石油开采区土壤污染物源解析、毒性及快速检测法研究
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摘要
在现代社会经济高速发展的今天,石油天然气等资源越来越重要,然而在石油的开采加工过程中会产生重金属、石油烃、多环芳烃等多种污染物,这些物质排放到水体、大气、土壤中,对周围环境产生了一定的影响。目前有关于石油开采加工产生的有机污染物对自然水体的污染研究较多,但是对陆地石油开采区土壤污染的研究较少。本论文对选定的陆地石油开采区土壤污染进行了初步研究,测试了石油开采区土壤特征污染物含量,研究了特征污染物空间、时间等分布规律,评价了土壤污染现状,揭示了土壤发光菌毒性与污染物含量间的关系,解析了主要污染物的来源及贡献率,并建立了相对快速的特征污染物检测方法。
通过对美国EPA相关标准方法的优化整合,建立了陆地石油开采区土壤中多环芳烃和石油烃同步前处理方法,并完成了土壤样品中12种重金属以及16种多环芳烃和石油烃的测试。结果显示除了重金属银和钡外,其他27种／类污染物均有检出。
利用基于地统计分析方法以及地理信息系统对陆地石油开采区土壤中特征污染物的空间、时间分布规律研究发现：特征污染物重金属、PAHs等在纵向上分布比较均匀,含量在95%置信区间没有显著性差异；横向上(0-30m)多数重金属和PAHs等污染物含量差异也不是很显著(95%置信区间),但是总体趋势是距井越近PAHs含量越高,而重金属是先增加(0-6m)然后再降低；各类污染物在不同采样井土壤中含量在95%置信区间差异较大,聚驱油井周围土壤PAHs含量显著增高,水驱油井周围土壤重金属含量显著增高,随着开采时间的增长污染物含量显著增加；另外,通过回归模拟建立的线性拟合模型及对数拟合模型,均可用于模拟石油开采场地土壤PHs浓度和TOC含量关系,可以通过TOC含量推断PHs含量分布规律。
通过比较国内外土壤污染等级评价方法优缺点,选取了适用于本论文的评价方法；通过对各国家和地区的标准值进行调研比较,选取较严格的标准值作为本论文评价标准；地累积指数法、单因子指数法以及内梅罗指数法评价结果显示所研究的石油开采区的土壤重金属污染较轻,而部分采样点的土壤均受到不同程度的PAHs的污染；内梅罗指数总体评价结果显示80%采样点污染较轻,处于清洁或者尚清洁状态,其他20%左右的采样点受到不同程度的污染。
相关性分析结果显示土壤发光菌毒性与重金属含量显著相关,而与PAHs含量不显著,而利用主成分回归分析方法建立了土壤发光菌毒性与重金属含量关系的模拟模型,发现重金属间交互作用可以影响重金属毒性在模型中的体现,影响模型的稳健性,从而影响土壤中重金属毒性作用的评估；对数据进行缺失值处理,并对主成分回归方法进行改进,引入重金属间交互作用,建立新的模拟土壤发光菌毒性与重金属含量关系模拟模型,模拟结果显示：重金属单独对发光菌毒性作用顺序是Cd>Co>Ni>Zn>Pb>Sb>Cr>Mn>Fe,而微量重金属Cu可能促进发光菌生存；重金属间交互作用较复杂,重金属Co-Zn、Co-Cu、Co-Cd、Cu-Pb、Cd-Sb、 Pb-Zn和Pb-Cd可能对土壤中发光菌的毒性是作用相互拮抗的,而Co-Ni、Co-Cr、 Co-Sb、Co-Pb、Cu-Sb、Cu-Cd、Cd-Ni、Cd-Fe、Pb-Cr和Pb-Sb等金属对发光菌的毒性作用是协同增加的。
通过比较选择了比值法、相关性分析以及多元统计分析的因子分析和聚类分析方法对陆地石油开采区重金属和PAHs来源进行识别,然后通过因子回归分析分别对重金属和PAHs来源贡献率进行估计,结果显示：陆地石油开采区土壤中重金属主要来源为自然来源、交通源、混合源、燃煤源、石油源和农业源,并且所占比例分别是37%、19%、18%、16%、5%和5%；解析的PAHs主要来源及比例是石油源占35%,其他依次为生物源20%、燃煤源17%、交通源16%。
初步研究发现,基于Raman光谱技术的多环芳烃测试可以应用于多环芳烃固体的鉴定,但是目前不适合应用于土壤样品中多环芳烃的直接检测。此外在现有工作基础上,建立了基于超声辅助-分散液液微萃取-上浮溶剂固化技术(UAE/DLLME/SFO)的土壤中的十六种PAHs快速测试方法,对影响土壤中多环芳烃UAE/DLLME/SFO萃取条件进行设计和优化,建立多环芳烃回收率与影响因子回归模型,模型决定系数R2在0.66-0.86之间,均可以通过统计学检验,预测值和实验值之间的标准偏差为0.49-3.01。
根据模型建立PAHs回收率响应曲面,确定UAE/DLLME/SFO萃取富集土壤中多环芳烃最优条件为萃取剂体积(X1)52.4μL、分散剂体积(X2)1.08mL、土壤质量(X3)0.54g、盐效应(X4)3.1%、超声时间(X5)3.1min、超声功率(X6)59kw；萃取回收率范围在40.91-94.91%(除CHR回收率9.12%外),相对偏差范围1.07-7.87%。所建立的多环芳烃测试方法的线性范围为两个数量级,检出限(S/N=3)为0.17-29.13μg/kg,相关系数为0.9864-0.9995。
期望本论文能为我国石油开采行业HSE(健康(Health)、安全(Safety)和环境(Environment))管理体系的建立、现代石油企业管理水平的提高、以及清洁生产和可持续发展的实现提供依据。
The significance of petroleum and natural gas to modern civilization is well known. However, it is inevitable for the oilfield to be contaminated during oil exploitation. These activities of oil industries have led to release of various organic and inorganic pollutants into the soil, air, and water, including trace elements, total petroleum hydrocarbons and polycyclic aromatic hydrocarbons. Numerous studies on the organic pollutants released into the water environment by oil and gas industries have been reported and a few researches focus on the contamination in soils generated by oil exploration and exploitation. The situation is worsening and already represents a threat to the environment, to food safety and to sustainable agriculture. In some areas of China, soil already suffers from varying degrees of pollution. Soil contamination has become one of the most pressing problems in our society.
