AIMC Topic: Polycyclic Aromatic Hydrocarbons
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Explainable no-code OECD-compliant machine learning models to predict the mutagenic activity of polycyclic aromatic hydrocarbons and their radical cation metabolites.
The Science of the total environment
Mar 18, 2025
Polycyclic aromatic hydrocarbons (PAHs) are persistent pollutants with well-known genotoxic and mutagenic effects, posing risks to ecosystems and human health. Their hydrophobic nature promotes accumulation in soils and aquatic environments, increasi...
Machine learning-enhanced surface-enhanced spectroscopic detection of polycyclic aromatic hydrocarbons in the human placenta.
Proceedings of the National Academy of Sciences of the United States of America
Feb 10, 2025
The detection and identification of polycyclic aromatic hydrocarbons (PAHs) and their derivatives, polycyclic aromatic compounds (PACs), are essential for environmental and health monitoring, for assessing toxicological exposure and their associated ...
Exposure experiments and machine learning revealed that personal care products can significantly increase transdermal exposure of SVOCs from the environment.
Journal of hazardous materials
Jan 17, 2025
We investigated the impacts of personal care products (PCPs) on dermal exposure to semi-volatile organic compounds (SVOCs), including phthalates, organophosphate esters, polycyclic aromatic hydrocarbons (PAHs), ultraviolet filters, and p-phenylenedia...
Predictions of the Optical Properties of Brown Carbon Aerosol by Machine Learning with Typical Chromophores.
Environmental science & technology
Nov 7, 2024
The linkages between BrC optical properties and chemical composition remain inadequately understood, with quantified chromophores explaining less than 25% of ambient aerosol light absorption. This study characterized 38 typical chromophores in aeroso...
Machine learning models reveal how polycyclic aromatic hydrocarbons influence environmental bacterial communities.
The Science of the total environment
Oct 22, 2024
Polycyclic aromatic hydrocarbons (PAHs) are harmful and widespread pollutants in the environment, posing an ecological threat. However, exploring the influence of PAHs on environmental bacterial communities in different habitats (soil, water, and sed...
Machine learning models to predict the bioaccessibility of parent and substituted polycyclic aromatic hydrocarbons (PAHs) in food: Impact on accurate health risk assessment.
Journal of hazardous materials
Oct 9, 2024
Food intake is the primary pathway for polycyclic aromatic hydrocarbons (PAHs) to enter the human body. Once ingested, PAHs tend to accumulate, posing health risks. To accurately assess the risk of PAHs from food, concentrations of 10 parent PAHs (PP...
Predicting the Occurrence of Substituted and Unsubstituted, Polycyclic Aromatic Compounds in Coking Wastewater Treatment Plant Effluent using Machine Learning Regression.
Chemosphere
May 28, 2024
Organic contaminants such as polycyclic aromatic compounds (PACs) occurring in industrial effluents can not only persist in wastewater but transform into more toxic and mobile, substituted heterocyclic products during treatment. Thus, predicting the ...
Ocean Stratification Impacts on Dissolved Polycyclic Aromatic Hydrocarbons (PAHs): From Global Observation to Deep Learning.
Environmental science & technology
Aug 31, 2023
Ocean stratification plays a crucial role in many biogeochemical processes of dissolved matter, but our understanding of its impact on widespread organic pollutants, such as polycyclic aromatic hydrocarbons (PAHs), remains limited. By analyzing disso...
Detection of 1-OHPyr in human urine using SERS with injection under wet liquid-liquid self-assembled films of β-CD-coated gold nanoparticles and deep learning.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Dec 12, 2022
1-Hydroxypyrene (1-OHPyr), a typical hydroxylated polycyclic aromatic hydrocarbon (OH-PAH), has been commonly regarded as a urinary biomarker for assessing human exposure and health risks of PAHs. Herein, a fast and sensitive method was developed for...
Visualization of a Machine Learning Framework toward Highly Sensitive Qualitative Analysis by SERS.
Analytical chemistry
Jul 6, 2022
Surface-enhanced Raman spectroscopy (SERS), providing near-single-molecule-level fingerprint information, is a powerful tool for the trace analysis of a target in a complicated matrix and is especially facilitated by the development of modern machine...
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