Simulation and Contamination. Keywords: Petroleum hydrocarbon pollution; source apportionment; PCA-MLR; Karst fissure groundwater

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1 nd International Conference on Sustainable Energy and Environment Protection (ICSEEP 2017) ISBN: Source Apportionment of Petroleum Hydrocarbon Contamination in Karst Fissure Groundwater Xiao Zhang 1,a, Zhenmin Ma 1,2,b*, XiYa Guo 3,c and Hao Lu 1,d 1 School of Resources and Environment, University of Jinan, Jinan , China 2 Shandong Provincial Engineering Technology Research Center for Groundwater Numerical Simulation and Contamination 3 University of the Chinese Academy of Science, Beijing , China a zhangx_0322@163.com, b* stu_mazm.stu.ujn.cn, c guoxiya16@mails.ucas.ac.cn, d jssqluhao@outlook.com Keywords: Petroleum hydrocarbon pollution; source apportionment; PCA-MLR; Karst fissure groundwater Abstract. Based on 22 groundwater samples collected from the monitoring wells in the study area, 10 petroleum hydrocarbons concentration data are applied to statistical methods, which the principal component analysis followed by multivariate linear regression(pca-mlr) was adopted to identify the possible sources and quantify the contributions of different pollution sources. Results showed that there existed three sources of pollution in the study area that are petroleum hydrocarbons pollution which derived from a refinery and an oil depot, chemical pollution which derived from a chemical plant, and machinery manufacturing pollution which derived from a mechanical factory. They contribute 68.26%, 10.11%, 21.63% to petroleum hydrocarbons pollution, respectively. Therefore, petroleum hydrocarbons pollution is the main source of groundwater pollution in the study area. Introduction Groundwater is very precious for us, however, with the rapid development of petrochemical industry, as well as the increasing demand for oil, groundwater has suffered from the pollution which derived from the accidental release of the surface, especially industrial products [1]. Leaking of the oil production, transportation and usage will allow oil infiltrate into the groundwater system, groundwater petroleum hydrocarbons pollution has become a widespread environmental problem. Petroleum hydrocarbons components are very complex, mainly including alkane, cycloalkanes, aromatic hydrocarbons, and some of these components such as benzene, naphthalene, anthracene and their derivatives have the so-called "three effects" which known as teratogenic, carcinogenic, mutagenic [2], causing a great threat to human health. The petroleum hydrocarbons pollution of groundwater is characterized by universality, harmfulness, complexity [3], difficulty removal and high repair cost [4]. Starting from the 1970, some studies had confirmed that the anthropogenic sources of petroleum hydrocarbons pollutants in groundwater, including the domestic and industrial wastes, the combustion of some fuels, as well as the transformation of non-hydrocarbon natural products into hydrocarbon-forming rocks [5]. Soils in the coastal areas of Mexico are severely polluted caused by refineries, and the main sources of pollutants result from the disposal of oil pipelines, valves, old tanks and untreated hydrocarbon deposits from cleaning tanks [6]. More than 30% petrol stations and most of the refineries, chemical plants in Britain existed the phenomena of leakage and arbitrary emission, leading to the serious petroleum hydrocarbons contamination of groundwater in gas station and petrochemical enterprises [7]. Therefore, in the process of oil exploitation, transportation, processing and storage, the oil hydrocarbon leakage caused by improper operation has seriously polluted the groundwater system. Multivariate statistical methods can able to identify the sources of pollution effectively. Using advanced statistical methods to interpret the temporal and spatial patterns of coastal water pollution problems in Hong Kong in order to identify the significant parameters and potential sources of 114