In this study, a terrestrial oilfield was selected for the study of soil pollution. The contents of the typical soil pollutants were detected; the distribution of the typical soil pollutants were studied; grade division for soil pollutions were evaluated; the relationship between soil biotoxicity and contents of the typical soil pollutants were modeled; the source of the typical soil pollutants were identified and apportioned; and the rapid detection methods of the typical soil pollutants were developed.
A method for extraction and separation of petroleum hydrocarbons (PHs) and polycyclic aromatic hydrocarbons (PAHs) from heavy oil-polluted soil was established. Effects of several factors such as ultrasonic power, temperature, and elution solvents on the extraction were investigated. The experimental results indicated the optimal experimental conditions were obtained as follows:300watt of ultrasonic power,0℃of extraction solvents, n-hexane and dichloromethane/n hexane (1:1, v/v) used as elution solvents. Then12heavy metals (Pb, Cd, Cu, Zn, Ni, Cr, Co, Sb, Fe, Mn, Ag, and Ba) and16PAHs and PHs were tested, and all of them were detected except the silver and barium.
The distribution of the typical soil pollutants from the terrestrial petroleum exploitation in horizonal, longitudinal, severce years, mining types, and individual oil wells were investgated by statistical analysis and geographic information systems (GIS). The distributions of the typical soil pollutants (heavy metals, PHs, PAHs) were not significant different in longitudinal (the level of significance is0.05in0-50cm). In horizontal (0-30m), the PAHs contents in soils decrease with increasing distance from the well, the heavy metals increase with increasing distance from0to6m and then decrease with the increase in distance, but the difference was not significant in the95%confidence interval. The distributions of all the typical soil pollutants were significantly different in various oil wells (the level of significance is0.05), the PAHs contents in soils of polymer flooding wells were significantly higher than the water flooding wells, on the contrary, the heavy metals contents in soils of water flooding wells were significantly higher than the polymer flooding wells, and the contents of heavy metals and PAHs were significantly increased with the increase in severe years.
