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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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Related in: MedlinePlus

The plotted graph of concentration of particulate metals and total metals.
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pone.0152491.g004: The plotted graph of concentration of particulate metals and total metals.

Mentions: All presented good linear relationship, leading to the conclusion that the occurrence of the five metals are directly related to suspended solids, which could be supported by the relationship of particulate metal (PM) concentrations and total metal (TM) concentrations. Metals in surface water exist in the form of particulate metals and dissolved metals. Fig 4 provides the concentrations of PM and TM from all observations (660 data sets) made in 2013, and both concentrations were similar in both rivers. As the figure demonstrates, metals in the two rivers are particle-bound pollutants with suspended solids associated with 81–98% of total metals in surface water, and these proportion values were also close to those in previous studies[21–24].


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)

The plotted graph of concentration of particulate metals and total metals.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0152491.g004: The plotted graph of concentration of particulate metals and total metals.
Mentions: All presented good linear relationship, leading to the conclusion that the occurrence of the five metals are directly related to suspended solids, which could be supported by the relationship of particulate metal (PM) concentrations and total metal (TM) concentrations. Metals in surface water exist in the form of particulate metals and dissolved metals. Fig 4 provides the concentrations of PM and TM from all observations (660 data sets) made in 2013, and both concentrations were similar in both rivers. As the figure demonstrates, metals in the two rivers are particle-bound pollutants with suspended solids associated with 81–98% of total metals in surface water, and these proportion values were also close to those in previous studies[21–24].

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