We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Innovative approaches to greywater micropollutant removal: AI-driven solutions and future outlook
AI summary Read the abstract
This review examines how artificial intelligence, particularly neural networks, can improve the treatment of greywater (household wastewater from sinks, showers, and laundry) that contains microplastics and other pollutants. AI models offer better predictions of treatment outcomes than traditional methods, which could lead to more effective removal of microplastics from wastewater. Since greywater is a significant source of microplastics entering the environment, better treatment could reduce human exposure.
Greywater constitutes a significant portion of urban wastewater and is laden with numerous emerging contaminants that have the potential to adversely impact public health and the ecosystem. Understanding greywater's characteristics and measuring the contamination levels is crucial for designing an effective recycling system. However, wastewater treatment is an intricate process involving significant uncertainties, leading to variations in effluent quality, costs, and environmental risks. This review addresses the existing knowledge gap in utilising artificial intelligence (AI) to enhance the laundry greywater recycling process and elucidates the optimal treatment technologies for the most prevalent micropollutants, including microplastics, nutrients, surfactants, synthetic dyes, pharmaceuticals, and organic matter. The development of laundry greywater treatment technologies is also highlighted with a critical discussion of physicochemical, biological, and advanced oxidation processes (AOPs) based on their functions, methods, associated limitations, and future trends. Artificial neural networks (ANN) stand out as the most prevalent and extensively applied AI model in the domain of wastewater treatment. Utilising ANN models mitigates certain limitations inherent in traditional adsorption models, particularly by offering enhanced predictive accuracy under varied operating conditions and multicomponent adsorption systems. Moreover, tremendous success has been recorded with the random forest (RF) model, exhibiting 100% prediction accuracy for both sessile and effluent microbial communities within a bioreactor. The precise prediction or simulation of membrane fouling behaviours using AI techniques is also of paramount importance for understanding fouling mechanisms and formulating efficient strategies to mitigate membrane fouling.
More Papers Like This
AI-assisted Microplastics Removal
AI summary Read the abstract
This review explored how artificial intelligence is being used to improve the detection and removal of microplastics from water and the environment. Researchers found that machine learning techniques can enhance the identification of microplastic particles and optimize treatment processes like filtration and coagulation. The study suggests that AI-driven approaches could overcome many of the efficiency and cost limitations of conventional microplastic removal methods.
The Role of Artificial Intelligence in Microplastic Pollution Studies and Management
AI summary Read the abstract
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.
Microplastic Detection in the Environmental Matrix Using Artificial Intelligence: Review of Recent Advancement
AI summary Read the abstract
Researchers reviewed how artificial intelligence (AI) — including machine learning and neural networks — is being applied to detect, count, and classify microplastics in environmental samples more accurately and efficiently than traditional manual methods. AI combined with microscopy or spectroscopy can automate the identification of microplastic type, size, and shape, helping scientists process large datasets faster.
Advancing environmental sustainability through emerging AI-based monitoring and mitigation strategies for microplastic pollution in aquatic ecosystems
AI summary Read the abstract
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.
A comprehensive review on current progress in microplastics removal from industrial and domestic wastewater
AI summary Read the abstract
This review summarizes existing research on tiny plastic particles called microplastics found in wastewater, including how they form and their links to organ problems and effects on animal reproduction. It also describes physical, chemical, and biological methods used to remove microplastics from wastewater, along with newer approaches like artificial intelligence and nanorobotics.
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.