AbstractEnvironmental contamination caused by leakage of fuels and lubricant oils at gas stations is of great c'/> Infrared spectroscopy and multivariate methods as a tool for identification and quantification of fuels and lubricant oils in soil
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Infrared spectroscopy and multivariate methods as a tool for identification and quantification of fuels and lubricant oils in soil

机译:红外光谱法和多元方法作为鉴定和定量土壤中燃料和润滑油的工具

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AbstractEnvironmental contamination caused by leakage of fuels and lubricant oils at gas stations is of great concern due to the presence of carcinogenic compounds in the composition of gasoline, diesel, and mineral lubricant oils. Chromatographic methods or non-selective infrared methods are usually used to assess soil contamination, which makes environmental monitoring costly or not appropriate. In this perspective, the present work proposes a methodology to identify the type of contaminant (gasoline, diesel, or lubricant oil) and, subsequently, to quantify the contaminant concentration using attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy and multivariate methods. Firstly, gasoline, diesel, and lubricating oil samples were acquired from gas stations and analyzed by gas chromatography to determine the total petroleum hydrocarbon (TPH) fractions (gasoline range organics, diesel range organics, and oil range organics). Then, solutions of these contaminants in hexane were prepared in the concentration range of about 5–10,000 mg kg−1. The infrared spectra of the solutions were obtained and used for the development of the pattern recognition model and the calibration models. The partial least square discriminant analysis (PLS-DA) model could correctly classify 100% of the samples of each type of contaminant and presented selectivity equal to 1.00, which provides a suitable method for the identification of the source of contamination. The PLS regression models were developed using multivariate filters, such as orthogonal signal correction (OSC) and general least square weighting (GLSW), and selection variable by genetic algorithm (GA). The validation of the models resulted in correlation coefficients above 0.96 and root-mean-square error of prediction values below the maximum permissible contamination limit (1000 mg kg−1). The methodology was validated through the addition of fuels and lubricating oil in soil samples and quantification of the TPH fractions through the developed models after the extraction of the analytes by the EPA 3550 method adapted by the authors. The recovery percentage of the analytes was within the acceptance limits of ASTM D7678 (70–130%), except for one sample (69% of recovery). Therefore, the methodology proposed here provides faster and less costly analyses than the chromatographic methods and it is adequate for the environmental monitoring of soil contamination by gas stations.
机译: 摘要 加油站燃料和润滑油泄漏造成的环境污染非常严重由于在汽油,柴油和矿物润滑油的成分中存在致癌化合物,因此引起关注。通常使用色谱法或非选择性红外法来评估土壤污染,这使环境监测成本高昂或不合适。从这个角度出发,本工作提出了一种方法来识别污染物(汽油,柴油或润滑油)的类型,然后使用衰减全反射傅里叶变换红外光谱(ATR-FTIR)光谱法和多元方法来量化污染物浓度。 。首先,从加油站获取汽油,柴油和润滑油样品,并通过气相色谱仪进行分析,以确定总石油烃(TPH)馏分(汽油范围的有机物,柴油范围的有机物和石油范围的有机物)。然后,制备这些污染物在己烷中的溶液,其浓度范围约为5–10,000 mg kg 上标> -1 。获得溶液的红外光谱,并将其用于模式识别模型和校准模型的开发。偏最小二乘判别分析(PLS-DA)模型可以正确分类每种类型的污染物的100%样品,其选择性等于1.00,这为识别污染源提供了一种合适的方法。 PLS回归模型是使用多元过滤器开发的,例如正交信号校正(OSC)和一般最小二乘加权(GLSW),以及通过遗传算法(GA)选择的变量。模型的验证导致相关系数高于0.96,预测值的均方根误差低于最大允许污染限值(1000 mg kg <上标> -1 )。作者通过改编的EPA 3550方法提取分析物之后,通过在开发的模型中添加燃料和润滑油并通过开发的模型对TPH分数进行定量,从而验证了该方法的有效性。除了一个样品(回收率的69%)外,分析物的回收率在ASTM D7678的接受范围内(70-130%)。因此,与色谱法相比,本文提出的方法提供了更快,成本更低的分析方法,对于加油站土壤污染的环境监测是足够的。

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