Modeling the adsorption of PAH mixture in silica nanopores by molecular dynamic simulation combined with machine learning

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.

Multiscale computational simulation of pollutant behavior at water interfaces

The investigation of pollutant behavior at water interfaces is critical to understand pollution in aquatic systems. Computational methods allow us to overcome the limitations of experimental analysis, delivering valuable insights into the chemical mechanisms and structural characteristics of pollutant behavior at interfaces across a range of scales, from microscopic to mesoscopic. Quantum mechanics, all-atom molecular dynamics simulations, coarse-grained molecular dynamics simulations, and dissipative particle dynamics simulations represent diverse molecular interaction calculation methods that can effectively model pollutant behavior at environmental interfaces from atomic to mesoscopic scales. These methods provide a rich variety of information on pollutant interactions with water surfaces. This review synthesizes the advancements in applying typical computational methods to the formation, adsorption, binding, and catalytic conversion of pollutants at water interfaces. By drawing on recent advancements, we critically examine the current challenges and offer our perspective on future directions. This review seeks to advance our understanding of computational techniques for elucidating pollutant behavior at water interfaces, a critical aspect of water research.

Advanced bioremediation by an amalgamation of nanotechnology and modern artificial intelligence for efficient restoration of crude petroleum oil-contaminated sites: a prospective study

Crude petroleum oil spillage is becoming a global concern for environmental pollution and poses a severe threat to flora and fauna. Bioremediation is considered a clean, eco-friendly, and cost-effective process to achieve success among the several technologies adopted to mitigate fossil fuel pollution. However, due to the hydrophobic and recalcitrant nature of the oily components, they are not readily bioavailable to the biological components for the remediation process. In the last decade, nanoparticle-based restoration of oil-contaminated, owing to several attractive properties, has gained significant momentum. Thus, intertwining nano- and bioremediation can lead to a suitable technology termed 'nanobioremediation' expected to nullify bioremediation's drawbacks. Furthermore, artificial intelligence (AI), an advanced and sophisticated technique that utilizes digital brains or software to perform different tasks, may radically transfer the bioremediation process to develop an efficient, faster, robust, and more accurate method for rehabilitating oil-contaminated systems. The present review outlines the critical issues associated with the conventional bioremediation process. It analyses the significance of the nanobioremediation process in combination with AI to overcome such drawbacks of a traditional approach for efficiently remedying crude petroleum oil-contaminated sites.

Adsorption characteristic analysis of PAHs on activated carbon with different functional groups by molecular simulation

As widespread organic pollutants in the environment, polycyclic aromatic hydrocarbons (PAHs) greatly threaten human health. The adsorption technology has become one of the main methods to deal with PAHs because of its low cost, simple design, and no secondary pollution. Among them, solid media have strong adsorption capacities for PAHs and are widely used. In this work, activated carbon was chosen as the solid adsorbent. The adsorption behavior of three PAHs (naphthalene, anthracene, and phenanthrene) on activated carbon was investigated at the molecular level by Grand Canonical Monte Carlo (GCMC) method. The effects of different functional groups (amino, carboxyl, hydroxyl, carbonyl, and hydrogen groups) and temperature effect on the adsorption isotherms and heat of adsorption of PAHs on activated carbon were calculated. The results showed that the carbonyl functional group increased the adsorption of PAH molecules by the most considerable amount among all the functional groups. Acid functional groups were more favorable to the adsorption of PAHs than alkali functional groups. The adsorption capacity and heat of adsorption of PAHs decreased when the temperature increased. The adsorption performance of bicyclic aromatic hydrocarbons was more influenced by temperature than that of tricyclic aromatic hydrocarbons.

