A risk assessment framework for geohydrological systems

Abstract

Groundwater pollution stands as a formidable challenge in South Africa, impacting human health, the environment, and overall sustainability. This research endeavours to tackle this issue by crafting adaptive risk assessment frameworks tailored to both local and regional contexts. With sustainability at its core, the study accomplishes seven key objectives: a comprehensive literature review, development of a tiered risk assessment framework, strategic application of fuzzy logic, identification of contamination hazards, establishment of a data information system, creation of a practical Excel-based support tool, and a real-world case study demonstration. To effectively meet the aims and objectives established for tackling the issue of groundwater pollution, the research delves into specific risk categories integral to the comprehensive assessment of the problem. These encompass Climate Change Impact, Aquifer Vulnerability, Groundwater Sustainability, and Human Health. 1. Climate Change Impact: Analysing climate patterns and groundwater dynamics is vital. Precipitation, temperature variations, and climatic shifts can significantly affect groundwater quality and contamination risks. 2. Aquifer Vulnerability: Evaluating aquifer vulnerability is crucial, considering geological and hydrological factors that influence susceptibility to pollutants, including soil characteristics and aquifer permeability. 3. Groundwater Sustainability: Ensuring the long-term sustainability of groundwater involves assessing borehole pumping rates and associated recharge of the area. 4. Human Health: Assessing health risks from groundwater pollution is paramount, involving the potential impact of contaminated water on human health, considering pollutant types, concentrations, and pathways affecting local communities. While each category is assessed uniquely, an integrated system that holistically evaluates all these categories requires a combination of their outputs. Fuzzy logic, a mathematical tool modelling uncertain and imprecise information, offers the advantage of a nonlinear response. Fuzzy logic functions by enabling a spectrum of truth values between 0 and 1, reflecting the extent of truth or membership of a specific proposition. This adaptability proves especially beneficial in scenarios characterized by uncertainty or incompleteness, such as in decision-making, control systems, and pattern recognition. Employing fuzzy logic, these categories can be skilfully amalgamated to formulate a novel risk indicator that captures the intricate andnonlinear connections among the various risk factors related to groundwater pollution and sustainability. The successful application of the risk assessment tool in a South African study area underscores its effectiveness in providing a nuanced analysis of groundwater pollution risks, emphasising the critical role of site-specific data. Recommendations include continual validation, integration of advanced modelling techniques, and the development of a user-friendly interface to enhance decision-making in groundwater pollution management. This study not only contributes to a holistic understanding of groundwater pollution risks but also offers practical tools for effective risk assessment and management, advancing the cause of sustainable groundwater resource utilisation in South Africa.