The overall goal of the project is to develop a mathematical model to assess the influence of natural factors (hydrology, climate, biogeochemistry) and anthropogenic factors (land use and social-economical indicators) on ecosystem exposure to waterborne chemical pollutants. The number of anthropogenic chemical substances in surface water is in the order of several hundreds of thousand, with most substances still to be identified. Chemical pollution discourse shifted from being a production process issue to be a product usage issue. Globalization of markets produces globally diffuse continuous emission of new pollutants, some of which are endocrine disruptors. There is a clear need of new sophisticated tools for exposure assessment.
Starting from a hydrological driven model of water, nutrients and organic carbon flow in catchments, I will develop a model aggregating elements of environmental chemistry, biogeochemistry, hydrology, geography and social science, to predict emissions and fate of contaminants in river catchments with particular focus on some emerging endocrine disruptors. In collaboration with a team of the host institution I will also measure concentrations of a range of the selected chemicals (including many endocrine disruptors) in specific points of rivers in two case study scenarios (selected to cover broad gradients of land use and social economical indicators in the respective sub-catchments).
I will run statistical regression models to look for quantitative relationships between concentration data and land use/social economical descriptors in the respective sub-catchments. The obtained regression models will be nested in the mechanistic catchment model to generate point source emissions, while the mechanistic model will project predicted concentration over space and time. The model will be a powerful site-specific aggregative tool to quantitatively analyze future exposure scenarios under hypothetical climatic, social-economical and regulation.