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ENVIRONMENTAL ENGINEERING

A Statistical Technique for Modelling Dissolved Oxygen in Salt Lakes

ORCID Icon, , , & | (Reviewing editor)
Article: 1875533 | Received 03 Feb 2020, Accepted 04 Jan 2021, Published online: 25 Jan 2021

Figures & data

Figure 1. The area of study; (a) Sawa Lake region retrieved by QGIS, and (b) Map of Sawa Lake retrieved from Landsat 8 OLI/TIRS Level-2 Data Products (WRS path/row: 168/038; acquired on 3 June 2017), showing the locations of samples used in this study

Figure 1. The area of study; (a) Sawa Lake region retrieved by QGIS, and (b) Map of Sawa Lake retrieved from Landsat 8 OLI/TIRS Level-2 Data Products (WRS path/row: 168/038; acquired on 3 June 2017), showing the locations of samples used in this study

Table 1. Monthly averaged water quality parameters of Sawa Lake in 2017

Figure 2. Statistical conceptual model

Figure 2. Statistical conceptual model

Figure 3. Correlation plots (a, b, and c) of Sawa Lake data; (a) Scatterplot matrix of Sawa Lake data, (b) Correlation color matrix of Sawa Lake data, and (c) Correlation pie plots of Sawa Lake data

Figure 3. Correlation plots (a, b, and c) of Sawa Lake data; (a) Scatterplot matrix of Sawa Lake data, (b) Correlation color matrix of Sawa Lake data, and (c) Correlation pie plots of Sawa Lake data

Figure 4. Boxplots and correlation matrix of dissolved oxygen, temperature, and turbidity in 2017 extracted from Sawa Lake raw data; (a) Dissolved oxygen, temperature, and turbidity boxplots from Sawa Lake data in 2017, and b) Dissolved oxygen, temperature, and turbidity correlation matrix from Sawa Lake data in 2017

Figure 4. Boxplots and correlation matrix of dissolved oxygen, temperature, and turbidity in 2017 extracted from Sawa Lake raw data; (a) Dissolved oxygen, temperature, and turbidity boxplots from Sawa Lake data in 2017, and b) Dissolved oxygen, temperature, and turbidity correlation matrix from Sawa Lake data in 2017

Table 2. The full linear regression model summary statistics of Equationequation 1

Table 3. The full linear regression model summary statistics of Equationequation 2

Figure 5. Linear regression validation graphs; (a) Model validation graphs of .EquationEq. 1, and (b) Model validation graphs of EquationEq. 2

Figure 5. Linear regression validation graphs; (a) Model validation graphs of .EquationEq. 1(1) DO=e2.47520−0.01997logTemp−0.08883logTurb(1) , and (b) Model validation graphs of EquationEq. 2(2) DO=e39.4915 −3.6715 logTDS(2)

Table 4. Regression models and summary statistics

Figure 6. Model auto-correlation plots

Figure 6. Model auto-correlation plots

Figure 7. Comparisons between the model predictions with raw data; (a) DO in Sawa Lake by fitting the model of EquationEq. 2 to the raw data in 2017, and (b) The interaction among dissolved oxygen, temperature, and turbidity, the circle size was scaled by the relevant temperature

Figure 7. Comparisons between the model predictions with raw data; (a) DO in Sawa Lake by fitting the model of EquationEq. 2(2) DO=e39.4915 −3.6715 logTDS(2) to the raw data in 2017, and (b) The interaction among dissolved oxygen, temperature, and turbidity, the circle size was scaled by the relevant temperature

Figure 8. The contribution of ionic compounds (a-h) to TDS and how they impact DO, the circle size was scaled by the relevant ion concentration

Figure 8. The contribution of ionic compounds (a-h) to TDS and how they impact DO, the circle size was scaled by the relevant ion concentration