Advanced Analytical Techniques for Characterizing Petroleum-Derived Contaminants in the Environment
Downloads
The characterization of petroleum-derived contaminants in the environment is crucial for understanding their impact on ecosystems and human health. Traditional analytical techniques such as Gas Chromatography (GC), Mass Spectrometry (MS), and High-Performance Liquid Chromatography (HPLC) have been instrumental in identifying and quantifying these contaminants. However, the complexity and diversity of petroleum-derived compounds necessitate the development and application of advanced analytical techniques for more comprehensive analysis. This paper reviews the most cutting-edge methods currently employed in environmental analysis, including Comprehensive Two-Dimensional Gas Chromatography (GC×GC), Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS), Nuclear Magnetic Resonance (NMR) Spectroscopy, and Synchrotron Radiation-Based Techniques such as X-ray Absorption Spectroscopy (XAS) and X-ray Fluorescence (XRF), as well as Laser-Induced Breakdown Spectroscopy (LIBS). Each technique's principles, capabilities, and applications are discussed, highlighting their roles in detecting and characterizing hydrocarbons, polycyclic aromatic hydrocarbons (PAHs), heavy metals, and volatile organic compounds (VOCs). Case studies demonstrate the practical applications of these advanced techniques in real-world scenarios, such as oil spill analysis and the identification of complex contaminant mixtures. The paper also addresses the advantages and limitations of these advanced techniques, considering factors like sensitivity, selectivity, complexity, and cost. Finally, future directions and emerging technologies, including nanotechnology, biosensors, and machine learning, are explored for their potential to enhance environmental monitoring and remediation efforts. This comprehensive review underscores the importance of continued innovation in analytical methods to effectively address the challenges posed by petroleum-derived contaminants in the environment.
Downloads
Duarte, H., Aliaño-González, M. J., Romano, A., & Medronho, B. (2024). Advancements in Detection and Mitigation Strategies for Petroleum-Derived Contaminants in Aquatic Environments: A Comprehensive Review. Sensors, 24(11), 3284.
Bojkovic, A., Vermeire, F. H., Kuzmanovic, M., Dao Thi, H., & Van Geem, K. M. (2021). Analytics driving kinetics: advanced mass spectrometric characterization of petroleum products. Energy & Fuels, 36(1), 6-59.
Barman, B. N., Cebolla, V. L., & Membrado, L. (2000). Chromatographic techniques for petroleum and related products. Critical Reviews in Analytical Chemistry, 30(2-3), 75-120.
Glattke, T. J., Chacón-Patiño, M. L., Hoque, S. S., Ennis, T. E., Greason, S., Marshall, A. G., & Rodgers, R. P. (2022). Complex mixture analysis of emerging contaminants generated from coal tar-and petroleum-derived pavement sealants: molecular compositions and correlations with toxicity revealed by fourier transform ion cyclotron resonance mass spectrometry. Environmental Science & Technology, 56(18), 12988-12998.
Seeley, M. E., Wang, Q., Bacosa, H., Rosenheim, B. E., & Liu, Z. (2018). Environmental petroleum pollution analysis using ramped pyrolysis-gas chromatography–mass spectrometry. Organic Geochemistry, 124, 180-189.
Roman-Hubers, A. T., Cordova, A. C., Barrow, M. P., & Rusyn, I. (2023). Analytical chemistry solutions to hazard evaluation of petroleum refining products. Regulatory Toxicology and Pharmacology, 137, 105310.
Anyaeche, R. O., Kaur, J., Li, W., & Kenttämaa, H. (2023). Tandem Mass Spectrometry in the Analysis of Petroleum-Based Compounds. Analytical Chemistry, 95(1), 128-133.
Anyaeche, R. O., Kaur, J., Li, W., & Kenttämaa, H. (2023). Tandem Mass Spectrometry in the Analysis of Petroleum-Based Compounds. Analytical Chemistry, 95(1), 128-133.
Rüger, C. P., Tiemann, O., Neumann, A., Streibel, T., & Zimmermann, R. (2021). Review on evolved gas analysis mass spectrometry with soft photoionization for the chemical description of petroleum, petroleum-derived materials, and alternative feedstocks. Energy & Fuels, 35(22), 18308-18332.
Zito, P., Ghannam, R., Bekins, B. A., & Podgorski, D. C. (2019). Examining the Extraction Efficiency of Petroleum‐Derived Dissolved Organic Matter in Contaminated Groundwater Plumes. Groundwater Monitoring & Remediation, 39(4), 25-31.
McKenna, A. M., Nelson, R. K., Reddy, C. M., Savory, J. J., Kaiser, N. K., Fitzsimmons, J. E., ... & Rodgers, R. P. (2013). Expansion of the analytical window for oil spill characterization by ultrahigh resolution mass spectrometry: beyond gas chromatography. Environmental science & technology, 47(13), 7530-7539.
Dell’Orco, S., Rowland, S. M., Harman-Ware, A. E., Carpenter, D., Foust, T., Christensen, E. D., & Mukarakate, C. (2022). Advanced spectrometric methods for characterizing bio-oils to enable refineries to reduce fuel carbon intensity during co-processing. Applied Spectroscopy Reviews, 57(1), 77-87.