The linear regression model and logarithmic fitting model were developed to simulate the concentrations of PHs and the contents of Total Organic Carbon (TOC) in the soil by comparing the different regression models. The determination coefficients (R2) for linear regression model and logarithmic fitting model were0.715and0.874, respectively. The relative average deviations for the predictive values were9.2%and3.3%in a validation data set of14, with the Nash-Sutcliffe simulation efficiency coefficients (NSC) of0.957for the linear regression model and0.959for the logarithmic fitting model. The established models could also provide some information for the application of spectroscopy and remote-sensing technology in the monitoring of petroleum hydrocarbons.
The relationship between soil biotoxicity and ten heavy metals (Pb, Cd, Cu, Zn, Ni, Cr, Co, Sb, Fe, and Mn) in an oilfield from China was investigated through multivariable analysis. Multiple regression analysis was conducted to predict the toxicity of the ten heavy metals after mitigated the multicollinearity among the metals by principal component analysis. A multiple regression model was developed to reveal the relationship between the biotoxicity and the contents of ten heavy metals. It was found that the biotoxicity positive correlated with contents of Zn, Ni, Cr, Sb, Fe, and Mn, and negative correlated with contents of Cd, Co, Pb, and Cu. According to the multiple regression model, the contribution to the biotoxicity of the heavy metals in soils was in the order of Cr>Ni>Mn>Sb>Zn>Fe, and a negative influence on the biotoxicity was caused by other four heavy metals, following the order of Cd, Co, Pb, and Cu.
The potential interactions of ten heavy metals (Pb, Cd, Cu, Zn, Ni, Cr, Co, Sb, Fe and Mn) on soil biotoxicity were also investigated using a modified principal component regression (MPCR) model in an oilfield in China. Principal component analysis (PCA) was used to mitigate the multicollinearity, and an Expectation-Maximisation (EM) algorithm was applied as a missing value treatment to make the principal components readily interpretable. It was found that the MPCR can be used to model the interaction between heavy metals in the biotoxicity analysis of the oilfield soil. According to the modified PCR model, the Cu acts as a micronutrient in the soil for the activities of luminescent marine bacteria (Photobacterium phosphoreum), and the other nine metals have a toxic effect on the bacteria. Additionally, the interactions of metals between Co-Zn, Co-Cu, Co-Cd, Cu-Pb, Cd-Sb, Pb-Zn and Pb-Cd can be described as antagonistic, while the interactions between Co-Ni, Co-Cr, Co-Sb, Co-Pb, Cu-Sb, Cu-Cd, Cd-Ni, Cd-Fe, Pb-Cr and Pb-Sb can be described as synergistic.
Sources of the heavy metals and PAHs in soil from the terrestrial petroleum exploitation were identified by isotope ratios, correlation analysis and multivariate statistical analysis (factor analysis and cluster analysis), and the contribution rate of the main source of the typical soil pollutants were calculated by factor regression analysis. The result showed that the main sources for heavy metals in soil from the terrestrial petroleum exploitation were natural source, traffic source, mixed source, coal-fired source, petroleum source and agricultural source, and their relative contributions were37%,19%,18%,16%,5%and5%; while the major PAHs source was petroleum source with the relative contribution of35%, followed by biological source which account for20%, coal-fired source for17%, and traffic source for16%.
Preliminary study found that the test based on the Raman spectra of polycyclic aromatic hydrocarbons can be applied to the identification of pure polycyclic aromatic hydrocarbons in solid, but not suitable for the direct detection of polycyclic aromatic hydrocarbons used in the soil samples.
The condition of the ultrasound-assisted dispersive liquid-liquid, micro-extraction method based on solidification of floating organic drop (UAE/DLLME/SFO) technique for PAHs in soils were designed by central composite design, and optimized by the response surface plot. The ideal conditions were extractant (dodecanol) volume52.4μL, emulsifier (methanol) volume1.08mL, weight of the soil0.54g, salt effect (contain of NaCl)3.1%, ultrasonic time3.1min, ultrasonic power59kw. The optimized results of the recoveries were9.12-94.91%, with the relative standard deviation (RSD) of1.07-7.87%. Under the selected conditions, the linear range of PAHs was0.02-0.5, with the limits of detection of0.17-29.13μg/L and the correlation coefficient ranged from0.9950to0.9999.
