Micro- and nanoplastic contamination in livestock production: Entry pathways, potential effects and analytical challenges

In-situ detection of microplastics in the aquatic environment: A systematic literature review

Microplastics are ubiquitous in the aquatic environment and have emerged as a significant environmental issue due to their potential impacts on human health and the ecosystem. Current laboratory-based microplastic detection methods suffer from various drawbacks, including a lack of standardisation, limited spatial and temporal coverage, high costs, and time-consuming procedures. Consequently, there is a need for the development of in-situ techniques to detect and monitor microplastics to effectively identify and understand their sources, pathways, and behaviours. Herein, we adopt a systematic literature review method to assess the development and application of experimental and field technologies designed for the in-situ detection and monitoring of aquatic microplastics, without the need for sample preparation. Four scientific databases were searched in March 2023, resulting in a review of 62 relevant studies. These studies were classified into seven sensor categories and their working principles were discussed. The sensor classes include optical devices, digital holography, Raman spectroscopy, other spectroscopy, hyperspectral imaging, remote sensing, and other methods. We also looked at how data from these technologies are integrated with machine learning models to develop classifiers capable of accurately characterising the physical and chemical properties of microplastics and discriminating them from other particles. This review concluded that in-situ detection of microplastics in aquatic environments is feasible and can be achieved with high accuracy, even though the methods are still in the early stages of development. Nonetheless, further research is still needed to enhance the in-situ detection of microplastics. This includes exploring the possibility of combining various detection methods and developing robust machine-learning classifiers. Additionally, there is a recommendation for in-situ implementation of the reviewed methods to assess their effectiveness in detecting microplastics and identify their limitations.

Plastisphere-hosted viruses: A review of interactions, behavior, and effects

Microbial communities, including bacteria, diatoms, and fungi, colonize plastic surfaces, forming biofilms known as the "plastisphere." Recent research has revealed that plastispheres also host a wide range of viruses, sparking interest in microbial ecology and virology. This shared habitat allows viruses to replicate, interact, infect, and spread, potentially impacting the environment and human health. Consequently, viruses attached to microplastics are now recognized to have broad effects on cellular and immune responses. However, the ecology and implications of viruses hosted in plastisphere habitats remain poorly understood, highlighting their fundamental importance as a subject of study. This review explores various pathways for virus attachment to plastispheres, factors influencing these interactions, their impacts within plastisphere and host-associated environments, and associated issues. It also summarizes current research and identifies knowledge gaps. We anticipate that this paper will help improve our predictive understanding of plastisphere viruses in natural settings and emphasizes the need for more research in real-world environments to advance the field.

Applicability of machine learning techniques to analyze Microplastic transportation in open channels with different hydro-environmental factors

This research utilized machine learning to analyze experiments conducted in an open channel laboratory setting to predict microplastic transport with varying discharge, velocity, water depth, vegetation pattern, and microplastic density. Four machine learning (ML) models, incorporating Random Forest (RF), Decision Tree (DT), Extreme Gradient Boost (XGB) and K-Nearest Neighbor (KNN) algorithms, were developed and compared with the Linear Regression (LR) statistical model, using 75% of the data for training and 25% for validation. The predictions of ML algorithms were more accurate than the LR, while XGB and RF provided the best predictions. To explain the ML results, Explainable artificial intelligence (XAI) was employed by using Shapley Additive Explanations (SHAP) to predict the global behavior of variables. RF was the most reliable model, with a coefficient of correlation of 0.97 and a mean absolute percentage error of 1.8% after hyperparameter tuning. Results indicated that discharge, velocity, water depth, and vegetation all influenced microplastic transport. Discharge and vegetation enhanced and reduced microplastic transport, respectively, and showed a response to different vegetation patterns. A strong linear positive correlation (R2 = 0.8) was noted between microplastic density and retention. In the absence of dedicated microplastic transport analytical models and infeasibility of using classical sediment transport models in predicting microplastic transport, ML proved to be helpful. Moreover, the use of XAI will reduce the black-box nature of ML models with effective interpretation enhancing the trust of domain experts in ML predictions. The developed model offers a promising tool for real-world open channel predictions, informing effective management strategies to mitigate microplastic pollution.

