Assessment of temporal and spatial variations in water quality using multivariate statistical methods: a case study of the Xin'anjiang River, China

Abstract

This study evaluated the temporal and spatial variations of water quality data sets for the Xin'anjiang River through the use of multivariate statistical techniques, including cluster analysis (CA), discriminant analysis (DA), correlation analysis, and principal component analysis (PCA). The water samples, measured by ten parameters, were collected every month for three years (2008–2010) from eight sampling stations located along the river. The hierarchical CA classified the 12 months into three periods (First, Second and Third Period) and the eight sampling sites into three groups (Groups 1, 2 and 3) based on seasonal differences and various pollution levels caused by physicochemical properties and anthropogenic activities. DA identified three significant parameters (temperature, pH and E.coli) to distinguish temporal groups with close to 76% correct assignment. The DA also discovered five parameters (temperature, electricity conductivity, total nitrogen, chemical oxygen demand and total phosphorus) for spatial variation analysis, with 80.56% correct assignment. The non–parametric correlation coefficient (Spearman R) explained the relationship between the water quality parameters and the basin characteristics, and the GIS made the results visual and direct. The PCA identified four PCs for Groups 1 and 2, and three PCs for Group 3. These PCs captured 68.94%, 67.48% and 70.35% of the total variance of Groups 1, 2 and 3, respectively. Although natural pollution affects the Xin'anjiang River, the main sources of pollution included agricultural activities, industrial waste, and domestic wastewater.

Keywords

Xin'anjiang River / multivariable statistical analysis / temporal variation / spatial variation / water quality

References

[1] Liu J, Diamond J. China’s environment in a globalizing world. Nature, 2005, 435(7046): 1179–1186

[2] Liu J, Yang W. Water management. Water sustainability for China and beyond. Science, 2012, 337(6095): 649–650

[3] Yu C, Gong P, Yin Y. China’s water crisis needs more than words. Nature, 2011, 470(7334): 307–307

[4] Ma X, Ortolano L. Environmental regulation in China: Institutions, enforcement, and compliance. Washington, DC: Rowman & Littlefield, 2000

[5] Christophersen N, Hooper R P. Multivariate analysis of stream water chemical data: The use of principal components analysis for the end‐member mixing problem. Water Resources Research, 1992, 28(1): 99–107

[6] Burns D A, McDonnell J J, Hooper R P, Peters N E, Freer J E, Kendall C, Beven K. Quantifying contributions to storm runoff through end‐member mixing analysis and hydrologic measurements at the Panola Mountain Research Watershed (Georgia, USA). Hydrological Processes, 2001, 15(10): 1903–1924

[7] Huang J, Li Q, Pontius R G Jr, Klemas V, Hong H. Detecting the dynamic linkage between landscape characteristics and water quality in a subtropical coastal watershed, Southeast China. Environmental Management, 2013, 51(1): 32–44

[8] Palma P, Ledo L, Soares S, Barbosa I R, Alvarenga P. Spatial and temporal variability of the water and sediments quality in the Alqueva reservoir (Guadiana Basin; southern Portugal). Science of the Total Environment, 2014, 470–471: 780–790

[9] Wang Y B, Liu C W, Liao P Y, Lee J J. Spatial pattern assessment of river water quality: implications of reducing the number of monitoring stations and chemical parameters. Environmental Monitoring and Assessment, 2014, 186(3): 1781–1792

[10] Mostafaei A. Application of multivariate statistical methods and water-quality index to evaluation of water quality in the Kashkan River. Environmental Management, 2014, 53(4): 865–881

[11] Shi W, Zeng W.Application of k-means clustering to environmental risk zoning of the chemical industrial area. Frontiers of Environmental Science & Engineering, 2014, 8(1): 117–127

[12] Li Q, Song J, Wei A, Zhang B. Changes in major factors affecting the ecosystem health of the Weihe River in Shaanxi Province, China. Frontiers of Environmental Science & Engineering, 2013, 7(6): 875–885

[13] Wang X, Cai Q, Ye L, Qu X. Evaluation of spatial and temporal variation in stream water quality by multivariate statistical techniques: A case study of the Xiangxi River basin, China. Quaternary International, 2012, 282: 137–144

[14] Wang Y, Zhang J, Feeley K, Jiang P, Ding P. Life‐history traits associated with fragmentation vulnerability of lizards in the Thousand Island Lake, China. Animal Conservation, 2009, 12(4): 329–337

[15] Wang Y, Chen S, Ding P. Testing multiple assembly rule models in avian communities on islands of an inundated lake, Zhejiang Province, China. Journal of Biogeography, 2011, 38(7): 1330–1344

[16] Singh K P, Malik A, Mohan D, Sinha S. Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River (India)—a case study. Water Research, 2004, 38(18): 3980–3992

