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Assessment of Surface Water Quality Using Wang et al., Irrigat Drainage Sys Eng 2018, 7:3 ge Syst ina em a s r D E DOI: 10.4172/2168-9768.1000214 n & g i n n o e i t e a r i g n i r g r Irrigation & Drainage Systems Engineering I ISSN: 2168-9768 Research Article Open Access Assessment of Surface Water Quality using Multivariate Statistical Techniques: A Case Study in China Wang Y*, Zhu G and Yu R Southeast University, Nanjing, Jiangsu, China Abstract In order to interpret the surface water quality of drinking water sources of Tongyu River and Mangshe River in Yancheng city, China, 18 water quality parameters were selected and data from 9 sampling sites during 2010 to 2015 from were collected and analyzed by multivariate statistical techniques, including cluster analysis (CA), principal component analysis (PCA), and factor analysis (FA). The sampling sites were classified into three clusters based on their similarities using a hierarchical CA, which represented relative low pollution sites, moderate pollution sites, and relative high pollution sites. By PCA/FA, six latent factors were identified that accounted for 75.39% of the total variance, representing the influences of organic pollution, fecal pollution, biochemical reactions, nutrients, domestic sewage, and natural factors, respectively. By pollution source analysis, the results were obtained that Sites 1, 2, and 3 were almost completely unaffected by various pollution sources, Sites 4 and 5 were polluted with industrial and domestic discharge, Sites 6, 7, and 8 were polluted with point and nonpoint sources from industrial activity, agriculture, and domestic drainage, and Site 9 was severely polluted with untreated domestic discharge from nearby inhabitants. The results verified that multivariate statistical techniques are useful, and may be necessary for analyzing and interpreting large, complex surface water quality databases, which could help managers optimize action plans to control drinking water quality. Keywords: Water quality; Cluster analysis; Principal component the east, and is adjacent to Yangzhou and Huai’an cities to the west, analysis; Factor analysis Lianyungang city to the north, and Nantong and Taizhou cities to the south. The district covers an area of about 14,983 km2, including Introduction 48.54 km2 in urban districts, while the remaining area is divided into Water quality has greatly deteriorated worldwide in the past nine counties, cities, and zones including Dongtai city, Dafeng city, decades, which is affected by both natural processes (precipitation Xiangshui County, Binhai County, Funing County, Jianhu county, rate, weathering processes, and soil erosion) and anthropogenic effects Sheyang County, Tinghu Zone, and Yandu Zone. The drinking water associated with excessive exploitation of water resources and untreated of Yancheng city is supplied by Mangshe River and Tongyu River, discharge of municipal and industrial wastewater [1-7]. The temporal which receive pollutants from domestic sewage, agricultural runoff, and spatial variations in surface water quality have been monitored aquaculture wastewater, and industrial effluent. Mangshe River by governments for years in order to prevent pollution of surface originates in Dazong Lake and discharges into the East Sea; with a total water bodies. However, long-term monitoring datasets are large, with length of nearly 50 km. Tongyu River has a total length of 415 km, complex matrixes comprising numerous physicochemical parameters. originating from Chang Jiang River and ending in Lianyungang city. Therefore, it is often difficult for planners to extract meaningful The middle reach of Tongyu River runs through Yancheng city, with 3 information from these datasets, identify significant parameters, and a length of 183.6 km and mean flow rate of about 100 m /s. At Wuyou apportion pollution sources [7-9]. Multivariate statistical techniques Port in Tongyu River, surface water is severely polluted by domestic such as cluster analysis (CA), principal component analysis (PCA), and wastewater from nearby settlements. Along the rivers there are nine factor analysis (FA) can be used to inspect complex datasets, evaluate monitoring sites (Fenghuang Bridge, Qinnanxi Bridge, Dazong Lake, water quality, and assess pollution