Advertisement

Environmental Monitoring and Assessment

, Volume 180, Issue 1–4, pp 501–520 | Cite as

Spatial and temporal variations of nitrogen pollution in Wen-Rui Tang River watershed, Zhejiang, China

  • Ping Lu
  • Kun Mei
  • Yujin Zhang
  • Lingling Liao
  • Bibo Long
  • Randy A. Dahlgren
  • Minghua Zhang
Open Access
Article

Abstract

Water quality has degraded dramatically in Wen-Rui Tang River watershed, Zhejiang, China, especially due to rapid economic development since 1995. This paper aims to assess spatial and temporal variations of the main pollutants (NH\(_{4}^{+}\)-N, TN, BOD5, CODMn, DO) of water quality in Wen-Rui Tang River watershed, using the geographic information system, cluster analysis (CA) and principal component analysis (PCA). Results showed that concentrations of BOD5, CODMn, NH\(_{4}^{+}\)-N, and TN were significantly higher in tertiary rivers than in primary and secondary rivers. From April 2006 to March 2007, the concentrations of NH\(_{4}^{+}\)-N (2.25–57.9 mg/L) and TN (3.78–70.4 mg/L) in all samples exceeded Type V national water quality standards (≥2 mg/L), while 5.3% of all CODMn (1.83–27.5 mg/L) and 33.6% of all BOD5 (0.34–50.4 mg/L) samples exceeded Type V national water quality standards (CODMn ≥ 15 mg/L, BOD5 ≥ 10 mg/L). Monthly changes of pollutant concentrations did not show a clear pattern, but correlation analysis indicated that NH\(_{4}^{+}\)-N and TN in tertiary rivers had a significant negative correlation with 5-day cumulative rainfall and monthly rainfall, while there were no significant correlations in primary and secondary rivers. The results of CA and spatial analysis showed that the northern part of Wen-Rui Tang River watershed was the most seriously polluted. This region is characterized by the high population density and industrial and commercial activities. The PCA and spatial analysis indicated that the degraded water quality is caused by anthropogenic activities and poor wastewater management.