2 pollution, as well as to quantify the contributions of pollution sources [8]. In recent years, multivariate statistical methods have been widely applied to groundwater hydrology and geochemical characteristics [9, 10] and to identify groundwater pollution sources [11]. In this paper, the study area is industrial zone, with machinery, chemical, petroleum and other factories. The pollutants entered into the groundwater system through various ways, which has caused the groundwater was polluted by varying degrees, and even the deep groundwater has been polluted, especially the petroleum hydrocarbons contamination of karst fissure water in the study area. Qualitative and quantitative analysis by using principal component analysis to extract the main components which can identify the types of pollution sources, and multivariate linear regression was to determine the contributions of various sources. This paper analyzes the types, contributions of petroleum hydrocarbons sources in karst fissure water in the study area, which can provide theoretical support for the development of water resources protection, water pollution control and environmental supervision. Materials and methods Study Area. The study area is located in the west of a city that is near the Yellow River, which is situated in the southern mountainous area to the North Hill sloping Plain in the transitional zone, while its area covers 63.51km 2. Its annual average temperature is 13.7, and the annual rainfall is approximately 635mm, while the annual evaporation is up to 1819mm. The study area belongs to the transitional zone of tectonic low hill and hilly land in the landform, and the terrain of high in south and low in north, with three sides surrounded by mountains, belonging to a single oblique structure. Paleozoic sedimentary rocks are dominated by the outcrop strata in the south of the study area, and the total thickness is about 500m, the lower is variegated shale, while the middle are purple, dark gray shale, sandy mica shale and thick layer oolitic limestone, as well as the upper mainly are gray shale, bamboo leaf-like limestone, thick layer gray rock and massive dolomite limestone. The magma rocks are distributed in the northern part of the study area, buried in the fourth line of loose deposits, the production primarily is based on rock plate and rock bed, which lithology of gabbro and diorite dominated; the fourth line of loose accumulation is mainly distributed in the north of the research area, which characterized by uneven distribution, thickness of 0-30m, increasing thickness from south to north. However, its thickness is less than 10m near a refinery, with lithology mainly for gravel, viscous sand, sand and silt and so on. The groundwater system in research area mainly includes karst fissure water and the fourth-system pore water. Sampling Collection and Data. On the basis of collecting the results and conducting field investigation on hydrogeological conditions in the study area, the monitoring points were arranged according to the point-surface control arrangement principle, as well as satisfy the purpose of monitoring. 22 groundwater monitoring points were established, which are shown as Fig. 1 and samples were taken from karst fissure water aquifers at the stationary period of pollutants emission. In the sampling process, on-site pumping sampling was adopted, and storing it in 2L glass bottles, then adding concentrated hydrochloric acid so that the PH of water sample is less than 2. Water Samples seal preservation at low temperatures and be sent to Shandong Province Analysis Test Center on the same day for petroleum hydrocarbons detection. 115

3 Figure 1. Map of the sampling locations in the study area. Multivariate statistical analysis. Principal component analysis (PCA) take advantage of the concept of data dimensionality reduction to explain the relevance of multiple variables. By using fewer complex variables to represent the most information of the original variables, which was preserved to the greatest extent. New variables are unrelated to each other and thus the original variables can be represented by the linear combinations of several principal components. The main components are arranged by the variance from large to small, the first variable has the maximum variance, as the first principal component, while the second variance is followed, as the second principal component, in turn. In order to obtain better explanations of the factors, certain factors will be extracted by max variance rotation, each factor corresponds to an eigenvalue, which greater than 1 is retained as the main component. We can infer the pollutants composition of each principal component according to their loadings, furthermore, the pollution sources reflected by each principal component are deduced. In this paper, multivariate linear regression (MLR) takes the factor scores obtained by principal component analysis as the independent variables, with the total quantity of concentration data standardized as dependent variables, accordingly, the contributions of different principal components have been worked out, which represent pollution sources. Results and discussion Principal component Analysis. After standardizing the data of 10 petroleum hydrocarbons concentration in 22 groundwater monitoring wells in the study area, the principal component analysis was carried out by SPASS, and the three main components which eigenvalue is greater than 1 were extracted. The cumulative interpretation rate of the variance reaches 95.91%. Then, implementing the maximum variance rotation, and the new variance cumulative explanation rate (Table 1) is still 95.91%. However, the first principal component F1 accounted for 47.14% of the variance, the second principal component accounted for 27.83% of the variance, and the third principal component accounted for 20.94% of the variance, and the rotation of the component graph 116

is shown as Fig. 2. Hence, the three main factors could illustrate the source of the groundwater hydrocarbon contamination. Table 1. Total Variance Explained.

97 3 1.88 18.83 95.91 2.09 20.94 95.91 4 0.14 1.37 97.27 5 0.10 1.04 98.31 6 0.06 0.55 98.86 7 0.05 0.48 99.34 8 0.04 0.39 99.73 9 0.02 0.21 99.93 10 0.01 0.07 100.

