Skip to main content
SpringerLink
Log in

Estimation of phosphorus flux into the sea through one reversing river using continuous turbidities and water quality modeling

  • Published:
Environment, Development and Sustainability Aims and scope Submit manuscript

Abstract

Based on continuous turbidity values (T) and water quality modeling, one methodology was proposed to estimate phosphorus (P) flux into the sea through the river. There are three steps in the procedure: (1) analyzing the relationship of P and total suspended solid (TSS), TSS and T; (2) estimating P concentrations with high temporal and spatial density along the river; and (3) calculating the amount of phosphorus flux into the sea. From September 2014 to December 2016, 224 data sets were collected at eight sites along the Tonglv River which feeds into the Yellow Sea, China. The linear regression of TSS and T provided a fit with R2 = 0.944, and TP and TSS presented linear relationship with R2 = 0.884. It was estimated that about 30,227 kg total phosphorus (TP) flows from the river into the sea from September 2014 to August 2015. It is practicable and credible to use T values and water quality modeling to estimate the process of phosphorus flux. Besides, the procedure proposed could be also applied in analyzing the influence on TP fluxes brought by pollution loading changes. Thus, to facilitate long-term pollutants’ fluxes estimation, combining the regression models with water quality modeling might be an effective technique.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Administration, S. E. P. (2002). Monitoring and analysis method of water and wastewater. Beijing, China: China Environment Science Press.

    Google Scholar 

  • Berndtsson, J. C. (2014). Storm water quality of first flush urban runoff in relation to different traffic characteristics. Urban Water Journal,11, 284–296.

    Article  CAS  Google Scholar 

  • Bin Omar, A. F., & Bin MatJafri, M. Z. (2009). Turbidimeter design and analysis: A review on optical fiber sensors for the measurement of water turbidity. Sensors,9, 8311–8335.

    Article  CAS  Google Scholar 

  • Campos, I., Alcañiz, M., Aguado, D., Barat, R., Ferrer, J., Gil, L., et al. (2012). A voltammetric electronic tongue as tool for water quality monitoring in wastewater treatment plants. Water Research,46, 2605–2614.

    Article  CAS  Google Scholar 

  • Environmental Protection Agency, P. C. (2002). Surface water quality standards (GB 3838-2002).

  • Grayson, R. B., Finlayson, B. L., Gippel, C. J., & Hart, B. T. (1996). The potential of field turbidity measurements for the computation of total phosphorus and suspended solids loads. Journal of Environmental Management,47, 257–267.

    Article  Google Scholar 

  • Huey, G. M., & Meyer, M. L. (2010). Turbidity as an indicator of water quality in diverse watersheds of the Upper Pecos River Basin. Water,2, 273–284.

    Article  CAS  Google Scholar 

  • Jiang, H., Li, J., Hu, X., Shen, J., Sun, X., Han, W., et al. (2017). Ordered mesoporous silica film as a novel fiber coating for solid-phase microextraction. Talanta,174, 307–313.

    Article  CAS  Google Scholar 

  • Kumar, R. N., Solanki, R., & Kumar, J. I. N. (2013). Seasonal variation in heavy metal contamination in water and sediments of river Sabarmati and Kharicut canal at Ahmedabad, Gujarat. Environmental Monitoring and Assessment,185, 359–368.

    Article  CAS  Google Scholar 

  • Li, Z., Ji, Y., Kong, D., Lu, J., Zhou, Q., & Yin, X. (2016). Simultaneous removal of bisphenol A and phosphate in zero-valent iron activated persulfate oxidation process. Chemical Engineering Journal,303, 458–466.

    Article  Google Scholar 

  • Loperfido, J. V., Just, C. L., Papanicolaou, A. N., & Schnoor, J. L. (2010). In situ sensing to understand diel turbidity cycles, suspended solids, and nutrient transport in Clear Creek, Iowa. Water Resources Research, 46, 1–8.

    Article  Google Scholar 

  • Marttila, H., & Klove, B. (2012). Use of turbidity measurements to estimate suspended solids and nutrient loads from peatland forestry drainage. Journal of Irrigation and Drainage Engineering-Asce,138, 1088–1096.

    Article  Google Scholar 

  • MEP (2011) The arrange for the first census of pollution sources. http://cpsc.mep.gov.cn/. Accessed 12 May 2016.

  • Mlynarczyk, N., Bartoszek, M., Polak, J., & Sulkowski, W. W. (2013). Forms of phosphorus in sediments from the Goczalkowice reservoir. Applied Geochemistry,37, 87–93.

    Article  CAS  Google Scholar 

  • Perianez, R. (2009). Environmental modelling in the Gulf of Cadiz: Heavy metal distributions in water and sediments. Science of the Total Environment,407, 3392–3406.

