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Towards Adaptive Water Quality Indexing: Integrating Fuzzy Logic for Improved Contaminant Detection and Treatment Planning
Summary
This study proposed integrating fuzzy logic into water quality index calculations to better handle the uncertainty and compounding effects of emerging contaminants such as pharmaceuticals, microplastics, and personal care products that conventional water quality indices were not designed to assess.
Water quality management is increasingly challenged by the compounded effects of climate change and the rise of contaminants of emerging concerns such as pharmaceutical residues, microplastics, Personal care products and other anthropogenic activities, which traditional treatment systems struggle to address efficiently.As such, monitoring and assessing water resources becomes mandatory not only to trace the levels and effects of such pollutants in water systems but also to estimate the actual cost of treating the water under varying contamination scenarios.Effective evaluation of water quality indicators is essential to determine the suitability of water for various human and ecological needs.Water Quality Indexing is an accepted method used to synthesise complex water quality data into a single representative value.Water quality index (WQI) models are essential to measure pollution levels and guide assessments of the impairment of specific water resources.However, prevalent WQI tools are typically site-specific and cannot be applied to rivers in locations separate from where they were developed, unless in cases where such rivers share similar physical characteristics, pollutant profiles, and water quality parameters.This limits their adaptability across regions with distinct hydrological conditions and land-use practices.Further, a significant limitation is the inability of the current index interpretation to identify and isolate "rogue" pollutants responsible for the negative scoring of the water quality.They also do not provide estimates of the cost associated with treating specific contaminants, aside from generic treatment costs.In response to these limitations, this paper proposes developing an adaptive and interpretable WQI framework based on fuzzy logic theory.The model offers improved accuracy and context and aligns treatment cost estimates with water quality status, enabling more efficient budget planning and resource allocation in water management systems.
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