Nanyang Environment And Water Research Institute (NEWRI), Nanyang Technological University of Singapore, 1 Cleantech Loop, 637141, Singapore.
Nanyang Environment And Water Research Institute (NEWRI), Nanyang Technological University of Singapore, 1 Cleantech Loop, 637141, Singapore; Tembusu College, National University of Singapore, 28 College Ave E, #B1-01, 138598, Singapore.
Sci Total Environ. 2021 Jan 10;751:141982. doi: 10.1016/j.scitotenv.2020.141982. Epub 2020 Aug 27.
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 (WQI) 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. WQI performed better in explaining the general water quality than WQI 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 WQI and WQI.
水质监测是水资源管理的重要支柱，但对于资源有限的发展中国家来说，这可能需要大量资源。因此，开发了水质指数 (WQI) 来总结一般水质状况，但评估 WQI 的效用、灵活性和实用性的努力有限。在本研究中，我们通过引入一种调整后的 WQI 形式 (WQI) 来处理缺失值，从而在传统的 WQI 开发框架中引入了一个额外的步骤，利用剩余的可用信息来开发 WQI。还开发了一个子 WQI 来解决当地的水质状况。使用不同的参数优化方法（多元线性回归和主成分分析）开发了加权和非加权的 WQI 结果，并进行了比较。为了在现有框架的基础上更进一步，我们开发了一种新的程序，根据与每个参数的缺失值分布相关的参数敏感性分析和不确定性来评估 WQI 的充分性。根据蒙特卡罗概率模拟，针对用户定义的 WQI 可接受变化，优化了开发稳健 WQI 所需的观测数量。马来西亚柔佛河流域 (JRB) 被用作该新框架应用的案例研究。JRB 是马来西亚人口最多的州之一柔佛和柔佛南部的新加坡的重要资源。与加权水质参数相比，WQI 更能有效地解释一般水质。采样频率的优化结果表明，如果可以容忍 WQI 变化 2%，则需要大约 130 个样本。结果（特定于 JRB）还表明，总大肠菌群是对缺失值最敏感的参数，敏感参数的分布对于 WQI 和 WQI 都是相似的。
