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Variation of Groundwater Quality in Seawater Intrusion Area Using Cluster and Multivariate Factor Analysis

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Variation of Groundwater Quality in Seawater Intrusion Area Using Cluster and Multivariate Factor Analysis 2010 Sixth International Conference on Natural Computation (ICNC 2010) The 6th International Conference on Natural Computation (ICNC'10) Variation of Groundwater Quality in Seawater Intrusion Area Using Cluster and Multivariate Factor Analysis 1 2 1 Yi-Chu Huang *, Chou-Ping Yang , Yu-Chun Lee , Ping-Kuei Tang1, Wen-Ming Hsu1 Tien-Nien Wu 1Department of Environmental Science and Engineering Department of Environmental Engineering 2Center for Teaching Excellence Kun Shan University of Technology National Pingtung University of Science and Technology Tainan County, Taiwan Pingtung, Taiwan [email protected] 0 Abstrac Numerous monitoring data and water quality index 30 N 1 t- 2 19 Mainland 20 21 China 22 Central obtained from 76 regional shallow-layer monitoring wells are Taiwan 24 4 23 26 5 Kao-Ping 27 Mountains simplified and classified by applying the multivariate statistical Tropic of 6 25 Cancer 33 29 River 7 28 32 methods such as factor and cluster analysis to search for the 0 31 9 120 E 30 34 36 8 35 39 37 40 41 44 Study Site 38 interrelation between the water quality parameters, factors 43 10 42 3 11 47 Pingtung 45 46 51 48 representing the characteristics as well as possible pollution 49 50 12 County 56 54 52 14 55 Pacific N 13 sources of groundwater quality in Pingtung Champaign, 57 53 64 59 58 60 Ocean 15 Taiwan. Four principal factors were recognized in concerned 61 66 62 63 67 69 65 16 68 area using principal component analysis (PCA) including 70 17 71 72 74 salinization factor, mineralization factor, inorganic factor, and Legend: 73 75 18 inorganic reduction factor. All four factors can interpret 81.0% Sampling Wells variances of the integrated groundwater characteristics. In addition four clusters were classified according to the similar Taiwan Strait 76 and dissimilar characteristics of water quality of monitoring wells in Pingtung Champaign. The results showed that the groundwater quality of hinterland was better than that of coastal area. Some coastal areas have already been affected by the seawater intrusion resulting in aquifer salinization. Multivariate statistical methods provided by this study can not only reduce the harassment of the missing items of monitoring water quality, but also refer as a management alternative for Figure 1. Study area with the location of sampling wells. groundwater resources. Keywords: seawater intrusion; salinization; cluster II. MATERIALS AND METHODS analysis, principal component analysis A. Study Area I. INTRODUCTION Pingtung County is a strip-shaped region that belongs to the Pingtung Champaign groundwater sub-regions, one Either naturally occurring processes or human of the major groundwater resources in Taiwan. The area of activities may have a significant impact on the water Pingtung County situated in the tropic region of southern quality of aquifer that may limit its use. Although in Taiwan is approximately 2,775 km2 and extends 112 km Pingtung plain groundwater resource is abundant, the from north to south and 47 km from east to west (Fig. 1). unregulated exploitation of groundwater resources would The area is enclosed by the Kao-Ping River to the west, result in undesirable adverse impacts such as land the central mountains to the north and east, and the subsidence, saline water intrusion, and aquifer salination, especially the groundwater over-pumping problems for Taiwan Strait to the south. The Kao-Ping, Tung-Kung, and aquaculture along the costal regions of study area. Several Lin-Bian are the three major rivers that flow through the indexes and their correlation are necessary to determine area. Annual precipitation concentrates from May to the regional water quality characteristics of aquifer. The September and is around 2,700 mm at average. multivariate statistical analysis is utilized to process the Agriculture and aquaculture are the primary sources problems of multi-factors and multi-characteristics. It has of revenue in Pingtung area. To boost earnings, many been applied to study the interrelation and correlation of farmers have transformed their croplands into aquafarms. variables in the fields of environmental science, hydrology, Over the past 30 years, a large quantity of groundwater geology, etc. [1-7]. has been overly extracted from the shallow layer aquifer to The objective of this study is to utilize the factor and mix with seawater to supply those aquafarms. The over- cluster analysis to understand the factors affecting the pumping behaviors have caused serious land subsidence, groundwater quality and discriminate their influence area. seawater intrusion, flooding and deterioration of the Multivariate statistical methods provided by this study can surrounding environment [4]. The most serious land not only reduce the harassment of the missing items of subsidence and seawater intrusion area occurred at the monitoring water quality, but also refer as a