We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Papers
61,005 resultsShowing papers similar to Artificial intelligence, evolution, and environmental disease: Rethinking the risk
ClearIntegrative toxicogenomics: Advancing precision medicine and toxicology through artificial intelligence and OMICs technology
Researchers reviewed how artificial intelligence combined with genomics (the study of genes) and multi-omics data is advancing personalized medicine and toxicology, enabling faster, more accurate predictions of how individuals will respond to drugs or toxic exposures. These tools could eventually help assess risks from environmental contaminants like microplastics based on a person's unique genetic makeup.
Microplastics in ecosystems: ecotoxicological threats and strategies for mitigation and governance
This review provides a broad assessment of microplastic pollution across ecosystems, covering sources, detection methods, ecological impacts, and cleanup strategies. The study highlights recent advances including AI-enhanced detection tools and microbe-based degradation approaches, and proposes a roadmap for working toward microplastic-free environments through coordinated scientific and policy action.
Ecotoxicological impacts of landfill sites: Towards risk assessment, mitigation policies and the role of artificial intelligence
This review examines the health and environmental risks posed by landfill sites, which act as reservoirs for both legacy and emerging pollutants including microplastics. Unregulated waste disposal and leachate contamination are linked to diseases in nearby communities, and laboratory studies show toxic effects on organisms from bacteria to birds. The authors recommend improving landfill design, leachate treatment, and exploring artificial intelligence to better predict and manage these pollution risks.
¿La IA usada en biología de la conservación es una buena estrategia de justicia ambiental?
This paper critically examines whether artificial intelligence applications in conservation biology serve environmental justice goals. It raises concerns that AI tools may reinforce existing power imbalances and overlook local community knowledge in conservation decisions.
Editorial: Bridging the Gap between Policy and Science in Assessing the Health Status of Marine Ecosystems
This editorial introduces a research collection focused on bridging the gap between policy and science in assessing health impacts of environmental contaminants including microplastics. It highlights the need for better integration of scientific evidence into regulatory and public health decision-making frameworks.
A Critical Review on Artificial Intelligence—Based Microplastics Imaging Technology: Recent Advances, Hot-Spots and Challenges
Researchers reviewed the use of artificial intelligence and machine learning techniques for detecting and identifying microplastics in environmental samples. The study found that AI-based imaging tools can significantly speed up analysis and improve accuracy compared to traditional manual methods. However, challenges remain around standardizing datasets and making these tools accessible for routine environmental monitoring.
Integrating artificial intelligence with microbial biotechnology for sustainable environmental remediation
This review examines how artificial intelligence is being combined with microbial biotechnology to improve the detection and breakdown of persistent environmental pollutants including microplastics. Researchers found that AI models achieve over 90 percent accuracy in classifying microplastics and have helped design enzymes that degrade PET plastic up to 46 times faster than conventional approaches. The integration of AI with biotechnology represents a significant advance in developing sustainable pollution remediation strategies.
Advancing environmental sustainability through emerging AI-based monitoring and mitigation strategies for microplastic pollution in aquatic ecosystems
This review explores how artificial intelligence technologies, including machine learning, computer vision, and remote sensing, can improve the detection, tracking, and removal of microplastic pollution in waterways. Researchers found that AI-based approaches offer significant advantages over traditional monitoring methods for identifying microplastic distribution patterns. The study highlights the potential of AI-driven robotic systems to support more efficient and scalable environmental cleanup efforts.
Artificial intelligence for modeling and reducing microplastic in marine environments: A review of current evidence
This review examines how artificial intelligence is being applied to address marine microplastic pollution, including modeling accumulation zones, developing real-time detection systems using remote sensing and robotics, and creating AI-based filtration technologies. The study suggests that while AI holds significant promise for predicting microplastic flows and supporting targeted cleanup efforts, challenges remain around data availability, model refinement, and international collaboration.
Editorial: Microbial Ecotoxicology Advances to Improve Environmental and Human Health Under Global Change
This editorial introduces a special journal issue on microbial ecotoxicology, highlighting how microorganisms are affected by environmental contaminants including microplastics, pharmaceuticals, and other emerging pollutants. Understanding microbial responses to pollution is critical for assessing broader ecosystem and human health risks.
Learning from Nature to Achieve Material Sustainability: Generative AI for Rigorous Bio-inspired Materials Design
This review explores how artificial intelligence can help design sustainable bio-inspired materials that could replace conventional plastics. By learning from nature's degradable materials, AI tools could help develop alternatives that do not persist in the environment as microplastics. While not a direct health study, this research addresses a root cause of microplastic pollution by working toward materials that break down safely in natural ecosystems.
