AIMC Topic: Metals, Heavy

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Advancing Whole-Cell Biosensors: Kinetics-Dependent Metabolic SERS Analytics for Pollutant Differentiation and Quantification.

Analytical chemistry
Whole-cell biosensors (WCBs), which detect targeting analytes through cellular responses, have become powerful tools for environmental monitoring. However, existing WCBs often rely on the single-channel low-dimension signal outputs (e.g., fluorescenc...

Integrating machine learning for enhanced spatial prediction and risk assessment of soil heavy metal(loid)s.

Environmental pollution (Barking, Essex : 1987)
Accurately predicting the concentrations and spatial distribution of soil heavy metal(loid)s is crucial for effective environmental management and human health risk assessment. However, existing studies are often limited by poor model accuracy, featu...

Interpretable machine learning approaches for predicting prostate cancer by using multiple heavy metal exposures based on the data from NHANES 2003-2018.

Ecotoxicology and environmental safety
Environmental pollution plays a major role in the development of prostate cancer (PCA). However, there has been no research on machine learning (ML) modelling between multiple heavy metal exposures and PCA risk. Based on the 8022 samples from the 200...

Machine learning-based source apportionment and source-oriented probabilistic ecological risk assessment of heavy metals in urban green spaces.

Ecotoxicology and environmental safety
Global urbanization has significantly contributed to soil contamination by heavy metals (HMs), posing serious ecological risks, particularly within urban green spaces (UGS). This study focused on UGS soils in Lanzhou, a major river-valley city in Chi...

Distribution, sources, and ecological risks of heavy metal contamination at the sediment-water interface in the Dongjiang Basin based on in situ high-resolution measurements.

Environmental pollution (Barking, Essex : 1987)
As a critical drinking water source for over 40 million people in southern China, the Dongjiang River faces growing ecological threats from sediment-derived heavy metals (HMs: As, Cd, Co, Cr, Cu, Mn, Ni, Pb, and Zn). This pioneering study is the firs...

Unlocking urban soil secrets: machine learning and spectrometry in Berlin's heavy metal pollution study considering spatial data.

Environmental monitoring and assessment
Berlin has historically been impacted by heavy metal (HM) emissions, raising concerns about soil pollution. In this study, machine learning (ML) techniques were applied to predict HM concentrations across the Berlin metropolitan area. A dataset of 66...

Predicting the Site-Specific Toxicity of Metals to Fishes Using a New Machine Learning-Based Approach.

Environmental science & technology
Fishes of various trophic levels play an important role in the stability and balance of aquatic ecosystems. Metal contaminants can impair the survival and population fitness of fish at elevated concentrations. When universal water quality criteria (W...

CRISPR/Cas-Based Biosensing Strategies for Non-Nucleic Acid Contaminants in Food Safety: Status, Challenges, and Perspectives.

Journal of agricultural and food chemistry
Non-nucleic acid targets (non-NATs), such as heavy metals, toxins, and pesticide residues, pose critical threats to food safety. Although CRISPR/Cas systems were initially developed for nucleic acid detection, recent advances have enabled their adapt...

Harnessing deep learning for fusion-based heavy metal contamination index prediction in groundwater.

Journal of contaminant hydrology
Groundwater contamination by heavy metals presents a major environmental threat with serious implications for public health and resource sustainability. This study proposes a novel deep learning-based data fusion framework to predict heavy metal cont...

Multi-functionalities of citric acid assisted thermal hydrolysis for sludge pretreatment: A novel method assisting in sludge treatment targeting multiple-objectives.

Journal of hazardous materials
Pretreatment is essential for enhancing sludge treatment efficiency, including anaerobic digestion, phosphorus recovery, sludge dewatering, and heavy metal removal. However, few techniques simultaneously address multiple treatment objectives. In this...