Soil contamination from oil spills is one of the biggest challenges facing oil-producing countries and firms. These spills are often caused during oil extraction and transportation and release volatile compounds that significantly impact the environment and human health.
Though there are some techniques such as Fourier-transform infrared spectroscopy (FTIR) and Gas Chromatography coupled with Mass Spectrometry (GC×GC-MS) to detect oil spills, however, such traditional methods often require complex procedures and long analysis times, making them less suitable for rapid field applications.
Researchers from the Skolkovo Institute of Science and Technology in Moscow and their colleagues from China and Kazakhstan proposed using an electronic nose (e-nose) technology to detect crude oil patterns and their mixtures, focusing on environmental monitoring of soil contamination caused by oil spills.
Since oil is a mixture of different hydrocarbons present in varying proportions depending on its source, the e-nose could be used to locate soil-contaminating oil spills, monitor the environment at refineries, and conduct oil field studies according to the study which was published in the Journal of Hazardous Materials in 2024.
The researchers analyzed crude oil samples from various fields in Kazakhstan using the FTIR and GC×GC-MS techniques to analyze the correlation between sensor responses (part of the e-nose system) and oil compositions.
FTIR allows for the identification of the overall composition of the oil, while GC×GC-MS provides detailed information on the volatile organic compounds (VOCs) present in the samples.
To address the limitations of these traditional techniques, the researchers employed e-nose, designed to mimic mammalian olfactory systems, to classify oil odors based on their VOCs.
Nine crude oil samples and seven mixtures were tested, with specific attention to their chemical properties and the impact of weathering on their odor patterns. The study utilized a gas-mixing setup to expose the e-nose to both pure oil vapors and contaminated soil samples.
The researchers observed that oil patterns change over time due to weathering, with specific clusters forming based on the origin of the oil.
Oil weathering refers to the natural processes that occur after an oil spill, leading to changes in the physical and chemical properties of the oil over time. This includes the evaporation of volatile compounds, dispersion, emulsification, and biodegradation, which can significantly alter the oil’s composition and behavior in the environment.
The researchers found that e-nose could accurately classify oil samples based on their chemical composition, achieving a classification accuracy of 100% using the Random Forest (RF) algorithm, an AI machine learning used to classify oil-related vapors based on the chemiresistive responses from the e-nose sensors.
The researchers tuned the processing of data from eight sensors, sensitive to various gas components so that the e-nose could accurately identify the origin of the oil, no matter how volatile. Eventually, the e-nose was able to detect oil in the soil even 12 hours after sampling, when some of the oil had already evaporated.
The study proved that the e-nose is 20 times less expensive than a tandem gas chromatograph-mass spectrometer that takes up several square meters of laboratory space. It is also portable and as compact as a paperback, making it suitable for a variety of applications.
E-nose technology offers a promising alternative to traditional laboratory methods, enabling quicker responses to oil contamination incidents. Through this innovative approach, researchers can track odor patterns which would help in early detection of pollution, assessing environmental impacts, ensuring regulatory compliance for oil and gas companies, mitigating climate change, and protecting public health.
