RESUMO
While the burden of disease from well-studied drinking water contaminants is declining, risks from emerging chemical and microbial contaminants arise because of social, technological, demographic and climatological developments. At present, emerging chemical and microbial drinking water contaminants are not assessed in a systematic way, but reactively and incidence based. Furthermore, they are assessed separately despite similar pollution sources. As a result, risks might be addressed ineffectively. Integrated risk assessment approaches are thus needed that elucidate the uncertainties in the risk evaluation of emerging drinking water contaminants, while considering risk assessors' values. This study therefore aimed to (1) construct an assessment hierarchy for the integrated evaluation of the potential risks from emerging chemical and microbial contaminants in drinking water and (2) develop a decision support tool, based on the agreed assessment hierarchy, to quantify (uncertain) risk scores. A multi-actor approach was used to construct the assessment hierarchy, involving chemical and microbial risk assessors, drinking water experts and members of responsible authorities. The concept of value-focused thinking was applied to guide the problem-structuring and model-building process. The development of the decision support tool was done using Decisi-o-rama, an open-source Python library. With the developed decision support tool (uncertain) risk scores can be calculated for emerging chemical and microbial drinking water contaminants, which can be used for the evidence-based prioritisation of actions on emerging chemical and microbial drinking water risks. The decision support tool improves existing prioritisation approaches as it combines uncertain indicator levels with a multi-stakeholder approach and integrated the risk assessment of chemical and microbial contaminants. By applying the concept of value-focused thinking, this study addressed difficulties in evidence-based decision-making related to emerging drinking water contaminants. Suggestions to improve the model were made to guide future research in assisting policy makers to effectively protect public health from emerging drinking water risks.
