in ES&E studies, including environmental quality assessment, prediction of chemical hazards, and optimization of pollution control techniques. Additionally, we discuss the challenges associated with implementing MML in ES&E and propose future research directions in this domain.

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.

Deriving ecological risk thresholds for soil molybdenum in China based on interspecies correlation estimation and quantitative ion character-activity relationship models

Soil molybdenum (Mo) levels can reach ecologically hazardous levels. China has not yet established the relevant thresholds, posing challenges for environmental management. Therefore, we present our data relevant to Mo toxicity for several important species. By normalizing soil properties, we obtained a correlation model of Mo toxicity to Hordeum vulgare, as well as 31 models for the toxicity of other elements including Cu and Ni to invertebrates and microbial processes. Using interspecies correlation estimation (ICE) extrapolation, the sensitivity coefficient (0.12-0.71) for five plants were found. For invertebrates and microbial processes lacking Mo data, we used regression analysis to establish Mo toxicity models based on the soil quantitative ion character-activity relationships (s-QICAR; R2 =0.70-0.95) and known toxicities of other metal elements to invertebrate and microbial processes. Furthermore, combining species sensitivity distribution calculations, the HC5 values for protecting 95% of soil species from Mo in three typical soil scenarios in China were calculated. After correction, the predicted no-effect concentrations were 6.8, 4.8, and 3.4 mg/kg, respectively. This study innovatively combined ICE and s - QICAR to derive soil Mo thresholds. Our results can provide a basis for decision-making in the assessment and management of soil Mo pollution.

Macrophyte assisted phytoremediation and toxicological profiling of metal(loid)s polluted water is influenced by hydraulic retention time

The present study reports findings related to the treatment of polluted groundwater using macrophyte-assisted phytoremediation. The potential of three macrophyte species (Phragmites australis, Scirpus holoschoenus, and Typha angustifolia) to tolerate exposure to multi-metal(loid) polluted groundwater was first evaluated in mesocosms for 7- and 14-day batch testing. In the 7-day batch test, the polluted water was completely replaced and renewed after 7 days, while for 14 days exposure, the same polluted water, added in the first week, was maintained. The initial biochemical screening results of macrophytes indicated that the selected plants were more tolerant to the provided conditions with 14 days of exposure. Based on these findings, the plants were exposed to HRT regimes of 15 and 30 days. The results showed that P. australis and S. holoschoenus performed better than T. angustifolia, in terms of metal(loid) accumulation and removal, biomass production, and toxicity reduction. In addition, the translocation and compartmentalization of metal(loid)s were dose-dependent. At the 30-day loading rate (higher HRT), below-ground phytostabilization was greater than phytoaccumulation, whereas at the 15-day loading rate (lower HRT), below- and above-ground phytoaccumulation was the dominant metal(loid) removal mechanism. However, higher levels of toxicity were noted in the water at the 15-day loading rate. Overall, this study provides valuable insights for macrophyte-assisted phytoremediation of polluted (ground)water streams that can help to improve the design and implementation of phytoremediation systems.

Ecotoxicological impacts of landfill sites: Towards risk assessment, mitigation policies and the role of artificial intelligence

Waste disposal in landfills remains a global concern. Despite technological developments, landfill leachate poses a hazard to ecosystems and human health since it acts as a secondary reservoir for legacy and emerging pollutants. This study provides a systematic and scientometric review of the nature and toxicity of pollutants generated by landfills and means of assessing their potential risks. Regarding human health, unregulated waste disposal and pathogens in leachate are the leading causes of diseases reported in local populations. Both in vitro and in vivo approaches have been employed in the ecotoxicological risk assessment of landfill leachate, with model organisms ranging from bacteria to birds. These studies demonstrate a wide range of toxic effects that reflect the complex composition of leachate and geographical variations in climate, resource availability and management practices. Based on bioassay (and other) evidence, categories of persistent chemicals of most concern include brominated flame retardants, per- and polyfluorinated chemicals, pharmaceuticals and alkyl phenol ethoxylates. However, the emerging and more general literature on microplastic toxicity suggests that these particles might also be problematic in leachate. Various mitigation strategies have been identified, with most focussing on improving landfill design or leachate treatment, developing alternative disposal methods and reducing waste volume through recycling or using more sustainable materials. The success of these efforts will rely on policies and practices and their enforcement, which is seen as a particular challenge in developing nations and at the international (and transboundary) level. Artificial intelligence and machine learning afford a wide range of options for evaluating and reducing the risks associated with leachates and gaseous emissions from landfills, and various approaches tested or having potential are discussed. However, addressing the limitations in data collection, model accuracy, real-time monitoring and our understanding of environmental impacts will be critical for realising this potential.

