QSPR models for predicting the adsorption capacity for microplastics of polyethylene, polypropylene and polystyrene

Interaction between antibiotics and microplastics: Recent advances and perspective

Both microplastics and antibiotics are emerging pollutants, which are ubiquitous in aquatic environments. With small size, high specific surface area, and attached biofilm, microplastics are capable of adsorbing or biodegrading antibiotic pollutants across aquatic environments. However, the interactions between them are poorly understood, especially factors that affect microplastics' chemical vector effects and the mechanisms driving these interactions. In this review, the properties of microplastics and their interaction behavior and mechanisms towards antibiotics were comprehensively summarized. Particularly, the impact of weathering properties of microplastics and the growth of attached biofilm was highlighted. We concluded that compared with virgin microplastics, aged microplastics usually adsorb more types and quantities of antibiotics from aquatic environments, whilst the attached biofilm could further enhance the adsorption capacities and biodegrade some antibiotics. This review can answer the knowledge gaps of the interaction between microplastics and antibiotics (or other pollutants), offer basic information for evaluating their combined toxicity, provide insights into the distribution of both emerging pollutants in the global water chemical cycle, and inform measures to remove microplastic-antibiotic pollution.

Predicting adsorption capacity of pharmaceuticals and personal care products on long-term aged microplastics using machine learning

We investigated the adsorption mechanism of 66 coexisting pharmaceuticals and personal care products (PPCPs) on microplastics treated with potassium persulfate, potassium hydroxide, and Fenton reagent for 54, 110, and 500 days. The total adsorption capacity (q_e) of 66 PPCPs on 15 original microplastics was 171.8 - 1043.7 μg/g, far below that of 177 long-term aged microplastics (7114.0 - 13,114.4 μg/g). Around 69.8% of q_e was primarily influenced by the total energy, energy of the highest occupied molecular orbital, and energy gap of PPCPs, calculated using the B3LYP/6-31 G* level. Furthermore, 111 aged microplastics exhibited similar total q_e values. Additionally, we developed predictive models based on attenuated total reflectance Fourier transform infrared spectroscopy to predict the individual and total q_e on 192 microplastics. These models, including the maximal information coefficient and gradient boosting decision tree regression, exhibited high accuracy with R_training² values of 0.9772 and 0.9661, respectively, and p-values below 0.001. Spectroscopic analysis and machine learning models highlighted surface functional group alterations and the importance of the 1528-1700 cm^-1 spectral region and carbon skeleton in the adsorption process. In summary, our findings contribute to understanding the adsorption of PPCPs on microplastics, particularly in the context of long-term aging effects.

Machine Learning to Predict the Adsorption Capacity of Microplastics

Nowadays, there is an extensive production and use of plastic materials for different industrial activities. These plastics, either from their primary production sources or through their own degradation processes, can contaminate ecosystems with micro- and nanoplastics. Once in the aquatic environment, these microplastics can be the basis for the adsorption of chemical pollutants, favoring that these chemical pollutants disperse more quickly in the environment and can affect living beings. Due to the lack of information on adsorption, three machine learning models (random forest, support vector machine, and artificial neural network) were developed to predict different microplastic/water partition coefficients (log K_d) using two different approximations (based on the number of input variables). The best-selected machine learning models present, in general, correlation coefficients above 0.92 in the query phase, which indicates that these types of models could be used for the rapid estimation of the absorption of organic contaminants on microplastics.

The impact of PET microplastic fibres on PVDF ultrafiltration performance - A short-term assessment of MP fouling in simple and complex matrices

Wastewater treatment plants (WWTPs) are key components for the capture of microplastics (MPs) before they are released into natural waterways. Removal efficiencies as high as 99% may be achieved but sub-micron MPs as well as nanoplastics have been overlooked because of analytical limitations. Furthermore, short MP fibres are of concern because of their low capture rate as well as the lack of understanding of their influence on purification system efficiency. This study has investigated the impact of poly(ethylene terephthalate) (PET) short nanofibres on the performance of polyvinylidene fluoride (PVDF) ultrafiltration membranes during cross-flow operation. Model MP fibres with an average length of 10 ± 7 μm and a diameter of 142 ± 40 nm were prepared via a combination of electrospinning and fine cutting using a cryomicrotome. The manufactured MPs were added to both pure and synthetic domestic wastewater at a concentration of 1 mg.L^-1 to determine their impact on the performance of PVDF ultrafiltration membranes. The results show that PET fibres attach to the membrane in a disorganised manner with low pore coverage. The water flux was decreased by 8% for MPs in pure water and no noticeable effect in wastewater after 3 days of filtration. Additionally, the nutrient removal efficiency of the membrane was not altered by the presence of PET MPs. These findings show that MP fibres do not significantly influence the early stages of filtration for a standard concentration of MPs in wastewater treatment plant studies.

