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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
Emerging contaminants (ECs) like pharmaceuticals, endocrine disruptors, PFAS, microplastics, and antibiotic resistance genes are increasingly detected in waste and wastewater systems. However, their low concentrations, chemical diversity, and transformation during treatment limit reliable monitoring using conventional target-based analytical methods. Wastewater treatment plants, landfill leachates, and sludge processing units act as critical accumulation and redistribution points for these contaminants, highlighting the need for more sensitive and adaptive monitoring approaches. This review examines recent advances in artificial intelligence (AI) for the detection and analysis of ECs in waste and wastewater matrices. The study focuses on the application of machine learning and deep learning techniques integrated with spectroscopic, chromatographic, sensor-based, imaging, and molecular analytical platforms. AI-driven methods are shown to improve signal extraction from complex datasets, enable non-target and suspect screening, and support pattern recognition and anomaly detection in multivariate environmental data. These capabilities enhance detection sensitivity, reduce analytical time, and allow early identification of contamination trends that are often missed by conventional workflows. Despite these advantages, practical implementation remains constrained by limited high-quality datasets, poor model robustness across sites, lack of standardized validation protocols, and challenges related to model interpretability and regulatory acceptance. This study highlights AI as a powerful analytical support tool that can shift monitoring of emerging contaminants from reactive, compound-specific analyses toward proactive, data-driven surveillance, thereby strengthening environmental risk assessment and management in waste and wastewater systems.
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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.
AI-based wastewater treatment for a circular economy and sustainable management of PFAS, heavy metals, microplastics, and antibiotics
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This review examined how artificial intelligence can be integrated into wastewater treatment systems to improve removal of emerging contaminants including PFAS, heavy metals, microplastics, and antibiotics. The authors conclude that AI-driven optimization offers significant potential for a circular economy approach to water treatment.
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
AI-Guided Adaptive Control in Membrane Bioreactors for Real-Time Micropollutant Removal: Bridging the Explainability and Multi-Contaminant Gap in Intelligent Wastewater Treatment
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Scientists are developing smarter wastewater treatment systems that use AI to better detect and remove harmful contaminants—like pharmaceutical residues, hormone-disrupting chemicals, and microplastics—from water before it's released back into the environment. This matters because these pollutants can end up in our drinking water and food supply, and current treatment plants don't always catch everything; a more adaptable, AI-guided system could close that gap. It's worth noting this is a technical proposal aimed at improving how existing treatment technology works, not a study on direct health outcomes in people.
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