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Estimating and Predicting Metal Concentration Using Online Turbidity Values and Water Quality Models in Two Rivers of the Taihu Basin, Eastern China

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Listed:
  • Hong Yao
  • Wei Zhuang
  • Yu Qian
  • Bisheng Xia
  • Yang Yang
  • Xin Qian

Abstract

Turbidity (T) has been widely used to detect the occurrence of pollutants in surface water. Using data collected from January 2013 to June 2014 at eleven sites along two rivers feeding the Taihu Basin, China, the relationship between the concentration of five metals (aluminum (Al), titanium (Ti), nickel (Ni), vanadium (V), lead (Pb)) and turbidity was investigated. Metal concentration was determined using inductively coupled plasma mass spectrometry (ICP-MS). The linear regression of metal concentration and turbidity provided a good fit, with R2 = 0.86–0.93 for 72 data sets collected in the industrial river and R2 = 0.60–0.85 for 60 data sets collected in the cleaner river. All the regression presented good linear relationship, leading to the conclusion that the occurrence of the five metals are directly related to suspended solids, and these metal concentration could be approximated using these regression equations. Thus, the linear regression equations were applied to estimate the metal concentration using online turbidity data from January 1 to June 30 in 2014. In the prediction, the WASP 7.5.2 (Water Quality Analysis Simulation Program) model was introduced to interpret the transport and fates of total suspended solids; in addition, metal concentration downstream of the two rivers was predicted. All the relative errors between the estimated and measured metal concentration were within 30%, and those between the predicted and measured values were within 40%. The estimation and prediction process of metals’ concentration indicated that exploring the relationship between metals and turbidity values might be one effective technique for efficient estimation and prediction of metal concentration to facilitate better long-term monitoring with high temporal and spatial density.

Suggested Citation

  • Hong Yao & Wei Zhuang & Yu Qian & Bisheng Xia & Yang Yang & Xin Qian, 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, Public Library of Science, vol. 11(3), pages 1-15, March.
    • Handle: RePEc:plo:pone00:0152491
    DOI: 10.1371/journal.pone.0152491
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    References listed on IDEAS

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    1. Hong Yao & Xin Qian & Hong Yin & Hailong Gao & Yulei Wang, 2015. "Regional Risk Assessment for Point Source Pollution Based on a Water Quality Model of the Taipu River, China," Risk Analysis, John Wiley & Sons, vol. 35(2), pages 265-277, February.
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    Cited by:

    1. Yuting Bai & Xuebo Jin & Xiaoyi Wang & Tingli Su & Jianlei Kong & Yutian Lu, 2019. "Compound Autoregressive Network for Prediction of Multivariate Time Series," Complexity, Hindawi, vol. 2019, pages 1-11, September.
    2. Hong Yao & Tianhua Ni & Tongzhu Zhang, 2020. "Estimation of phosphorus flux into the sea through one reversing river using continuous turbidities and water quality modeling," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(5), pages 4251-4265, June.
    3. Bai, Jing & Zhao, Jian & Zhang, Zhenyu & Tian, Ziqiang, 2022. "Assessment and a review of research on surface water quality modeling," Ecological Modelling, Elsevier, vol. 466(C).

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