School of Water, Energy and Environment, Cranfield University, Cranfield MK43 0AL, UK.
Department of Environment, Ghent University, Coupure 653, 9000 Gent, Belgium.
Sci Total Environ. 2018 Jun 1;626:1108-1120. doi: 10.1016/j.scitotenv.2018.01.122. Epub 2018 Feb 19.
This study investigated the sensitivity of visible near-infrared spectroscopy (vis-NIR) to discriminate between fresh and weathered oil contaminated soils. The performance of random forest (RF) and partial least squares regression (PLSR) for the estimation of total petroleum hydrocarbon (TPH) throughout the time was also explored. Soil samples (n = 13) with 5 different textures of sandy loam, sandy clay loam, clay loam, sandy clay and clay were collected from 10 different locations across the Cranfield University's Research Farm (UK). A series of soil mesocosms was then set up where each soil sample was spiked with 10 ml of Alaskan crude oil (equivalent to 8450 mg/kg), allowed to equilibrate for 48 h (T2 d) and further kept at room temperature (21 °C). Soils scanning was carried out before spiking (control TC) and then after 2 days (T2 d) and months 4 (T4 m), 8 (T8 m), 12 (T12 m), 16 (T16 m), 20 (T20 m), 24 (T24 m), whereas gas chromatography mass spectroscopy (GC-MS) analysis was performed on T2 d, T4 m, T12 m, T16 m, T20 m, and T24 m. Soil scanning was done simultaneously using an AgroSpec spectrometer (305 to 2200 nm) (tec5 Technology for Spectroscopy, Germany) and Analytical Spectral Device (ASD) spectrometer (350 to 2500 nm) (ASDI, USA) to assess and compare their sensitivity and response against GC-MS data. Principle component analysis (PCA) showed that ASD performed better than tec5 for discriminating weathered versus fresh oil contaminated soil samples. The prediction results proved that RF models outperformed PLSR and resulted in coefficient of determination (R) of 0.92, ratio of prediction deviation (RPD) of 3.79, and root mean square error of prediction (RMSEP) of 108.56 mg/kg. Overall, the results demonstrate that vis-NIR is a promising tool for rapid site investigation of weathered oil contamination in soils and for TPH monitoring without the need of collecting soil samples and lengthy hydrocarbon extraction for further quantification analysis.
本研究旨在探讨可见近红外光谱（vis-NIR）对鉴别新鲜和风化油污染土壤的灵敏度。还探索了随机森林（RF）和偏最小二乘回归（PLSR）在整个时间内估算总石油烃（TPH）的性能。从克兰菲尔德大学研究农场（英国）的 10 个不同地点收集了具有砂壤土、砂壤土、壤土、砂壤土和粘土 5 种不同质地的土壤样本（n=13）。然后设置了一系列土壤中试，在每个土壤样本中加入 10ml 的阿拉斯加原油（相当于 8450mg/kg），平衡 48 小时（T2d），并在室温（21°C）下进一步保持。在喷洒前（对照 TC）和 2 天后（T2d）、4 个月（T4m）、8 个月（T8m）、12 个月（T12m）、16 个月（T16m）、20 个月（T20m）、24 个月（T24m）进行土壤扫描，然后进行气相色谱-质谱（GC-MS）分析。同时使用 AgroSpec 光谱仪（305 至 2200nm）（tec5 光谱技术，德国）和 Analytical Spectral Device（ASD）光谱仪（350 至 2500nm）（ASDI，美国）对土壤进行扫描，以评估和比较它们对 GC-MS 数据的灵敏度和响应。主成分分析（PCA）表明，ASD 比 tec5 更能区分风化油和新鲜油污染的土壤样本。预测结果表明，RF 模型优于 PLSR，得到了 0.92 的决定系数（R）、3.79 的预测偏差比（RPD）和 108.56mg/kg 的预测均方根误差（RMSEP）。总的来说，结果表明 vis-NIR 是一种很有前途的工具，可用于快速现场调查土壤中风化油的污染情况，以及进行 TPH 监测，而无需采集土壤样本和进行冗长的烃类提取进行进一步定量分析。