Polyelectrolytes for Environmental, Agricultural, and Medical Applications

In recent decades, polyelectrolytes (PELs) have attracted significant interest owing to a surge in research dedicated to the development of new technologies and applications at the biological level. Polyelectrolytes are macromolecules of which a substantial portion of the constituent units contains ionizable or ionic groups. These macromolecules demonstrate varied behaviors across different pH ranges, ionic strengths, and concentrations, making them fascinating subjects within the scientific community. The aim of this review is to present a comprehensive survey of the progress in the application studies of polyelectrolytes and their derivatives in various fields that are vital for the advancement, conservation, and technological progress of the planet, including agriculture, environmental science, and medicine. Through this bibliographic review, we seek to highlight the significance of these materials and their extensive range of applications in modern times.

Engaging One Health in Heavy Metal Pollution in Some Selected Nigerian Niger Delta Cities. A Systematic Review of Pervasiveness, Bioaccumulation and Subduing Environmental Health Challenges

The Niger Delta environment is under serious threat due to heavy metal pollution. Many studies have been conducted on the heavy metal contamination in soils, water, seafood and plants in the Niger Delta ecosystem. However, there is a lack of clear understanding of the health consequences for people and strategies for attaining One Health, and a dispersion of information that is accessible. The study focused on investigating the contamination levels, distributions, risks, sources and impacts of heavy metals in selected regions of the Niger Delta. Prior studies revealed that the levels of certain heavy metals, including Cd, Pb, Cu, Cr, Mn, Fe and Ni, in water, sediment, fish and plants in most Niger Delta ecosystems were higher than the acceptable threshold attributed to various anthropogenic stressors. In the reviewed Niger Delta states, ecosystems in Rivers state showed the highest concentrations of heavy metals in most sampled sites. Groundwater quality was recorded at concentrations higher than 0.3 mg/L World Health Organization drinking water guideline. High concentrations of copper (147.915 mg/L) and zinc (10.878 mg/L) were found in Rivers State. The heavy metals concentrations were greater in bottom-dwelling organisms such as bivalves, gastropods and shrimp than in other fishery species. Heavy metal exposure in the region poses risks of communicable and non-communicable diseases. Diverse remediation methods are crucial to reduce contamination levels, but comprehensive strategies and international cooperation are essential to address the health hazards. Actively reducing heavy metals in the environment can achieve One Health objectives and mitigate disease and economic burdens.

Application of the partial least square regression method in determining the natural background of soil heavy metals: A case study in the Songhua River basin, China

The "background" is an essential index for identifying anthropogenic inputs and potential ecological risks of soil heavy metals. However, the lithology of bedrock can cause significant spatial variation in the natural background of soil elements, posing considerable difficulties in estimating background values. In this study, an attempt was made to calculate the natural background through regression analysis of soil chemical composition, and reasonably evaluate the impact of lithology. A total of 1771 surface soil samples were collected from the Songhua River Basin, China, for chemical composition analysis, and the partial least square regression (PLSR) method was employed to establish the relationship between heavy metals (As, Hg, Cr, Cd, Pb, Cu, Zn, and Ni) and soil chemical composition/environmental parameters (SiO2, Al2O3, TFe2O3, MgO, CaO, K2O, Na2O, La, Y, Zr, V, Sc, Sr, Li and pH). The result shows that As, Cr, Pb, Cu, Zn, and Ni have significant linear relationships with soil chemical composition. Each of these six heavy metals obtained 1771 regression background values; some were higher than the uniform background value obtained from the boxplot, while others were lower. The regression background values recognized not only subtle anthropogenic inputs and potential ecological risks in low-background regions but also spurious contamination in high-background areas. All these indicate that the PLSR method can effectively improve the determination accuracy of the natural background of soil heavy metals. More attention should be paid to the serious anthropogenic inputs appearing in some places of the study area.