Versatile in silico modelling of microplastics adsorption capacity in aqueous environment based on molecular descriptor and machine learning

To comprehensively evaluate the hazards of microplastics and their coexisting organic pollutants, the sorption capacity of microplastics is a major issue that is quantified through the microplastic-aqueous sorption coefficient (K_d). Almost all quantitative structure-property relationship (QSPR) models that describe K_d apply only to narrow, relatively homogeneous groups of reactants. Herein, non-hybrid QSPR-based models were developed to predict PE-water (K_PE-w), PE-seawater (K_PE-sw), PVC-water (K_PVC-w) and PP-seawater (K_PP-sw) sorption coefficients at different temperatures, with eight machine learning algorithms. Moreover, novel hybrid intelligent models for predicting K_d more accurately were innovatively developed by applying GA, PSO and AdaBoost algorithms to optimize MLP and ELM models. The results indicated that all three optimization algorithms could improve the robustness and predictability of the standalone MLP and ELM models. In all models trained with K_PE-w, K_PE-sw, K_PVC-w and K_PP-sw data sets, GBDT-1 and XGBoost-1 models, MLP-GA-2 and MLP-PSO-2 models, MLR-3 and MLR-4 models performed better in terms of goodness of fit (R_adj²: 0.907-0.999), robustness (Q_BOOT²: 0.900-0.937) and predictability (R_ext²: 0.889-0.970), respectively. Analyzing the descriptors revealed that temperature, lipophilicity, ionization potential and molecular size were correlated closely with the adsorption capacity of microplastics to organic pollutants. The proposed QSPR models may assist in initial environmental exposure assessments without imposing heavy costs in the early experimental phase.

Predictive Models of Gas/Particulate Partition Coefficients (KP) for Polycyclic Aromatic Hydrocarbons and Their Oxygen/Nitrogen Derivatives

Polycyclic aromatic hydrocarbons (PAHs) and their oxygen/nitrogen derivatives released into the atmosphere can alternate between a gas phase and a particulate phase, further affecting their environmental behavior and fate. The gas/particulate partition coefficient (KP) is generally used to characterize such partitioning equilibrium. In this study, the correlation between log KP of fifty PAH derivatives and their n-octanol/air partition coefficient (log KOA) was first analyzed, yielding a strong linear correlation (R2 = 0.801). Then, Gaussian 09 software was used to calculate quantum chemical descriptors of all chemicals at M062X/6-311+G (d,p) level. Both stepwise multiple linear regression (MLR) and support vector machine (SVM) methods were used to develop the quantitative structure-property relationship (QSPR) prediction models of log KP. They yield better statistical performance (R2 > 0.847, RMSE < 0.584) than the log KOA model. Simulation external validation and cross validation were further used to characterize the fitting performance, predictive ability, and robustness of the models. The mechanism analysis shows intermolecular dispersion interaction and hydrogen bonding as the main factors to dominate the distribution of PAH derivatives between the gas phase and particulate phase. The developed models can be used to predict log KP values of other PAH derivatives in the application domain, providing basic data for their ecological risk assessment.

Prediction study on the distribution of polycyclic aromatic hydrocarbons and their halogenated derivatives in the atmospheric particulate phase

Polycyclic aromatic hydrocarbons (PAHs) and their halogenated derivatives (X-PAHs), which generally produced from photochemical and thermal reactions of parent PAHs, widely exist in the environment. They are semi-volatile organic chemicals (SVOCs) and the partitioning between gas/particulate phases affects their environmental migration, transformation and fate, which further impacts their toxicity and health risk to human. However, there is a large data missing of the experimental distribution ratio in the atmospheric particulate phase (f), especially for X-PAHs. In this study, we first checked the correlation between experimental f values of 53 PAH derivatives and their octanol-air partitioning coefficients (log K_OA), which is frequently used to characterize the distribution of chemicals in organic phase, and yielded R² = 0.803. Then, quantum chemical descriptors derived from molecular structural optimization by M06-2X/6-311 +G (d,p) method were further employed to develop Quantitative Structure-Property Relationship (QSPR) model. The model contains two descriptors, the average molecular polarizability (α) and the equilibrium parameter of molecular electrostatic potential (τ), and yields better performance with R² = 0.846 and RMSE = 0.122. The mechanism analysis and validation results by different strategies prove that the model can reveal the molecular properties that dominate the distribution between gas and particulate phases and it can be used to predict f values of other PAHs/X-PAHs, providing basic data for their environmental ecological risk assessment.

Ecotoxic effects of microplastics and contaminated microplastics - Emerging evidence and perspective

The high prevalence and persistence of microplastics (MPs) in pristine habitats along with their accumulation across environmental compartments globally, has become a matter of grave concern. The resilience conferred to MPs using the material engineering approaches for outperforming other materials has become key to the challenge that they now represent. The characteristics that make MPs hazardous are their micro to nano scale dimensions, surface varied wettability and often hydrophobicity, leading to non-biodegradability. In addition, MPs exhibit a strong tendency to bind to other contaminants along with the ability to sustain extreme chemical conditions thus increasing their residence time in the environment. Adsorption of these co-contaminants leads to modification in toxicity varying from additive, synergistic, and sometimes antagonistic, having consequences on flora, fauna, and ultimately the end of the food chain, human health. The resulting environmental fate and associated risks of MPs, therefore greatly depend upon their complex interactions with the co-contaminants and the nature of the environment in which they reside. Net outcomes of such complex interactions vary with core characteristics of MPs, the properties of co-contaminants and the abiotic factors, and are required to be better understood to minimize the inherent risks. Toxicity assays addressing these concerns should be ecologically relevant, assessing the impacts at different levels of biological organization to develop an environmental perspective. This review analyzed and evaluated 171 studies to present research status on MP toxicity. This analysis supported the identification and development of research gaps and recommended priority areas of research, accounting for disproportionate risks faced by different countries. An ecological perspective is also developed on the environmental toxicity of contaminated MPs in the light of multi-variant stressors and directions are provided to conduct an ecologically relevant risk assessment. The presented analyses will also serve as a foundation for developing environmentally appropriate remediation methods and evaluation frameworks.

Current status of microplastics pollution in the aquatic environment, interaction with other pollutants, and effects on aquatic organisms

Microplastics, as emerging pollutants, have received great attention in the past few decades due to its adverse effects on the environment. Microplastics are ubiquitous in the atmosphere, soil, and water bodies, and mostly reported in aqueous environment. This paper summarizes the abundance and types of microplastics in different aqueous environments and discusses the interactions of microplastics with other contaminants such as polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), antibiotics, and heavy metals. The toxicity of microplastics to aquatic organisms and microorganisms is addressed. Particularly, the combined toxic effects of microplastics and other pollutants are discussed, demonstrating either synergetic or antagonistic effects. Future prospectives should be focused on the characterization of different types and shapes of microplastics, the standardization of microplastic units, exploring the interaction and toxicity of microplastics with other pollutants, and the degradation of microplastics, for a better understanding of the ecological risks of microplastics.

Enhanced propagation of intracellular and extracellular antibiotic resistance genes in municipal wastewater by microplastics

Microplastics (MPs) are an emerging global concern as they are abundant in the environment and can act as vectors of various contaminants. However, whether and how MPs can be vectors of antibiotic resistance genes (ARGs), especially extracellular ARGs (eARGs), remains far from explicit. This study addresses the adsorption of both intracellular ARGs (iARGs) and eARGs by four types of MPs in municipal wastewater, and then explores the potential horizontal gene transfer of iARGs and eARGs exposed to MPs. Results indicate that though MPs significantly adsorbed both iARGs and eARGs, eARGs were adsorbed with a significantly higher fold enrichment (2.0-5.0 log versus 2.0-3.3 log) and rate (0.0056 min^-1 versus 0.0037 min^-1) than iARGs. While all four types of MPs adsorbed ARGs, polypropylene MPs showed the highest adsorption capacity for ARGs. Background constituents such as humic acid and antibiotics significantly inhibited adsorption of iARGs, but not eARGs on MPs. The presence of sodium chloride didn't significantly affect adsorption of iARGs or eARGs. The adsorption of ARGs was well explained by the extended Derjaguin-Landau-Verwey-Overbeek (XDLVO) interaction energy profile. Higher eARG adsorption was attributed to a lower energy barrier between MPs and eARGs than that between MPs and iARGs. Exposure to MPs enhanced horizontal gene transfer of both iARGs and eARGs by 1.5 and 2.0 times, respectively. The improved contact potential between donors and recipients, as well as the increased cell permeability of recipients induced the improved horizontal gene transfer by MPs. This study underscores the need to address ARG propagation through adsorption to MPs.

Are microplastics destabilizing the global network of terrestrial and aquatic ecosystem services?

Plastic has created a new man-made ecosystem called plastisphere. The plastic pieces including microplastics (MPs) and nanoplastics (NPs) have emerged as a global concern due to their omnipresence in ecosystems and their ability to interact with the biological systems. Nevertheless, the long-term impacts of MPs on biotic and abiotic resources are not completely understood, and existing evidence suggests that MPs are hazardous to various keystones species of the global biomes. MP-contaminated ecosystems show reduced floral and faunal biomass, productivity, nitrogen cycling, oxygen-generation and carbon sequestration, suggesting that MPs have already started affecting ecological biomes. However, not much is known about the influence of MPs towards the ecosystem services (ESs) cascade and its correlation with the biodiversity loss. MPs are perceived as a menace to the global ecosystems, but their possible impacts on the provisional, regulatory, and socio-economic ESs have not been extensively studied. This review investigates not only the potentiality of MPs to perturb the functioning of terrestrial and aquatic biomes, but also the associated social, ecological and economic repercussions. The possible long-term fluxes in the ES network of terrestrial and aquatic niches are also discussed.

Prediction of organic compounds adsorbed by polyethylene and chlorinated polyethylene microplastics in freshwater using QSAR

Microplastics (MPs), a growing class of emerging pollutants in the environment, have attracted widespread attention due to their adsorption properties. Recent research on MPs has mainly concentrated on seawater, and little work has been conducted on freshwater. Investigating and predicting the adsorption behavior of organic pollutants by MPs are necessary in freshwater. In this study, the adsorption behavior of 13 organic chemicals by polyethylene (PE) and chlorinated polyethylene (CPE) MPs was determined under freshwater conditions. Results shows the majority of the organic chemicals exhibit no distinctive differences in their adsorption on two MPs. However, the adsorption of polycyclic aromatic hydrocarbons and chlorobenzene on CPE is obviously stronger than that on PE, and the result is a counter for two pesticides. Quantitative structure activity relationship (QSAR) analysis was performed for the prediction of adsorption capacity. A QSAR model with acceptable performance (R² = 0.8586) was built to predict the adsorptive affinity (expressed as logK_d) of organic compounds on the PE MPs via multivariable linear regression (MLR) on forty-nine determined and collected data. The octanol/water partition coefficient (logK_ow) and excess molar refractive index (E) play dominant roles in the model. A QSAR model with satisfactory performance (R² = 0.9302) was also established for logK_d values from CPE MPs in freshwater by using 13 adsorption data determined. The logK_ow and most negative charge on Cl atom (Q^-_max,cl) play decisive roles in the adsorption. The findings can provide a scientific basis for the risk assessment of waters contaminated by MPs and organic pollutants.

Polarizability and aromaticity index govern AhR-mediated potencies of PAHs: A QSAR with consideration of freely dissolved concentrations

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous environmental pollutants associated with adverse human effects including cancer, and the aryl hydrocarbon receptor (AhR) is a key ligand-activated transcription factor mediating their toxicity. However, there is presently a lack of data on AhR potencies of PAHs. Simple, transparent, interpretable and predictive quantitative structure-activity relationship (QSAR) models are helpful, especially with the consideration of freely dissolved concentrations linked to bioavailability. Here, QSAR models on AhR-mediated luciferase activity of PAHs were developed with nominal median effect concentrations (EC₅₀, _nom) and freely dissolved concentration (EC_{50, free}) as endpoints, and quantum chemical and Dragon descriptors as predictor variables. Results indicated that only the EC_{50, free} model met the acceptable criteria of QSAR model (determination coefficient (R²) > 0.600, leave-one-out cross validation (Q_LOO²) > 0.500, and external validation coefficient (Q_EXT²) > 0.500), implying that it has good goodness-of-fit, robustness and external predictive power. Molecular polarizability and aromaticity index reflecting the partition behavior and intermolecular interactions can effectively predict AhR-mediated potencies of PAHs. The results highlight the necessity of adoption of the freely dissolved concentration in the QSAR modeling and more in silico models need to be further developed for different animal models (in vivo or in vitro).