Researchers in China have developed a novel workflow for near-infrared reflectance spectroscopy (NIRS or NIR) that enhances the detection of low-level petroleum hydrocarbon pollution in soils, revealing new diagnostic features and significantly improving sensitivity for environmental monitoring.
Environmental monitoring is an important part of environmental protection. One of the main functions of environmental monitoring is analyzing contaminants in soil. The soil is essential for the production of crops, which help feed humans around the globe (1). However, because we live in a highly industrial world that emits significant pollution, contaminants entering the soil has only become more commonplace.
Petroleum hydrocarbons are one of these pollutants that routinely contaminate soil. These are chemicals comprised of carbon and hydrogen, and they are commonly found in diesel fuel, motor oil, and kerosene (2). Recently, a team of researchers from multiple Chinese institutions collaborated on a research study to test and develop a technical workflow for detecting low levels of petroleum hydrocarbon pollution in soils. This study, published in the journal Science of the Total Environment, introduced a diagnostic spectrum construction and parameterization method that significantly enhances the sensitivity and reliability of near-infrared reflectance spectroscopy (NIRS) for pollution detection (3).
Petroleum hydrocarbons are among the most pervasive contaminants in soils worldwide, posing significant risks to ecosystems and human health (2,3). Current methods of detection using NIRS rely on diagnostic features in sample spectra, which become less distinct at low pollutant levels (3). The research team looked to solve this limitation in their new method.
Traditional NIRS approaches face challenges in identifying low concentrations of petroleum hydrocarbons because the spectral response is often negligible or inconclusive (3). To address this, the research team developed a method based on spectral subtraction, allowing them to construct diagnostic spectra that reveal hidden patterns even in lightly polluted samples.
Applying this workflow to soil samples contaminated with varying levels of petroleum hydrocarbons (ranging from 178 to 1,716 mg/kg), the researchers identified two key diagnostic spectral features. The first was downward concave spectral features. The research team observed that within the spectral ranges of 2290–2370 nm and 1700–1780 nm, this feature was consistent even in samples with pollution levels below 200 mg/kg (3). The second was asymmetric "W-Shaped" absorption valleys. These distinct patterns were evident in samples with contamination levels exceeding 1,000 mg/kg (3). The valleys were consistently located around 2310 nm, 2348 nm, 1727 nm, and 1762 nm (3).
By enabling the detection of petroleum hydrocarbons at extremely low levels, the proposed workflow extends the applicability of NIRS in environmental management. The study’s methodology involved comparing NIRS data from pre- and post-contamination soil samples, visually analyzing the diagnostic spectra, and quantifying them using a series of spectral parameters (3).
This study helps to advance environmental monitoring of petroleum hydrocarbons. The method shows that it is possible to improve on traditional NIRS techniques. The study also provides scientists with a roadmap in regard to setting up future studies in this space. With the downward concave and asymmetric absorption valley features now established as reliable markers for low-level contamination, researchers and environmental managers can improve the accuracy and efficiency of pollution monitoring programs (3).
The researchers also suggest in their study that their workflow can be integrated with remote sensing technologies (3). This capability is particularly valuable for managing the environmental impact of industrial activities and mitigating the risks associated with petroleum hydrocarbon pollution.
While the study marks a significant leap forward, the researchers emphasize the need for further validation and adaptation of their workflow in diverse environmental settings (3). By extending the diagnostic spectrum approach to other soil types and contaminants, the scientific community can continue to refine and expand its tools for safeguarding soil and environmental health (3).