    Article  CAS  Google Scholar 

  • Price, O. R., Williams, R. J., van Egmond, R., Wilkinson, M. J., & Whelan, M. J. (2010). Predicting accurate and ecologically relevant regional scale concentrations of triclosan in rivers for use in higher-tier aquatic risk assessments. Environment International,36, 521–526.

    Article  CAS  Google Scholar 

  • Rugner, H., Schwientek, M., Beckingham, B., Kuch, B., & Grathwohl, P. (2013). Turbidity as a proxy for total suspended solids (TSS) and particle facilitated pollutant transport in catchments. Environmental Earth Sciences,69, 373–380.

    Article  Google Scholar 

  • Sanchez, P. B., Oliver, D. P., Castillo, H. C., & Kookana, R. S. (2012). Nutrient and sediment concentrations in the Pagsanjan-Lumban catchment of Laguna de Bay, Philippines. Agricultural Water Management,106, 17–26.

    Article  Google Scholar 

  • Suk, N. S., Guo, Q., & Psuty, N. P. (1998). Feasibility of using a turbidimeter to quantify suspended solids concentration in a tidal saltmarsh creek. Estuarine, Coastal and Shelf Science,46, 383–391.

    Article  Google Scholar 

  • Tang, D. H., Zou, X. Q., Liu, X. J., Liu, P. T., Zhamangulova, N., Xu, X. G., et al. (2015). Integrated ecosystem health assessment based on eco-exergy theory: A case study of the Jiangsu coastal area. Ecological Indicators,48, 107–119.

    Article  Google Scholar 

  • Toor, G. S., Han, L., & Stanley, C. D. (2013). Temporal variability in water quality parameters-a case study of drinking water reservoir in Florida, USA. Environmental Monitoring and Assessment,185, 4305–4320.

    Article  CAS  Google Scholar 

  • USEPA (2006). National recommended water quality criteria. http://water.epa.gov/scitech/swguidance/standards/criteria/current/index.cfm. Accessed 10 Nov 2018.

  • Van Moorleghem, C., Six, L., Degryse, F., Smolders, E., & Merckx, R. (2011). Effect of organic P forms and P present in inorganic colloids on the determination of dissolved p in environmental samples by the diffusive gradient in thin films technique, ion chromatography, and colorimetry. Analytical Chemistry,83, 5317–5323.

    Article  Google Scholar 

  • Xu, X. Z., Song, G. D., Dang, T. M., Liu, J. W., Zhang, H. W., Gao, H., et al. (2017). Environment and sustainability of the middle route, south-to-north water transfer project in China: A close look. Environment, Development and Sustainability, 20(1), 1–12.

    Google Scholar 

  • Yao, H., Liu, B., You, Z., & Zhao, L. (2018). Risk perception of aquatic pollution originated from chemical industry clusters in the coastal area of Jiangsu province, China. Environmental Science and Pollution Research,25, 5711–5721.

    Article  Google Scholar 

  • Yao, H., Qian, X., Gao, H., Wang, Y., & Xia, B. (2014). Seasonal and spatial variations of heavy metals in two typical chinese rivers: concentrations, environmental risks, and possible sources. International Journal of Environmental Research and Public Health,11, 11860–11878.

    Article  CAS  Google Scholar 

  • Yao, H., Qian, X., Yin, H., Gao, H. L., & Wang, Y. L. (2015). Regional risk assessment for point source pollution based on a water quality model of the Taipu River, China. Risk Analysis,35, 265–277.

    Article  Google Scholar 

  • Yao, H., Zhuang, W., Qian, Y., Xia, B., Yang, Y., & Qian, X. (2016). Estimating and predicting metal concentration using online turbidity values and water quality models in two rivers of the Taihu Basin, Eastern China. PloS one,11, e0152491.

    Article  Google Scholar 

Download references

Acknowledgements

This work was sponsored by the National Natural Science Foundation of China (41501601), the Natural Science and Technology Project of Nantong (MS12018035), and the doctoral research start-up fund of Nantong University (15B01).

Author information

Author notes

  1. Hong Yao and Tianhua Ni contributed equally to this work.

Authors and Affiliations

  1. School of Geography, Nantong University, Nantong, 226019, China

    Hong Yao

  2. Program in Public Health, University of California, Irvine, CA, 92697, USA

    Hong Yao

  3. School of Geographic and Oceanographic Science, Nanjing University, Nanjing, 210023, China

    Tianhua Ni

  4. School of Liberal Arts, Nantong University, Nantong, 226019, China

    Tongzhu Zhang

Authors
  1. Hong Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tianhua Ni
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tongzhu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hong Yao.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yao, H., Ni, T. & Zhang, T. Estimation of phosphorus flux into the sea through one reversing river using continuous turbidities and water quality modeling. Environ Dev Sustain 22, 4251–4265 (2020). https://doi.org/10.1007/s10668-019-00382-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10668-019-00382-9

Keywords

Search

Navigation