The author hopes this paper would provide the basis for the establishment of the HSE (Health, Safety and Environment) management system in China's petroleum mining industry, and improve management level of the modern oil companies, as well as the cleaner production and sustainable development.
通过对美国EPA相关标准方法的优化整合,建立了陆地石油开采区土壤中多环芳烃和石油烃同步前处理方法,并完成了土壤样品中12种重金属以及16种多环芳烃和石油烃的测试。结果显示除了重金属银和钡外,其他27种／类污染物均有检出。
利用基于地统计分析方法以及地理信息系统对陆地石油开采区土壤中特征污染物的空间、时间分布规律研究发现：特征污染物重金属、PAHs等在纵向上分布比较均匀,含量在95%置信区间没有显著性差异；横向上(0-30m)多数重金属和PAHs等污染物含量差异也不是很显著(95%置信区间),但是总体趋势是距井越近PAHs含量越高,而重金属是先增加(0-6m)然后再降低；各类污染物在不同采样井土壤中含量在95%置信区间差异较大,聚驱油井周围土壤PAHs含量显著增高,水驱油井周围土壤重金属含量显著增高,随着开采时间的增长污染物含量显著增加；另外,通过回归模拟建立的线性拟合模型及对数拟合模型,均可用于模拟石油开采场地土壤PHs浓度和TOC含量关系,可以通过TOC含量推断PHs含量分布规律。
通过比较国内外土壤污染等级评价方法优缺点,选取了适用于本论文的评价方法；通过对各国家和地区的标准值进行调研比较,选取较严格的标准值作为本论文评价标准；地累积指数法、单因子指数法以及内梅罗指数法评价结果显示所研究的石油开采区的土壤重金属污染较轻,而部分采样点的土壤均受到不同程度的PAHs的污染；内梅罗指数总体评价结果显示80%采样点污染较轻,处于清洁或者尚清洁状态,其他20%左右的采样点受到不同程度的污染。
相关性分析结果显示土壤发光菌毒性与重金属含量显著相关,而与PAHs含量不显著,而利用主成分回归分析方法建立了土壤发光菌毒性与重金属含量关系的模拟模型,发现重金属间交互作用可以影响重金属毒性在模型中的体现,影响模型的稳健性,从而影响土壤中重金属毒性作用的评估；对数据进行缺失值处理,并对主成分回归方法进行改进,引入重金属间交互作用,建立新的模拟土壤发光菌毒性与重金属含量关系模拟模型,模拟结果显示：重金属单独对发光菌毒性作用顺序是Cd>Co>Ni>Zn>Pb>Sb>Cr>Mn>Fe,而微量重金属Cu可能促进发光菌生存；重金属间交互作用较复杂,重金属Co-Zn、Co-Cu、Co-Cd、Cu-Pb、Cd-Sb、 Pb-Zn和Pb-Cd可能对土壤中发光菌的毒性是作用相互拮抗的,而Co-Ni、Co-Cr、 Co-Sb、Co-Pb、Cu-Sb、Cu-Cd、Cd-Ni、Cd-Fe、Pb-Cr和Pb-Sb等金属对发光菌的毒性作用是协同增加的。
通过比较选择了比值法、相关性分析以及多元统计分析的因子分析和聚类分析方法对陆地石油开采区重金属和PAHs来源进行识别,然后通过因子回归分析分别对重金属和PAHs来源贡献率进行估计,结果显示：陆地石油开采区土壤中重金属主要来源为自然来源、交通源、混合源、燃煤源、石油源和农业源,并且所占比例分别是37%、19%、18%、16%、5%和5%；解析的PAHs主要来源及比例是石油源占35%,其他依次为生物源20%、燃煤源17%、交通源16%。
初步研究发现,基于Raman光谱技术的多环芳烃测试可以应用于多环芳烃固体的鉴定,但是目前不适合应用于土壤样品中多环芳烃的直接检测。此外在现有工作基础上,建立了基于超声辅助-分散液液微萃取-上浮溶剂固化技术(UAE/DLLME/SFO)的土壤中的十六种PAHs快速测试方法,对影响土壤中多环芳烃UAE/DLLME/SFO萃取条件进行设计和优化,建立多环芳烃回收率与影响因子回归模型,模型决定系数R2在0.66-0.86之间,均可以通过统计学检验,预测值和实验值之间的标准偏差为0.49-3.01。
根据模型建立PAHs回收率响应曲面,确定UAE/DLLME/SFO萃取富集土壤中多环芳烃最优条件为萃取剂体积(X1)52.4μL、分散剂体积(X2)1.08mL、土壤质量(X3)0.54g、盐效应(X4)3.1%、超声时间(X5)3.1min、超声功率(X6)59kw；萃取回收率范围在40.91-94.91%(除CHR回收率9.12%外),相对偏差范围1.07-7.87%。所建立的多环芳烃测试方法的线性范围为两个数量级,检出限(S/N=3)为0.17-29.13μg/kg,相关系数为0.9864-0.9995。
期望本论文能为我国石油开采行业HSE(健康(Health)、安全(Safety)和环境(Environment))管理体系的建立、现代石油企业管理水平的提高、以及清洁生产和可持续发展的实现提供依据。
The significance of petroleum and natural gas to modern civilization is well known. However, it is inevitable for the oilfield to be contaminated during oil exploitation. These activities of oil industries have led to release of various organic and inorganic pollutants into the soil, air, and water, including trace elements, total petroleum hydrocarbons and polycyclic aromatic hydrocarbons. Numerous studies on the organic pollutants released into the water environment by oil and gas industries have been reported and a few researches focus on the contamination in soils generated by oil exploration and exploitation. The situation is worsening and already represents a threat to the environment, to food safety and to sustainable agriculture. In some areas of China, soil already suffers from varying degrees of pollution. Soil contamination has become one of the most pressing problems in our society.
