Optimisation of water quality monitoring network based on land use changes

The evaluation of the importance of having accurate and representative stations in a network for river water quality monitoring is always a matter of concern. The minimal budget and time demands of water quality monitoring programme may appear very attractive, especially when dealing with large-scale river watersheds. This research proposes an improved methodology for optimising water quality monitoring network for present and forthcoming monitoring of water quality under a case study of the Selangor River basin in Malaysia. To achieve this goal, various data and analyses were utilised. In the first stage, two sets of water quality data acquired from 9 stations of Department of Environment (DOE) monitoring network and 12 monitoring stations proposed by Selangor Water Management Authority (SWMA) were used. A geo-statistical technique coupled with Kendall's W was first applied to analyse the performance of each monitoring station in the existing networks under the monitored water quality parameters. Based on this approach, four stations were identified as the most informative, five stations were identified as the least informative while another 12 stations were moderately informative. In the second stage, land use data for 2006, 2010 and 2015 and the corresponding years of frequently sampled water quality data were utilised to analyse the spatiotemporally varying relationship between land use and water quality by using Geographically Weighted Regression (GWR). The results indicated that, in 2015, agricultural land most predicted the change in most water quality variables compared with its prediction proportion in 2010 and 2006, while urban area most predicted the change in most water quality variables in 2010 compared to other years. However, other land uses were more positively associated with most of the water pollutants compared to forest, agricultural, and urban areas. In the last stage, the present and future changes in non-point pollution sources were simulated through land use mapping by using the integrated Cellular Automata and Markov chain model (CA Markov). The performance of the model was very good in its overall ability to simulate the actual land use map of 2015, with Kstandard (90 %), Kno (92 %) and Klocation (97 %), which indicated the reliability of the model to successfully simulate land use changes in 2024 and 2033. Therefore, the Station Potential Pollution Score (SPPS) determined based on Analytic Hierarchy Process (AHP) was used to weight each station under the changes of non-point pollution sources for 2015, 2024, and 2033 prior to prioritisation sequencing of stations in the monitoring weights of non-point sources from the AHP evaluation and fuzzy membership functions, six (6) most efficient sampling stations were identified to build a robust network for the present and future monitoring of water quality status in the Selangor River basin. Additionally, six (6) other stations considered to be the second most efficient sampling stations were also identified for possible expansion of the monitoring network in the future. The methodology proposed in this study implies an optimal procedure for the evaluation and allocation of an optimised water quality monitoring network. The method also enhances the reliability in data classification and rankings.

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