Bioaugmentation and vermicompost facilitated the hydrocarbon bioremediation: scaling up from lab to field for petroleum-contaminated soils

The biodegradation of total petroleum hydrocarbon (TPH) in soil is very challenging due to the complex recalcitrant nature of hydrocarbon, hydrophobicity, indigenous microbial adaptation and competition, and harsh environmental conditions. This work further confirmed that limited natural attenuation of petroleum hydrocarbons (TPHs) (15% removal) necessitates efficient bioremediation strategies. Hence, a scaling-up experiment for testing and optimizing the use of biopiles for bioremediation of TPH polluted soils was conducted with three 500-kg pilots of polluted soil, and respective treatments were implemented: including control soil (CT), bioaugmentation and vermicompost treatment (BAVC), and a combined application of BAVC along with bioelectrochemical snorkels (BESBAVC), all maintained at 40% field capacity. This study identified that at pilot scale level, a successful application of BAVC treatment can achieve 90.3% TPH removal after 90 days. BAVC's effectiveness stemmed from synergistic mechanisms. Introduced microbial consortia were capable of TPH degradation, while vermicompost provided essential nutrients, enhanced aeration, and, potentially, acted as a biosorbent. Hence, it can be concluded that the combined application of BAVC significantly enhances TPH removal compared to natural attenuation. While the combined application of a bioelectrochemical snorkel (BES) with BAVC also showed a significant TPH removal, it did not differ statistically from the individual application of BAVC, under applied conditions. Further research is needed to optimize BES integration with BAVC for broader applicability. This study demonstrates BAVC as a scalable and mechanistically sound approach for TPH bioremediation in soil.

Recent Development of Metal-Organic Frameworks for Water Purification

Water contamination is an increasing concern to mankind because of the increasing amount of pollutants in aquatic ecosystems. To purify the polluted water, various techniques have been used to remove hazardous components. Unfortunately, traditional cleanup techniques with a low uptake capacity are unable to achieve water purification. Metal-organic frameworks (MOFs) have recently shown potential in effective water pollutant isolation in terms of selectivity and adsorption capacity over traditional porous materials. The high surface area and versatile functionality of MOFs allow for the development of new adsorbents. The development of MOFs in a range of water treatments in the recent five years will be highlighted in this review, along with assessments of the adsorption performance relevant to the particular task. Moreover, the outlook on future opportunities for water purification using MOFs is also provided.

The hidden threat: Environmental toxins and their effects on gut microbiota

The human gut microbiota (GM), which consists of a complex and diverse ecosystem of bacteria, plays a vital role in overall wellness. However, the delicate balance of this intricate system is being compromised by the widespread presence of environmental toxins. The intricate connection between contaminants in the environment and human well-being has garnered significant attention in recent times. Although many environmental pollutants and their toxicity have been identified and studied in laboratory settings and animal models, there is insufficient data concerning their relevance to human physiology. Consequently, research on the toxicity of environmental toxins in GM has gained prominence in recent years. Various factors, such as air pollution, chemicals, heavy metals, and pesticides, have a detrimental impact on the composition and functioning of the GM. This comprehensive review aims to comprehend the toxic effects of numerous environmental pollutants, including antibiotics, endocrine-disrupting chemicals, heavy metals, and pesticides, on GM by examining recent research findings. The current analysis concludes that different types of environmental toxins can lead to GM dysbiosis and have various potential adverse effects on the well-being of animals. We investigate the alterations to the GM composition induced by contaminants and their impact on overall well-being, providing a fresh perspective on research related to pollutant exposure.

Field application of hydroxyapatite and humic acid for remediation of metal-contaminated alkaline soil

The quality of soil is essential for ensuring the safety and quality of agricultural products. However, soils contaminated with toxic metals pose a significant threat to agricultural production and human health. Therefore, remediation of contaminated soils is an urgent task, and humic acid (HA) with hydroxyapatite (HAP) materials was applied for this study in contaminated alkaline soils to remediate Cd, Pb, Cu, and Zn. Physiochemical properties, improved BCR sequential extraction, microbial community composition in soils with superoxide dismutase (SOD), peroxidase (POD), and chlorophyll content in plants were determined. Among the studied treatments, application of HAP-HA (2:1) (T7) had the most significant impact on reducing the active forms of toxic metals from soil such as Cd, Pb, Cu, and Zn decreased by 18.59%, 9.12%, 11.83%, and 3.33%, respectively, but HAP and HA had a minor impact on metal accumulation in Juncao. HAP (T2) had a beneficial impact on reducing the TCleaf/root of Cd, Cu, and Zn, whereas HAP-HA (T5) showed the best performance for reducing Cd and Cu in EFleaf/soil. HAP-HA (T5 and T7) showed higher biomass (57.3%) and chlorophyll (17.9%), whereas HAP (T4) showed better performance in POD (25.8%) than T0 in Juncao. The bacterial diversity in soil was increased after applying amendments of various treatments and enhancing metal remediation. The combined application of HAP and HA effectively reduced active toxic metals in alkaline soil. HAP-HA mixtures notably improved soil health, plant growth, and microbial diversity, advocating for their use in remediating contaminated soils.

