Assessment of Water Quality Profile Using Numerical Modeling Approach in Major Climate Classes of Asia
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Site
- The study selected the rivers with the comparably similar hydraulic characteristics, having a natural flow, unlike artificial channels.
- All the rivers have mostly similar land type characteristics passing through urban as well as vegetative areas.
- All the rivers section having several urban networks of wastewater drains flowing into them.
- All the rivers have common data characteristics which are useful for their comparative water quality profile analysis and assessment.
2.1.1. Yamuna River
2.1.2. Baghmati River
2.1.3. Galing River
2.1.4. Nakdong River
2.2. Input Data Sets
2.3. Water Quality Modeling
QUAL2Kw
2.4. Calibration and Validation of the Model
2.5. Assessment of the Model Accuracy
2.6. Water Quality Index (WQI) Development
- This technique integrates information from several water quality variables into a numerical form that measures the fitness of the water ecosystem with the number scale.
- Fewer variables are needed in evaluating the overall water quality for specific use.
- Advantageous for the report of overall water bodies health for the corresponding community and policymakers.
- Mirrors the overall influence of different water quality variables that are significant for the management and administration of water environments.
3. Results and Discussion
3.1. Model Calibration and Validation
3.2. Models Accuracy Assessment
3.3. Assessment of Water Quality Index Using Validated Results
3.4. Spatial Scale Interrelationship between Water Quality Parameters and Flow Profile
3.5. Study Significance and Limitations
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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WQI | Water Quality Status (WQS) |
---|---|
90–100 | Excellent |
70–90 | Good |
50–70 | Average |
25–50 | Poor |
0–25 | Very Poor |
River | Parameter | MAE | MSE | RMSE | NRMSE | MAPE | R2 | ME | PMB | CF | IOA |
---|---|---|---|---|---|---|---|---|---|---|---|
Yamuna River Calibration | DO | 0.45 | 0.25 | 0.50 | 0.21 | 0.67 | 0.90 | 0.86 | −19.36 | 0.11 | 0.95 |
BOD | 12.28 | 172.1 | 13.12 | 0.31 | 0.23 | 0.86 | 0.84 | −25.76 | 0.03 | 0.91 | |
TN | 3.97 | 22.91 | 4.79 | 0.20 | 0.14 | 0.87 | 0.86 | 17.57 | 0.05 | 0.92 | |
PH | 0.10 | 0.02 | 0.14 | 0.01 | 0.01 | 0.79 | 0.81 | −0.83 | 0.21 | 0.92 | |
Yamuna River Validation | DO | 0.48 | 0.27 | 0.52 | 0.222 | 0.715 | 0.96 | 0.92 | −10.6 | 0.12 | 0.97 |
BOD | 13.2 | 185 | 13.6 | 0.335 | 0.251 | 0.93 | 0.90 | −17.7 | 0.03 | 0.93 | |
TN | 4.32 | 24.9 | 4.99 | 0.221 | 0.149 | 0.95 | 0.93 | 19.1 | 0.05 | 0.94 | |
PH | 0.11 | 0.02 | 0.14 | 0.016 | 0.014 | 0.87 | 0.89 | −0.91 | 0.23 | 0.94 | |
Baghmati River Calibration | DO | 0.03 | 0.01 | 0.1 | 0.01 | 1.02 | 0.93 | 0.86 | −0.29 | 0.01 | 0.96 |
