Spatial assessment of water quality using chemometrics in the Pearl River Estuary, China

Abstract

A cruise was commissioned in the summer of 2009 to evaluate water quality in the Pearl River Estuary (PRE). Chemometrics such as Principal Component Analysis (PCA), Cluster analysis (CA) and Self-Organizing Map (SOM) were employed to identify anthropogenic and natural influences on estuary water quality. The scores of stations in the surface layer in the first principal component (PC1) were related to NH₄-N, PO₄-P, NO₂-N, NO₃-N, TP, and Chlorophyll a while salinity, turbidity, and SiO₃-Si in the second principal component (PC2). Similarly, the scores of stations in the bottom layers in PC1 were related to PO₄-P, NO₂-N, NO₃-N, and TP, while salinity, Chlorophyll a, NH₄-N, and SiO₃-Si in PC2. Results of the PCA identified the spatial distribution of the surface and bottom water quality, namely the Guangzhou urban reach, Middle reach, and Lower reach of the estuary. Both cluster analysis and PCA produced the similar results. Self-organizing map delineated the Guangzhou urban reach of the Pearl River that was mainly influenced by human activities. The middle and lower reaches of the PRE were mainly influenced by the waters in the South China Sea. The information extracted by PCA, CA, and SOM would be very useful to regional agencies in developing a strategy to carry out scientific plans for resource use based on marine system functions.

Keywords

principal component analysis / self-organizing map / estuarine water quality / the Pearl River Estuary / spatial variation

References

[1] Astel A, Tsakovski S, Barbieri P, Simeonov V (2007). Comparison of self-organizing maps classification approach with cluster and principal components analysis for large environmental data sets. Water Res, 41(19): 4566–4578

[2] Astel A, Tsakovski S, Simeonov V, Reisenhofer E, Piselli S, Barbieri P (2008). Multivariate classification and modeling in surface water pollution estimation. Anal Bioanal Chem, 390(5): 1283–1292

[3] Cereghino R, Park Y S (2009). Review of the Self-Organizing Map (SOM) approach in water resources. Environ Model Softw, 24(8): 945–947 (Commentary)

[4] Chen J C, Heinke G W, Zhou M J (2004). The Pearl River Estuary Pollution Project (PREPP). Cont Shelf Res, 24(16): 1739–1744

[5] Cheung K C, Leung H M, Wong M H (2008). Metal concentrations of common freshwater and marine fish from the Pearl River Delta, South China. Arch Environ Contam Toxicol, 54(4): 705–715

[6] Çinar O, Merdun H (2009). Application of an unsupervised artificial neural network technique to multivariant surface water quality data. Ecol Res, 24(1): 163–173

[7] Dou L, Du H Y, He J M, You Y H, Zhou Y Z, Liu Z N, Zhu X (2009). Spatial distribution and sources of heavy metals in soil of Dongguan City, Pearl River Delta Economic Zone, Guangdong Province, China. Environ Sci, 29(12): 3496–3502

[8] Duzgoren-Aydin N S (2007). Sources and characteristics of lead pollution in the urban environment of Guangzhou. Sci Total Environ, 385(1−3): 182–195

[9] Hall P O J, Holby O, Kollberg S, Samuelson M O (1992). Chemical fluxes and mass balances in a marine fish cage farm.4. Nitrogen. Mar Ecol Prog Ser, 89: 81–91

[10] Harrison P J, Yin K D, Lee J H W, Gan J P, Liu H B (2005). Physical-biological coupling in the Pearl River Estuary. Symposium on Coastal Ecosystem Responses to Changing Nutrient Inputs from Large Temperate and Sub-Tropical Rivers, Fujian, PEOPLES R CHINA, 1405–1415

[11] Hodgkiss I J, Lu S H (2004). The effects of nutrients and their ratios on phytoplankton abundance in Junk Bay, Hong Kong. Hydrobiologia, 512(1−3): 215–229

[12] Holby O, Hall P O J (1991). Chemical fluxes and mass balances in a marine fish cage farm. II: Phosphor. Mar Ecol Prog Ser, 70: 263–272

[13] Hu J T, Li S Y (2009). Modeling the mass fluxes and transformations of nutrients in the Pearl River Delta, China. J Mar Syst, 78(1): 146–167

[14] Huang X P, Huang L M, Yue W Z (2003). The characteristics of nutrients and eutrophication in the Pearl River Estuary, South China. Mar Pollut Bull, 47(1−6): 30–36

[15] Kohonen T (1982). Self-organized formation of topologically correct feature maps. Biol Cybern, 43(1): 59–69

[16] Kohonen T (2001). Self-Organizing Maps (3rd ed). Berlin: Springer

[17] Li J, Zhang G, Xu Y, Liu X A, Li X D (2010). Dry and wet particle deposition of polybrominated diphenyl ethers (PBDEs) in Guangzhou and Hong Kong, South China. J Environ Monit, 12(9): 1730–1736

[18] Li K Z, Yin J Q, Huang L M, Tan Y H (2006). Spatial and temporal variations of mesozooplankton in the Pearl River Estuary, China. Estuar Coast Shelf Sci, 67(4): 543–552

