Determination of petroleum hydrocarbon contamination in soil using VNIR DRS and PLSR modeling

Abstract

Visible near infrared diffuse reflectance spectroscopy (VNIR DRS) is being proposed as a rapid and cheaper alternative to conventional soil analysis. This approach to soil analysis will be especially useful when conducting an environmental risk management for petroleum contamination in soil. This study evaluated the potential of VNIR diffuse reflectance spectra for rapid non-destructive quantitative analysis of extractible total petroleum hydrocarbon (ETPH) in soils. It also assessed the effect soil organic carbon (SOC) has on the performance of partial least square regression (PLSR) models developed for characterizing ETPH in soils. Model performance was evaluated based on the coefficient of determination (R 2 ), ratio of performance to deviation (RPD) and the root means square error (RMSE). Result show that VNIR DRS can be a potentially viable analytical tool for petroleum hydrocarbon contamination in soils. However, model quality was found to be affected by spatial variations within soil samples. Models developed from contaminated soils from highly variable geological origins had fair but promising model statistics (R 2 = 0.72, RPD = 1.4) as against excellent predictions obtained from contaminated soils with similar geology (R 2 = 0.97, RPD = 4.5) implying that the VNIR DR approach to characterizing petroleum hydrocarbon contamination in soils will be better suited to development of local prediction models. PLSR models developed for soil groups with SOC range (0.94–26.5% OC) gave quite robust prediction (R 2 = 0.90–0.97, RPD = 2.7–4.5), though a high SOC content slightly lowered PLSR model statistics. These results suggest that VNIR DRS can be quite useful for rapid characterization of petroleum hydrocarbon contamination especially when low budgets and reduced timelines are desirable for remediation purposes.

Analyzing Physical-Mechanical and Hydrophysical Properties of Sandy Soils Exposed to Long-Term Hydrocarbon Contamination

“…It is seen from Figure 9 that input of petroleum hydrocarbons in the upper part of the profile, namely the unsaturated zone built of air-dried sands, is accompanied by a sorption process [27][28][29]. According to published data, the sorption capacity of soils is measured by their granulometric size composition [30][31][32][33]. The highest capacity, in relation to hydrocarbons, is typical of clay soils, which are able to sorb and retain more than 40 L/m 3 .…”

Hyperspectral Estimation of Soil Organic Carbon Content Based on Continuous Wavelet Transform and Successive Projection Algorithm in Arid Area of Xinjiang, China

“…CWT-1/R 2 3 ,2 4 ,2 5 ,2 6 ,2 9 2 3 -1550,2 4 -1548,2 4 -1550,2 5 -1461,2 5 -1541,2 5 -1547,2 6 -2015,2 6 -2018,2 6 -2023,2 6 -2028,2 6 -2031, 2 6 -2033,2 9 -428,2 9 -439,2 9 -451 CWT-LgR 2 5 ,2 6 ,2 7 ,2 8 2 5 -2012,2 5 -2016,2 6 -415,2 6 -2004,2 6 -2038,2 7 -867,2 7 -1998,2 7 -2014,2 7 -2049,2 8 -2011,2 8 -2045…”

“…In contrast, the visible-near-infrared (NIR) spectroscopy technique is efficient, convenient, and pollution-free, with significant advantages in analyzing soil information, thus providing a new way to achieve large-scale and rapid monitoring of SOC. The new technique has gradually become a powerful tool for analyzing soil physical and chemical properties [6], facilitating the study of soil contamination with heavy metals, rare earth elements, and petroleum hydrocarbons in the field of ecology and environment [7][8][9]. Moreover, it is widely used to estimate soil salinity and soil nutrient content [10][11][12][13][14].…”

A Method for Quality Control of Bauxites: Case Study of Brazilian Bauxites Using PLSR on Transmission XRD Data

“…The RPD indicates how well the model performs compared to using only the average of the original data [26]. Some authors argue that RPD < 1.0 denotes a very poor model, 1.0 ≤ RPD < 1.4 a poor model, 1.4 ≤ RPD < 1.8 a fair model, 1.8 ≤ RPD < 2.0 a good model and RPD ≥ 2.0 an excellent model [26][27][28]. It can be observed that the models C1 and C2 for both AvAl 2 O 3 and RxSiO 2 performed well with RPD~2.0.…”

“…and to determine the composition of mineral-organic mixtures in soils, while PCA was used to compare the synthetic mixtures with respective soils. Olatunde [27] reported excellent results using PLSR on infrared data to estimate the extractible total petroleum hydrocarbon (ETPH) in soils. The author highlights the accuracy and rapidness of this method.…”