Skip to main content
Log in

Characterization and source apportionment of water pollution in Jinjiang River, China

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

Characterizing water quality and identifying potential pollution sources could greatly improve our knowledge about human impacts on the river ecosystem. In this study, fuzzy comprehensive assessment (FCA), pollution index (PI), principal component analysis (PCA), and absolute principal component score–multiple linear regression (APCS–MLR) were combined to obtain a deeper understanding of temporal–spatial characterization and sources of water pollution with a case study of the Jinjiang River, China. Measurement data were obtained with 17 water quality variables from 20 sampling sites in the December 2010 (withered water period) and June 2011 (high flow period). FCA and PI were used to comprehensively estimate the water quality variables and compare temporal–spatial variations, respectively. Rotated PCA and receptor model (APCS–MLR) revealed potential pollution sources and their corresponding contributions. Application results showed that comprehensive application of various multivariate methods were effective for water quality assessment and management. In the withered water period, most sampling sites were assessed as low or moderate pollution with characteristics pollutants of permanganate index and total nitrogen (TN), whereas 90 % sites were classified as high pollution in the high flow period with higher TN and total phosphorus. Agricultural non-point sources, industrial wastewater discharge, and domestic sewage were identified as major pollution sources. Apportionment results revealed that most variables were complicatedly influenced by industrial wastewater discharge and agricultural activities in withered water period and primarily dominated by agricultural runoff in high flow period.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Chen, H. W., Chang, N. B., & Shaw, D. (2005). Valuation of in-stream water quality improvement via fuzzy contingent valuation method. Stochastic Environmental Research and Risk Assessment, 19(2), 158–171.

    Article  Google Scholar 

  • Chen, H. Y., Teng, Y. G., & Wang, J. S. (2012a). Load estimation and source apportionment of non-point source nitrogen and phosphorus based on integrated application of SLURP model, ECM and RUSLE: a case study in Jinjiang River watershed, China. Environmental Monitoring and Assessment. doi:10.1007/s10661-012-2684-z.

    Google Scholar 

  • Chen, H. Y., Teng, Y. G., & Wang, J. S. (2012b). Source apportionment of polycyclic aromatic hydrocarbons (PAHs) in surface sediments of the Rizhao coastal area (China) using diagnostic ratios and factor analysis with nonnegative constraints. Science of the Total Environment, 414, 293–300.

    Article  CAS  Google Scholar 

  • Chen, H. Y., Teng, Y. G., Wang, J. S., & Song, L. T. (2012c). Source apportionment of water pollution in the Jinjiang River (China) using factor analysis with nonnegative constraints and support vector machines. Environmental Forensics, 13(2), 175–184.

    Article  Google Scholar 

  • CN-EPA (2002). National Environmental Quality Standards for surface waters, China (GB3838-2002), Chinese Environmental Protection Agency.

  • Fharnham, I. M., Singh, A. K., Stetzenbach, K. J., & Lohannesson, K. H. (2002). Treatment of nondetects in multivariate analysis of groundwater geochemistry data. Chemometrics and Intelligent Laboratory Systems, 60, 265–281.

    Article  Google Scholar 

  • Goldhaber, S. B. (2003). Trace elements risk assessments: essentiality vs. toxicity. Regulatory Toxicology and Pharmacology, 38, 232–242.

    Article  CAS  Google Scholar 

  • Guleda, O. E., Ibrahim, D., & Halil, H. (2004). Assessment of urban air quality in Istanbul using fuzzy synthetic evaluation. Atmospheric Environment, 38(23), 3809–3815.

    Article  Google Scholar 

  • Guo, H., Wang, T., & Louie, P. K. K. (2004). Source apportionment of ambient non-methane hydrocarbons in Hong Kong: application of a principal component analysis/absolute principal component scores (PCA/APCS) receptor model. Environmental Pollution, 129, 489–498.

    Article  CAS  Google Scholar 

  • Huang, F., Wang, X., Lou, L., Zhou, Z., & Wu, J. (2010). Spatial variation and source apportionment of water Pollution in Qiantang River (China) using statistical techniques. Water Research, 44(5), 1562–1572.

