A framework for assessing the adequacy of Water Quality Index - quantifying parameter sensitivity and uncertainties in missing values distribution
Water quality monitoring is a pillar in water resource management, but it can be resource intensive, especially for developing countries with limited resources. As such, Water Quality Indices (WQI) are developed to summarise general water quality, but efforts to assess the utility, flexibility, and...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2021
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| Online Access: | https://hdl.handle.net/10356/154614 |
| _version_ | 1826127720737669120 |
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| author | Pak, Hui Ying Chuah, C Joon Tan, Mou Leong Yong, Ee Ling Snyder, Shane A |
| author2 | Nanyang Environment and Water Research Institute |
| author_facet | Nanyang Environment and Water Research Institute Pak, Hui Ying Chuah, C Joon Tan, Mou Leong Yong, Ee Ling Snyder, Shane A |
| author_sort | Pak, Hui Ying |
| collection | NTU |
| description | Water quality monitoring is a pillar in water resource management, but it can be resource intensive, especially for developing countries with limited resources. As such, Water Quality Indices (WQI) are developed to summarise general water quality, but efforts to assess the utility, flexibility, and practicality of WQI have been limited. In this study, we introduced an additional step to the traditional WQI development framework by introducing an adjusted form of WQI (WQIADJUSTED) to handle missing values, and capitalise on the remaining available information for the development of a WQI. A Sub-WQI was also developed to address local water quality conditions. WQI results (weighted and non-weighted) developed using different parameter optimisation methods, namely Multivariate Linear Regression and Principal Component Analysis were compared. To build upon the current framework, a new procedure was developed to assess the adequacy of WQI based on the sensitivity analysis of parameters and uncertainties associated with each parameter's missing values distribution. The number of observations needed for the development of a robust WQI was optimised with respect to user-defined acceptable change in WQI, based on Monte Carlo probabilistic simulation. The Johor River Basin (JRB), Malaysia is used as a case-study for the application of this new framework. The JRB serves as an important resource for Johor, one of the most populous state in Malaysia, and Singapore, a country south of Johor. WQIMLR performed better in explaining the general water quality than WQIPCA for weighted water quality parameters. Optimisation of sampling frequency revealed that around 130 samples will be required if a 2% change in WQI can be tolerated. The results (specific to the JRB) also revealed that total coliform is the most sensitivity parameter to missing values, and the distribution of sensitive parameters are similar for both WQINON-ADJUSTED and WQIADJUSTED. |
| first_indexed | 2024-10-01T07:13:29Z |
| format | Journal Article |
| id | ntu-10356/154614 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:13:29Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | ntu-10356/1546142021-12-29T05:57:40Z A framework for assessing the adequacy of Water Quality Index - quantifying parameter sensitivity and uncertainties in missing values distribution Pak, Hui Ying Chuah, C Joon Tan, Mou Leong Yong, Ee Ling Snyder, Shane A Nanyang Environment and Water Research Institute Engineering::Environmental engineering Water Quality Index Multivariate Linear Regression Water quality monitoring is a pillar in water resource management, but it can be resource intensive, especially for developing countries with limited resources. As such, Water Quality Indices (WQI) are developed to summarise general water quality, but efforts to assess the utility, flexibility, and practicality of WQI have been limited. In this study, we introduced an additional step to the traditional WQI development framework by introducing an adjusted form of WQI (WQIADJUSTED) to handle missing values, and capitalise on the remaining available information for the development of a WQI. A Sub-WQI was also developed to address local water quality conditions. WQI results (weighted and non-weighted) developed using different parameter optimisation methods, namely Multivariate Linear Regression and Principal Component Analysis were compared. To build upon the current framework, a new procedure was developed to assess the adequacy of WQI based on the sensitivity analysis of parameters and uncertainties associated with each parameter's missing values distribution. The number of observations needed for the development of a robust WQI was optimised with respect to user-defined acceptable change in WQI, based on Monte Carlo probabilistic simulation. The Johor River Basin (JRB), Malaysia is used as a case-study for the application of this new framework. The JRB serves as an important resource for Johor, one of the most populous state in Malaysia, and Singapore, a country south of Johor. WQIMLR performed better in explaining the general water quality than WQIPCA for weighted water quality parameters. Optimisation of sampling frequency revealed that around 130 samples will be required if a 2% change in WQI can be tolerated. The results (specific to the JRB) also revealed that total coliform is the most sensitivity parameter to missing values, and the distribution of sensitive parameters are similar for both WQINON-ADJUSTED and WQIADJUSTED. Economic Development Board (EDB) Nanyang Technological University Funding: The authors are grateful for the financial support provided by the Economic Development Board - Singapore and also for the Research Fund for the Masters Programme by the Nanyang Environment and Water Research Institute, Nanyang Technological University and for the support of the School of Civil and Environmental Engineering, Nanyang Technological University. 2021-12-29T05:57:40Z 2021-12-29T05:57:40Z 2021 Journal Article Pak, H. Y., Chuah, C. J., Tan, M. L., Yong, E. L. & Snyder, S. A. (2021). A framework for assessing the adequacy of Water Quality Index - quantifying parameter sensitivity and uncertainties in missing values distribution. Science of the Total Environment, 751, 141982-. https://dx.doi.org/10.1016/j.scitotenv.2020.141982 0048-9697 https://hdl.handle.net/10356/154614 10.1016/j.scitotenv.2020.141982 33181998 2-s2.0-85090880195 751 141982 en Science of the Total Environment © 2020 Elsevier B.V. All rights reserved |
| spellingShingle | Engineering::Environmental engineering Water Quality Index Multivariate Linear Regression Pak, Hui Ying Chuah, C Joon Tan, Mou Leong Yong, Ee Ling Snyder, Shane A A framework for assessing the adequacy of Water Quality Index - quantifying parameter sensitivity and uncertainties in missing values distribution |
| title | A framework for assessing the adequacy of Water Quality Index - quantifying parameter sensitivity and uncertainties in missing values distribution |
| title_full | A framework for assessing the adequacy of Water Quality Index - quantifying parameter sensitivity and uncertainties in missing values distribution |
| title_fullStr | A framework for assessing the adequacy of Water Quality Index - quantifying parameter sensitivity and uncertainties in missing values distribution |
| title_full_unstemmed | A framework for assessing the adequacy of Water Quality Index - quantifying parameter sensitivity and uncertainties in missing values distribution |
| title_short | A framework for assessing the adequacy of Water Quality Index - quantifying parameter sensitivity and uncertainties in missing values distribution |
| title_sort | framework for assessing the adequacy of water quality index quantifying parameter sensitivity and uncertainties in missing values distribution |
| topic | Engineering::Environmental engineering Water Quality Index Multivariate Linear Regression |
| url | https://hdl.handle.net/10356/154614 |
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