We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Artificial intelligence and IoT driven technologies for environmental pollution monitoring and management
Summary
This review explores how artificial intelligence and Internet of Things sensors can be used to detect and monitor environmental pollutants, including microplastics, in air, water, and soil. Machine learning methods show promise for improving pollution tracking and prediction, but challenges remain around data sharing and model reliability. Advanced monitoring technology could play a key role in identifying and managing microplastic contamination in the environment.
Detecting hazardous substances in the environment is crucial for protecting human wellbeing and ecosystems. As technology continues to advance, artificial intelligence (AI) has emerged as a promising tool for creating sensors that can effectively detect and analyze these hazardous substances. The increasing advancements in information technology have led to a growing interest in utilizing this technology for environmental pollution detection. AI-driven sensor systems, AI and Internet of Things (IoT) can be efficiently used for environmental monitoring, such as those for detecting air pollutants, water contaminants, and soil toxins. With the increasing concerns about the detrimental impact of legacy and emerging hazardous substances on ecosystems and human health, it is necessary to develop advanced monitoring systems that can efficiently detect, analyze, and respond to potential risks. Therefore, this review aims to explore recent advancements in using AI, sensors and IOTs for environmental pollution monitoring, taking into account the complexities of predicting and tracking pollution changes due to the dynamic nature of the environment. Integrating machine learning (ML) methods has the potential to revolutionize environmental science, but it also poses challenges. Important considerations include balancing model performance and interpretability, understanding ML model requirements, selecting appropriate models, and addressing concerns related to data sharing. Through examining these issues, this study seeks to highlight the latest trends in leveraging AI and IOT for environmental pollution monitoring.
Sign in to start a discussion.
More Papers Like This
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.
Integrating Machine Learning and IoT Technologies for Smart Water Quality Monitoring: Methods, Challenges, and Future Directions
Machine learning and IoT sensor technologies were integrated into a smart environmental monitoring system designed for real-time detection of pollutants including microplastics. The platform demonstrates how digital technologies can improve the spatial and temporal resolution of environmental contamination surveillance.
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 and Machine Learning Approaches for Automatic Microplastics Identification and Characterization
This review examines how artificial intelligence and machine learning algorithms are being applied to identify, characterize, and model microplastic pollution in the environment. The authors found that these tools can analyze large sensor datasets to detect microplastics in water bodies, predict transport patterns, and model adsorption behavior under various environmental conditions. The study highlights the growing role of computational approaches in understanding and mitigating microplastic contamination.
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.