Chakraborty Somsubhra, Weindorf David C, Zhu Yuanda, Li Bin, Morgan Cristine L S, Ge Yufeng, Galbraith John
IRDM Faculty Center, Ramakrishna Mission Vivekananda University, Kolkata 700103, India.
J Environ Monit. 2012 Nov;14(11):2886-92. doi: 10.1039/c2em30330b.
Visible near-infrared (VisNIR) diffuse reflectance spectroscopy (DRS) is a rapid, non-destructive method for sensing the presence and amount of total petroleum hydrocarbon (TPH) contamination in soil. This study demonstrates the feasibility of VisNIR DRS to be used in the field to proximally sense and then map the areal extent of TPH contamination in soil. More specifically, we evaluated whether a combination of two methods, penalized spline regression and geostatistics could provide an efficient approach to assess spatial variability of soil TPH using VisNIR DRS data from soils collected from an 80 ha crude oil spill in central Louisiana, USA. Initially, a penalized spline model was calibrated to predict TPH contamination in soil by combining lab TPH values of 46 contaminated and uncontaminated soil samples and the first-derivative of VisNIR reflectance spectra of these samples. The r(2), RMSE, and bias of the calibrated penalized spline model were 0.81, 0.289 log(10) mg kg(-1), and 0.010 log(10) mg kg(-1), respectively. Subsequently, the penalized spline model was used to predict soil TPH content for 128 soil samples collected over the 80 ha study site. When assessed with a randomly chosen validation subset (n = 10) from the 128 samples, the penalized spline model performed satisfactorily (r(2) = 0.70; residual prediction deviation = 2.0). The same validation subset was used to assess point kriging interpolation after the remaining 118 predictions were used to produce an experimental semivariogram and map. The experimental semivariogram was fitted with an exponential model which revealed strong spatial dependence among soil TPH [r(2) = 0.76, nugget = 0.001 (log(10) mg kg(-1))(2), and sill 1.044 (log(10) mg kg(-1))(2)]. Kriging interpolation adequately interpolated TPH with r(2) and RMSE values of 0.88 and 0.312 log(10) mg kg(-1), respectively. Furthermore, in the kriged map, TPH distribution matched with the expected TPH variability of the study site. Since the combined use of VisNIR prediction and geostatistics was promising to identify the spatial patterns of TPH contamination in soils, future research is warranted to evaluate the approach for mapping spatial variability of petroleum contaminated soils.
可见近红外（VisNIR）漫反射光谱法（DRS）是一种快速、无损的方法，用于检测土壤中总石油烃（TPH）污染的存在和含量。本研究证明了VisNIR DRS用于现场近端检测并绘制土壤中TPH污染区域范围的可行性。更具体地说，我们评估了惩罚样条回归和地质统计学这两种方法的组合是否能提供一种有效的方法，利用从美国路易斯安那州中部一个80公顷原油泄漏现场采集的土壤的VisNIR DRS数据来评估土壤TPH的空间变异性。最初，通过结合46个受污染和未受污染土壤样本的实验室TPH值以及这些样本的VisNIR反射光谱的一阶导数，校准了一个惩罚样条模型来预测土壤中的TPH污染。校准后的惩罚样条模型的r(2)、RMSE和偏差分别为0.81、0.289 log(10) mg kg(-1)和0.010 log(10) mg kg(-1)。随后，使用惩罚样条模型预测在80公顷研究区域采集的128个土壤样本的土壤TPH含量。当用从128个样本中随机选择的验证子集（n = 10）进行评估时，惩罚样条模型表现令人满意（r(2) = 0.70；残差预测偏差 = 2.0）。在将其余118个预测值用于生成实验半方差图和地图后，使用相同的验证子集评估点克里金插值。实验半方差图用指数模型拟合，结果显示土壤TPH之间存在很强的空间依赖性[r(2) = 0.76，块金值 = 0.001 (log(10) mg kg(-1))(2)，基台值 = 1.044 (log(10) mg kg(-1))(2)]。克里金插值能够充分插值TPH，r(2)和RMSE值分别为0.88和0.312 log(10) mg kg(-1)。此外，在克里金图中，TPH分布与研究区域预期的TPH变异性相匹配。由于VisNIR预测和地质统计学的联合使用有望识别土壤中TPH污染的空间模式，因此有必要开展未来研究来评估该方法用于绘制石油污染土壤空间变异性的效果。