    Article  CAS  Google Scholar 

  • Hulya, B., & Hayal, B. (2008). Water pollution sources assessment by multivariate statistical methods in the Tahtali Basin, Turkey. Environmental Geology, 54(2), 275–282.

    Article  Google Scholar 

  • Jonnalagadda, S. B., & Mhere, G. (2001). Water quality of the Odzi River in eastern highlands of Zimbabwe. Water Research, 35, 2371–2376.

    Article  CAS  Google Scholar 

  • Kannel, P. R., Lee, S., & Lee, Y. S. (2008). Assessment of spatial–temporal patterns of surface and ground water qualities and factors influencing management strategy of groundwater system in an urban river corridor of Nepal. Journal of Environmental Management, 86(4), 595–604.

    Article  CAS  Google Scholar 

  • Leeuwen, F. X. R. V. (2000). Safe drinking water; the toxicologist’s approach. Food and Chemical Toxicology, 38, 51–58.

    Article  Google Scholar 

  • Lu, R. S., & Lo, S. L. (2002). Diagnosing reservoir water quality using self-organizing maps and fuzzy theory. Water Research, 36(9), 2265–2274.

    Article  CAS  Google Scholar 

  • Marmur, A., Unal, A., Mulholland, J. A., & Russell, A. G. (2005). Optimization-based source apportionment of PM2.5 incorporating gas-to-particle ratios. Environmental Science and Technology, 39, 3245–3254.

    Article  CAS  Google Scholar 

  • Pekey, H., Karakas, D., & Bakoglu, M. (2004). Source apportionment of trace metals in surface waters of a polluted stream using multivariate statistical analyses. Marine Pollution Bulletin, 49(9–10), 809–818.

    Article  CAS  Google Scholar 

  • Rachdawong, P., & Christensen, E. R. (1997). Determination of PCB sources by a principal component method with nonnegative constraints. Environmental Science and Technology, 31, 2686–2691.

    Article  CAS  Google Scholar 

  • Rowan, J. S., Black, S., & Franks, S. W. (2012). Sediment fingerprinting as an environmental forensics tool explaining cyanobacteria blooms in lakes. Applied Geography, 32, 832–843.

    Article  Google Scholar 

  • Shrestha, 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(4), 464–475.

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  • Su, S. L., Li, D., Zhang, Q., Xiao, R., Huang, F., & Wu, J. P. (2011). Temporal trend and source apportionment of water pollution in different functional zones of Qiantang River, China. Water Research, 45, 1781–1795.

    Article  CAS  Google Scholar 

  • Wang, H. Y. (2002). Assessment and prediction of overall environmental quality of Zhuzhou City, Hunan Province, China. Journal of Environmental Management, 66(3), 329–340.

    Google Scholar 

  • Wang, J., Da, L., Song, K., & Li, B. (2008). Temporal variations of surface water quality in urban, suburban and rural areas during rapid urbanization in Shanghai, China. Environmental Pollution, 152(2), 387–393.

    Article  CAS  Google Scholar 

  • World Health Organization (WHO) (2008). Guidelines for drinking water quality. In: Recommendations, 3rd ed., vol. 1, Geneva.

  • Yuan, Z. W., Ramaswami, B., Casaletto, D., Falke, S., Angenent, L. T., & Giammar, D. E. (2007). Evaluation of chemical indicators for tracking and apportionment of phosphorus sources to Table Rock Lake in Southwest Missouri, USA. Water Research, 41, 1525–1523.

    Article  CAS  Google Scholar 

  • Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353.

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

Download references

Acknowledgments

This study was financially supported by the Chinese important special project (No. 2009ZX07419-003) and MOE program of China (NECT-09-0230).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Haiyang Chen.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chen, H., Teng, Y., Yue, W. et al. Characterization and source apportionment of water pollution in Jinjiang River, China. Environ Monit Assess 185, 9639–9650 (2013). https://doi.org/10.1007/s10661-013-3279-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10661-013-3279-z

Keywords

Navigation