management Donggang, Linbian, Jiadung, and Fangliau townships of alternative for the groundwater resources. Pingtung County. The most recent case, the rainfall of the typhoon Morakot on August 8 of 2009 reached 2,000 mm 978-1-4244-5959-9/10/$26.00 ©2010 IEEE 3021 The 6th International Conference on Natural Computation (ICNC'10) in two days resulting in serious disasters including stream As the measurement scales and numerical ranges of swollen, instantaneous inundation and seawater flooding the original variables vary widely, all raw data should be in Pingtung region. Vertical infiltration from the first standardized by Z-score mode to exclude the effects aquafarms and lateral intrusion of seawater are the main of different units of measurement, and make the data causes of local shallow groundwater salinization along the dimensionless. Standardization is to make each variable coastal area of Pingtung. within the original data matrix subtract the column mean and then is divided by the column standard deviation. The Regional groundwater recharges are mainly from the variances/co-variances and correlation coefficients infiltration of the western Kao-Ping River as well as the measures how well the variance of each constituent can be northern and eastern mountains of the Pingtung Plain. The explained by relationships with each of the others. Then, hydro-geological stratification from top to bottom can be eigenvalues and eigenvectors were calculated for the divided into four aquifers and three aquitards within depth covariance matrix and the data were also transformed into of 220 m in the groundwater sub-regions of Pingtung factors [2, 4-5]. Champaign. In general, the thickness of aquifers is large PCA is based on the diagonalization of the correlation and extends over the whole region. Aquitard is matrix that can give the overall coherence of the data set. sandwiched between the aquifers within 220 m below the By utilizing PCA, the original p-dimensional standardized ground. Monitoring wells whose permeability coefficients data matrix is transformed into an m-dimensional PC ranging from 1.0×10-4 to 9.9×10-4 m s-1, from 1.0×10-5 to matrix with less degree of freedom, i.e. p<m. Considering 9.9×10-5 m s-1, and greater than 1.0×10-3 m s-1 are the correlations present in the original data, PCs can classified to good-class (68.5% of monitoring wells), reduce the overall complexity of the data and still reserve medium (14.2%), and excellent level permeability inherent inter-dependencies. Only factors with eigenvalues (13.4%), respectively. The thickness of the first aquifer greater than 1 are preserved for further processed [11]. covering the entire region ranges from 23.5 to 83.5 m with Generally speaking, the first few PCs explain the majority an average thickness of about 49.9 m. In this study, the of the variance within the original dataset, then the first periodically sampled groundwater quality data are mainly one accounts for the most variance and each subsequent from the first aquifer. PC explains progressively less. The factor loadings are B. Groundwater Data responsible for the correlations between PCs and selected variables, and those with the greatest positive and negative There are 76 monitoring wells set up on the shallow loadings make the largest contribution. As a result, the aquifer and periodically surveyed since 2003 (Fig. 1). loadings can offer more information to track the sources These wells with depth ranging from 8 to 30 m were that are responsible for the similarities of collected designed to monitor the groundwater quality with an samples in groundwater quality [2]. emphasis on aquifer salinization and potential groundwater contamination in the concerned region. From D. Cluster Analysis 2003 to 2008, groundwater quality including total Cluster analysis (CA) is a statistical tool to classify - hardness (TH), total dissolved solids (TDS), chloride (Cl ), the true groups of data according to their similarities to 2- sulfate (SO4 ), total organic carbon (TOC), nitrate each other. All variables were also standardized by Z- nitrogen (NO3-N), nitrite nitrogen (NO2-N), ammonia score mode before being subjected to CA. A short nitrogen (NH3-N), iron (Fe) and manganese (Mn) were Euclidean distant implies the high similarity between the selected as their more complete geochemical data [8]. measured objects. In clustering, the distinct groups can C. Factor Analysis reveal either the interaction among the variables (R-mode) or the interrelation among the samples (Q-mode) [4-5]. The multivariate statistical process of environmental Two types of CA methods, hierarchical cluster analysis data is widely used to characterize and assess the surface and nonhierarchical cluster analysis, have been performed water and groundwater quality, and it is useful for in two-step procedures in this study. Ward’s clustering evidencing temporal and spatial variations caused by procedure commonly used in hierarchical method of natural and human factors linked to seasonality [4, 9-10]. cluster analysis was employed to identify the number of With the aid of multivariate statistical techniques, the clusters.
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