AQUAROAD: Advancing Groundwater Quality in the Global South: Geochemical Processes, Remediation, Optimisation & Co-Designed Decision-Making Frameworks

Access to safe drinking water is centrally linked to public health, well-being and economic prosperity. Although water quality is strongly linked to many of the UN Sustainable Development Goals (SDG 6: Clean Water & Sanitation, 3: Good Health & Well-Being, 5: Gender Equality, and 2: Zero Hunger), there is still a long way to go to achieve equitable access to safe drinking water, particularly in the Global South. To accelerate progress, we need new interdisciplinary approaches to tackle complex water quality challenges, especially with increasing stressors like rapid urbanisation and climate change impacting groundwater resources widely used for drinking. The aim of my FLF is to create a roadmap towards improved groundwater quality management in the context of the Global South by bringing together systematic approaches to improve the understanding of dominant groundwater processes and to support evidence-based decision-making for effective groundwater remediation. We will develop and demonstrate this approach in relation to two selected contrasting locations in South Asia (e.g. Bihar, India) and East Africa (e.g. Uganda) and for selected priority groundwater contaminants relevant to those locations. The roadmap approach developed here could then be applied to different scenarios in the future. We will bring together expertise in groundwater pollution (e.g. chemical, microbial, emerging contaminants, antimicrobial resistance), (bio)geochemical processes, remediation technologies, machine learning, decision science (e.g. agent based modelling, multi-criteria decision analysis) and social science to address local water quality and remediation challenges in these two areas. We will co-design decision tools, iteratively integrating scientific data with modelled predictions, to enable informed, locally-relevant decision-making for effective groundwater remediation. We will address an integrated set of key objectives and hypothesis (see objectives) through a series of Workpackages (WP) implemented as: (i) WP 1: Field-based Investigations comprising of WP 1.1 Multipollutant & Process Investigation and WP 1.2 Community Science; (ii) WP 2: Lab-based Investigations comprising of WP 2.1: Water & Sediment Characterisation and WP 2.2 Remediation Evaluation; (iii) WP 3: Predictive Modelling comprising of WP 3.1 Machine Learning; WP 3.2 Agent Based Modelling; and WP 3.3 Multi-Criteria Decision Analysis; and (iv) WP 4: Synthesis & Communication comprising of WP 4.1 Stakeholder Engagement and WP 4.2 Open Resource Bank Development. Our project team brings together highly complementary expertise and skillsets. I am an environmental engineer with expertise in groundwater pollution and remediation, with substantial experience managing and implementing complex, multi-partner research projects in South/Southeast Asia, Africa and South America. I am joined by Co-Investigators from The University of Manchester, British Geological Survey, University of Birmingham and University of Bath, along with international Project Partners from University of Melbourne (Australia), KTH Royal Institute of Technology (Sweden), Mahavir Cancer Sansthan (India), University of Heidelberg (Germany), Mbarara University of Science and Technology (Uganda) and independent affiliates from India and Malaysia. Collectively we bring together decades of interdisciplinary expertise in water science, remediation, water management, water and health, biotechnology, decision science, social science, participatory science, stakeholder engagement and extensive local knowledge in India and East Africa. The results and tools generated will improve the understanding of the complex natural and anthropogenic processes impacting groundwater quality in the selected locations and will better enable evidence-based decision making for effective groundwater remediation, with the roadmap generated able to be applied to other scenarios in the future.