AIMC Topic: Polycyclic Aromatic Hydrocarbons
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Predicting polycyclic aromatic hydrocarbons in surface water by a multiscale feature extraction-based deep learning approach.
The Science of the total environment
Aug 5, 2021
Accurate and effective prediction of polycyclic aromatic hydrocarbons (PAHs) in surface water remains a substantial challenge due to the limited understanding of the dynamic processes. To assist integrated surface water management, a novel hybrid sur...
Inter-regional multimedia fate analysis of PAHs and potential risk assessment by integrating deep learning and climate change scenarios.
Journal of hazardous materials
Jan 15, 2021
Polycyclic aromatic hydrocarbons (PAHs) are hazardous compounds associated with respiratory disease and lung cancer. Increasing fossil fuel consumption, which causes climate change, has accelerated the emissions of PAHs. However, potential risks by P...
Effects of corn straw on dissipation of polycyclic aromatic hydrocarbons and potential application of backpropagation artificial neural network prediction model for PAHs bioremediation.
Ecotoxicology and environmental safety
Oct 10, 2019
In order to provide a viable option for remediation of PAHs-contaminated soils, a greenhouse experiment was conducted to assess the effect of corn straw amendment (1%, 2%, 4% or 6%, w/w) on dissipation of aged polycyclic aromatic hydrocarbons (PAHs) ...
Using GIS, geostatistics and Fuzzy logic to study spatial structure of sedimentary total PAHs and potential eco-risks; An Eastern Persian Gulf case study.
Marine pollution bulletin
Aug 16, 2019
GIS, geo-statistics and autocorrelation analysis were employed to reveal spatial structure of sedimentary ∑16PAHs. Global Moran's I index outlined significant ∑PAHs clusters for the entire region (Moran's I index =0.62, Z-score = 25.6). Anselin Moran...
Application of backpropagation artificial neural network prediction model for the PAH bioremediation of polluted soil.
Chemosphere
Jul 15, 2016
The backpropagation (BP) artificial neural network (ANN) is a renowned and extensively functional mathematical tool used for time-series predictions and approximations; which also define results for non-linear functions. ANNs are vital tools in the p...
The use of diagnostic ratios, biomarkers and 3-way Kohonen neural networks to monitor the temporal evolution of oil spills.
Marine pollution bulletin
Apr 29, 2015
Oil spill identification relies usually on a wealth of chromatographic data which requires advanced data treatment (chemometrics). A simple approach based on Kohonen neural networks to handle three-dimensional arrays is presented. A suite of 28 diagn...
Refining source-specific lung cancer risk assessment from PM-bound PAHs: Integrating component-based potency factors and machine learning in Ningbo, China.
Ecotoxicology and environmental safety
Jun 1, 2025
The component-based potency factor approach, combined with benzo[a]pyrene (BaP) unit risk values from the World Health Organization (WHO), is commonly used to assess lung excess cancer risk (LECR) from polycyclic aromatic hydrocarbons (PAHs). However...
Toward Accurate PAH IR Spectra Prediction: Handling Charge Effects with Classical and Deep Learning Models.
Journal of chemical information and modeling
May 26, 2025
Polycyclic aromatic hydrocarbons (PAHs) play a crucial role in astrochemistry, environmental studies, and combustion chemistry, yet interpreting their infrared (IR) spectra remains challenging due to the similarity of spectral features of many molecu...
The impact of multipollutant exposure on hepatic steatosis: a machine learning-based investigation into multipollutant synergistic effects.
Frontiers in public health
Jan 1, 2025
INTRODUCTION: This study examines the synergistic effects of multi-pollutant exposure on hepatic lipid accumulation in non-alcoholic fatty liver disease (NAFLD) through the application of an explainable machine learning framework. This approach addre...
[Determination of polycyclic aromatic hydrocarbon metabolites in urine by liquid-liquid extraction-high resolution gas chromatography-high resolution dual-focus magnetic mass spectrometry].
Se pu = Chinese journal of chromatography
Jun 8, 2020
A sensitive and accurate method was developed to quantify eight polycyclic aromatic hydrocarbon (PAH) metabolites in human urine by liquid-liquid extraction-high resolution gas chromatography-high resolution dual-focus magnetic mass spectrometry (LLC...
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