2 that the first principal component is mainly benzene, toluene, dichloroethane, chloroform and bromine-dichloroethane, and all of them are petroleum hydrocarbon products, which derived from

Based on further field surveys, the pollutants are mainly from a refinery and a certain oil depot in study area; The second main component is 1,2-dichloroethylene and carbon tetrachloride, which

4 is shown as Fig. 2. Hence, the three main factors could illustrate the source of the groundwater hydrocarbon contamination. Table 1. Total Variance Explained. Component Initial Eigenvalues Rotation Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative % Extraction Method: Principal Component Analysis It may be seen from Fig. 2 that the first principal component is mainly benzene, toluene, dichloroethane, chloroform and bromine-dichloroethane, and all of them are petroleum hydrocarbon products, which derived from petroleum hydrocarbon sources. Based on further field surveys, the pollutants are mainly from a refinery and a certain oil depot in study area; The second main component is 1,2-dichloroethylene and carbon tetrachloride, which belong to the chemical pollution source, and mainly originate from a chemical plant in study area; The third main component is trichloroethylene, perchloroethylene and 1,2-dichloroethane, mainly used for parts processing and cleaning, which belong to the mechanical manufacturing source, and mainly derived from a mechanical factory in the study area. Figure 2. PCA results with A for benzene, B for toluene, C for dichloroethane, D for chloroform, E for bromine-dichloroethane, F for 1,2-dichloroethylene, G for carbon tetrachloride, H for trichloroethylene, I for perchloroethylene, J for 1,2-dichloroethane. 117

5 Source apportionment. The quantitative analysis of groundwater pollution sources was operated by calculating the contributions of pollution sources to total petroleum hydrocarbons contamination. The three types of pollution sources obtained by the principal component analysis are defined as three variables, the main components factor scores are F1, F2 and F3, respectively, representing the petroleum, chemical and mechanical manufacturing sources, which are as the independent variables, and the total amount of the concentration data standardized as the dependent variables, using SPASS for multivariate linear regression (MLR) to calculate the contributions of various sources. Regression coefficients of multivariate linear regression equations are as shown in Table 2. Table 2. Regression coefficients. Model Non-standardized coefficient Sig. R 2 B Standard error Constant 9.41E F F F The regression equation is: Z=6.69F1+1.02F2+1.87F3, in addition, the contributions of three sources can be obtained which are F1=68.26%, F2=10.11%, F3=21.63, respectively. In other words, as far as the contribution of petroleum hydrocarbons pollution is concerned, oil refineries, manufacturers and chemical plants are 68.26%, 21.63%, 10.11%, respectively. Therefore, the principal component analysis-multivariate linear regression(pca-mlr) method illustrates the main sources for petroleum hydrocarbons, chemical pollution and mechanical manufacturing pollution. Among them, petroleum hydrocarbon accounted for a larger proportion, and is the largest source of petroleum hydrocarbons pollution in groundwater system in the study area. Conclusions In this study, using statistical methods, the concentration datas of 10 petroleum hydrocarbons pollutants after standardized treatment were carried out on the principal component analysis and multivariate linear regression to determine the possible sources and the contributions of the pollution sources. Based on these works we draw the following conclusions: 1. The petroleum hydrocarbons pollutants can be classified into three sources, the first category is petroleum hydrocarbons source, including benzene, toluene, dichloroethane, chloroform and bromine-dichloroethane, which derived from a refinery and an oil depot; the second category is chemical source, including 1,2-dichloroethylene and carbon tetrachloride, which derived from a chemical plant; the third category is mechanical manufacturing source, including trichloroethylene, perchloroethylene and 1,2-dichloroethane, which derived from a mechanical factory. 2. The contributions of three pollution sources are 68.26%, 10.11% and 21.63%, respectively. It may be indicated that petroleum hydrocarbons pollution is the main source of petroleum hydrocarbons pollution in groundwater. Acknowledgements This work was financially supported by the Shandong Provincial Natural Science Foundation, China (ZR2014DM011) and Shandong provincial key research project, China (2015GSF117025). References [1] M. Yang, Y. S. Yang, X. Du, Y. Cao, and Y. Lei, Fate and Transport of Petroleum Hydrocarbons in Vadose Zone: Compound-specific Natural Attenuation, Water, Air, & Soil Pollution, vol. 224 (2013), p [2] S. Lesage, H. Xu, and K. S. Novakowski, Distinguishing Natural Hydrocarbons from Anthropogenic Contamination in Ground Water, Ground Water, vol. 35 (1996), p

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