Molecular clusters played an important role in the adsorption of polycyclic aromatic hydrocarbons (PAHs) on carbonaceous materials

Polycyclic aromatic hydrocarbons (PAHs) are one of the most frequently detected hydrophobic organic contaminants (HOCs) in the environment. They may form clusters because of the strong hydrophobic and π-π electron-donor-acceptor (EDA) interactions among PAHs molecules. However, previous experimental studies and theoretical simulations generally ignored the impact of molecular clusters on the adsorption, which may result in the misunderstanding of the environmental fate and risk. In this work, naphthalene (NAP), phenanthrene (PHE), and pyrene (PYR) were selected to investigate intermolecular interaction as well as the consequent impact on their adsorption on graphene. The density field of C atoms in equilibrium configurations of self-interacted PAHs suggested that the formation of PAHs molecular clusters was a spontaneous process, and was favored in solvents with stronger polarity and for PAHs with more benzene rings. It should be noted that the molecular dynamics simulations with the initial state of molecular clusters matched better with the published experimental results compared with those of individual PAHs. The formed compact PAHs clusters in polar solvents increased the apparent PAHs adsorption, because of their higher hydrophobic and π-π EDA interactions. This study emphasized that the self-interaction of PAHs should be carefully considered in both experimental and theoretical simulation studies.

Soil microbial ecological effect of shale gas oil-based drilling cuttings pyrolysis residue used as soil covering material

The residue derived from oil-based drilling cutting pyrolysis could be used as paving materials. Some petroleum hydrocarbons remain in the residue after pyrolysis and cause severe environmental pollution. In this study, the soil column leaching experiments were carried out under different leaching amounts, and the vertical migration characteristics of petroleum hydrocarbons in soil and the dynamic response mechanism of microorganisms to petroleum hydrocarbons were analyzed. The result showed that the soil pH value and water content with different leaching amounts did not differ significantly, but the vertical migration ability of each petroleum hydrocarbon component was different. In petroleum hydrocarbon contaminated soil, the relative abundance of Proteobacteria maintained a high level (23.6%-60.7%). At the genus level, the relative abundance of Massilia decreased with the leaching amount increased. According to PICRUSt, Monooxygenase [EC: 1.14.13.-] played a significant role in petroleum hydrocarbon degradation. While Long-chain-fatty-acid-CoA ligase [EC: 6.2.1.3] had the highest relative abundance. By studying the influence of shale gas oil-based drilling cuttings pyrolysis residue on soil physical and chemical properties and soil microorganisms, this work provides scientific ecological assessment for the resource application of pyrolysis residue.

Predicting Carbon Residual in Biomass Wastes Using Soft Computing Techniques

In recent decades, the development of complex materials developed a class of biomass waste-derived porous carbons (BWDPCs), which are used for carbon capture and sustainable waste management. It is difficult in understanding the adsorption mechanism of CO2 in the air as it has a wide range of properties associated with its diverse textures, functional group existence, pressure, and temperature of varying range. These properties influence diversely the adsorption mechanism of CO2 and pose serious challenges in the process. To resolve this multiobjective formulation, we use a machine learning classifier that maps systematically the CO2 adsorption as a function of compositional and textural properties and adsorption parameters. The machine learning classifier helps in the classification of various porous carbon materials during the time of training and testing. The results of the simulation show that the proposed method is more efficient in classifying the porous nature of the CO2 adsorption materials than other methods.

Competitive adsorption of methanol co-solvent and dioctyl phthalate on functionalized graphene sheet: Integrated investigation by molecular dynamics simulations and quantum chemical calculations

HYPOTHESIS

Organic co-solvents, which are universally employed in adsorption studies of hydrophobic organic chemicals (HOCs), can inhibit HOC adsorption by competing for active sites on the adsorbent. The adsorbent structure can influence co-solvent interference of HOC adsorption; however, this effect remains unclear, leading to an incomplete understanding of the adsorption mechanism.

EXPERIMENTS

In this study, dioctyl phthalate (DOP) was used to investigate competitive adsorption on functionalized graphene sheet in a water-methanol co-solvent system through molecular dynamics simulations and quantum chemical calculations.

FINDINGS

The simulations showed that the functional groups in the graphene defects had a strong adsorption affinity for methanol. The adsorbed methanol occupied a large number of active sites at the graphene center, thereby weakening DOP adsorption. However, the methanol adsorbed at the graphene edges could not compete with DOP for the active sites. -COOH had the strongest binding affinity for methanol among the functional groups and thus predominantly controlled the interaction between graphene and methanol. This study makes an innovative contribution toward understanding the competitive adsorption of methanol and DOP on functionalized graphene sheet, especially in visualizing the competition for active sites, and provides theoretical guidance for the removal of HOCs and practical application of graphene.