BOD | 1.40 | 6.14 | 2.48 | 0.05 | 2.46 | 0.90 | 0.92 | −0.80 | 0.49 | 0.92 | |
TN | 0.73 | 1.04 | 1.02 | 0.03 | 2.10 | 0.90 | 0.89 | −0.97 | 0.25 | 0.93 | |
PH | 0.05 | 0.01 | 0.10 | 0.01 | 0.61 | 0.87 | 0.79 | −0.60 | 0.02 | 0.93 | |
Baghmati River Validation | DO | 0.03 | 0.01 | 0.10 | 0.009 | 1.071 | 0.98 | 0.91 | −0.30 | 0.01 | 0.97 |
BOD | 1.49 | 6.53 | 2.56 | 0.057 | 2.612 | 0.96 | 0.98 | −0.85 | 0.52 | 0.93 | |
TN | 0.78 | 1.12 | 1.06 | 0.035 | 2.255 | 0.97 | 0.96 | −1.04 | 0.27 | 0.94 | |
PH | 0.05 | 0.01 | 0.10 | 0.011 | 0.661 | 0.95 | 0.86 | −0.65 | 0.02 | 0.94 | |
Galing River Calibration | DO | 0.25 | 0.12 | 0.35 | 0.12 | 0.11 | 0.87 | 0.73 | −5.89 | 0.32 | 0.90 |
BOD | 1.46 | 3.52 | 1.88 | 0.11 | 0.09 | 0.90 | 0.90 | 8.66 | 1.84 | 0.91 | |
TN | 0.44 | 0.27 | 0.52 | 0.20 | 0.19 | 0.84 | 0.43 | 17.57 | 0.56 | 0.88 | |
PH | 0.05 | 0.02 | 0.14 | 0.01 | 0.01 | 0.72 | 0.79 | −0.31 | 0.05 | 0.91 | |
Galing River Validation | DO | 0.27 | 0.13 | 0.36 | 0.124 | 0.115 | 0.93 | 0.78 | −6.27 | 0.34 | 0.93 |
BOD | 1.57 | 3.78 | 1.94 | 0.122 | 0.094 | 0.97 | 0.97 | 9.31 | 1.98 | 0.94 | |
TN | 0.48 | 0.29 | 0.54 | 0.216 | 0.202 | 0.91 | 0.47 | 19.1 | 0.61 | 0.91 | |
PH | 0.05 | 0.02 | 0.14 | 0.009 | 0.007 | 0.79 | 0.87 | −0.34 | 0.06 | 0.94 | |
Nakdong River Calibration | DO | 0.21 | 0.13 | 0.26 | 0.06 | 0.02 | 0.86 | 0.88 | −0.93 | 0.05 | 0.96 |
BOD | 0.14 | 0.03 | 0.17 | 0.03 | 0.07 | 0.80 | 0.86 | −1.02 | 0.07 | 0.92 | |
TN | 0.22 | 0.07 | 0.26 | 0.10 | 0.09 | 0.86 | 0.86 | 9.09 | 0.13 | 0.93 | |
PH | 0.11 | 0.02 | 0.14 | 0.02 | 0.01 | 0.65 | 0.84 | 0.18 | 0.06 | 0.93 | |
Nakdong River Validation | DO | 0.22 | 0.14 | 0.37 | 0.065 | 0.019 | 0.91 | 0.93 | −0.98 | 0.05 | 0.97 |
BOD | 0.15 | 0.03 | 0.17 | 0.033 | 0.072 | 0.85 | 0.91 | −0.49 | 0.07 | 0.93 | |
TN | 0.24 | 0.07 | 0.26 | 0.112 | 0.096 | 0.93 | 0.92 | 9.77 | 0.14 | 0.94 | |
PH | 0.12 | 0.02 | 0.14 | 0.017 | 0.015 | 0.71 | 0.91 | 0.20 | 0.06 | 0.94 |
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Iqbal, M.M.; Shoaib, M.; Farid, H.U.; Lee, J.L. Assessment of Water Quality Profile Using Numerical Modeling Approach in Major Climate Classes of Asia. Int. J. Environ. Res. Public Health 2018, 15, 2258. https://doi.org/10.3390/ijerph15102258
Iqbal MM, Shoaib M, Farid HU, Lee JL. Assessment of Water Quality Profile Using Numerical Modeling Approach in Major Climate Classes of Asia. International Journal of Environmental Research and Public Health. 2018; 15(10):2258. https://doi.org/10.3390/ijerph15102258
Chicago/Turabian StyleIqbal, Muhammad Mazhar, Muhammad Shoaib, Hafiz Umar Farid, and Jung Lyul Lee. 2018. "Assessment of Water Quality Profile Using Numerical Modeling Approach in Major Climate Classes of Asia" International Journal of Environmental Research and Public Health 15, no. 10: 2258. https://doi.org/10.3390/ijerph15102258