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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.

Yao H, Zhuang W, Qian Y, Xia B, Yang Y, Qian X - PLoS ONE (2016)

Bottom Line: Metal concentration was determined using inductively coupled plasma mass spectrometry (ICP-MS).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%.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, China.

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 R(2) = 0.86-0.93 for 72 data sets collected in the industrial river and R(2) = 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.

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Framework of estimating and predicting metal concentration.
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pone.0152491.g002: Framework of estimating and predicting metal concentration.

Mentions: The proposed method to estimate and predict metal concentration using online turbidity values and water quality models includes four different steps (Fig 2): (1) determining water quality and hydrological parameters, including turbidity, total suspended solid concentration, metals, some other water quality parameters, discharges and particle size distributions, (2) statistical analysis of the metal concentration and turbidity relationship, (3) estimating metal concentration using online turbidity data with high temporal density, (4) predicting metal concentration by water quality modeling and obtaining metal concentration with high temporal and spatial density. Detailed explanations of these four components are provided below.


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

Yao H, Zhuang W, Qian Y, Xia B, Yang Y, Qian X - PLoS ONE (2016)

Framework of estimating and predicting metal concentration.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4814083&req=5

pone.0152491.g002: Framework of estimating and predicting metal concentration.
Mentions: The proposed method to estimate and predict metal concentration using online turbidity values and water quality models includes four different steps (Fig 2): (1) determining water quality and hydrological parameters, including turbidity, total suspended solid concentration, metals, some other water quality parameters, discharges and particle size distributions, (2) statistical analysis of the metal concentration and turbidity relationship, (3) estimating metal concentration using online turbidity data with high temporal density, (4) predicting metal concentration by water quality modeling and obtaining metal concentration with high temporal and spatial density. Detailed explanations of these four components are provided below.

Bottom Line: Metal concentration was determined using inductively coupled plasma mass spectrometry (ICP-MS).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%.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, China.

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 R(2) = 0.86-0.93 for 72 data sets collected in the industrial river and R(2) = 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.

Show MeSH
Related in: MedlinePlus