Chemical fingerprinting analyses of 29 hydrocarbon-contaminated soils were performed to assess the soil quality and determine the main contaminant sources. The results were compared to an assessment based on concentrations of the 16 priority polycyclic aromatic hydrocarbons pointed out by the U.S. Environmental Protection Agency (EPAPAH16) and total petroleum hydrocarbon (TPH). The chemical fingerprinting strategy proposed in this study included four tiers: (i) qualitative analysis of GC-FID chromatograms, (ii) comparison of the chemical composition of both un-substituted and alkyl-substituted polycyclic aromatic compounds (PACs), (iii) diagnostic ratios of selected PACs, and (iv) multivariate data analysis of sum-normalized PAC concentrations. The assessment criteria included quantitative analysis of 19 PACs and C1-C4 alkyl-substituted homologues of naphthalene, fluorene, dibenzothiophene, phenanthrene, pyrene, and chrysene; and 13 oxygenated polycyclic aromatic compounds (O-PACs). The chemical composition of un-substituted and alkyl-substituted PACs and visual interpretation of GC-FID chromatograms were in combination successful in differentiating pyrogenic and petrogenic hydrocarbon sources and in assessing weathering trends of hydrocarbon contamination in the soils. Multivariate data analysis of sum-normalized concentrations could as a stand-alone tool distinguish between hydrocarbon sources of petrogenic and pyrogenic origin, differentiate within petrogenic sources, and detect weathering trends. Diagnostic ratios of PACs were not successful for source identification of the heavily weathered hydrocarbon sources in the soils. The fingerprinting of contaminated soils revealed an underestimation of PACs in petrogenic contaminated soils when the assessment was based solely on EPAPAH16. As alkyl-substituted PACs are dominant in petrogenic sources, the evaluation of the total load of PACs based on EPAPAH16 was not representative. Likewise, the O-PACs are not represented in soil quality assessments based on EPAPAH16 and TPH. The ∑O-PACs ranged between
Download full-text PDF
Source
http://dx.doi.org/10.1039/c3em00631j DOI Listing Publication Analysis
Top Keywords
Similar Publications
Glycan Sequencing Based on Glycosidase-Assisted Nanopore Sensing.
J Am Chem Soc
January 2025
State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
Nanopores are promising sensors for glycan analysis with the accurate identification of complex glycans laying the foundation for nanopore-based sequencing. However, their applicability toward continuous glycan sequencing has not yet been demonstrated. Here, we present a proof-of-concept of glycan sequencing by combining nanopore technology with glycosidase-hydrolyzing reactions.
View Article and Find Full Text PDFNeuTox 2.0: A hybrid deep learning architecture for screening potential neurotoxicity of chemicals based on multimodal feature fusion.
Environ Int
December 2024
Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China.
Chemically induced neurotoxicity is a critical aspect of chemical safety assessment. Traditional and costly experimental methods call for the development of high-throughput virtual screening. However, the small datasets of neurotoxicity have limited the application of advanced deep learning techniques.
View Article and Find Full Text PDFAnalysis strategy of contamination source using chemical fingerprint information based on GC-HRMS: A case study of landfill leachate.
Water Res
December 2024
College of Environment, Ministry of Education Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Hohai University, Nanjing 210098, PR China.; Suzhou Research Institute, Hohai University, Suzhou 215100, PR China.. Electronic address:
With the increasing prevalence of emerging contaminants (ECs) in the environment, gaining a deeper understanding of the chemical information pertaining to the contamination source is a crucial step toward effective prevention and control of these ECs. This study presents a novel strategy for analyzing the chemical information of contamination sources using gas chromatography-high resolution mass spectrometry (GC-HRMS) and demonstrates it on landfill leachate, a common and representative environmental contamination source. Initially, a non-targeted screening approach using HRMS was used to characterize a total of 5344 organic compounds with identification confidence levels 1 and 2 in 14 landfill leachate samples.
View Article and Find Full Text PDFJoint Metabolites for Avocado Oil Identity: Fatty Acid Profiles and Fatty Alcohol Esters as Unique Derivatives.
J Agric Food Chem
January 2025
Escuela de Ingenieria y Ciencias, Tecnologico de Monterrey, Ave. Eugenio Garza Sada 2501, Monterrey, Nuevo Leon 64849, Mexico.
Fatty acid (FA), tocopherol, and phytosterol profiles are used in avocado oil purity standards. However, blends with other oils can mimic the profile of pure avocado oil, resulting in similar ranges for these molecules. Therefore, fatty alcohol esters (FAEs) uniquely of spp.
View Article and Find Full Text PDFQSAR Classification Modeling Using Machine Learning with a Consensus-Based Approach for Multivariate Chemical Hazard End Points.
ACS Omega
December 2024
Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea.
This study introduces an innovative computational approach using hybrid machine learning models to predict toxicity across eight critical end points: cardiac toxicity, inhalation toxicity, dermal toxicity, oral toxicity, skin irritation, skin sensitization, eye irritation, and respiratory irritation. Leveraging advanced cheminformatics tools, we extracted relevant features from curated data sets, incorporating a range of descriptors such as Morgan circular fingerprints, MACCS keys, Mordred calculation descriptors, and physicochemical properties. The consensus model was developed by selecting the best-performing classifier-Random Forest (RF), eXtreme Gradient Boosting (XGBoost), or Support Vector Machines (SVM)-for each descriptor, optimizing predictive accuracy and robustness across the end points.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!