Abstract:
Oil-contaminated soils are a major environmental problem for Kazakhstan. Oil spills or leaks lead to
profound changes in the physical and agrochemical properties of the soil and the accumulation of hazardous
substances. Whilst there are many remote sensing techniques and complex laboratory methods for oil spill
detection, developing simple, reliable, and inexpensive tools for detecting the presence of pollutants in the soil
is a relevant research task. The study aims to research the possibilities of an electronic nose combining a
chemical sensor array with pattern recognition techniques to distinguish volatile organic compounds from
several types of hydrocarbon soil pollutants. An electronic nose system was assembled in our laboratory. It
includes eight gas metal oxide sensors, a humidity and temperature sensor, an analog-digital processing unit,
and a data communication unit. We measured changes in the electrical conductivity of sensors in the presence
of volatile organic compounds released from oil and petroleum products and samples of contaminated and
uncontaminated soils. The list of experimental samples includes six types of soils corresponding to different
soil zones of Kazakhstan, crude oil from three oil fields in Kazakhstan, and five types of locally produced fuel
oil (including gasoline, kerosene, diesel fuel, engine oil, and used engine oil). We used principal component
analysis to statistically process multidimensional sensor data, feature extraction, and collect the volatile
fingerprint dataset. Pattern recognition using machine learning algorithms made it possible to classify digital
fingerprints of samples with an average accuracy of about 92%. The study results show that electronic nose
sensors are sensitive to soil hydrocarbon content. The proposed approach based on machine olfaction is a fast,
accurate, and inexpensive method for detecting oil spills and leaks, and it can complement remote sensing
methods based on computer vision.