In this study, a terrestrial oilfield was selected for the study of soil pollution. The contents of the typical soil pollutants were detected; the distribution of the typical soil pollutants were studied; grade division for soil pollutions were evaluated; the relationship between soil biotoxicity and contents of the typical soil pollutants were modeled; the source of the typical soil pollutants were identified and apportioned; and the rapid detection methods of the typical soil pollutants were developed.
A method for extraction and separation of petroleum hydrocarbons (PHs) and polycyclic aromatic hydrocarbons (PAHs) from heavy oil-polluted soil was established. Effects of several factors such as ultrasonic power, temperature, and elution solvents on the extraction were investigated. The experimental results indicated the optimal experimental conditions were obtained as follows:300watt of ultrasonic power,0℃of extraction solvents, n-hexane and dichloromethane/n hexane (1:1, v/v) used as elution solvents. Then12heavy metals (Pb, Cd, Cu, Zn, Ni, Cr, Co, Sb, Fe, Mn, Ag, and Ba) and16PAHs and PHs were tested, and all of them were detected except the silver and barium.
The distribution of the typical soil pollutants from the terrestrial petroleum exploitation in horizonal, longitudinal, severce years, mining types, and individual oil wells were investgated by statistical analysis and geographic information systems (GIS). The distributions of the typical soil pollutants (heavy metals, PHs, PAHs) were not significant different in longitudinal (the level of significance is0.05in0-50cm). In horizontal (0-30m), the PAHs contents in soils decrease with increasing distance from the well, the heavy metals increase with increasing distance from0to6m and then decrease with the increase in distance, but the difference was not significant in the95%confidence interval. The distributions of all the typical soil pollutants were significantly different in various oil wells (the level of significance is0.05), the PAHs contents in soils of polymer flooding wells were significantly higher than the water flooding wells, on the contrary, the heavy metals contents in soils of water flooding wells were significantly higher than the polymer flooding wells, and the contents of heavy metals and PAHs were significantly increased with the increase in severe years.
The linear regression model and logarithmic fitting model were developed to simulate the concentrations of PHs and the contents of Total Organic Carbon (TOC) in the soil by comparing the different regression models. The determination coefficients (R2) for linear regression model and logarithmic fitting model were0.715and0.874, respectively. The relative average deviations for the predictive values were9.2%and3.3%in a validation data set of14, with the Nash-Sutcliffe simulation efficiency coefficients (NSC) of0.957for the linear regression model and0.959for the logarithmic fitting model. The established models could also provide some information for the application of spectroscopy and remote-sensing technology in the monitoring of petroleum hydrocarbons.