Insights into the estimation of capacitance for carbon-based supercapacitors

Carbon-based materials are broadly used as the active component of electric double layer capacitors (EDLCs) in energy storage systems with a high power density. Most of the reported computational studies have investigated the electrochemical properties under equilibrium conditions, limiting the direct and practical use of the results to design electrochemical energy systems. In the present study, for the first time, the experimental data from more than 300 published papers have been extracted and then analyzed through an optimized support vector machine (SVM) by a grey wolf optimization (GWO) algorithm to obtain a correlation between carbon-based structural features and EDLC performance. Several structural features, including calculated pore size, specific surface area, N-doping level, I D/I G ratio, and applied potential window were selected as the input variables to determine their impact on the respective capacitances. Sensitivity analysis, which has only been performed in this study for approximating the EDLC capacitance, indicated that the specific surface area of the carbon-based supercapacitors is of the greatest effect on the corresponding capacitance. The proposed SVM-GWO, with an R 2 value of 0.92, showed more accuracy than all the other proposed machine learning (ML) models employed for this purpose.

Molecular dynamics simulation of the interaction of water and humic acid in the adsorption of polycyclic aromatic hydrocarbons

Humic acid (HA) and water play an important role in polycyclic aromatic hydrocarbons (PAHs) adsorption and biodegradation in soil. In this work, molecular dynamics (MD) and electrostatic potential surfaces (EPSs) simulations are conducted to research the contribution of quartz surface, leonardite humic acid (LHA), and water to PAH adsorption. The adsorption energies between PAHs and LHA are much higher than that between PAHs and quartz. Simulation shows that the hydroxyl and carboxyl groups' attraction by LHA is the main adsorption force between PAHs and LHA. The π-π interaction between PAHs and LHA also contributes to the adsorption process. In addition, the mobility of water on quartz surface is much higher than that of LHA. Water should be regarded as an adsorbate in the system as well as PAHs. However, the presence of water has a remarkable negative effect on the adsorption of PAHs on LHA and quartz. The bridging effect of water could only enhance the stability of the aggregation system. The adsorption contribution of quartz and LHA to PAHs in the soil model tends to 0 if the water layer reaches 2.0 nm. Graphical abstract.

Predicting the capacitance of carbon-based electric double layer capacitors by machine learning

Machine learning (ML) methods were applied to predict the capacitance of carbon-based supercapacitors. Hundreds of published experimental datasets are collected for training ML models to identify the relative importance of seven electrode features. This present method could be used to predict and screen better carbon electrode materials.

A coupled multivariate statistics, geostatistical and machine-learning approach to address soil pollution in a prototypical Hg-mining site in a natural reserve

The impact of mining activities on the environment is vast. In this regard, many mines were operating well before the introduction of environmental law. This is particularly true of cinnabar mines, whose activity has declined for decades due to growing public concern regarding Hg high toxicity. Here we present the exemplary case study of an abandoned Hg mine located in the Somiedo Natural Reserve (Spain). Until its closure in the 1970s, this mine operated under no environmental regulations, its tailings dumped in two spoil heaps, one of them located uphill and the other in the surroundings of the village of Caunedo. This study attempts to outline the degree to which soil and other environmental compartments have been affected by the two heaps. To this end, we used a novel combination of multivariate statistical, geostatistical and machine-learning methodologies. The techniques used included principal component and clustering analysis, Bayesian networks, indicator kriging, and sequential Gaussian simulations. Our results revealed high concentrations of Hg and, secondarily, As in soil but not in water or sediments. The innovative methodology abovementioned allowed us to identify natural and anthropogenic associations between 25 elements and to conclude that soil pollution was attributable mainly to natural weathering of the uphill heap. Moreover, the probability of surpassing the threshold limits and the local backgrounds was found to be high in a large extension of the area. The methodology used herein demonstrated to be effective for addressing complex pollution scenarios and therefore they are applicable to similar cases.