Sampling of microplastics at a materials recovery facility

Detecting, separating, and characterizing airborne microplastics from other airborne particulates is currently challenging due to the various instrumental constraints and related sample preparation hurdles that must be overcome. The ability to measure these real-world environments is needed to better assess the risks associated with microplastics. To that end, the current study focused on developing a methodology for sampling and characterizing airborne microplastics. Particulate sampling was carried out at a municipal materials recovery facility near a conveyer belt containing sorted plastic materials to collect airborne environmental particles on filters. Nucleopore filters were mounted on Teflon support rings, coated with 100 nm aluminum to reduce the background signal for micro-Raman spectroscopy, and marked with a fiducial pattern using a laser engraver. The fiducial pattern was crucial in identifying samples, relocating particles, and efficiently enabling orthogonal measurements on the same samples. Optimum sampling conditions of 2 h at 25 L/min were determined using light microscopy to evaluate the particle loadings. The filters were then cut into slices which were attached to sections of thin beryllium-copper sheeting for easy transfer of the filter between microscopy platforms. Scanning electron microscopy was used to identify carbon-rich particles. Light microscopy was used to identify colored particles which were also carbon-rich which were then analyzed using micro-Raman spectroscopy to identify specific polymers.

Smart carbon-based sensors for the detection of non-coding RNAs associated with exposure to micro(nano)plastics: an artificial intelligence perspective

Micro(nano)plastics (MNPs) are pervasive environmental pollutants that individuals eventually consume. Despite this, little is known about MNP's impact on public health. In this article, we assess the evidence for potentially harmful consequences of MNPs in the human body, concentrating on molecular toxicity and exposure routes. Since MNPs are present in various consumer products, foodstuffs, and the air we breathe, exposure can occur through ingestion, inhalation, and skin contact. MNPs exposure can cause mitochondrial oxidative stress, inflammatory lesions, and epigenetic modifications, releasing specific non-coding RNAs in circulation, which can be detected to diagnose non-communicable diseases. This article examines the most fascinating smart carbon-based nanobiosensors for detecting circulating non-coding RNAs (lncRNAs and microRNAs). Carbon-based smart nanomaterials offer many advantages over traditional methods, such as ease of use, sensitivity, specificity, and efficiency, for capturing non-coding RNAs. In particular, the synthetic methods, conjugation chemistries, doping, and in silico approach for the characterization of synthesized carbon nanodots and their adaptability to identify and measure non-coding RNAs associated with MNPs exposure is discussed. Furthermore, the article provides insights into the use of artificial intelligence tools for designing smart carbon nanomaterials.

Are microplastics in livestock and poultry manure an emerging threat to agricultural soil safety?

Microplastics (MPs) have attracted much attention in recent years, due to the difficulty of degradation and threats to ecological systems and humans. Based on the analysis of 1429 articles on MPs in soil, we found that we know little about the behavior and fate of manure-born MPs from the livestock and poultry production systems to agriculture soils. This review summarizes the analytical methods for sampling, separation, and identification and the occurrence of MPs in livestock and poultry manure, mainly based on 7 surveys related to manure-born MPs. Then, the sources, fate, and environmental risks of MPs in livestock and poultry manure are discussed. MPs, heavy metals, pathogens, antibiotic resistance genes, and persistent organic pollutants are common pollutants in livestock and poultry manure. Worse, manure-born MPs will become smaller, rougher, and more numerous and could easily form more toxic compound pollution after complicated processes of manure treatment, which seriously threatens agricultural soil safety. Finally, an outlook is offered for future research. We hope this article to attract attention to the risks of MPs in livestock and poultry manure and provide a reference for future research.

Advances on micro/nanoplastics and their effects on the living organisms: A review

Micro/nanoplastics (MPs) are attracting increasing attention owing to the potential threats they pose to the sustainability of the environment and the health of living organisms. Thus, a comprehensive understanding of the influence of MPs on living organisms is vital for developing countermeasures. We conducted an extensive literature search to retrieve the articles related to MPs via the Web of Science. Accordingly, 152 articles published in the last decade and in influential journals were selected to analyze the effects of MPs on plants, animals, microorganisms, and humans as well as the current status, hotspots, and trends of studies on MPs. The results showed that owing to the special characteristics of MPs and anthropogenic activities, MPs have become ubiquitous worldwide. MPs are ingested by plants and animals and enter the human body through various pathways, resulting in numerous adverse effects, such as growth inhibition, oxidative stress, inflammation, organ damage, and germ cell lesions. Moreover, they affect microorganisms by reshaping the structure and function of microbial communities and changing the spread pathway. However, microorganisms can also contribute to the degradation of MPs. With increasing evidence of the adverse effects of MPs on biota, coping with MP pollution and mitigating harmful outcomes have emerged as major challenges. This review focuses on (1) the main effects of MPs on living organisms, ranging from microorganisms to humans, (2) the current status and hotspots of studies related to MPs, and (3) the challenges and prospects of further studies on MPs.