[17] Zhang Y, Guo F, Meng W, Wang X Q. Water quality assessment and source identification of Daliao River Basin using multivariate statistical methods. Environmental Monitoring and Assessment, 2009, 152(1–4): 105–121

[18] Sundaray S K. Application of multivariate statistical techniques in hydrogeochemical studies—a case study: Brahmani–Koel River (India). Environmental Monitoring and Assessment, 2010, 164(1–4): 297–310

[19] Alberto W D, María del Pilar D, María Valeria A, Fabiana P S, Cecilia H A, María de los Ángeles B. Marrí del Pilar D a, Marrí Valeria A, Fabiana P S, Cecilia H A, Marrí de los Ángeles B. Pattern recognition techniques for the evaluation of spatial and temporal variations in water quality. A case study: Suquía A River Basin (Córdoba–Argentina). Water Research, 2001, 35(12): 2881–2894

[20] Awadallah A G, Yousry M. Identifying homogeneous water quality regions in the Nile River using multivariate statistical analysis. Water Resources Management, 2012, 26(7): 2039–2055

[21] Guo L, Zhao Y, Wang P. Determination of the principal factors of river water quality through cluster analysis method and its prediction. Frontiers of Environmental Science & Engineering, 2012, 6(2): 238–245

[22] Singh K P, Malik A, Sinha S. Water quality assessment and apportionment of pollution sources of Gomti river (India) using multivariate statistical techniques—a case study. Analytica Chimica Acta, 2005, 538(1): 355–374

[23] Sultana J, Farooqi A, Ali U. Arsenic concentration variability, health risk assessment, and source identification using multivariate analysis in selected villages of public water system, Lahore, Pakistan. Environmental Monitoring and Assessment, 2014, 186(2): 1241–1251

[24] Gomes A I, Pires J C, Figueiredo S A, Boaventura R A. Optimization of river water quality surveys by multivariate analysis of physicochemical, bacteriological and ecotoxicological data. Water Resources Management, 2014, 28: 1345–1361

[25] Heckler C E. Applied multivariate statistical analysis. Technometrics, 2005, 47(4): 517–517

[26] Juahir H, Zain S M, Yusoff M K, Hanidza T I, Armi A S, Toriman M E, Mokhtar M. Spatial water quality assessment of Langat River Basin (Malaysia) using environmetric techniques. Environmental Monitoring and Assessment, 2011, 173(1–4): 625–641

[27] Wang L, Wang Y, Zhang W, Xu C, An Z. Multivariate statistical techniques for evaluating and identifying the environmental significance of heavy metal contamination in sediments of the Yangtze River, China. Environmental Earth Sciences, 2014, 71(3): 1183–1193

[28] Jang C S, Chen J S, Lin Y B, Liu C W. Characterizing hydrochemical properties of springs in Taiwan based on their geological origins. Environmental Monitoring and Assessment, 2012, 184(1): 63–75

[29] Vega M, Pardo R, Barrado E, Debán L. Assessment of seasonal and polluting effects on the quality of river water by exploratory data analysis. Water Research, 1998, 32(12): 3581–3592

[30] Filik Iscen C, Emiroglu O, Ilhan S, Arslan N, Yilmaz V, Ahiska S. Application of multivariate statistical techniques in the assessment of surface water quality in Uluabat Lake, Turkey. Environmental Monitoring and Assessment, 2008, 144(1–3): 269–276

[31] Li Y, Tang C, Yu Z, Acharya K. Correlations between algae and water quality: factors driving eutrophication in Lake Taihu, China. International Journal of Environmental Science and Technology, 2014, 11(1): 169–182

[32] Frenzel S A, Couvillion C S. Fecal‐indicator bacteria in streams along a gradient of residential development. Journal of the American Water Resources Association, 2002, 38(1): 265–273

[33] Zhou F, Huang G H, Guo H, Zhang W, Hao Z. Spatio–temporal patterns and source apportionment of coastal water pollution in eastern Hong Kong. Water Research, 2007, 41(15): 3429–3439

[34] Shrestha S, Kazama F. Assessment of surface water quality using multivariate statistical techniques: A case study of the Fuji river basin, Japan. Environmental Modelling & Software, 2007, 22(4): 464–475

[35] Simeonov V, Stratis J A, Samara C, Zachariadis G, Voutsa D, Anthemidis A, Sofoniou M, Kouimtzis T. Assessment of the surface water quality in Northern Greece. Water Research, 2003, 37(17): 4119–4124

Acknowledgements

This work was supported by the Major Science and Technology Program for Water Pollution Control and Treatment Foundation (Grant No.2008ZX07631-001) and Comprehensive Program for Water Pollution Control and Treatment (Grant No.2012A012).Appendix is available in the online version of this article at http://dx.doi.org/10.1007/s11783-014-0736-z and is accessible for authorized users.

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg