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Advancing the Discovery of Emerging Contaminants: A Leap in Technology and Data
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There are countless chemicals in our environment—like PFAS "forever chemicals," drug residues, and microplastics—that scientists still can't fully detect or track, which means new health risks could be slipping under the radar for years before we catch them. This review argues that combining better lab technology, artificial intelligence, and shared global data could help researchers spot harmful chemicals *before* they become widespread problems, rather than reacting after damage is done. For consumers, this matters because catching risky chemicals earlier—including new plastic-related pollutants—could mean faster warnings and safer products, instead of learning about dangers only after long-term exposure
Emerging contaminants pose substantial threats to ecosystems and public health. While advances in analytical techniques and chemicals assessment have revealed the impacts of contaminants such as per- and polyfluoroalkyl substances, pharmaceutical residues, and microplastics, knowledge of the vast chemical universe remains limited. This Perspective examines the evolving attention toward emerging contaminants and drivers of their discovery. It identifies key challenges, including methodological limitations in detecting diverse chemical classes, fragmented data landscapes, and delays in translating scientific evidence into regulatory action. We highlight how technological and methodological innovations, particularly advanced analytical technologies, artificial intelligence, and future-oriented assessment frameworks, can shift paradigms in identifying and managing emerging contaminants from reactive to predictive. Critical to this transition is the adoption of open and FAIR principles (Findable, Accessible, Interoperable, and Reusable) and harmonized data standards to enable integration of heterogeneous evidence streams. Furthermore, we advocate coupling early warning systems with "Safe and Sustainable by Design" approaches to prevent regrettable substitution and reduce hazards at the source. Coordinated action across academia, regulators, industry, and funders is essential to establish proactive prevention as the norm in emerging contaminant management.
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Artificial intelligence enabled detection of emerging contaminants in waste and wastewater systems
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Our wastewater often contains tiny amounts of hard-to-detect pollutants—like drug residues, hormone-disrupting chemicals, PFAS ("forever chemicals"), microplastics, and antibiotic-resistant bacteria—that regular lab tests can easily miss. This review paper (which summarizes existing research rather than presenting new experiments) explains how artificial intelligence can scan complex wastewater data to catch these contaminants faster and more accurately than older methods, which could help catch pollution problems before they spread into our water and food systems. That said, the technology isn't ready for widespread real-world use y
Artificial intelligence for microplastic pollution monitoring, predictive modeling, and risk assessment: advances, challenges, and future perspectives
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This review paper explains how artificial intelligence is helping scientists detect, track, and analyze microplastics faster and more accurately than older lab methods allow. This matters because better detection tools could speed up efforts to understand how these tiny plastic particles spread through our environment, food, and bodies, ultimately supporting stronger health protections and cleanup strategies.
Artificial intelligence-powered new approach methodologies for assessing combined toxicity of chemical mixtures
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In real life, we're never exposed to just one chemical at a time — we're surrounded by mixtures like microplastics carrying antibiotics and heavy metals, which can interact in ways that are harder to predict and study than single toxins. This review looks at how new AI tools could help scientists better predict how these chemical combinations affect our health, potentially catching dangerous interactions that older, one-chemical-at-a-time testing methods miss. It's not a new experiment, but rather a roadmap summarizing how AI could improve chemical safety testing, while stressing that these tools still need rigorous validation before regulators can fully trust their pred
Emerging Contaminants in Wastewater: Mitigation Approaches for Environmental Management and Future Sustainability
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Researchers synthesized five years of literature on emerging contaminants including microplastics, PFAS, and pharmaceuticals in wastewater, cataloguing detection advances from high-resolution mass spectrometry to AI-assisted sensors, and mapping regulatory gaps that leave most of these compounds unmonitored and untreated at global scale.
Modelling the fate of chemicals of emerging concern in wastewater treatment: From mechanistic models to hybrid data driven approaches
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Everyday chemicals from medicines, cleaning products, and plastics often slip through wastewater treatment plants and end up back in our environment, potentially in our water supply. This review looked at different computer modeling approaches—including newer AI-based methods—that scientists use to predict how well treatment plants remove these pollutants, finding that combining traditional science-based models with machine learning works best. The authors note we still need better tools to track particularly concerning substances like PFAS ("forever chemicals") and microplastics, which matters because understanding how well treatment plants filter these out helps protect the water we drink and use every day.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.