sources. In recent years, a number of Dongtai Bridge, Baiju Bridge, Shuini Bridge, Xin-Gou, Datuan Bridge, studies have comprehensively applied different multivariate statistical and Wuyou Bridge) (Figure 1). Water quality parameters including techniques in water quality assessments for optimizing monitoring water temperature (T), pH, dissolved oxygen (DO), chemical oxygen networks, selecting representative water quality parameters without demand (CODCr), 5-day biochemical oxygen demand (BOD5), + losing meaningful information [5,6,10-12]. ammonia nitrogen (NH4 ), total phosphorus (TP), total nitrogen (TN), fluoride (F-), sulfate (SO 2-), chloride (Cl-), nitrate (NO -), alkalinity, In this study, 18 water quality parameters were selected and 4 3 turbidity, total dissolved solids (TDS), nitrite (NO -), Fecal coliforms collected from 2010 to 2015 at 9 sampling stations in Yancheng city, 2 (F. coli), and Escherichia coliforms (E. Coli) were selected to represent China. The multivariate statistical methods (i.e., CA, PCA, and FA) were applied to analyze the water quality data. Firstly, similarities and dissimilarities among 9 sampling stations were classified by mean of *Corresponding author: Wang Y, School of Energy and Environment, CA. Secondly, the complex water quality datasets were analyzed to Southeast University, Nanjing, Jiangsu, China, Tel: +86-13776415656; E-mail: extract latent water quality factors using PCA and FA. Finally, the [email protected] effects of possible pollution sources on water quality were identified. Received July 24, 2018; Accepted August 07, 2018; Published August 20, 2018 Methods Citation: Wang Y, Zhu G, Yu R (2018) Assessment of Surface Water Quality using Multivariate Statistical Techniques: A Case Study in China. Irrigat Drainage Sys Study area Eng 7: 214. doi: 10.4172/2168-9768.1000214 Yancheng city (32°51′–34°12′N, 119°34′–120°27′E) is an eastern Copyright: © 2018 Wang Y, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted coastal district in the center of Jiangsu Province, China, with a use, distribution, and reproduction in any medium, provided the original author and population of more than 8 million. It is bordered by the Yellow Sea to source are credited. Irrigat Drainage Sys Eng, an open access journal Volume 7 • Issue 3 • 1000214 ISSN: 2168-9768 Citation: Wang Y, Zhu G, Yu R (2018) Assessment of Surface Water Quality using Multivariate Statistical Techniques: A Case Study in China. Irrigat Drainage Sys Eng 7: 214. doi: 10.4172/2168-9768.1000214 Page 2 of 7 water quality characteristics, and analyzed semiannually from 2010 pH, turbidity (NTU), fecal coliforms (CFU/100 mL), and Escherichia to 2015 according to standard methods (APHA, 1998). All the water coliforms (CFU/100 mL). The statistical summary of the water quality quality parameters are expressed in mg L-1, except temperature (°C), parameters sampled at nine monitoring site was shown in Table 1. 1 = Xiangshui County 6 = Tinghu Zone 2 = Binhai County 7 = Yandu Zone 3 = Funing County 8 = Dafeng County 4 = Sheyang County 9 = Dongtai County 5 = Jinahu County Figure 1: Sampling sites in Yancheng city. Parameters Site 1 Site 2 Site 3 Site 4 Site 5 Site 6 Site 7 Site 8 Site 9 Range 14.3-27 14.2-26.8 14.6-26.6 5.2-27.4 5.2-27.2 16.2-19.1 14.2-19.2 5.6-26.8 18.4-26.8 T (°C) Mean 21.14 21.45 21.34 20.63 20.38 18.8 18.4 20.3 24.6 S.D. 5.21 4.99 5.03 6.66 6.64 4.51 5.01 7.07 3.99 Range 7.2-8.2 7.3-8.1 7.5-8.3 7.3-8.1 7.3-8.1 7.44-7.58 7.42-7.64 7.3-8.1 7.4-8 pH Mean 7.70 7.61 7.74 7.58 7.60 7.6 7.6 7.6 7.7 S.D. 0.29 0.26 0.33 0.19 0.23 0.12 0.09 0.26 0.32 Range 3.3-8.5 3.5-7.7 3.1-8.6 3.5-8.1 3.90-8.60 6.5-7.1 6.8-7.1 3.1-7.2 4-5 DO (mg/L) Mean 5.71 5.85 6.27 5.79 5.58 6.6 6.4 5.5 4.3 S.D. 1.63 1.42 1.80 1.75 1.66 0.91 1.33 1.47 0.45 Range 20.9-45.8 16.9-35.5 17.8-50.4 20.3-38.3 19.6-40.7 29.7-32.2 18.5-30.7 18.7-42.0 25.7-46.9 CODCr (mg/L) Mean 33.35 29.80 33.38 31.35 30.67 29.5 28.9 32.2 33.9 S.D. 8.21 5.06 7.88 5.22 6.24 4.82 5.88 7.54 10.21 Range 2.0-6.0 2.0-4.0 2.0-4.0 2.0-5.0 2.0-7.0 2.5-3 2.5-4 2-3.5 1-3 BOD 5 Mean 2.86 2.77 2.82 2.88 2.88 2.6 2.9 2.5 2.0 (mg/L) S.D. 1.19 0.69 0.64 0.88 1.37 0.22 0.65 0.56 0.82 Range 0.1-1.0 0.1-0.9 0.1-1.1 0.1-0.9 0.1-1.2 0.5-1.3 0.4-0.8 0.2-1.0 0.2-0.8 NH + 4 Mean 0.49 0.51 0.44 0.56 0.62 0.8 0.6 0.6 0.6 (mg/L) S.D.
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