AIMC Topic: Water Pollutants, Chemical
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Accurate prediction of pollution and health risks of iodinated X-ray contrast media in Taihu Lake with machine learning and revealing key environmental factors.
Water research
Dec 17, 2024
Iodinated X-ray contrast media (ICM) are commonly detected at considerable concentrations in aquatic environments. The long-term pollution trends in ICM at the whole lake/river scale have not yet been investigated; therefore, the risks associated wit...
Predicting the adsorption of ammonia nitrogen by biochar in water bodies using machine learning strategies: Model optimization and analysis of key characteristic variables.
Environmental research
Dec 14, 2024
Biochar adsorption technology has been widely used to remove ammonia nitrogen from water bodies. However, existing methods for predicting adsorption efficiency often lack sufficient accuracy and practical usability. This study evaluated eight machine...
Machine learning-based prediction and model interpretability analysis for algal growth affected by microplastics.
The Science of the total environment
Dec 14, 2024
Microplastics (MPs), the plastic debris smaller than 5 mm, are ubiquitous in waterbodies and have been shown to be toxic to aquatic organisms, especially to microalgae. The aim of this study is to use machine learning models to predict the effects of...
Towards A universal settling model for microplastics with diverse shapes: Machine learning breaking morphological barriers.
Water research
Dec 12, 2024
Accurately predicting the settling velocity of microplastics in aquatic environments is a prerequisite for reliably modeling their transport processes. An increasing number of settling models have been proposed for microplastics with fragmented, film...
Modeling the global ocean distribution of dissolved cadmium based on machine learning-SHAP algorithm.
The Science of the total environment
Dec 12, 2024
Cadmium (Cd) is a bio-essential trace metal in the ocean that can be toxic at high concentrations, significantly impacting the marine environment and phytoplankton growth. Its distribution pattern is closely proportional to that of phosphate (PO), al...
Evaluation of plant-based coagulants for turbidity removal and coagulant dosage prediction using machine learning.
Environmental technology
Dec 11, 2024
This study investigates the use of six plant-based coagulants - , , , , , and for the removal of turbidity from wastewater effluent. The coagulants were characterized using Scanning Electron Microscopy (SEM) to determine morphological structure, X-r...
Selectively Quantify Toxic Pollutants in Water by Machine Learning Empowered Electrochemical Biosensors.
Environmental science & technology
Dec 6, 2024
Electroactive biofilm (EAB) sensors have become pivotal in water quality detection and early ecological risk warnings due to their remarkable sensitivity. However, it is challenging to identify multiple toxicants in complex water bodies concurrently....
Modeling and predicting caffeine contamination in surface waters using artificial intelligence and standard statistical methods.
Environmental monitoring and assessment
Dec 5, 2024
Caffeine, considered an emerging contaminant, serves as an indicator of anthropic influence on water resources. This research employs various modeling techniques, including Artificial Neural Networks (ANN), Random Forest (RF), and more, along with hy...
Identifying Organic Chemicals with Acetylcholinesterase Inhibition in Nationwide Estuarine Waters by Machine Learning-Assisted Mass Spectrometric Screening.
Environmental science & technology
Dec 4, 2024
Neurotoxicity is frequently observed in the global aquatic environment, threatening aquatic ecosystems and human health. However, a very limited proportion of neurotoxic effects (∼1%) has been explained by known chemicals of concern. Here, we integra...
Enhancing groundwater quality prediction through ensemble machine learning techniques.
Environmental monitoring and assessment
Dec 4, 2024
Groundwater quality is assessed by conducting water sampling and laboratory analysis. Field-based measurements are costly and time-consuming. This study introduces a machine learning (ML)-based framework and innovative application of stacking ensembl...
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