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Artificial Intelligence and Environmental Challenges
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This book pulls together research on how AI can help tackle environmental problems, from predicting solar and wind energy to spotting solar panel malfunctions to simulating how nanoplastics interact with living cells. The bottom line: AI shows real promise, but its usefulness depends heavily on having good-quality data and results that humans can actually understand and trust—especially since decisions about pollution, plastics exposure, and climate ultimately require human judgment, not just algorithms. For everyday health, this matters because better AI tools could eventually improve our ability to track air quality, emissions, and plastic contamination that affect the air we breathe and the
This volume examines where artificial intelligence can provide genuine insight into environmental problems, and at what cost. Across eight chapters, contributors apply machine learning, deep learning, econometric modelling, and computational simulation to a range of pressing challenges: forecasting wind and solar energy output, deploying efficient AI on resource-constrained edge devices, quantifying risk in sustainable finance, detecting faults in photovoltaic installations, analysing air quality and CO₂ emissions data, simulating nanoplastic interactions with biological systems, and modelling urban heat transfer. A recurring theme is the critical importance of data quality — sparse, biased, or poorly curated datasets remain a fundamental obstacle to trustworthy modelling. The volume equally emphasises interpretability, recognising that environmental decision-making is ultimately a human and political process. Taken together, the chapters offer an honest, domain-grounded assessment of the current capabilities and limitations of AI as a tool for addressing environmental challenges.
More Papers Like This
Environmental Pollution Control: Measures, Prevention and Safety Management
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This book pulls together existing research on how pollution in our air, water, and soil threatens both human health and our food supply, covering issues like plastic waste, industrial pollution, and carbon emissions. It matters because these forms of pollution don't just harm the environment—they can contaminate the food we eat and disproportionately hurt vulnerable communities, making pollution control a public health issue, not just an environmental one. The book also looks at practical solutions, including new technology like AI, that could help monitor and reduce these risks going forward.
Artificial intelligence in microplastics domain: Current progress, challenges, and sustainable prospects
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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.
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.
Implementing AI in Ocean Waste Tracking and Management
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This review paper looks at how artificial intelligence, like satellite image analysis, can help spot and clean up ocean pollution such as plastic waste and oil spills more efficiently than traditional methods. This matters because ocean plastic breaks down into microplastics that enter the seafood we eat and even our drinking water, so better detection and cleanup could mean less of this pollution ending up in our bodies. The authors note that AI isn't a perfect fix yet, it requires huge amounts of data and has its own environmental costs, like energy use, so more research and teamwork across science, government, and industry are still needed.
Implementing AI in Ocean Waste Tracking and Management
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This review paper looks at how artificial intelligence, like satellite image analysis and smart tracking systems, can help spot and clean up ocean pollution, including plastic waste and oil spills, faster and more accurately than older methods. This matters because ocean plastic breaks down into microplastics that end up in seafood and drinking water, so better cleanup technology could mean less of that pollution making its way into our bodies. That said, this is a summary of existing studies rather than new experiments, and the authors note that AI tools still need more data, cross-team collaboration, and attention to their own environmental costs before they can fully deliver on this promise.
Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.