[19] Liu S M, Zhang J, Chen H T, Zhang G S (2005). Factors influencing nutrient dynamics in the eutrophic Jiaozhou Bay, North China. Prog Oceanogr, 66(1): 66–85

[20] Lu R S, Lo S L (2002). Diagnosing reservoir water quality using self-organizing maps and fuzzy theory. Water Res, 36(9): 2265–2274

[21] Pereira-Filho J, Schettini C A F, Rörig L, Siegle E (2001). Intratidal variation and net transport of dissolved inorganic nutrients, POC and chlorophyll a in the Camboriu River Estuary, Brazil. Estuar Coast Shelf Sci, 53(2): 249–257

[22] Qiao M, An T C, Zeng X Y, Zhang D L, Li G Y, Sheng G Y, Fu J M, Zhang G X, Guo J (2010). Safety assessment of the source water within the Pearl River Delta on the aspect of organochlorine pesticides contamination. J Environ Monit, 12(9): 1666–1677

[23] Redfield A C, Ketchum B H, Richards F A (1963). The influence of organisms on the composition of seawater. In: Hill M N, ed. The Sea. New York: John Wiley, 26–77

[24] Shrestha S, Kazama F (2007). Assessment of surface water quality using multivariate statistical techniques: a case study of the Fuji river basin, Japan. Environ Model Softw, 22(4): 464–475

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

[26] Song M Y, Hwang H J, Kwak I S, Ji C W, Oh Y N, Youn B J, Chon T S (2007). Self-organizing mapping of benthic macroinvertebrate communities implemented to community assessment and water quality evaluation. Ecol Modell, 203(1−2): 18–25

[27] Tao Y X, Hills P (1999). Assessment of alternative wastewater treatment approaches in Guangzhou, China. Water Sci Technol, 39(5): 227–234

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

[29] Wang Y S (2013). Quantitative Marine Ecology-Theory, Method and Practice. Beijing: Science Press

[30] Wei P, Huang L M (2010). Water quality and eutrophication in the Guangzhou Sea Zone of the Pearl River Estuary. Chin J Oceanology Limnol, 28(1): 113–121

[31] Wen W Y, Zhang G X, Du W C (1995). A study on water pollution in the Zhujing (Pearl River) Estuary. In: Wong C K, Chu K H, Chen Q C, Ma X L, eds. Environmental Research in Pearl River and Coastal Area. Guangzhou: Guangdong Higher Education Press, 99–108

[32] Wong L A, Chen J, Xue H, Dong L X, Su J L, Heinke G (2003). A model study of the circulation in the Pearl River Estuary (PRE) and its adjacent coastal waters: 1. Simulations and comparison with observations. Journal of Geophysical Research, 108(C5): 249–260

[33] Wu M L, Ling J, Long L J, Zhang S, Zhang Y Y, Wang Y S, Dong J D (2012). Influence of human activity and monsoon dynamics on spatial and temporal hydrochemistry in tropical coastal waters (Sanya Bay, South China Sea). Chem Ecol, 28(4): 375–390

[34] Wu M L, Wang Y S (2007). Using chemometrics to evaluate anthropogenic effects in Daya Bay, China. Estuar Coast Shelf Sci, 72(4): 732–742

[35] Wu M L, Wang Y S, Sun C C, Wang H L, Dong J D, Yin J P, Han S H (2010). Identification of coastal water quality by statistical analysis methods in Daya Bay, South China Sea. Mar Pollut Bull, 60(6): 852–860

[36] Ye L, Cai Q H, Liu R Q, Cao M (2009). The influence of topography and land use on water quality of Xiangxi River in Three Gorges Reservoir region. Environmental Geology, 58(5): 937–942

[37] Yin K D, Qian P Y, Wu M C S, Chen J C, Huang L M, Song X Y, Jian W J (2001). Shift from P to N limitation of phytoplankton growth across the Pearl River estuarine plume during summer. Mar Ecol Prog Ser, 221: 17–28

[38] Zhang J (1996). Nutrient elements in large Chinese estuaries. Cont Shelf Res, 16(8): 1023–1045

[39] Zhao H (1990). Evolution of the Pearl River Estuary. Beijing: Ocean Press

[40] Zhou F, Liu Y, Guo H C (2007). Application of multivariate statistical methods to water quality assessment of the watercourses in northwestern new territories, Hong Kong. Environ Monit Assess, 132(1–3): 1–13

[41] Zhou H Y, Peng X T, Pan J M (2004). Distribution, source and enrichment of some chemical elements in sediments of the Pearl River Estuary, China. Cont Shelf Res, 24(16): 1857–1875

Acknowledgements

This research was supported by the National Natural Science Foundation of China (Grant Nos. 41206082 and 31270528), Natural Science Foundation of Guangdong (Nos. S2013020012823), Scientific Research Project of Guangzhou (No. 15020023), the project of Guangdong Provincial Department of Science and Technology (No. 2012A032100004), the projects of knowledge innovation program of State Key Laboratory of Tropical Oceanography (Nos. LTOZZ1402 and LTOZZ1604), the Key Laboratory for Ecological Environment in Coastal Area, State Oceanic Administation (No. 201507), Key Laboratory of Fishery Ecology and Environment, Guangdong Province (No. LFE-2010-14) and the visiting scholar project of the Chinese Academy Sciences overseas study program.

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