Proteolysis and therapeutic potential of bioactive peptides derived from Cheddar cheese

Cheddar cheese-derived bioactive peptides are considered a potential component of functional foods. A positive impact of bioactive peptides on diet-related chronic, non-communicable diseases, like obesity, cardiovascular diseases, and diabetes, has been observed. Bioactive peptides possess multifunctional therapeutic potentials, including antimicrobial, immunomodulatory, antioxidant, enzyme inhibitory effects, anti-thrombotic, and phyto-pathological activities against various toxic compounds. Peptides can regulate human immune, gastrointestinal, hormonal, and neurological responses, which play an integral role in the deterrence and treatment of certain diseases like cancer, osteoporosis, hypertension, and other health disorders, as described in the present review. This review summarizes the categories of the Cheddar cheese-derived bioactive peptides, their general characteristics, physiological functions, and possible applications in healthcare.

Effects of microplastics, pesticides and nano-materials on fish health, oxidative stress and antioxidant defense mechanism

Microplastics and pesticides are emerging contaminants in the marine biota, which cause many harmful effects on aquatic organisms, especially on fish. Fish is a staple and affordable food source, rich in animal protein, along with various vitamins, essential amino acids, and minerals. Exposure of fish to microplastics, pesticides, and various nanoparticles generates ROS and induces oxidative stress, inflammation, immunotoxicity, genotoxicity, and DNA damage and alters gut microbiota, thus reducing the growth and quality of fish. Changes in fish behavioral patterns, swimming, and feeding habits were also observed under exposures to the above contaminants. These contaminants also affect the Nrf-2, JNK, ERK, NF-κB, and MAPK signaling pathways. And Nrf2-KEAP1 signalling modulates redox status marinating enzymes in fish. Effects of pesticides, microplastics, and nanoparticles found to modulate many antioxidant enzymes, including superoxide dismutase, catalase, and glutathione system. So, to protect fish health from stress, the contribution of nano-technology or nano-formulations was researched. A decrease in fish nutritional quality and population significantly impacts on the human diet, influencing traditions and economics worldwide. On the other hand, traces of microplastics and pesticides in the habitat water can enter humans by consuming contaminated fish which may result in serious health hazards. This review summarizes the oxidative stress caused due to microplastics, pesticides and nano-particle contamination or exposure in fish habitat water and their impact on human health. As a rescue mechanism, the use of nano-technology in the management of fish health and disease was discussed.

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.

High concentration of ammonia sensitizes the response of microbial electrolysis cells to tetracycline

Microbial electrolysis cell (MEC) is a promising technology for effective energy conversion of wastewater organics to biogas. Yet, in swine wastewater treatment, the complex contaminants including antibiotics may affect MEC performance, while the high ammonia concentration might increase this risk by increasing cell membrane permeability. In this work, the responses of MECs on tetracycline (TC) with low and high ammonia loadings (80 and 1000 mg L^-1) were fully investigated. The TC of 0 to 1 mg L^-1 slightly improved MEC performance in current production and electrochemical characteristics with low ammonia loading, while TC ≥ 4 mg L^-1 started to show negative effects. Generally, the high ammonia loading sensitized MECs to TC concentration, inducing the current and COD removal of MECs to sharply decline with TC ≥ 0.5 mg L^-1. The positive effect of high ammonia loading on MEC due to conductivity increase was counteracted with TC ≥ 1 mg L^-1. The co-contamination of TC and ammonia significantly decreased the bioactivity and biomass of anode biofilm. Although the high concentration of co-existing TC and ammonia inhibited MEC performance, the reactors still obtained positive energy feedback. The network analyses indicated that the effluent suspension contributed much to antibiotic resistance gene (ARG) transmission, while the microplastics (MPs) in wastewater greatly raised the risks of ARGs spreading. This work systematically examined the synergetic effects of TC and ammonia and the transmission of ARGs in MEC operation, which is conducive to expediting the application of MECs in swine wastewater treatment.