Artificial Intelligence as an Aid: Regulating Plastic and Microplastic Pollution
Researchers reviewed how India is tackling plastic and microplastic pollution through legislation and cleanup campaigns, while also examining how artificial intelligence tools could improve monitoring, detection, and regulation of plastic waste. The article argues that AI integration into environmental policy could significantly accelerate progress against this global health and ecological crisis.
One Health
This editorial introduces a journal issue focused on the One Health framework, which recognizes the interconnection between human, animal, and ecosystem health, and highlights how environmental pollutants including microplastics are increasingly central to One Health concerns.
Artificial Intelligence – Source of Inspiration or a Problem?
Not relevant to microplastics — this paper reviews the history and challenges of defining artificial intelligence as a field of computer science.
Editorial: Emerging contaminants and aquatic ecosystem health
This editorial introduces a research collection on emerging contaminants and aquatic ecosystem health, highlighting the vulnerability of aquatic systems to pollutants including microplastics and the ecological and human health implications of contamination.
Role of AI in Microplastic Pollution Detection and management studies
This review assessed how artificial intelligence approaches—including machine learning and deep learning—are being applied to detect, identify, and monitor microplastics in environmental and biological samples. The authors found AI substantially accelerates microplastic characterization workflows but that training data quality and standardization across studies remains a limiting factor.
The Role of Artificial Intelligence in Microplastic Pollution Studies and Management
This review explores how artificial intelligence is transforming microplastic research, from automating detection in microscopy images and spectral analysis to predicting how plastics interact with pollutants and living organisms. AI-powered sensors and real-time monitoring systems are also being integrated into wastewater treatment to reduce microplastic release, making the technology a powerful tool for both understanding and managing plastic pollution.
Using artificial intelligence to rapidly identify microplastics pollution and predict microplastics environmental behaviors
This review summarizes how artificial intelligence and machine learning are being used to identify, track, and predict the environmental behavior of microplastics in soil and water. AI methods can analyze the chemical composition, shape, and distribution of microplastics faster and more accurately than traditional techniques. The technology could help scientists better understand where microplastics accumulate and what risks they pose to ecosystems and human health.
Artificial Intelligence-Driven Environmental Toxicology: Predictive Toxicity Modelling, Forensic Pollution Analysis, and AI-Enabled Public Health Surveillance
This research review shows how artificial intelligence and machine learning can help scientists better predict how environmental pollutants might harm human health, replacing slower traditional testing methods. AI can analyze huge amounts of environmental data to identify pollution sources, predict toxic effects, and track public health threats in real-time. This technology could help protect communities by catching environmental health risks earlier and providing better evidence for legal cases against polluters.
Artificial intelligence in microplastics domain: Current progress, challenges, and sustainable prospects
This critical review assesses how artificial intelligence tools—including machine learning and image recognition—are being applied to detect, characterize, and predict the behavior of microplastics in the environment. AI approaches show promise for overcoming persistent bottlenecks in large-scale microplastic analysis, but the authors highlight challenges around data quality, model interpretability, and standardization that must be addressed for these tools to reach their potential.
Ecotoxicological Impacts of Micro(Nano)plastics in the Environment: Biotic and Abiotic Interactions
This editorial or overview paper addresses the ecotoxicological impacts of micro- and nanoplastics across both biotic (organisms) and abiotic (physical and chemical) dimensions, framing the problem as a multifaceted challenge involving environmental contamination, ecosystem health, and potential human health risks. It underscores the need for integrated approaches across disciplines and stakeholder groups to fully understand and manage plastic pollution. The work contributes a broad conceptual framing for ongoing research into microplastic hazards.
Artificial intelligence in environmental health and public safety: A comprehensive review of USA strategies
This review explores how artificial intelligence is being used in the United States to improve environmental health monitoring and public safety, including pollution tracking and disease surveillance. While not specifically about microplastics, the AI tools discussed, such as real-time sensor networks and predictive models, could be applied to monitoring microplastic contamination in air and water. The review highlights how technology could help identify and reduce human exposure to environmental pollutants including microplastics.
Editorial: Using Ecological Models to Support and Shape Environmental Policy Decisions
This editorial introduces a special journal collection on using ecological models to guide environmental policy, covering a range of marine and coastal ecosystems. Ecological models are increasingly used to predict how pollutants including microplastics move through and affect marine food webs.
Environmental Toxicology and Human Health
This is an editorial introduction to a journal special issue on environmental toxicology and human health. The abstract is too brief to determine specific content, but the broader issue covers how people and animals are exposed to mixtures of environmental contaminants daily. It is not specifically focused on microplastics and appears to be a false positive in this dataset.