The relationship between soil biotoxicity and ten heavy metals (Pb, Cd, Cu, Zn, Ni, Cr, Co, Sb, Fe, and Mn) in an oilfield from China was investigated through multivariable analysis. Multiple regression analysis was conducted to predict the toxicity of the ten heavy metals after mitigated the multicollinearity among the metals by principal component analysis. A multiple regression model was developed to reveal the relationship between the biotoxicity and the contents of ten heavy metals. It was found that the biotoxicity positive correlated with contents of Zn, Ni, Cr, Sb, Fe, and Mn, and negative correlated with contents of Cd, Co, Pb, and Cu. According to the multiple regression model, the contribution to the biotoxicity of the heavy metals in soils was in the order of Cr>Ni>Mn>Sb>Zn>Fe, and a negative influence on the biotoxicity was caused by other four heavy metals, following the order of Cd, Co, Pb, and Cu.
The potential interactions of ten heavy metals (Pb, Cd, Cu, Zn, Ni, Cr, Co, Sb, Fe and Mn) on soil biotoxicity were also investigated using a modified principal component regression (MPCR) model in an oilfield in China. Principal component analysis (PCA) was used to mitigate the multicollinearity, and an Expectation-Maximisation (EM) algorithm was applied as a missing value treatment to make the principal components readily interpretable. It was found that the MPCR can be used to model the interaction between heavy metals in the biotoxicity analysis of the oilfield soil. According to the modified PCR model, the Cu acts as a micronutrient in the soil for the activities of luminescent marine bacteria (Photobacterium phosphoreum), and the other nine metals have a toxic effect on the bacteria. Additionally, the interactions of metals between Co-Zn, Co-Cu, Co-Cd, Cu-Pb, Cd-Sb, Pb-Zn and Pb-Cd can be described as antagonistic, while the interactions between Co-Ni, Co-Cr, Co-Sb, Co-Pb, Cu-Sb, Cu-Cd, Cd-Ni, Cd-Fe, Pb-Cr and Pb-Sb can be described as synergistic.
Sources of the heavy metals and PAHs in soil from the terrestrial petroleum exploitation were identified by isotope ratios, correlation analysis and multivariate statistical analysis (factor analysis and cluster analysis), and the contribution rate of the main source of the typical soil pollutants were calculated by factor regression analysis. The result showed that the main sources for heavy metals in soil from the terrestrial petroleum exploitation were natural source, traffic source, mixed source, coal-fired source, petroleum source and agricultural source, and their relative contributions were37%,19%,18%,16%,5%and5%; while the major PAHs source was petroleum source with the relative contribution of35%, followed by biological source which account for20%, coal-fired source for17%, and traffic source for16%.
Preliminary study found that the test based on the Raman spectra of polycyclic aromatic hydrocarbons can be applied to the identification of pure polycyclic aromatic hydrocarbons in solid, but not suitable for the direct detection of polycyclic aromatic hydrocarbons used in the soil samples.
The condition of the ultrasound-assisted dispersive liquid-liquid, micro-extraction method based on solidification of floating organic drop (UAE/DLLME/SFO) technique for PAHs in soils were designed by central composite design, and optimized by the response surface plot. The ideal conditions were extractant (dodecanol) volume52.4μL, emulsifier (methanol) volume1.08mL, weight of the soil0.54g, salt effect (contain of NaCl)3.1%, ultrasonic time3.1min, ultrasonic power59kw. The optimized results of the recoveries were9.12-94.91%, with the relative standard deviation (RSD) of1.07-7.87%. Under the selected conditions, the linear range of PAHs was0.02-0.5, with the limits of detection of0.17-29.13μg/L and the correlation coefficient ranged from0.9950to0.9999.
The author hopes this paper would provide the basis for the establishment of the HSE (Health, Safety and Environment) management system in China's petroleum mining industry, and improve management level of the modern oil companies, as well as the cleaner production and sustainable development.
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