Removal of anthracene in water by MIL-88(Fe), NH2-MIL-88(Fe), and mixed-MIL-88(Fe) metal–organic frameworks

Three adsorbents based on the metal–organic frameworks (MOFs), viz.; MIL-88(Fe), NH₂-MIL-88(Fe), and mixed-MIL-88(Fe) were synthesized using a microwave-assisted solvothermal technique.

Biological and analytical techniques used for detection of polyaromatic hydrocarbons

Polycyclic aromatic hydrocarbons contain two or more fused benzene rings that are considered as cosmo-pollutants ubiquitously found in the environment. The identification and monitoring of polycyclic aromatic hydrocarbons (PAHs) are of great interests for rapid and on-site detection. Therefore, many analytical and biological techniques have been proposed for the qualitative and quantitative assessments of PAHs. Non-biological analytical techniques such as infrared, Raman, and fluorescence spectroscopies are commonly exploited as non-destructive techniques while gas chromatography (GC) and high-performance liquid chromatography (HPLC) with multiple detectors are extensively employed for the separation and detection of an analyte. Even though spectroscopy and chromatography are more accurate, convenient, and feasible techniques, often, these methods are expensive and sophisticated which require high maintenance cost. On the other hand, biological approaches, i.e., immunoassay, PCR, and microarray, offer comprehensive high-throughput specificity and sensitivity for a similar analyte. Biosensor- and immunoassay-mediated detections of PAHs have opened up new avenues in terms of low cost, rapid determination, and higher sensitivity. In this review, we have discussed the strengths and limitations of biological and analytical techniques that were explored for precise evaluation and were trusted at both the legislation and research levels.

Characterisation and risk assessment of polycyclic aromatic hydrocarbons (PAHs) in soils and plants around e-waste dismantling sites in southern China

Environmental pollution due to primitive e-waste dismantling activities has been intensively investigated over the last decade in the south-eastern coastal region of China. In the present study, we investigated the distribution and composition of polycyclic aromatic hydrocarbons (PAHs) in soils and plants around e-waste recycling sites in Longtang, Guangdong province, South China. The results indicated that PAH concentrations in rhizosphere soil and non-rhizosphere soil were in the range of 133 to 626 ng/g and 60 to 816 ng/g, respectively, while PAH levels in plant tissue were 96 to 388 ng/g in shoots and 143 to 605 ng/g in roots. PAHs were enriched in rhizosphere soils in comparison with non-rhizosphere soils. The concentrations of PAHs in plant tissues varied greatly among plant cultivars, indicating that the uptake of PAHs by plants is species-dependent. Different profiles of PAHs in the soil and the corresponding plant tissue implied that PAH uptake and translocation by plants were selective.The total daily intakes of PAHs and carcinogenic PAHs through vegetables at the e-waste recycling site were estimated to be 99 and 22 ng/kg/day, respectively, suggesting that potential health risks associated with the consumption of contaminated vegetables should not be ignored.

Insights into asphaltene aggregation in the Na-montmorillonite interlayer

This study aimed to provide insights into the diffusion and aggregation of asphaltenes in the Na-montmorillonite (MMT) interlayer with different water saturation, salinity, interlayer space and humic substances. The molecular configuration, density profile, diffusion coefficient and aggregation intensity were determined by molecular dynamic simulation, while the 3D topography and particle size of the aggregates were characterized by atomic force microscopy. Results indicated that the diffusivity of asphaltenes was up to 5-fold higher in the MMT interlayer filled with fresh water than with saline water (salinity: 35‰). However, salinity had little impact on the asphaltene aggregation. This study also showed a marked decrease in the mobility of asphaltenes with decrease in the pore water content and the interlayer space of MMT. This was more pronounced in the organo-MMT where the humic substances were present. The co-aggregation process resulted in the sequestration of asphaltenes in the hollow cone-shaped cavity of humic substances in the MMT interlayer, which decreased the asphaltene diffusion by up to one-order of magnitude and increased the asphaltene aggregation by about 33%. These findings have important ramifications for evaluating the fate and transport of heavy fractions of the residual oil in the contaminated soils.