Khanmohammadi, M., Garmarudi, A. B., & de la Guardia, M. (2012). Characterization of petroleum-based products by infrared spectroscopy and chemometrics. TrAC Trends in Analytical Chemistry, 35, 135-149.
Birch, Q. T., Potter, P. M., Pinto, P. X., Dionysiou, D. D., & Al-Abed, S. R. (2021). Isotope ratio mass spectrometry and spectroscopic techniques for microplastics characterization. Talanta, 224, 121743.
Malins, D. C., Krahn, M. M., Brown, D. W., MacLeod, W. D., & Collier, T. K. (1980). Analysis for petroleum products in marine environments. Helgoländer Meeresuntersuchungen, 33, 257-271.
Malins, D. C., Krahn, M. M., Brown, D. W., MacLeod, W. D., & Collier, T. K. (1980). Analysis for petroleum products in marine environments. Helgoländer Meeresuntersuchungen, 33, 257-271.
Wang, Z., Stout, S. A., & Fingas, M. (2006). Forensic fingerprinting of biomarkers for oil spill characterization and source identification. Environmental Forensics, 7(2), 105-146.
Güney, B., & Aladağ, A. (2022). Microstructural analysis of liquefied petroleum gas vehicle emissions, one of the anthropogenic environmental pollutants. International Journal of Environmental Science and Technology, 19(1), 249-260.
Vempatapu, B. P., & Kanaujia, P. K. (2017). Monitoring petroleum fuel adulteration: A review of analytical methods. TrAC Trends in Analytical Chemistry, 92, 1-11.
Essumang, D. K., Kowalski, K., & Sogaard, E. G. (2011). Levels, distribution and source characterization of polycyclic aromatic hydrocarbons (PAHs) in topsoils and roadside soils in Esbjerg, Denmark. Bulletin of environmental contamination and toxicology, 86, 438-443.
Nagesh, C., Chaganti, K. R., Chaganti, S., Khaleelullah, S., Naresh, P., & Hussan, M. (2023). Leveraging Machine Learning based Ensemble Time Series Prediction Model for Rainfall Using SVM, KNN and Advanced ARIMA+ E-GARCH. International Journal on Recent and Innovation Trends in Computing and Communication, 11(7s), 353-358.
Jiang, B., Seif, M., Tandon, R., & Li, M. (2021). Context-aware local information privacy. IEEE Transactions on Information Forensics and Security, 16, 3694-3708.
Surabhi, S. N. R. D., Mandala, V., & Shah, C. V. AI-Enabled Statistical Quality Control Techniques for Achieving Uniformity in Automobile Gap Control.
Chaganti, K. R., Ramula, U. S., Sathyanarayana, C., Changala, R., Kirankumar, N., & Gupta, K. G. (2023, November). UI/UX Design for Online Learning Approach by Predictive Student Experience. In 2023 7th International Conference on Electronics, Communication and Aerospace Technology (ICECA) (pp. 794-799). IEEE.
Jiang, B., Li, M., & Tandon, R. (2020). Local information privacy and its application to privacy-preserving data aggregation. IEEE Transactions on Dependable and Secure Computing, 19(3), 1918-1935.
Mandala, V., & Surabhi, M. D. Intelligent Engines: Revolutionizing Manufacturing and Supply Chains with AI.
Chaganti, K. R., & Chaganti, S. Deep Learning Based NLP and LSTM Models for Sentiment Classification of Consumer Tweets.
Jiang, B., Li, M., & Tandon, R. (2018, May). Context-aware data aggregation with localized information privacy. In 2018 IEEE Conference on Communications and Network Security (CNS) (pp. 1-9). IEEE.
Zhang, W., Jiang, B., Li, M., & Lin, X. (2022). Privacy-preserving aggregate mobility data release: An information-theoretic deep reinforcement learning approach. IEEE Transactions on Information Forensics and Security, 17, 849-864.
Adeyeri, T. B. (2024). Enhancing Financial Analysis Through Artificial Intelligence: A Comprehensive Review. Journal of Science & Technology, 5(2), 102-120.
Jiang, B., Li, M., & Tandon, R. (2019, May). Local information privacy with bounded prior. In ICC 2019-2019 IEEE International Conference on Communications (ICC) (pp. 1-7). IEEE.
Adeyeri, T. B. (2024). Automating Accounting Processes: How AI is Streamlining Financial Reporting. Journal of Artificial Intelligence Research, 4(1), 72-90.
Mandala, V., & Surabhi, S. N. R. D. Intelligent Systems for Vehicle Reliability and Safety: Exploring AI in Predictive Failure Analysis.
Adeyeri, T. B. (2024). Blockchain and AI Synergy: Transforming Financial Transactions and Auditing. Blockchain Technology and Distributed Systems, 4(1), 24-44.
Shah, C. V., Surabhi, S. N. R. D., & Mandala, V. ENHANCING DRIVER ALERTNESS USING COMPUTER VISION DETECTION IN AUTONOMOUS VEHICLE.
Copyright (c) 2024 Syed Masroor Hassan Rizvu
This work is licensed under a Creative Commons Attribution 4.0 International License.
