Development and testing of an efficient micropollutant monitoring strategy across a large watershed.

In recent decades, extensive monitoring programmes have been conducted at the national, international, and project levels with the objective of expanding our understanding of the contamination of surface waters with micropollutants, which are often referred to as hazardous substances (HS). It has been demonstrated that HS enter surface waters via a number of pathways, including groundwater, atmospheric deposition, soil erosion, and urban systems. Given the ever-growing list of substances and the high resource demand associated with laboratory analysis, it is common practice to quantify the listed pathways based on emission factors derived from temporally and spatially constrained monitoring programmes. The derivation calculations are subject to high uncertainties, and substantial knowledge gaps remain regarding the relative importance of the unique pathways, territories, and periods. This publication presents a monitoring method designed to quantify the unique emission pathways of HS in large geographical areas characterized by differences in land use, population, and economic development. The method will be tested for a wide range of HS (ubiquitous organic and inorganic pollutants, pesticides, pharmaceuticals) throughout small sub-catchments located on tributaries. The results of the test application demonstrate a high diversity of both emission loads and instream concentrations throughout different regions for numerous substances. Riverine concentrations are found to be highly dependent on the flow status. Soil concentration levels of polycyclic aromatic hydrocarbons (PAH) and perfluoroalkyl substances (PFAS) are found to be in proportion, whereas that of potentially toxic elements (PTE) in a reverse relationship with economic development. In many instances, concentration levels are also contingent upon land use. The findings of this study reinforce the necessity for the implementation of harmonised and concerted HS monitoring programmes, which should encompass a diverse range of substances, emission sources, pathways and geographical areas. This is essential for the reliable development of emission factors.

Coupling a pathway-oriented approach with tailor-made monitoring as key to well-performing regionalized modelling of PFAS emissions and river concentrations.

Surface water pollution with poly- and perfluorinated compounds (PFAS) is a well-recognized problem, but knowledge about contribution of different emission pathways, especially diffuse ones, is very limited. This study investigates the potential of the pathway oriented MoRE model in shedding light on the relevance of different emission pathways on regional scale and in predicting concentrations and loads in unmonitored rivers. Modelling was supported with a tailor-made monitoring programme aimed to fill gaps on PFAS concentration in different environmental compartments. The study area covers the whole Austrian territory including some additional transboundary catchments and it focuses on perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS). These two PFAS are regulated and therefore their production and use in Europe are currently decreasing. Nevertheless, these compounds are still emitted into the environment via legacy pollution and as transformation products from other PFAS. These two compounds were selected for this study in view of the larger information availability compared to other PFAS. Despite considerable uncertainties in the input data, model validations show that this approach performs significantly better than previous modelling frameworks based on population-specific emission factors, population density and wastewater treatment plant information. The study reveals the predominance of emissions via municipal wastewater treatment plants for PFOS and a relevant role of diffuse emission pathways for PFOA. Results suggest that unpaved areas contribute the biggest share to total diffuse emissions, but the estimation of these pathways is affected by the highest uncertainty in the input data and requires better input data from monitoring. Once the currently growing substance-specific data sets on the concentration of PFAS, others than PFOS and PFOA, in different environmental compartments, will reach an adequate quality, the model presented here will be easily applicable to them.