Keywords

Wen-Rui Tang River Water quality CA PCA GIS 

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 datasets. Water Research, 41, 4566–4578.CrossRefGoogle Scholar
  2. Avalanja, M. C. R., & Bonner, M. R. (2005). Pesticides and human cancers. Cancer Investigation, 23, 700–711.CrossRefGoogle Scholar
  3. Barrett, J. H., Parslow, R. C., McKinney, P. A., Law, G. R., & Forman, D. (1998). Nitrate in drinking water and the incidence of gastric, esophageal, and brain cancer in Yorkshire, England. Cancer Causes and Control, 9, 153–159.CrossRefGoogle Scholar
  4. Boesch, D. F., Brinsfield, R. B., & Magnien, R. E. (2001). Chesapeake Bay eutrophication: Scientific understanding, ecosystem restoration, and challenges for agriculture. Journal of Environmental Quality, 30, 303–320.CrossRefGoogle Scholar
  5. Camargo, J. A. , & Alonso, A. (2006). Ecological and toxicological effects of inorganic nitrogen pollution in aquatic ecosystems: A global assessment. Environment International, 32, 831–849.CrossRefGoogle Scholar
  6. Chang, H. (2008). Spatial analysis of water quality trends in the Han River basin, South Korea. Water Research, 42, 3285–3304.CrossRefGoogle Scholar
  7. Chen, J., Wang, Y., & Zhang, H. (2006). Overview on the studies of nitrate pollution in groundwater. Progress in Geography, 25, 34–44.Google Scholar
  8. Diaz, R. J., & Rosenberg, R. (2008). Spreading dead zones and consequences for marine ecosystems. Science, 321, 926–929.CrossRefGoogle Scholar
  9. Fewtrell, L. (2004). Drinking-water nitrate, methemoglobinaemia, and global burden of disease: A discussion. Environmental Health Perspectives, 112, 1371–1374.CrossRefGoogle Scholar
  10. Fytianos, K., & Christophoridis, C. (2004). Nitrate, arsenic and chloride pollution of drinking water in Northern Greece. Elaboration by applying GIS. Environmental Monitoring and Assessment, 93, 55–67.CrossRefGoogle Scholar
  11. Gelberg, K. H., Church, L., Casey, G., London, M., Roerig, D. S., Boyd, J., et al. (1999). Nitrate levels in drinking water in rural New York State. Environmental Research, 80, 34–40.CrossRefGoogle Scholar
  12. Girija, T. R., Mahanta, C., & Chandramouli, V. (2007). Water quality assessment of an untreated effluent impacted urban stream: The Bharalu tributary of the Brahmaputra River, India. Environmental Monitoring and Assessment, 130, 221–236.CrossRefGoogle Scholar
  13. Grande, J. A., Borrego, J., Morales, J. A., & de la Torre, M. L. (2003). A description of how metal pollution occurs in the Tinto-Odiel rias (Huelva-Spain) through the application of cluster analysis. Marine Pollution Bulletin, 46, 475–480.CrossRefGoogle Scholar
  14. Helena, B., Pardo, R., Vega, M., Barrado, E., Fernandez, J. M., & Fernandez, L. (2000). Temporal evolution of groundwater composition in an alluvial aquifer (Pisuerga River, Spain) by principal component analysis. Water Research, 34, 807–816.CrossRefGoogle Scholar
  15. Holbrook, R. D., Yen, J. H., & Grizzard, T. J. (2006). Characterizing natural organic material from the Occoquan watershed (Northern Virginia, US) using fluorescence spectroscopy and PARAFAC. Science of the Total Environment, 361, 249–266.CrossRefGoogle Scholar
  16. Horrigan, L., Lawrence, R. S., & Walker, P. (2002). How sustainable agriculture can address the environmental and human health harms of industrial agriculture. Environmental Health Perspectives, 110, 445–456.CrossRefGoogle Scholar
  17. Kallioinen, M., Huuhilo, T., Reinikainen, S. P., Nuortila-Jokinen, J., & Mänttäri, M. (2006). Examination of membrane performance with multivariate methods: A case study within a pulp and paper mill filtration application. Chemometrics and Intelligent Laboratory Systems, 84, 98–105.CrossRefGoogle Scholar
  18. Kowalkowski, T., Zbytniewski, R., Szpejna, J., & Buszewski, B. (2006). Application chemometrics in river water classification. Water Research, 40, 744–752.CrossRefGoogle Scholar
  19. Lattin, J., Carroll, D., & Green, P. (2003). Analyzing multivariate data. New York: Duxbury.Google Scholar
  20. Lin, H. M., & Zhang, W. L. (2005). Differences from principal component analysis and component analysis and SPSS. Statistical Research, 3, 65–68.Google Scholar
  21. Ma, J. Z., Ding, Z. Y., Wei, G. X., Zhao, H., & Huang, T. M. (2009). Sources of water pollution and evolution of water quality in the Wuwei basin of Shiyang river, Northwest China. Journal of Environmental Management, 90, 1168–1177.CrossRefGoogle Scholar
  22. McKenna, J. (2003). An enhanced cluster analysis program with bootstrap significance testing for ecological community analysis. Environmental Modelling and Software, 18, 205–220.CrossRefGoogle Scholar
  23. Mingoti, S. A., & Lima, J. O. (2006). Comparing SOM neural network with fuzzy c-means, K-means and traditional hierarchical clustering algorithms. European Journal of Operational Research, 174, 1742–1759.CrossRefGoogle Scholar
  24. Nas, B., & Berktay, A. (2006). Groundwater contamination by nitrates in the city of Konya, (Turkey): a GIS perspective. Journal of Environmental Management, 79, 30–37.CrossRefGoogle Scholar
  25. Ocean Commission (2004). An ocean blueprint for the 21st century. Final Report. U.S. Commission on ocean policy. Washington, DC.Google Scholar
  26. Papatheodorou, G., Demopoulou, G., & Lambrakis, N. (2006). A long-term study of temporal hydrochemical data in a shallow lake using multivariate statistical techniques. Ecological Modelling, 193, 759–776.CrossRefGoogle Scholar
  27. Pekey, H., Karakas, D., & Bakog, L. M. (2004). Source apportionment of trace metals in surface waters of a polluted stream using multivariate statistical analyses. Marine Pollution Bulletin, 49, 809–818.CrossRefGoogle Scholar
  28. Sarkar, B. C., Mahanta, B. N., Saikia, K., Paul, P. R., & Singh, G. (2007). Geo-environmental quality assessment in Jharia coalfield, India, using multivariate statistics and geographic information system. Environmental Geology, 51, 1177–1196.CrossRefGoogle Scholar
  29. SEPBC (2002a). Environmental quality standards for surface water; Prepared and published jointly by: State Environment Protection Bureau of China (SEPBC), State Quality Supervision, Inspection and Quarantine Bureau of China (SQSIQBC), Beijing.Google Scholar
  30. SEPBC (2002b). Water and wastewater analysis method. Prepared and published by: State Environment Protection Bureau of China (SEPBC), China Environmental Science Press, Beijing.Google Scholar
  31. Shreshtha, S., & Kazama, F. (2007). Assessment of surface water quality using multivariate statistical techniques: a case study of the Fuji river basin, Japan. Environmental Modelling and Software, 22, 464–475.CrossRefGoogle Scholar
  32. Singh, K. P., Malik, A., Singh, V. K., & Sinha, S. (2006). Multi-way data analysis of soils irrigated with wastewater—a case study. Chemometrics and Intelligent Laboratory Systems, 83, 1–12.CrossRefGoogle Scholar
  33. Tilman, D., Cassman, K. G., Matson, P. A., Naylor, R., & Polasky, S. (2002). Agricultural sustainability and intensive production practices. Nature, 418, 671–677.CrossRefGoogle Scholar
  34. 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 Research, 32, 3581–3592.CrossRefGoogle Scholar
  35. Zhang, Y., Guo, F., Meng, W., & Wang, X. Q. (2009). Water quality assessment and source identification of Daliao river basin using multivariate statistical methods. Environmental Monitoring and Assessment, 152, 105–121.CrossRefGoogle Scholar
  36. Zhou, F., Guo, H. C., Liu, Y., & Jiang, Y. M. (2007). Chemometrics data analysis of marine water quality and source identification in Southern Hong Kong. Marine Pollution Bulletin, 54, 745–756.CrossRefGoogle Scholar

Copyright information

© The Author(s) 2010

Authors and Affiliations

  • Ping Lu
    • 1
  • Kun Mei
    • 1
  • Yujin Zhang
    • 1
  • Lingling Liao
    • 1
  • Bibo Long
    • 1
  • Randy A. Dahlgren
    • 1
    • 2
  • Minghua Zhang
    • 1
    • 2
  1. 1.The Environmental Geographic Information System (EGIS) Laboratory, School of Environmental Science and Public HealthWenzhou Medical CollegeWenzhouChina
  2. 2.Department of Land, Air and Water ResourcesUniversity of California, DavisCAUSA

Personalised recommendations