Data-based interpretation of emerging contaminants occurrence in rivers using a simple advection-reaction model

Abstract

Our knowledge of the river’s qualitative status generally relies on discrete spatial and temporal observations organized under what is commonly known as a “monitoring network”. Network performance is constrained by its spatial - temporal resolution, which is severely limited by the costs associated with the whole sampling and analytical process. Alternatively, modeling allows predicting the spatial - temporal variable profile at any resolution at affordable computing costs. However, it involves high uncertainty in the parameterization and requires experimental validation as well. Here, we aimed at reconciling monitoring and modeling, deriving simple steady-state advection-reaction (reactive-transport) models from monitoring data. They are based on graph-theoretical concepts, notably the use of the Laplacian matrix, which captures the river network topology, the interaction between adjacent sites, and the advection process between them. The local reactive process is described by a first-order decay reaction. The application of these models provided relevant information about the variables monitored, such as the local dynamics, the distance of the site’s influence, the degree of synchronization, or the external input/output to the system, which is useful for both scientific and management purposes.

The model was tested in the Llobregat River (NE Spain) basin, with 70 emerging contaminants of different classes (pharmaceuticals, pesticides, perfluorinated substances, endocrine disruptors, and drugs of abuse). The monitoring network included 14 sites (7 in the mainstream, 4 in the Cardener, and 3 in the Anoia tributaries) and was monitored in 2 campaigns. These models can help water managers to optimize the design of river monitoring networks, a key aspect of environmental regulations.

Keywords

Monitoring network / emerging contaminants / advection-reaction / reactive transport / graph theory / Laplacian matrix

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