A Review about the Mycoremediation of Soil Impacted by War-like Activities: Challenges and Gaps

(1) Background: The frequency and intensity of war-like activities (war, military training, and shooting ranges) worldwide cause soil pollution by metals, metalloids, explosives, radionuclides, and herbicides. Despite this environmentally worrying scenario, soil decontamination in former war zones almost always involves incineration. Nevertheless, this practice is expensive, and its efficiency is suitable only for organic pollutants. Therefore, treating soils polluted by wars requires efficient and economically viable alternatives. In this sense, this manuscript reviews the status and knowledge gaps of mycoremediation. (2) Methods: The literature review consisted of searches on ScienceDirect and Web of Science for articles (1980 to 2023) on the mycoremediation of soils containing pollutants derived from war-like activities. (3) Results: This review highlighted that mycoremediation has many successful applications for removing all pollutants of war-like activities. However, the mycoremediation of soils in former war zones and those impacted by military training and shooting ranges is still very incipient, with most applications emphasizing explosives. (4) Conclusion: The mycoremediation of soils from conflict zones is an entirely open field of research, and the main challenge is to optimize experimental conditions on a field scale.

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.

Biochar applications for treating potentially toxic elements (PTEs) contaminated soils and water: a review

Environmental pollution with potentially toxic elements (PTEs) has become one of the critical and pressing issues worldwide. Although these pollutants occur naturally in the environment, their concentrations are continuously increasing, probably as a consequence of anthropic activities. They are very toxic even at very low concentrations and hence cause undesirable ecological impacts. Thus, the cleanup of polluted soils and water has become an obligation to ensure the safe handling of the available natural resources. Several remediation technologies can be followed to attain successful remediation, i.e., chemical, physical, and biological procedures; yet many of these techniques are expensive and/or may have negative impacts on the surroundings. Recycling agricultural wastes still represents the most promising economical, safe, and successful approach to achieving a healthy and sustainable environment. Briefly, biochar acts as an efficient biosorbent for many PTEs in soils and waters. Furthermore, biochar can considerably reduce concentrations of herbicides in solutions. This review article explains the main reasons for the increasing levels of potentially toxic elements in the environment and their negative impacts on the ecosystem. Moreover, it briefly describes the advantages and disadvantages of using conventional methods for soil and water remediation then clarifies the reasons for using biochar in the clean-up practice of polluted soils and waters, either solely or in combination with other methods such as phytoremediation and soil washing technologies to attain more efficient remediation protocols for the removal of some PTEs, e.g., Cr and As from soils and water.

Prediction of the Impact of Land Use and Soil Type on Concentrations of Heavy Metals and Phthalates in Soil Based on Model Simulation

The main objective of this study is to determine the possibility of predicting the impact of land use and soil type on concentrations of heavy metals (HMs) and phthalates (PAEs) in soil based on an artificial neural network model (ANN). Qualitative analysis of HMs was performed with inductively coupled plasma-optical emission spectrometry (ICP/OES) and Direct Mercury Analyzer. Determination of PAEs was performed with gas chromatography (GC) coupled with a single quadrupole mass spectrometry (MS). An ANN, based on the Broyden-Fletcher-Goldfarb-Shanno (BFGS) iterative algorithm, for the prediction of HM and PAE concentrations, based on land use and soil type parameters, showed good prediction capabilities (the coefficient of determination (r²) values during the training cycle for HM concentration variables were 0.895, 0.927, 0.885, 0.813, 0.883, 0.917, 0.931, and 0.883, respectively, and for PAEs, the concentration variables were 0.950, 0.974, 0.958, 0.974, and 0.943, respectively). The results of this study indicate that HM and PAE concentrations, based on land use and soil type, can be predicted using ANN.